MicroRNA Expression Detection Methods

MicroRNA Expression Detection Methods Zhiguo Wang Baofeng Yang l MicroRNA Expression Detection Methods Dr. Zhiguo ...

Author: Zhiguo Wang | Baofeng Yang

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MicroRNA Expression Detection Methods

Zhiguo Wang Baofeng Yang l

MicroRNA Expression Detection Methods

Dr. Zhiguo Wang Montreal Heart Institute Research Center 5000 Belanger Street Montreal QC H1T 1C8 Canada [email protected] or [email protected]

Dr. Baofeng Yang Harbin Medical University Dept. Pharmacology 150086 Harbin China, People’s Republic [email protected]

ISBN: 978-3-642-04927-9 e-ISBN: 978-3-642-04928-6 DOI 10.1007/978-3-642-04928-6 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: PCN Applied for # Springer-Verlag Berlin Heidelberg 2010 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: WMXDesign GmbH, Heidelberg, Germany Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Preface

MicroRNAs (miRNAs), endogenous noncoding regulatory mRNAs of 22nucleotides, have rapidly emerged as the central players in gene expression regulation. Owing to their ever-increasing implications in the control of various biological and pathological processes, miRNAs have now been considered novel biomarkers of various human diseases including, cancer, viral disease, cardiovascular disorders, metabolic disturbances, etc. Particular expression profiles have been associated with particular pathological states. Expression profiling of miRNAs have therefore become extremely important not only for fundamentalists but also for clinicians. However, the methodologies used for detecting protein-coding mRNAs cannot be directly applied to miRNAs because of their small size. Over the past years, researchers have made great efforts to developing techniques suitable for miRNA detection and quantification; a wide spectrum of creative and innovative techniques (more than 30 different methods) have been invented and validated. It has come to the time now to summarize these methods and present them in an orderly manner for better understanding and utilization of these methods to miRNA research and applications. In particular, the development of methods for quantifying circulating miRNAs opens up a fascinating opportunity for realizing miRNA as diagnostic and prognostic biomarkers of human disease. A book on this subject may help boosting up the passion of researchers to further improve the existing techniques and develop more new methods to fit to new application needs. These considerations prompted us and urged us to undertake the work: writing a book focusing on miRNA expression detection methods. This book is aimed to target a wide range of readers from graduate students to post-doctoral fellow and senior researchers involving miRNA research of any fields in universities and research institutions. The contents of the book are also suitable for medical practitioners from residents to professors of various types of medical fields, who are interested in developing or utilizing miRNA profiling as a complementary and an alternative strategy for clinical diagnosis of human disease. It provides state-of-the art approaches, cutting-edge methods, and practical protocols as powerful, efficient tools for miRNA detection, profiling and quantification for

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Preface

both basic research and diagnostic analysis of miRNA-related diseases. The contents of chapters are organized essentially based on the hands-on laboratory experience from many outstanding investigators worldwide. In microRNA Expression Detection Methods, the authors provide comprehensive descriptions of the innovative strategies and methodologies for detecting miRNA expression, and their applications to miRNA research and their potential as tools for clinical diagnosis and prognosis. The book is divided into 11 sections that include a total of 33 chapters. The book begins with Sect. 1 introducing the overall concept and strategies of miRNA expression detection methods emphasizing the need of a wide variety of miRNA detection methods to suit specific requirements for research and clinical examination in the laboratories. From Sect. 2 to Sect. 11, each of the 32 chapters is focused on an independent, unique method of miRNA detection. Each single chapter contains five subsections: Summary, Introduction, Protocol (including Materials, Instrument, Reagent, and Procedure), Application and Limitation, and Reference. The development of the technique, ideas behind it, and mechanisms underlying the method are given in Introduction of each chapter. The step-by-step protocols are detailed in Protocol section. Then, the applications and limitations of the methods are discussed. Finally, the literature citations are listed in Reference section. Schematic diagrams are included where needed and appropriate for better illustrating the principle of the methodologies. In addition, flowcharts are also provided to outline the protocols for each of miRNA expression detection methods. Canada China

Zhiguo Wang, PhD Baofeng Yang, MD, PhD

Acknowledgements

There are old Chinese sayings, “everything is difficult to do at the very beginning” and “as soon as revives, two chapter of ripeness.” Yet despite that this is the second one we have written in the line of miRNA book series, we were still feeling nervous and sometimes unconfident. Thanks God! We have finally made it, after a mixture of the bitter and sweet. But we have been enjoying the process more than the harvest. We entirely credit to the actual contributors and inventors of the techniques and methodologies described in this book. Since the beginning of the writing process, we have been immediately inspired by the actual contributors by their ingenious, creative thoughts, strongly amazed by their serious efforts of searing scientific doctrine, and deeply touched by their non-conserved sharing-out of every detail of their experimental protocols. We were not only gathering the information from their studies for our book writing, but are also learning a great deal from these investigators for “great works are performed not by strength but by perseverance (Samuel Johnson)” and “the talent of success is nothing more than doing well whatever you do without a thought of fame (Longfellow)”. We give our most sincere and respectful thanks to all these people, whether we knew them in person or not; we know their names from their papers and their works by heart. Without their sweat and intellect, this book would have been impossible. We give our hearty gratitude to our wives who are standing behind us and selflessly supporting us, while sharing with us the hash times and the joyfulness as well. We also thank our kids for understanding our long-time unavailability of being with them for fun and our lack of care of them as a result of deprivation of our attention from them by the writing task.

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About the Authors

Wang, Zhiguo He received his PhD in cardiovascular pharmacology from McGill University, Canada. He is presently a Professor of Biomedical Sciences, University of Montreal (Canada) and Department of Phamacology, Harbin Medical University, Harbin, Heilongjiang, China. He is the Director of the Cardiovascular Research Institute of Harbin Medical University. He is also a ChangJiang Scholar Endowed Professor (China), the Ministry of Education of China and a Longjiang Scholar Endowed Professor, the Education Committee of Heilongjiang province, China. His current research interests include cardiovascular disease and gene therapy related to microRNAs and ion channels. Yang, Baofeng He received his PhD in pharmacology from Tongji Medical University, China. He is presently a Professor of Pharmacology and the President, Harbin Medical University, China. He is an Academician, the Academy of Engineering of China. He is a Chief Scientist for the National Program on Key Basic Research Project of China, the Vice President of Chinese Pharmacologic Society, a Visiting Professor of West Virginia University, University of Missouri-Kansas City and Nippon Medical University, and an Honorable Professor of Perm Pharmaceutical Academy and Shiga-University of Medical Science. His current research interests include cardiovascular and molecular pharmacology involving microRNAs and ion channels.

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Contents

Part I 1

Detection, Profiling, and Quantification of miRNA Expression . . . . . . . 1.1 miRNA Biology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.1 Biogenesis of miRNAs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.2 Actions of miRNAs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Human Disease-Related Expression Profiles of miRNAs . . . . . . . . . . . . . 1.2.1 Spatiotemporal Expression Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 miRNA Transcriptome and Human Physiology . . . . . . . . . . . . . . 1.2.3 miRNA Transcriptome in Diseased States . . . . . . . . . . . . . . . . . . . . 1.3 miRNAs as Biomarkers for Human Disease . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Methods for Analyzing miRNAs Expression . . . . . . . . . . . . . . . . . . . . . . . . 1.4.1 Ideal Methods for miRNA Detection . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.2 Classification of Methods for miRNA Detection . . . . . . . . . . . . . 1.4.3 Brief Introduction to the Currently Available miRNA Detection Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Part II 2

General Remarks 3 3 3 7 9 9 17 20 37 38 39 39 40 53

miRNA Microarray Methods

Microarray and Its Variants for miRNA Profiling . . . . . . . . . . . . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

67 68 70 70 71 72 73 77 78

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Part III 3

Northern Blotting and Its Variants for Detecting Expression and Analyzing Tissue Distribution of miRNAs . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Basic Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Protocols with Improved Sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Protocols with Improved Specificity and Sensitivity . . . . . . . . . . 3.2.4 Protocols with Nonisotopic Detection . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Part IV 4

83 83 85 85 89 91 97 99 99

In Situ Hybridization Methods

In Situ Hybridization and Its Variants for Detecting Expression and Analyzing Cellular Distribution of miRNAs . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Basic Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 LNA-Modified Protocol with Enhanced Sensitivity and Specificity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3 Ultramer Extension Protocol with Reduced Stringency and Expense . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Part V 5

Northern Blotting Methods

103 104 106 106 116 122 126 127

Real-Time RT-PCR Methods

End-Point Stem-Loop Real-Time RT-PCR for miRNA Quantification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

131 132 134 134 135 135 135 139 139

Contents

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6

miR-Q RT-PCR for miRNA Quantification . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

141 141 142 142 142 143 143 146 146

7

Poly(A)-Tailed Universal Reverse Transcription . . . . . . . . . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

147 147 148 148 149 149 149 151 151

8

Multiplexing RT-PCR for High-Throughput miRNA Profiling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

153 154 155 155 155 155 155 156 157

miRNA Amplification Profiling (mRAP) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

159 159 160 160 162 163 164 169 169

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Part VI 10

miRNA Serial Analysis of Gene Expression (miRAGE or SAGE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.3 Reagnets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Part VII 11

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13

Cloning Methods

173 173 174 174 176 176 177 187 188

Nanoparticle Methods

Electrocatalytic Nanoparticle Tags Technique for High-Sensitivity miRNA Expression Analysis . . . . . . . . . . . . . . . . . . . . 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

191 191 194 194 194 194 195 197 197

Nanoparticle-Amplified SPR Imaging for High-Sensitivity miRNA Profiling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

199 199 201 201 201 202 202 205 206

Conducting Polymer Nanowires Technique for High-Sensitivity miRNA Expression Analysis . . . . . . . . . . . . . . . . . . . . . . . . 13.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

207 207 209 209 209

Contents

14

xv

13.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

209 211 214 215

Gold Nanoparticle Probe Method for miRNA Quantification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

217 217 220 220 220 220 221 224 224

Part VIII

Other Methods

15

Splinted Ligation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

229 230 230 230 230 232 233 238 239

16

Padlock-Probes and Rolling-Circle Amplification . . . . . . . . . . . . . . . . . . . 16.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.3 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.3.1 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.3.2 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.3.3 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.4 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.4.1 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.4.2 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

241 241 243 243 243 243 244 246 246 247 247

17

Invader Assay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

249 249 250 250

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Contents

17.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

250 250 251 255 255

18

Single Molecule Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

257 257 258 258 259 259 259 263 264

19

Enzymatic Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

267 267 269 269 269 269 270 274 274

20

Surface-Enhanced Raman Spectroscopy Method . . . . . . . . . . . . . . . . . . . . 20.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

275 275 277 277 277 277 278 280 280

21

RAKE Assay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

281 281 283 283 283 283 284

Contents

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21.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 22

Bead-Based Flow Cytometric miRNA Expression Profiling . . . . . . . . . 22.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

289 289 290 290 290 290 290 293 294

23

Bioluminescence miRNA Detection Method . . . . . . . . . . . . . . . . . . . . . . . . . . 23.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

295 295 296 296 297 297 298 301 302

24

Molecular Beacon Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

303 303 306 306 307 307 307 310 310

25

Ribozyme Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

313 313 315 315 315 315 316 318 319

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26

Contents

Electrocatalytic Moiety Labeling Technique for High-Sensitivity miRNA Expression Analysis . . . . . . . . . . . . . . . . . . . . . . . . 26.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Part IX

321 321 322 322 323 324 324 327 328

Circulating miRNA Detection Methods

27

Serum and Plasma miRNA Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

331 331 332 332 333 333 333 337 337

28

miRNA Detection from Peripheral Blood Microvesicles . . . . . . . . . . . . 28.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

339 339 340 340 340 341 341 343 343

29

Detection of Placental miRNAs in Maternal Plasma . . . . . . . . . . . . . . . . . 29.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

345 345 346 346 346 346 347 348 349

Contents

Part X 30

xix

Single-cell miRNA Detection Methods

Quantitative LNA-ELF-FISH Method for miRNA Detection in Single Mammalian Cell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

353 353 354 354 354 355 355 358 358

31

Single Cell Stem-Looped Real-Time PCR . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

361 361 363 363 363 364 364 367 367

32

miRNA Function-Reporter Expression Assay . . . . . . . . . . . . . . . . . . . . . . . . 32.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

369 369 370 370 371 371 371 373 374

Part XI 33

Whole Mount In Situ Analysis

Whole Mount In Situ Hybridization (WM-ISH) for miRNA Expression Profiling During Vertebrate Development . . . . . . . . . . . . . . 33.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33.2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33.2.2 Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

377 377 379 379 379

xx

Contents

33.2.3 Reagents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33.2.4 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33.3 Application and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

379 380 383 384

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385

Part I General Remarks

Chapter 1

Detection, Profiling, and Quantification of miRNA Expression

Abstract MicroRNAs (miRNAs), endogenous noncoding regulatory mRNAs of 22 nucleotides, have rapidly emerged as the central players in gene expression regulation. Owing to their ever-increasing implications in the control of various biological and pathological processes, miRNAs have now been considered novel biomarkers of various human diseases including, cancer, viral diseases, cardiovascular disorders, metabolic disturbances, etc. Particular expression profiles have been associated with particular pathological states. Expression profiling of miRNAs have therefore become extremely important not only for fundamentalists but also for clinicians. Over the past years, researchers have made great efforts to develop techniques suitable for miRNA detection and quantification; a wide spectrum of creative and innovative techniques (more than 30 different methods) have been invented and validated. While the aim of this book is to introduce these miRNA expression detection methods, this chapter serves to pave the way for better understanding of these techniques. The chapter begins with the basics of miRNAs in terms of the fundamental aspects of miRNA biology: the biogenesis and actions of miRNAs. Next, miRNA transcriptome related to human physiology and pathology is described. The concept of miRNAs as potential biomarkers of human disease is then introduced. And finally, a brief introduction to miRNA expression detection technologies is given to link to the following chapters.

1.1

miRNA Biology

1.1.1

Biogenesis of miRNAs

1.1.1.1

Transcriptional Regulation

Genes for miRNAs are located in the chromosomes, and many of them are identified in clusters that can be transcribed as polycistronic primary transcripts. Some miRNAs Z. Wang and B. Yang, MicroRNA Expression Detection Methods, DOI 10.1007/978-3-642-04928-6_1, # Springer-Verlag Berlin Heidelberg 2010

3

4

1 Detection, Profiling, and Quantification of miRNA Expression

are encoded by their own genes and others are encoded by the sequences as a part of the host protein-coding genes. On the basis of the genomic arrangement of their genes, miRNAs can be grouped into two classes (Wang 2009b): 1. Intergenic miRNAs (miRNA-coding genes located in between protein-coding genes) 2. Intragenic miRNAs (miRNA-coding genes located within their host proteincoding genes). Further, the intragenic miRNAs can be divided into the following subclasses: (a) Intronic miRNAs (miRNA-coding genes located within introns of their host protein-coding genes) (b) Exonic miRNAs (miRNA-coding genes located within exons of host protein-coding genes) (c) 30 UTR miRNAs (miRNA-coding genes located within 30 UTR of host protein-coding genes) (d) 50 UTR miRNAs (miRNA-coding genes located within 50 UTR of host protein-coding genes) According to our analysis, in the human miRNAs identified thus far, a majority of miRNAs belong to intergenic and intronic miRNAs comprising ~42 and ~44% of the total, respectively, and the other three categories are rare, with the exonic miRNAs being ~7%, 30 UTR miRNAs being 1.5%, and 50 UTR miRNAs being 1%. Clearly, miRNAs either have their own genes or are associated with their host genes; accordingly, miRNAs are generated by two different mechanisms. Biogenesis of miRNAs can be summarized as a five-step process as detailed below (see also Fig. 1.1). 1. Generation of primary miRNAs: transcription of miRNA genes. The intergenic miRNA genes are first transcribed as long transcripts, called primary miRNAs (pri-miRNAs), mostly by RNA polymerase II or RNA polymerase III (Ying and Lin 2005). The pri-miRNAs are capped and polyadenylated and can reach several kilobases (kb) in length (Cullen 2004; Kim 2005). The clustered miRNA genes in polycistronic transcripts are likely to be coordinately regulated (Bartel 2004). The intronic miRNAs are processed by sharing the same promoter and other regulatory elements of the host genes. They are first transcribed along with their host genes by RNA polymerase II and then processed by Droshaindependent pathway from excised introns by the RNA splicing machinery for their biogenesis in Drosophila, C elegans, and mammals (Berezikov et al. 2007; Okamura et al. 2007; Ruby et al. 2007).

1.1.1.2

Post-transcriptional Processing

2. Generation of precursor miRNAs: endonuclease processing of pri-miRNAs. The pri-miRNAs are processed to precursor miRNAs (pre-miRNAs) by the RNase

1.1 miRNA Biology

5

CTD TEFb

TFIIF

Pol II

Transcription

AAAA

m7G

Targeted mRNA cleavage

Cytoplasm

Nucleus

pri-miRNA

miRNA

pre-miRNA

degradation

miRISC passenger strand RISC

protein-coding mRNA m7G

guide strand RISC

RISC

translation inhibition

~ ~ ~ ~ ~ ~

RISC

AAAA

ORF

3’UTR

II Protein

Fig. 1.1 Illustrative diagram for the action of miRNAs. A protein-coding gene is transcribed to an mRNA that is subsequently translated to a protein. On the other hand, a small RNA-coding gene is initially transcribed into a long primary miRNA (pri-miRNA) with stem-loop structure which is rapidly processed to remove branches and become precursor miRNA (pre-miRNA). Pre-miRNA is then transported out of the nucleus to the cytoplasm where it is further processed to become a double-stranded mature miRNA. A mature miRNA is incorporated into a protein complex called RISC (RNA-induced silencing complex). One of the strands is then removed from the complex and gets degraded, being a passenger strand. The remaining strand called guide strand can guide the RISC to find its complementary sites in the 30 UTR of target genes. The binding of RISC then primarily causes translation inhibition of protein-coding mRNA and may cause mRNA degradation as well

endonuclease-III Drosha and its partner DGCR8/Pasha in the nucleus (Lee et al. 2002b; Denli et al. 2004; Gregory et al. 2004; Landthaler et al. 2004). These premiRNAs are ~60–100 nts with a stem-loop or hairpin secondary structure. Specific RNA cleavage by Drosha predetermines the mature miRNA sequence and provides the substrates for subsequent processing steps. Cleavage of a primiRNA by microprocessor begins with DGCR8 recognizing the single-stranded RNA (ssRNA)–double-stranded RNA (dsRNA) junction typical of a pri-miRNA (Han et al. 2006). Then, Drosha is brought close to its substrate through interaction with DGCR8 and cleaves the stem of a pri-miRNA ~11 nt away from the two single-stranded segments. miRNA precursor-containing introns have recently been designated “mirtrons” (Miranda et al. 2006). Mirtrons are derived from certain debranched introns that fold into hairpin structures with 50 monophosphates and 30 2-nt

6


hydroxyl overhangs, which mimic the structural hallmarks of pre-miRNAs and enter the miRNA-processing pathway (Okamura et al. 2007; Ruby et al. 2007). The discovery of mirtrons suggests that any RNA, with a size comparable to a pre-miRNA and all the structural features of a pre-miRNA, can be utilized by the miRNA processing machinery and can potentially give rise to a functional miRNA. 3. Nucleus to cytoplasm translocation of pre-miRNAs. Pre-miRNAs then get exported to the cytoplasm from the nucleus through nuclear pores by RanGTP and exportin-5 (Bohnsack et al. 2004; Lund et al. 2004; Yi et al. 2003). After a pre-miRNA is exported to the cytoplasm, RanGTP is hydrolyzed by RanGAP to RanGDP, and the pre-miRNA is released from Exp-5. 4. Generation of mature miRNAs: endonuclease processing of pre-miRNAs. In the cytoplasm, pre-miRNAs are further processed by Dicer in animals, which is a highly conserved, cytoplasmic RNase III ribonuclease that chops pre-miRNAs into ~22-nt duplexes of mature miRNAs containing a guide strand and a passenger strand (miRNA/miRNA*), with 2-nt overhangs at the 30 termini (Kim 2005). Like other RNase III family proteins, Dicer interacts with doublestranded RNA-binding protein (dsRBP) partners. In mammalian cells, Dicer associates with transactivation-response element RNA-binding protein (TRBP) and protein activator of the interferon-induced protein kinase (PACT) (Chendrimada et al. 2005; Lee et al. 2006). In plants, miRNAs are cleaved into miRNA:miRNA* duplex, possibly by Dicer-like enzyme 1 (DCL1) in the nucleus rather than in the cytoplasm (Bartel 2004; Lee et al. 2002a), then the duplex is translocated into the cytoplasm by HASTY, the plant ortholog of exportin 5 (Bartel 2004). The strands of this duplex separate and release mature miRNA of 19–25 nts in length (Bartel 2004; Lee et al. 2002). Plant miRNAs undergo further modification by methylation at the 30 end by HEN1 (Yu et al. 2005). 5. Formation of miRISC. Mature miRNAs get integrated into a RNA-induced silencing complex (RISC) to form the miRNA:RISC complex (miRISC). Only one strand of miRNA/miRNA*, the guide strand, is successfully incorporated into RISC, while the other strand, the passenger strand, is eliminated. Strand selection may be determined by the relative thermodynamic stability of two ends of miRNA duplexes (Khvorova et al. 2003; Schwarz et al. 2003). The strand with less stability at the 50 end is favorably loaded onto RISC, whereas the passenger strand is released or destroyed. miRISC contains several proteins, such as Dicer, TRBP, PACT, and Gemin3, but the components directly associated with miRNAs are Argonaute proteins (Ago). These proteins contain four domains: the N-terminal, PAZ, middle, and Piwi domains. The PAZ domain binds to the 30 end of guide miRNA, while the other three domains form a unique structure, creating grooves for target mRNA and guide miRNA interactions (Liu et al. 2002b; Song et al. 2004; Ma et al. 2005; Parker et al. 2005). In mammalian cells, four Ago proteins have been identified, all of which can bind to endogenous miRNAs (Meister and Tuschl 2004). Despite the sequence similarity among these Ago proteins, only Ago2 exhibits endonuclease activity to slice

1.1 miRNA Biology

7

complementary mRNA sequences between positions 10 and 11 in the 50 end of guide strand miRNA. Therefore, human Ago2 is a component not only of miRISC but also of siRISC (siRNA-induced silencing complex), a RISC assembled with exogenously introduced siRNA. The roles of various Ago proteins in mammalian RISC are ambiguous, but the division of labor among Ago proteins in Drosophila is well defined. Drosophila Ago1 and Ago2 have been shown by biochemical and genetic evidence to participate in two separate pathways: Ago1 interacts with miRNA in translational repression, whereas Ago2 associates with siRNA for target cleavage (Carmell et al. 2002; Okamura et al. 2004).

1.1.2

Actions of miRNAs

1.1.2.1

Mechanisms of Actions

miRNAs exist in double-stranded form (duplex), activate in single-stranded form (simplex), and act in complex form miRISC. Mature miRNAs confer sequence specificities to the RISC complex. Subsequently, a miRNA in the miRISC binds to the 30 untranslated region (30 UTR) of its target mRNA through a WatsonCrick basepairing mechanism with its 50 -end 2–8 nts exactly complementary to recognition motif within the target (taking into account that an RNA– RNA hybrid can also contain G–U matches). This 50 -end 2–8 nt region is termed “seed sequence” or “seed site” as it is critical for miRNA actions (Lewis et al. 2003, 2005). Partial complementarity with the rest of the sequences of a miRNA also plays a role in producing post-transcriptional regulation of gene expression, presumably by stabilizing the miRNA:mRNA interaction. Moreover, the mid and 30 -end regions of a miRNA may also be important for forming miRISC. Studies have shown that in addition to 30 UTR, coding region and 50 UTR can also interact with miRNAs to induce gene silencing (Jopling et al. 2005; Luo et al. 2008; Tay et al. 2008). In mammalian species, the assembly of the miRNA/RISC on a 30 UTR can potentially influence protein production by enhancing de-adenylation with subsequent degradation of the mRNA or by repressing translation initiation or both (Yekta et al. 2004; Lim et al. 2005; Giraldez et al. 2005; Pillai et al. 2007), depending upon at least the following factors (see Fig. 1.1): 1. The overall degree of complementarity of the binding site 2. The number of recognition motif corresponding to 50 -end 2–8 nts of the miRNA, and 3. The accessibility of the bindings sites (as determined by free energy states) (Jackson and Standart 2007; Nilsen 2007; Pillai et al. 2007) The greater the degree of complementarity of accessible binding sites, the more likely a miRNA degrades its targeted mRNA. The loose binding constraints allow

8


one miRNA to bind to several sites within one 30 UTR. Perfectly complementary targets (full miRNA:mRNA interaction) are efficiently silenced by the endonucleolytic cleavage activity of some Argonaute proteins (Hutva´gner and Zamore 2002; Yekta et al. 2004; Davis et al. 2005), but the vast majority of predicted targets in animals are only partially paired (Partial miRNA:mRNA interaction) (Lewis et al. 2003, 2005; Grun et al. 2005; Krek et al. 2005; Rajewsky and Socci 2004; Brennecke et al. 2005) and can hardly be cleaved (Haley and Zamore 2004). Some miRNAs have only seed-site complementarity (seed-site miRNA:mRNA) and this interaction primarily leads to translation inhibition. And those miRNAs that display imperfect sequence complementarities with target mRNAs primarily lead to translational inhibition (Lewis et al. 2003, 2005; Jackson and Standart 2007; Nilsen 2007; Pillai et al. 2007). The mechanisms for translational inhibition remain largely unkown, although inhibition of translation initiation has been identified as one such mechanism by several studies (Humphreys et al. 2005; Pillai et al. 2005). Greater actions may be elicited by a miRNA if it has more than one accessible binding sites in its targeted miRNA, presumably by the cooperative miRNA:mRNA interactions from different sites. mRNA degradation by miRISC is initiated by deadenylation and decapping of the targeted mRNAs (Pillai et al. 2007). A recent study demonstrated, however, that miRNAs can also act to enhance translation when AU-rich elements and miRNA target sites coexist at proximity in the target mRNA and when the cells are in the state of cell-cycle arrest (Vasudevan et al. 2007). In plants, miRNAs base pair with their mRNA targets by precise or nearly precise complementarity (Wang et al. 2006). The loose binding constraints also allow one miRNA to bind to multiple mRNA targets within the transcriptome. This multiplicity endows miRNAs in principle with the ability to inhibit several genes at once, leading to a much stronger biological response due to multiple effects on one pathway or coordinated effects on several pathways. It has been predicted that each single miRNA can have >1,000 target genes and each single protein-coding gene can be regulated by multiple miRNAs (Lewis et al. 2003, 2005; Jackson and Standart 2007; Nilsen 2007; Pillai et al. 2007; Alvarez-Garcia and Miska 2005; Ambros 2004). This is at least partially a result of a lax requirement of complementarity for miRNA:mRNA interaction (Lim et al. 2005). This implies that actions of miRNAs are sequence- or motif-specific, but not gene-specific; different genes can have same binding motifs for a given miRNA and a given gene can have multiple binding motifs for distinct miRNAs. On the downside, the relaxed stringency of miRNAs, with regard to their potential targets, enhances the possibility of disadvantageous off-target effects on inappropriate mRNAs. Another disadvantage is the difficulty it raises for researchers trying to interpret the biological significance of altered expression of a given miRNA by determination of its relevant downstream targets. On the basis of the characteristics of miRNA actions, we postulated that a miRNA should be viewed as a regulator of a cellular function or a cellular program, not of a single gene (Wang et al. 2008).

1.2 Human Disease-Related Expression Profiles of miRNAs

1.1.2.2

9

Cellular Functions of miRNAs

miRNAs are an abundant RNA species constituting >3% of the predicted human genes, which regulates ~30% of protein-coding genes (Lim et al. 2005). The high sequence conservation across metazoan species and the ability of individual miRNAs to regulate the expression of multiple genes confer strong evolutionary pressure and participation of miRNAs in essential biologic processes such as cell proliferation, differentiation, apoptosis, metabolism, stress, etc. (Alvarez-Garcia and Miska 2005; Ambros 2004; Wang and Blelloch 2009; Wu et al. 2009a; Yang et al. 2009).

1.2 1.2.1

Human Disease-Related Expression Profiles of miRNAs Spatiotemporal Expression Profiles

Expression of miRNAs in mammalian species under normal conditions is genetically programmed with certain spatial (depending on cell-, tissue-, or organ-type) and temporal (depending on developmental stage) patterns. On one hand, expression of miRNAs is not spatially uniform; instead, some miRNAs are expressed in cell/tissue/organ-restricted manners and others may be ubiquitously expressed. On the other hand, expression of miRNAs is dynamic, but not static, along with the lifespan of an organism from fetal development to aging process. These spatial heterogeneities and temporal differences are critical for the involvement of miRNAs in the fine regulation of versatile cellular functions and and cell lineage decisions with right timings in right places. Chromosomal location and genomic distribution of a miRNA gene are important determinants of its expression from at least three perspectives. (1) Approximately 60% of miRNA genes are located within introns of defined transcription units, and their expression is frequently correlated with the expression profiles of their host genes. (2) Many miRNA genes are distributed as clusters, and a microarray expression profiling of 175 miRNAs in 24 human tissues showed that proximally paired miRNA genes at a distance up to 50 kb are generally co-expressed. The best example may be the four miRNA genes (miR-196b, miR-10a, miR-196a-2, and miR-10b) that are embedded in the Hox gene clusters (Hox A, Hox B, Hox C, and Hox D, respectively). By histochemical staining and in situ hybridization, expression patterns of miR-10a and Hoxb4 mRNA are very similar, suggesting that they share regulatory control of transcription. (3) miRNA genes are frequently located at fragile sites, as well as in regions of loss of heterozygosity, regions of amplification, or common breakpoint regions. Expression of miRNA genes within the regions afflicted by chromosomal aberration, a hallmark characteristic of neoplastic cells, could also be directly affected. For example, miR-15a and miR-16-1 are located

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at a frequently deleted site in most of the B cell chronic lymphocytic leukemia (CLL) patients, and induce apoptosis in a leukemia cell line model.

1.2.1.1

Spatial Heterogeneity of miRNA Expression

miRNA Distribution in Mouse Tissues The first attempt to characterize tissue distribution of miRNAs in mammalian species was made by Lagos-Quintana et al. (2002) with mice. They used the cloning method to investigate the tissue-specific distribution of miRNAs in nine different mouse tissues including heart, liver, small intestine, colon, spleen, cortex, cerebellum, and midbrain. For the purpose of tissue-specificity of miRNA expression, cloning of miRNAs from specific tissues is preferred over whole organism-based cloning because low-abundance miRNAs that normally go undetected by Northern blot analysis are identified clonally. They demonstrated that miR-1 accounts for 45% of all mouse miRNAs found in heart, yet miR-1 was still expressed at a low level in liver and midbrain, even though it remained undetectable by Northern analysis. In liver, variants of miR-122 account for 72% of all cloned miRNAs, and miR-122 was undetected in all other tissues analyzed. In spleen, miR-143 appeared to be most abundant, at a frequency of ~30%. In colon, miR-142-as was cloned several times and also appeared at a frequency of 30%. Variants of a particular miRNA, miR-124, dominated and accounted for 25–48% of all brain miRNAs. miR-101, -127, -128, -131, and -132, also cloned from brain tissues, were further analyzed by Northern blotting and shown to be predominantly brain specific (Lagos-Quintana et al. 2002). A study also performed with mice examined miRNA expression profiles from 13 neuroanatomically distinct areas of the adult mouse central nervous system (CNS), employing microarray profiling (see Chap. 2) in combination with real-time RT-PCR (see Chap. 5) and LNA (locked nucleic acid)-based in situ hybridization (see Chap. 4) (Bak et al. 2008). The authors uncovered 44 miRNAs displaying more than threefold enrichment in the spinal cord, cerebellum, medulla oblongata, pons, hypothalamus, hippocampus, neocortex, olfactory bulb, eye, and pituitary gland. These include miR-9, miR-124a, miR-125b, miR-127, miR-128, and members of the let-7 family. This is consistent with some previopus studies (Babak et al. 2004; Barad et al. 2004; Miska et al. 2004; Sempere et al. 2004; Shingara et al. 2005; Thomson et al. 2004). More than 50% of the identified mouse CNS-enriched miRNAs showed different expression patterns compared to those reported in zebrafish, although the mature miRNA sequences are nearly 100% conserved between the two vertebrate species. Their data further revealed that miR-195, miR-497, and miR-30b are enriched in the cerebellum. The medulla oblongata displayed enrichment of miR-34a, miR-451, miR-219, miR-338, miR-10a, and miR-10b. miR-7 and miR-7b were enriched in the hypothalamus. The hippocampus showed accumulation of miR-218, miR-221, miR-222, miR-26a, miR-128a/b,


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miR-138, and let-7c and enrichment of any miRNAs in the amygdala, mesencephalon, and thalamus. It was found that miR-7 and miR-7b were enriched in the pituitary and hypothalamus (Farth et al. 2005); miR-195 in the cerebellum (Hohjoh and Fukushima 2007); miR-375, miR-141, and miR-200a in the pituitary (Landgraf et al. 2007); whereas miR-10a and miR-10b were enriched in the spinal cord (Kloosterman and Plasterk 2006). These findings suggest that a large number of mouse CNS-expressed miRNAs may be associated with specific functions within these regions. Another investigation compared two different methodologies (linear amplification of miRNAs – labeled-aRNA or using a direct labeling strategy – labeled-cDNA) for the preparation of labeled miRNAs from mouse CNS tissue for microarray analysis (Saba and Booth 2006). The most abundant miRNAs, including brain specific or enriched miRNAs, miR-124a-1, -9-1, -9*-1, -127, -136, -138-1, 149, -154, -218-1, -219-1, -222, -125a, -125b-1, -128a, -26a-1, -29a, -29b-1, -30c-1, and -34a were equally identified by both types of microarray labeling techniques. In mouse heart, miR-1, miR-133, miR-125, miR-30, let-7, miR-23, miR-24, miR-26, miR-29, miR-99, and miR-143 are highly enriched. Many miRNAs are enriched in a tissue-/cell-specific manner (Landgraf et al. 2007): miR-1, miR-16, miR-27b, miR-30d, miR-126, miR-133, miR-143, and the let-7 family are abundantly but not exclusively expressed in adult cardiac tissue. In addition to cardiomyocytes, the heart contains many other “non-cardiomyocyte” cell types, such as endothelial cells, smooth muscle cells, fibroblasts, and immune cells, which may have completely distinct miRNA expression profiles. Indeed, (skin) fibroblasts mainly express miR-16, miR-21, miR-22, miR-23a, miR-24, miR-27a, and others, an expression pattern that is highly different from that of cardiomyocytes. In artery smooth muscle, the most abundant miRNAs are miR145, let-7, miR-125b, miR-125a, miR-23, and miR-143 (Ji et al. 2007), despite the fact that the “muscle-specific” miR-1 and miR-133 are also expressed in artery smooth muscle. Other miRNAs, such as the let-7 family, miR-126, miR-221, and miR-222, are highly expressed in human endothelial cells (Kuehbacher et al. 2007; Harris et al. 2008). In addition, miRNA expression profiles can change during cardiac development, and many miRNAs that are only normally expressed at significant levels in the fetal human heart are re-expressed in cardiac disease, such as heart failure (Landgraf et al. 2007; Bauersachs and Thum 2007).

miRNA Distribution in Human Tissues Tissue distribution of miRNAs has also been measured in humans. One report used a bead-based detection platform (see Chap. 21) to profile expression of 217 miRNAs in a broad spectrum of normal human tissues, but low sensitivity and specificity make the results problematic for miRNAs that are less abundant (Lu et al. 2007). Thus far, the study recently reported by Liang et al. (2007) has been the only one systematic, detailed miRNA expression profiling in human tissues. These authors

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used real-time RT-PCR-based method (see Chap. 5) to examine global profiles of distribution and expression of 345 unique miRNAs in 40 normal human tissues, in combination with public datasets to systematically analyze the association between genomic locations of miRNAs and their expression, and the correlation of expression between miRNAs and their predicted target genes. This study provides a wealth of invaluable information regarding the tissue distribution of miRNAs and the pertinent genomic association, target gene correlation. A number of findings were revealed: 1. A considerable portion of miRNAs have tissue-specific expression patterns and the average miRNA copy numbers in all tissues are highly variable. 2. MicroRNA genes localized within a genomic cluster are preferentially co-expressed as a “transcription unit.” In general, normal human tissues derived from similar anatomical locations or with related physiological functions were primarily clustered together according to the clusters of miRNAs. For example, tissues derived from different parts of heart (atrium versus ventricle) were clustered with skeletal muscle. Hence, localization in the same genomic cluster is the most recognizable feature for miRNAs that have correlated abundance and expression patterns among tissues. 3. The clustering of tissues using the miRNA expression profiles is very similar to that obtained by the mRNA expression profiles. The authors explain this finding on the ground that miRNAs preserve more of the “cellular identity signature” compared to mRNAs under the genomic instability and heterogeneity that characterize neoplastic cells, whereas in normal tissues such a variable environment does not exist, so the performance of both miRNA and mRNA expression profiles on tissue classification is comparable. However, among some tissue types the clustering patterns by their mRNA and miRNA expression profiles are quite different. Lung is clustered together with female reproductive organs and esophagus by mRNA expression profile, but miRNA expression profile of lung is similar only to that of fallopian tube. Thyroid is similar to different parts of the heart in miRNA expression but not in mRNA expression. 4. A group of 15 miRNAs are universally expressed at similar levels in all 40 different normal tissues. These universal miRNAs include miR-15, miR-16b, miR-29a, miR-29aN, miR-29bN, miR-30e, miR-92, miR-92N, miR-93, miR103, miR-106b, miR-140, miR-152b, miR-324-3p, and miR-423. Such a feature characterizes these miRNAs as a candidate of universal reference to normalize miRNA expression in normal human tissues. 5. Seventy-seven percent of the miRNAs have coherent expression patterns with their host genes. This result further corroborates the hypothesis that expression of intronic miRNAs is co-regulated with their host genes, and it also identifies the host genes that could be surrogate markers for expression of their intronic miRNAs. 6. A large number (~100) of miRNAs have pronounced expression in placenta compared to most of the other tissues.


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Important to note is that this human miRNA profiling confirms many of the results from murine studies described earlier. A few examples are given below (Readers are strongly referred to the original article by Liang et al. (2007a); see Table 1.1). 1. miR-1 and miR-133 show highest expression in different parts of the heart and skeletal muscle as well as in vena cava. Other muscle-preferential miRNAs include miR-30, miR-125, miR-26, miR-23, miR-126, miR-92, miR-99, miR100, let-7c, and let-7f. 2. The miRNA expression profile is quite distinct in brain and not shared by other tissue types. Brain expresses high levels of miR-124, miR-221, miR-222, miR514, miR-524, miR-299, miR-320, miR-196, miR-527, miR-34a, and let-7a. 3. Intriguingly, in addition to the expression of liver-specific miR-122a (Jopling et al. 2005), liver also shares similar high levels of expression of several miRNAs as in heart: miR-30, miR-125, miR-26, miR-23, miR-92, miR-148, and miR-126. In addition, miR-192, miR-321, and miR-19 are abundantly expressed in liver but sparsely in other tissues. 4. Strikingly, the gender-related organs/tissues, including testicle, prostate, ovary, fallopian tube, uterus, and breast, express a quite similar set of miRNAs: miR125, miR-26, miR-21, miR-24, miR-30, let-7c, miR-100, miR-99, and miR-92. 5. miR-126, miR-21, miR26, miR-30, miR-24, miR-223, miR-92, and miR-142-3p are among the most abundant miRNAs in human lung. Interestingly, these miRNAs are also highly expressed in spleen. 6. The characteristic set of miRNAs in pancreas includes miR-375, miR-21, miR200c, miR-148, miR-30, miR-26, miR-29, and miR-125. 7. Surprisingly, the muscle-preferential miRNAs miR-133, miR-1, and miR-206 are also among the most abundant miRNA species in thyroid. Moreover, other miRNAs (miR-30, miR-125, miR-26, miR-126, miR-92, let-7c, and let-7f) that are enriched in heart also demonstrate their predominance in thyroid. Apparently, the restriction of miRNA expression can be qualitative (some miRNAs are expressed exclusively in certain tissue or cell types but not in others) or quantitative (some miRNAs are abundantly expressed only in certain tissue or cell types and modestly in others). To be more appropriate, while each individual miRNA may not be expressed in a tissue/cell-specific manner, the expression profile of miRNAs (co-expression of miRNAs) appears to be tissue/cell-specific. The differential tissue distributions of miRNAs suggest tissue– or even cell type– specific functions of these molecules. For instance, the cell lineage-specific miRNA expression patterns may be required to control timing of development and tissue specification. Probably more important is the fact that the expression profile of miRNAs is disease-dependent. A particular pathological process may be associated with the expression of a particular group of miRNAs; this is what the signature expression pattern of miRNAs implies. This issue will be discussed in the following sections of this chapter.

Table 1.1 Top ten abundant miRNAs expressed in various normal human tissues 1 2 3 4 5 6 7 Brain miR-514 let-7 miR-524 miR-299 miR-233 miR-222 miR-124a Heart LV miR-133 miR-1 miR-26 miR-30 miR-24 miR-125 miR-126 RV miR-133 miR-1 miR-26 miR-30 miR-126 miR-24 miR-125 LA miR-133 miR-26 miR-1 miR-125 miR-30 miR-24 miR-126 RA miR-133 miR-26 miR-125 miR-30 miR-1 miR-24 miR-126 Pericardium miR-26 miR-21 miR-16 miR-126 let-7 miR-321 miR-125 Vena Cava miR-26 miR-16 miR-133 miR-125 miR-126 miR-30 let-7 Lung miR-26 miR-126 miR-21 miR-30 miR-223 miR-16 miR-24 Trachea miR-26 miR-16 miR-21 miR-125 miR-200 let-7 miR-24 Liver miR-30e miR-26 miR-30 miR-122 miR-192 miR-92 miR-19 Kidney miR-26 miR-30 miR-21 miR-125 miR-16 miR-29 miR-24 Spleen miR-26 miR-16 miR-223 miR-126 miR-142-3p miR-21 miR-150 Pancreas miR-21 miR-375 miR-26 miR-30 miR-200 miR-16 miR-148 Thyroid miR-133 miR-26 miR-1 miR-26 miR-16 miR-126 miR-30 Esophagus miR-26 miR-125 miR-21 miR-24 miR-16 miR-30 miR-145 Stomach miR-26 miR-200 miR-30 miR-21 miR-375 miR-16 miR-148 Small Intestine miR-26 miR-21 miR-192 miR-321 miR-194 miR-16 miR-92 Colon miR-192 miR-194 miR-26 miR-21 miR-200 miR-215 miR-16 Lymph Node miR-142-3p miR-150 miR-26 miR-16 miR-21 miR-126 miR-29 Thymus miR-26 miR-16 miR-142-3p miR-125 miR-92 miR-20 miR-21 Adrenal miR-26 miR-125 miR-16 let-7 miR-21 miR-126 miR-30 Adipose miR-26 miR-126 miR-125 let-7 miR-16 miR-30 miR-24 Ovary miR-26 miR-125 let-7 miR-92 miR-195 miR-100 miR-99 Uterus miR-26 miR-125 let-7 miR-16 miR-100 miR-24 miR-30 Cervix miR-26 miR-125 let-7 miR-99 miR-100 miR-29 miR-21 Breast miR-26 miR-126 miR-125 miR-30 miR-21 miR-16 let-7 Testicle miR-26 miR-125 let-7 miR-514 miR-16 miR-21 miR-30 Prostate miR-26 miR-125 miR-21 let-7 miR-24 miR-16 miR-27 Bladder miR-26 miR-125 miR-16 miR-21 let-7 miR-126 miR-24 Ileum miR-26 miR-21 miR-192 miR-16 miR-194 miR-30 miR-24 PBMC miR-125 miR-92 miR-29 miR-124 miR-150 miR-30 miR-26 Source: Liang et al. (2007) LV left ventricle; RV right ventricle; LA left atrium; RA right atrium; PBMC peripheral blood mononuclear cells 8 miR-15b miR-23 miR-23 let-7 let-7 miR-199* miR-24 let-7 miR-29 miR-125 let-7 miR-30 let-7 miR-29 miR-27 miR-24 miR-30 miR-321 let-7 miR-150 miR-24 miR-100 miR-30 miR-99 miR-24 miR-24 miR-92 miR-100 miR-30 miR-92 miR-9

9 miR-196b miR-16 miR-27 miR-23 miR-100 miR-92 miR-21 miR-92 miR-126 miR-126 miR-126 miR-92 miR-29 let-7 miR-23 miR-125 miR-200 miR-30 miR-30 miR-30 miR-321 miR-99 miR-199 miR-10 miR-195 miR-30 miR-20 miR-30 miR-321 miR-20 miR-16

10 miR-29bN let-7 let-7 miR-21 miR-99 miR-24 miR-1 miR-29 miR-223 miR-21 miR-192 miR-20 miR-125 miR-125 miR-126 miR-321 miR-20 miR-92 miR-20 miR-321 miR-29 miR-92 miR-29 miR-27 miR-199 miR-195 miR-24 miR-99 miR-100 miR-126 miR-21

14 1 Detection, Profiling, and Quantification of miRNA Expression


1.2.1.2

15

Temporal Difference of miRNA Expression

miRNAs in Normal Bone Marrow Cell Lineages Differentiation Hematopoietic stem cells, an example of adult stem cells, are undifferentiated cells that reside in specific niches of the bone marrow and have the capacity to differentiate into any type of blood cell. miRNAs play an important role in hematopoiesis, not only in regulation of hematopoietic stem cell self-renewal but also in hematopoietic differentiation (Lawrie 2007). In mouse BM were identified lineage specific miRNA: miR-223 for myeloid cells and miR-181 for B cells (Chen et al. 2004). miR-150 is usually expressed in mature B and T cells and a premature expression of miR-150 in B cell progenitors stops pro-B cell transition. An up-regulation of miR150 in early B cell development stages blocks the expression of genes that are crucial for B cell maturation and function (Xiao et al. 2007; Fazi et al. 2005). The miR-181a is a key regulator of lymphoid cells differentiation; it is expressed in BM B cells and promotes B cell differentiation possibly through repression of Notch signals (Chen et al. 2005). A screening in naive, effector, and memory T cells showed the importance of miR-21 in T cell differentiation and function (Wu et al. 2007).

miRNA Expression Signatures of Human Mesenchymal Stromal Cells Human mesenchymal stromal cells (MSCs) can produce osteocytes and chondrocytes for bone and cartilage development, and adipocytes for maintaining fat tissue as well. MSCs offer great hope for the treatment of tissue degenerative and immune diseases, but their phenotypic similarity to dermal fibroblasts may hinder robust cell identification and isolation from diverse tissue harvests. To identify genetic elements that can reliably discriminate MSCs from fibroblasts, Bae et al. (2009) performed comparative gene and miRNA expression profiling analyzes with genome-wide oligonucleotides microarrays. They observed similar miRNA expression profiles between MSCs and fibroblasts. A notable exception to the homologous miRNA pattern shared by MSCs and fibroblasts is miR-335 whose expression was found to be about 44-fold higher in MSCs than in fibroblasts. In addition, another four miRNAs, miR-520f, miR-181a-2, miR-340, and miR-431, show >3-fold higher levels in MSCs than in fibroblasts. In agreement, MEST, the host gene for the inronic mirR-335, was also found to be up-regulated in MSCs.

miRNA Expression Signatures of Human Embryonic Stem Cells Embryonic stem (ES) cells share several unique features, including unlimited self-renewal and the ability to differentiate into any of the three embryonal lineages – ectoderm, endoderm, and mesoderm. For the cell fate decision to be made in response to internal and/or niche-specific signals, a complex set of dynamic

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feedback loops and cross-regulation of pathways is required. Recently, miRNA expression in stem cells has been shown to differ significantly from other cell types tested to date (Houbaviy et al. 2003; Suh et al. 2004; Tang et al. 2006). The role of miRNA-mediated regulation of stem cell division (Hatfield et al. 2005), as well as adipocyte (Esau et al. 2004), cardiac (Zhao et al. 2005), neural (Kuwabara et al. 2004; Wu and Belasco 2005) and hematopoietic lineage differentiation (Chen et al. 2004, 2005), is well known. More recently, a unique set of miRNAs has been shown to be associated with mouse ES cells and embryoid body (EB) formation (Houbaviy et al. 2003; Tang et al. 2006a, b; Murchison et al. 2005). In the study performed by Suh et al. (2004), 14 miRNAs were found to be expressed in a human ES cell-specific manner; miR-302b*, miR-302b, miR-302c*, miR-302c, miR-302a*, miR-302d, miR-367, miR-200c, miR-368, miR-154*, miR371, miR-372, miR-373*, and miR-373 (Suh et al. 2004). This study suggests that the expression patterns of miRNAs cloned from ES cells can be classified into four groups: (1) miRNAs that are expressed in ES cells as well as in EC cells; miR302b*, miR-302b, miR-302c*, miR-302c, miR-302a*, hsc-3, miR-302d, and miR367. These miRNAs may have conserved roles in mammalian pluripotent stem cells. (2) miRNAs that are expressed specifically in ES cells but not in other cells including EC cells; miR-200c, miR-368, miR-154*, miR-371, miR-372, miR-373*, and miR-373. These miRNAs may have functions specific to ES cells. (3) miRNAs that are rare in ES cells but abundant in HeLa and STO cells; let-7a, miR-301 (hsc11), miR-374, miR-21, miR-29b, and miR-29. These stage-specific miRNAs may play roles in the regulation of development and differentiation, like let-7 in C. elegans. (4) The last class consists of miR-16, miR-17-5p, miR-19b, miR-26a, miR-92, miR-103, miR-130a, and miR-222. These are expressed in most tested cell lines, so they may contribute to basic cellular functions. Lakshmipathy et al. (2007a, b) identified the differences in miRNA expression between undifferentiated ES cells and their corresponding differentiated cells that underwent differentiation in vitro over a period of 2 weeks, confirming the identity of a signature miRNA profile in pluripotent cells, comprising a small subset of differentially expressed miRNAs in ES cells. ES cells express high levels of miR200c, miR-371, miR-372, miR-302a, miR-320d, miR-373, miR-302c, miR-21, miR-222, miR-296, miR-494, miR-367, miR-miR154, miR-29a, miR-143, miR29c, and let7a, relative to their corresponding differentiated cells. By comparison, miR17M, miR-92, and miR-93 are more abundantly expressed in differentiated cells than in ES cells.

miRNA Signatures During Human Erythroid Cell Differentiation Although the stages of erythroid cell differentiation are well-characterized, the molecular mechanisms that orchestrate the coordinated changes from erythroid lineage commitment to terminal maturation remain poorly understood. In a study documented by Zhan et al. (2007), the authors explored the expression profile of miRNAs in erythroid cells at different stages of differentiation using miRNA


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microarray analysis. Real-time RT-PCR was used to confirm the results of miRNA microarray. MEL cells were used, which are derived from Friend virus transformed mouse spleen erythroid precursors that are blocked at about the pronormoblast stage of differentiation. Their studies show that, of 295 miRNAs assayed, more than 100 are expressed in erythroid cells with varied abundances. Of the miRNAs on the array, miR-298 was the most abundant miRNA with signals of about 50,000 in uninduced MEL cells. miR-320 was the second most abundant miRNA in uninduced MEL cells. miR-29b, miR-140*, miR-193, miR-382, and miR-434-5p, which were undetectable in untreated MEL cells, became detectable following induction of erythroid differentiation In contrast, the levels of both miR-298 and miR-320, the two most abundant miRNAs in untreated MEL cells, decreased upon induction of erythroid maturation. The level of miR-451 is the most significantly increased (more than sevenfold), whereas the levels of several miRNAs, such as miR-29a, miR-26a, miR-22, miR-144, miR-15b, miR-292-5p, and miR-30a-5p, increased more than twofold upon induction of erythroid differentiation. Functional studies using gain of function and loss of function approaches showed that miR-451 is associated with erythroid maturation. The findings indicate that dynamic changes in miRNA expression occurred during erythroid differentiation, with an overall increase in the levels of miRNAs upon terminal differentiation of erythroid cells.

1.2.2

miRNA Transcriptome and Human Physiology

1.2.2.1

Glucose-Regulated miRNAs from Pancreatic b Cells

Tang et al. (2009b) carried out a screen in the pancreatic b-cell line MIN6 to identify miRNAs with altered abundance in response to changes in glucose concentrations. This screen resulted in identification of 61 glucose-regulated miRNAs from a total of 108 miRNAs detectable in MIN6 cells. Fifty of the identified miRNAs, including miR-124a, miR-107, and miR-30d, are up-regulated in the presence of high glucose. Only a few of the miRNAs, including miR-296, miR484, miR-612, miR-638, and miR-690, are significantly down-regulated by high glucose treatment. Overexpression of miR-30d increases insulin gene expression, while inhibition of miR-30d abolished glucose-stimulated insulin gene transcription. Overexpression or inhibition of miR-30d has no effect on insulin secretion. The findings suggest that the putative target genes of miR-30d may be negative regulators of insulin gene expression. Several miRNAs including miR-375 and miR-124a, which are highly expressed. in pancreatic b cells cells (Baroukh et al. 2007; Kloosterman et al. 2007; Poy et al. 2007), have been implicated to negatively regulate insulin exocytosis (Poy et al. 2004; Krek et al. 2005; Lovis et al. 2008a, b). In addition, both miR-375 and miR124a appear to have important roles in pancreas development (Baroukh et al. 2007; Kloosterman et al. 2007; Poy et al. 2007).

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1.2.2.2


miRNAs in Human Omental and Subcutaneous Adipose Tissue

miRNAs have been shown to regulate metabolic processes, which are associated with type 2 diabetes including insulin signaling and glucose homeostasis (Poy et al. 2004; Gauthier and Wollheim 2006). To gain further insight into the association between miRNA expression in human adipose tissue and parameters of obesity, fat distribution, and glucose metabolism, Klo¨ting et al. (2009) performed a global miRNA gene expression array in paired omental and abdominal subcutaneous adipose tissue samples from 15 overweight or obese individuals with either normal glucose tolerance or type 2 diabetes. Expression of 155 miRNAs was carried out using the TaqManHMicroRNA Assays Human Panel Early Access Kit (Applied Biosystems, Darmstadt, Germany). These authors identified expression of 106 (68%) miRNAs in human omental and subcutaneous adipose tissue, but did not observe any miRNAs exclusively expressed in either fat depot, suggesting common developmental origin of both fat depots. They further identified significant correlations between the expressions of miRNA-17-5p, miR-132, miR-99a, miR-134, miR-181a, miR-145, and miR-197 and both adipose tissue morphology and key metabolic parameters, including visceral fat area, HbA1c, fasting plasma glucose, circulating leptin, adiponectin, and interleukin-6. They suggested that miRNA expression differences may contribute to intrinsic differences between omental and subcutaneous adipose tissue; human adipose tissue miRNA expression correlates with adipocyte phenotype, parameters of obesity, and glucose metabolism.

1.2.2.3

miRNAs in Circadian Rhythmicity

The daily cycling of light and temperature, generated by the earth’s rotation, is one of the most important driving forces in the evolution of the circadian clock, allowing organisms to anticipate and adapt to their daily (and seasonally) changing environment. Recently, a few studies have suggested that miRNAs may be important regulators of circadian rhythmicity, providing a new dimension (posttranscriptional) of our understanding of biological clocks. In one study with mice, miR-132 and miR-219-1 show daily oscillation in the suprachiasmatic nucleus (SCN), but not in other regions, peaking during the subjective day, which was abolished in mCry1/mCry2 double mutant, strongly indicating a clock control of the expression of these miRNAs in vivo. In another recent study on the mouse, 78 miRNAs were found expressed in adult mouse retina, 21 of which are potentially retina-specific (Xu et al. 2007b). This study identified a polycistronic, sensory organ-specific paralogous miRNA cluster that includes miR-96, miR-182, and miR-183 on mouse chromosome 6qA3 with conservation of synteny to human chromosome 7q32.2. In situ hybridization showed that members of this cluster are expressed in photoreceptors, retinal bipolar and amacrine cells. To identify miRNAs potentially involved in circadian rhythm regulation of the retina, these authors performed miRNA expression profiling with retinal RNA harvested at noon (Zeitgeber time 5) and midnight (Zeitgeber time 17) and identified a subgroup of 12 miRNAs,


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including members of the miR-183/96/182 cluster with diurnal variation in expression pattern. The daily cycling of expression of a number of miRNAs including miR-96, miR-124a, miR-103, miR-182, miR-106b, miR-422a, and miR-422b was found in the retina.

1.2.2.4

miRNAs in Human Pregnancy and Parturition

Montenegro et al. (2009) performed to determine gestational age-dependent changes in miRNA expression in the chorioamniotic membranes and to assess the significance of miRNAs in human pregnancy and parturition. The expression profile of 455 miRNAs was compared between patients at term without labor, in labor, and preterm labor using microarrays. A total of 39 miRNAs are differentially expressed between term and preterm cases, of which 31 (79.5%) are downregulated at term. A comparison between the preterm labor and term labor groups revealed differential expression of ten miRNAs, all of which are down-regulated at term. A comparison between the preterm labor and term without labor cases also showed decreased expression of 28 miRNAs and increased expression of ten miRNAs at term. The down-regulation of nine miRNAs at term (miR-25, miR338, miR-101, miR-449, miR-154, miR-135a, miR-142-3p, miR-202∗, and miR136) was shared by the two groups compared, preterm labor versus term labor and preterm labor versus term without labor. Overall, the majority (79.5%) of all differentially expressed miRNAs have decreased expression at term. Decreased expression of miR-338, miR-449, miR-136, and miR-199a∗ at term was confirmed by qRT-PCR.

1.2.2.5

Gender difference of miRNAs in liver

It has been shown that hepatic transcript profiles are different in men and women. An interesting study tested the role of miRNAs in the gender-differences of gene expression (Cheung et al. 2009). Using microarrays, miRNA screening was performed to identify sex-dependent miRNAs in rat liver. Out of 324 unique probes on the array, 254 were expressed in the liver and eight (3% of 254;) of those were found to be different between the sexes, with miR-21, miR-148a, miR-451, and miR-526c being male-predominant and miR-29b, miR-122a, miR-193, and miR205 being female-predominant. Among the eight putative sex-different miRNAs, only one female-predominant miRNA (miR-29b) was confirmed using quantitative real-time PCR. Furthermore, 1 week of continuous growth hormone treatment in male rats reduced the levels of miR-451 and miR-29b, whereas mild starvation (12 h) raised the levels of miR-451, miR-122a, and miR-29b in both sexes. The biggest effects were obtained on miR-29b with growth hormone treatment. It appears that hepatic miRNA levels depend on the hormonal and nutritional status of the animal and that miR-29b is a female-predominant and growth hormoneregulated miRNA in rat liver.

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1.2.3


miRNA Transcriptome in Diseased States

The first evidence of the involvement of miRNA in a human disease was obtained in 2002, when for the first time a deregulation of two miRNAs (miR-15a and miR-161) was related to CLL (Calin et al. 2002). Thus, if a disease occurs specifically in a given tissue, the miRNAs specifically expressed in that tissue will have a great potential to be related to that disease. The dissection of these relationships will be valuable for studying the functions of these miRNAs and their mechanisms in diseases and can be used to discovering novel disease-associated miRNAs. For example, 5 of the 6 papers revealed a down-regulation of let-7 in cancers, which is consistent with previous report, while all papers reported an up-regulation of let-7 in Alzheimer’s disease (see our HMDD database). 1. miRNAs tend to show similar or different dysfunctional evidences for the similar or different disease clusters, respectively. 2. A negative correlation between the tissue specificity of a miRNA and the number of diseases it associated. 3. An association between miRNA conservation and disease. 4. miRNAs associated with the same disease tend to emerge as predefined miRNA groups. 5. Disease-associated miRNAs show various dysfunctions, such as mutation, up-regulation, deletion, and down-regulation. 6. The miRNAs that have a higher degree of conservation tend to be associated with diseases with a higher probability significantly. miRNA conservation is associated with human disease susceptibility. 7. Revealed that miRNAs in 57% of the diseases have at least one family member in that disease associated miRNAs, which is significantly higher than the random. This finding suggested that the miRNA family members might have similar functions and play roles in similar biological processes and therefore their dysfunction would lead to similar phenotype. 8. Bartel and his colleagues reported that neighboring miRNAs show significant coexpression by a microarray profiling analysis (Farth et al. 2005). We found that miRNAs in 46% of the diseases have at least one neighboring member, which is significantly higher than the random. For example, all the six miRNAs implicated in hematopoietic malignancies are located in the miR-17 cluster. This result indicated that neighboring miRNAs might be regulated by common regulators at similar conditions and function together, and then their dysfunctions would result in the same disease.

1.2.3.1

Cancers

The initial study establishing a role for miRNAs in cancer came with the observation that two miRNA genes, miR-15a and miR-16a, are deleted in the majority of B


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cell chronic lymphocytic leukemia patients (Calin et al. 2002). Since then, roles for miRNAs in various cancers have been reported, primarily on the basis of differential miRNA profiling between cancer and normal tissue or from genetic screens with miRNA expression libraries. An important step toward the understanding of miRNA transcriptomes as biomarkers of human cancers was reached by the study reported by Volinia et al. (2006). In this study, the authors conducted a large-scale miRNA transcriptome analysis on 540 samples of six solid tumors including lung, breast, stomach, prostate, colon, and pancreatic tumors, and were able to identify a solid cancer miRNA signature composed by a large portion of overexpressed miRNAs. The up-regulated miRNAs in three or more types of solid cancers include miR-17-5p/miR-20a/miR-92-2, miR-21, miR-24, miR-25, miR-29, miR-30c, miR32, miR-106a, miR-107, miR-128b, miR-146, miR-155, miR-181-1b, miR-191, miR-199a-1, miR-214, and miR-221. The predicted targets for the differentially expressed miRNAs are significantly enriched for protein-coding tumor suppressors and oncogenes. Nonetheless, apart from the common panel aberrantly altered miRNAs, each type of tumor entities has its own miRNA signature, which can be used as a biomarker for distinguishing the types and progonosis of cancers.

Solid Cancers Probably the most classical study linking miRNAs and human cancers is the one reported by Volinia et al. (2006), describing a large-scale detailed analysis of the miRNA profiles in 540 samples from six solid tumors. The clustering of miRNA expression profiles derived from 228 miRNAs in 363 solid cancer and 177 normal samples. Comparison of all tumors against all normal tissues identified 26 overexpressed and 17 underexpressed miRNAs, out of 137 miRNAs expressed in at least 90% of the samples and shows a very good separation between the different tissues. These results indicated that, in solid cancers, the spectrum of expressed miRNAs is very different from that of normal cells (43 of 137 miRNAs, 31%). Among these miRNAs are some with well characterized cancer association, such as miR-17-5p, miR-20a, miR-21, miR-92, miR-106a, and miR-155. Strikingly, miR21, miR-191, and miR-17-5p are significantly overexpressed in all the tumor types examined or in five of six. The predicted targets for the differentially expressed miRNAs are significantly enriched for protein-coding tumor suppressors and oncogenes.

Breast Cancer The human “breast”-specific signature is characterized by the expression profile of 23 miRNA (Heneghan et al. 2009). In this study, the breast has been reported to be the tissue with the lower number of detected miRNA. Expression of 222 premiRNA was studied by real-time PCR in 32 commonly used cell lines, including

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five breast cancer ones (T47D, SKBR3, MDA361, MCF7, and MDA231) (Jiang et al. 2005). This study revealed that let-7f-1 is increased by seven-fold in the epithelial-derived breast, lung, and colorectal cancer cells (Silveri et al. 2006). The miRNA microarray was used to evaluate miRNA expression profiles in ten normal and 76 neoplastic breast tissues (Iorio et al. 2005). Twenty-nine miRNAs were found to be differentially regulated, of which a set of 15 could be used to predict the nature of the cell sample analyzed with 100% accuracy (i.e., tumor or normal breast tissue). Expression of some miRNA could be correlated with specific breast cancer biopathologic features, such as estrogen and progesterone receptor expression, tumor stage, vascular invasion, or proliferation index. Among the differentially expressed miRNAs, miR-10b, miR-125b, miR145, miR-21, and miR-155 emerged as the most consistently deregulated in breast cancer. Three of them, miR-10b, miR-125b, and miR-145, were down-regulated and the remaining two, miR-21 and miR-155, were up-regulated, suggesting that they may potentially act as tumor suppressor genes or oncogenes, respectively. miR-145 and miR-21, whose expression could differentiate cancer versus normal tissues, were also differentially expressed in cancers with different proliferation indexes or different tumor stages. In particular, miR-145 is progressively down-regulated from normal breast to cancer with high proliferation index. Similarly, but in opposite direction, miR-21 is progressively up-regulated from normal breast to cancers with high tumor stage. It was observed that breast cancer primary tumors have a decreased expression level of miR-125b compared to normal breast tissue, suggesting that downregulation of miR-125b impairs the differentiation capability of cancer cells.

Lung Cancer Yanaihara et al. (2006) found that expression levels of the five miRNAs (hsa-mir155, hsa-mir-17-3p, hsa-mir-let-7a-2, hsa-mir-145, and hsa-mir-21) were statistically altered in lung cancers, and these also had a prognostic effect on patient survival. An expression meta-analysis of predicted gene targets of three lung-enriched miRNAs, miR-34b/miR-34c/miR-449, identifies a diagnostic signature for lung cancer (Liang 2008). The study by Markou et al. (2008) suggests that overexpression of mature miR21 is an independent negative prognostic factor for overall survival in non–small cell lung cancer patients.

Liver Cancer The miRNA expression profiles in a large set of 52 human primary liver tumors consisting of premalignant dysplastic liver nodules and hepatocellular carcinomas were examined by qRT-PCR (Varnholt et al. 2008). Eighty miRNAs were examined


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in a subset of tumors, which yielded 10 up-regulated and 19 down-regulated miRNAs compared to normal liver. Subsequently, five miRNAs (miR-122, miR100, miR-10a, miR-198, and miR-145) were selected on the basis of the initial results and further examined in an extended tumor sample set of 43 hepatocellular carcinomas and nine dysplastic nodules. miR-122, miR-100, and miR-10a were overexpressed whereas miR-198 and miR-145 were up to five-fold down-regulated in hepatic tumors compared to normal liver parenchyma.

Colon Cancer Dıáz et al. (2008) assessed in 110 colon cancer patients the levels of miR-17-5p, miR-106a, miR-126, E2F1, and EGFL7 by quantitative real-time RT-PCR. Altered expression of miR-17-5p, miR-106a, and EGFL7 was associated with pathological tumor features of poor prognosis. Down-regulation of miR-106a predicted shortened disease-free survival and overall survival. miR-17-5p correlated with disease-free survival only at early stages. Inverse correlations were found between miR-17-5p and miR-106a levels and their target expression, E2F1. No correlation was found between miR-126 expression and its host gene levels, EGFL7. miR-106a deregulation was therefore considered as a marker of disease-free survival and overall survival independent of tumor stage. The lack of association between expression of miR-126 and its host gene EGFL7 suggests their regulation by independent stimuli. Inverse correlation between miR-17-5p and miR-106a and E2F1 levels supports E2F1 as a target mRNA for the two miRNAs. In a separate study, Schetter et al. (2008) reported that 37 miRNAs were differentially expressed in colon tumors from the test cohort. Selected for validation were miR-20a, miR-21, miR-106a, miR-181b, and miR-203, and all five were enriched in tumors from the validation cohort. Higher miR-21 expression was present in adenomas and in tumors with more advanced TNM staging. In situ hybridization demonstrated miR-21 to be expressed at high levels in colonic carcinoma cells. The 5-year cancer-specific survival rate was 57.5% for the Maryland cohort and was 49.5% for the Hong Kong cohort. High miR-21 expression was associated with poor survival, independent of clinical covariates, including TNM staging, and was associated with a poor therapeutic outcome. Among the miRNAs identified, some have a well-characterized association with colon cancer progression, eg miR-10b, miR-21, miR-30a, miR-30e, miR125b, miR-141, miR-200b, miR-200c, and miR-205 (Baffa et al. 2009). The expression levels of miR-143 and miR-145 were decreased in human colon tumors (Akao et al. 2007; Schepeler et al. 2008; Takagi et al. 2009). A decrease of miR101 levels could represent one of the leading causes of COX-2 overexpression in colon cancer cells (Strillacci et al. 2009). Guo et al. reported a ubiquitous loss of miR-126 expression in colon cancer lines when compared to normal human colon epithelia that may provide a selective growth advantage during colon carcinogenesis (Guo et al. 2008a).

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Colorectal Cancer Role of miRNAs in human colorectal cancer has been evidenced by several studies (Tang and Fang 2009). Ng et al. (2009) tested miRNAs using real-time PCR-based miRNA profiling on plasma and tissues from colorectal cancer. Of the panel of 95 miRNAs analyzed, five miRNAs were up-regulated both in plasma and tissue samples (miR-17-3p, miR-135b, miR-222, miR-92, and miR-95). The plasma levels of these markers were significantly reduced after surgery in ten colorectal cancer patients. Further validation with an independent set of plasma samples indicated that miR-92 differentiates colorectal cancer from gastric cancer, IBD, and normal subjects. Another study presented the expression of 156 mature miRNAs with several bioinformatics algorithms in colorectal tumors and adjacent non-neoplastic tissues from patients and colorectal cancer cell lines (Bandre´s et al. 2006). A group of 13 miRNAs were significantly altered in their expression in this tumor. The most significantly deregulated miRNA being miR-31, miR-96, miR-133b, miR-135b, miR-145, and miR-183. In addition, the expression level of miR-31 was correlated with the stage of colorecta tumor.

Gastric Cancer Human gastric cancer is among the major causes of cancer mortality worldwide. Using miRNA microarray assay, a group reported the miRNA expression profile in gastric cancer as compared with non-tumor tissues. The study revealed that the most highly expressed miRNAs in non-tumorous tissues are miR-768-3p, miR-1395p, miR-378, miR-31, miR-195, miR-497, and miR-133b. The most highly expressed miRNAs in gastric cancer tissues include miR-20b, miR-20a, miR-17, miR-106a, miR-18a, miR-21, miR-106b, miR-18b, miR-421, miR-340*, miR-19a, and miR-658. Unfortunately, verification of this expression pattern was not done with more accurate methods. The expression levels of three miRNAs (miR-34b, miR-34c, and miR-128a) were significantly up-regulated and those of three miRNAs (miR-128b, miR-129, and miR-148) were down-regulated in undifferentiated gastric cancer tissue when compared with those of the paired normal tissues (Katada et al. 2009). The probability of survival was significantly lower in patients with high expression levels of miR-20b or miR-150. There was a correlation between miR-27a and lymph node metastasis.

Esophageal Cancer Esophageal cancer is the sixth leading cause of death from cancer and one of the least studied cancers worldwide. With cryopreserved esophageal cancer tissues using advanced microRNA microarray techniques, Guo et al. (2008a, b) identified


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seven miRNAs that could distinguish malignant esophageal cancer lesions from adjacent normal tissues. Among the seven miRNAs, three miRNAs (miR-25, miR424, and miR-151) showed up-regulation and four miRNAs (miR-100, miR-99a, miR-29c, and mmu-miR-140*) showed reduction in cancer versus normal tissue. Some of these miRNAs could be correlated with the different clinicopathologic classifications. High expression of miR-103 and miR-107 correlates with poor survival. Five miRNAs (miR-335, miR-181d, miR-25, miR-7, and miR-495) correlate with gross pathologic classification (fungating versus medullary) and two miRNAs (miR-25 and miR-130b) correlate with differentiation classification (high versus middle versus low).

Thyroid Papillary Carcinomas A significant increase in miRNAs miR-221, miR-222, and miR-181b was detected in thyroid papillary carcinomas in comparison with normal thyroid tissue (Pallante et al. 2006). These results were further confirmed by Northern blot and quantitative RT-PCR analyzes. Moreover, RT-PCR revealed miR-221, miR-222, and miR-181b overexpression in fine needle aspiration biopsies corresponding to thyroid nodules, which were eventually diagnosed as papillary carcinomas after surgery. Finally, miR-221, miR-222, and miR-181b overexpression was also demonstrated in transformed rat thyroid cell lines and in mouse models of thyroid carcinogenesis. Functional studies, performed by blocking miR-221 function and by overexpressing miR-221 in human thyroid papillary carcinomas-derived cell lines, suggest a critical role of miR-221 overexpression in thyroid carcinogenesis.

Pancreatic Cancer Differential expression of 95 miRNAs with their potential functions related to cancer biology, cell development, and apoptosis was analyzed by qRT-PCR for pancreatic cancer tissue samples or cancer cell lines in comparison with those in relatively normal pancreatic tissues or normal human pancreatic ductal epithelial (HPDE) cells (Zhang et al. 2009). Human pancreatic tissue with chronic pancreatitis also was included for analysis. Analysis was performed on ten pancreatic cancer cell lines and 17 pairs of pancreatic cancer/normal tissues. Eight miRNAs were significantly up-regulated in most pancreatic cancer tissues and cell lines, including miR-196a, miR-190, miR-186, miR-221, miR-222, miR-200b, miR-15b, and miR95. The incidence of up-regulation of these eight genes between normal control subjects and tumor cells or tissues ranged from 70 to 100%. The magnitude of increase of these miRNAs in pancreatic cancer samples ranged from 3- to 2,018fold of normal control subjects. Roldo et al. (2006) showed that the expression of has-miR-103 and has-miR-107 and lack of expression of has-miR-155 could discriminate pancreatic tumors from normal pancreatic tissues.

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Hematological Cancers Acute myeloid leukemia (AML). AML is the most common acute leukemia in adults; it is characterized by clonal expansion of hematopoietic stem cells blocked at different stages of erythroid, granulocytic, monocytic, or megakaryocytic differentiation. It was shown that miRNA expression profiles are linked to the karyotype (Dixon-McIver et al. 2008) and expression of a specific miRNA (miR-181a) correlated with AML morphological subtype (Debernardi et al. 2007). miRNA signatures were associated with cytogenetic abnormalities in AML and the high expression of miR-191 and miR-199a correlated with patients having poor prognosis (Garzon et al. 2008). Chronic myeloid leukemia (CML). CML is a multi-step chronic BM disorder involving progression from chronic phase to an accelerated phase characterized by a translocation involving chromosomes 9 and 22, generating the Philadelphia chromosome. In CML CD34þ cells from patients from chronic phase, the miR17–92 polycistron, also called oncomir-1 comprising seven miRNA (miR-17-5p, miR-17-3p, miR-18a, miR-19a, miR-19b-1, miR-20a, and miR-92a-1) was up-regulated compared to blast phase (Venturini et al. 2007). Acute lymphocytic leukemia (ALL). ALL arises from either T or B lymphocyte precursors; however, B-ALL is the most common type. ALL is the predominant cancer in childhood and has a favorable prognosis compared to AML. Using the TaqMan MicroRNA Assays Human Panel (Applied Biosystems), Zanette et al. (2007) analyzed miRNA expression profiles in leukemia samples and CD19þ samples from healthy individuals.The five most highly expressed miRNAs were miR-128b, miR-204, miR-218, miR-331, and miR-181b-1 in ALL, and miR-331, miR-29a, miR-195, miR-34a, and miR-29c in CLL (Zanette et al. 2007), allowing for distinction between ALL and CLL. The miR-17-92 cluster was also found to be up-regulated in ALL. Another study demonstrated that several miRNAs were differentially expressed between AML and ALL, where miR-128a, miR-128b, miR-223, and let-7b were the most significant and discriminatory. The authors found that a signature of only two of these four miRNA was sufficient to discriminate these diseases. A subset of AML patients showed up-regulation of miR-155, which may repress genes implicated in hematopoietic development and disease (O’Connell et al. 2008). Chronic lymphocytic leukemia (CLL). CLL is characterized by elevated number of clonal lymphocytes B in circulation, usually arrested in G0/G1 phase. Calin et al. (2004a) reported that a unique 13-miRNA expression signature (hsa-miR-15a, hsamiR-195, hsa-miR-221, miR-23b, miR-155, miR-223, miR-29a-2, miR-24-1, miR29b-2, miR-146, miR-16-1, miR-16-2, and miR-29c) was a prognostic indicator of CLL. miR-15a and miR-16 are in a cluster located in 13q14.3, a chromosome region frequently deleted in CLL patients, which could explain the loss or downregulation of these miRNAs (Calin et al. 2002). These two miRNAs have potential for CLL prognosis: patients with good prognosis showed down-regulation of miR15a and miR-16, whereas bad prognosis was associated with down-regulation of miR-29 (Calin et al. 2002, 2004a). The miR-143 and miR-145 could also be used as


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disease markers, in combination with miR-15a and miR-16, as they were significantly down-regulated in B-cell diseases, including CLL (Akao et al. 2007). By using a cloning-based method, miR-21, miR-150, and miR-155 were shown to be up-regulated in CLL (Fulci et al. 2007), while miR-181a, let-7a and miR-30d were down-regulated (Marton et al. 2008). In addition, Marton et al. (2008) identified five new miRNA (miR-1200, let-7i*, miR-1201, miR-1202, and miR-1203) associated to CLL. Zanette et al. (2007) described up-regulation of miR-331, miR-29a, miR-195, miR-34a, and miR-29c in CLL.

Hodgkin Lymphoma Hodgkin lymphoma (HL) is derived from preapoptotic germinal center B cells, although a general loss of B cell phenotype is noted. HL is one of the most frequently occurring lymphomas, with an annual incidence rate of three to four new cases per 100,000 persons in the Western world. Using quantitative reverse transcription–polymerase chain reaction and miRNA microarray, we determined the miRNA profile of HL and compared this with the profile of a panel of B-cell non–Hodgkin lymphomas. The two methods showed a strong correlation for the detection of miRNA expression levels. The HL-specific miRNA included miR-1792 cluster members, miR-16, miR-21, miR-24, and miR-155. Using a large panel of cell lines, we found differential expression between HL and other B-cell lymphoma-derived cell lines for 27 miRNAs. A significant down-regulation in HL compared to non-Hodgkin lymphoma was observed only for miR-150.

Ovarian Cancer Epithelial ovarian cancer is the sixth most common cancer in women worldwide. In comparison to normal ovary, miRNAs are aberrantly expressed in human ovarian cancer. The most significantly overexpressed miRNAs were miR-200a, miR-141, miR-200c, and miR-200b, whereas miR-199a, miR-140, miR-145, and miR-125b1 were among the most down-modulated miRNAs (Iorio et al. 2007). The overall miRNA expression could clearly separate normal versus cancer tissues. The expression of these miRNAs can also be correlated with specific ovarian cancer biopathologic features, such as histotype, lymphovascular and organ invasion, and involvement of ovarian surface.

Prostate Cancer Expression profile of 40 prostatectomy specimens from stage T2a/b, early relapse, and non-relapse cancer patients was examined (Tong et al. 2009). Paired analysis was carried out with microdissected, malignant and non-involved areas of each specimen, using high-throughput liquid-phase hybridization (mirMASA) reactions

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and 114 miRNA probes. Five miRNAs (miR-23b, -100, -145, -221, and -222) were significantly down-regulated in malignant tissues. Ectopic expression of these miRNAs significantly reduced LNCaP cancer cell growth, suggesting growth modulatory roles for these miRNAs. Patient subset analysis showed that those with post-surgery elevation of prostate-specific antigen (chemical relapse) displayed a distinct expression profile of 16 miRNAs, as compared with patients with non-relapse disease. A trend of increased expression (>40%) of miR-135b and miR-194 was confirmed by qRT-PCR in 11 patients from each clinical subset.

Kidney Cancer The expression profiles of 245 miRNAs in kidney primary tumors were analyzed using a microarray containing 245 human and mouse miRNA genes (Gottardo et al. 2007). The specimens include a total of 27 kidney specimens (20 carcinomas, four benign renal tumors, and three normal parenchyma). A set of four human miRNAs (has-miR-28, has-miR-185, has-miR-27, and has-let-7f-2) were found significantly up-regulated in renal cell carcinoma compared to normal kidney. Of the kidney cancers studied, there was no differential miRNA expression across various stages, whereas with increasing tumor-nodes-metastasis staging in bladder cancer, miR26b showed a moderate decreasing trend.

Bladder Cancer Urothelial carcinoma is the most common form of cancer in the bladder and can be divided into two groups: the low-grade tumors which are always papillary and usually superficial, and the high-grade tumors which can be either papillary or nonpapillary and often invasive. Superficial tumors account for 75–80% of bladder neoplasms, while the remaining 20–25% are invasive or metastatic. Neely et al. (2009) performed microarray analysis and identified several miRNAs that were differentially expressed between the noninvasive and invasive bladder carcinoma cell lines including miR-21, miR-31, miR-200a, miR-200c, miR-205, miR-373*, miR-487b, miR-498, and miR-503. Cell lines characterized as invasive showed a miR-21:miR-205 ratio at least ten-fold higher than the quantitative ratio obtained from non-invasive cell lines. miR-21 and miR-205 expression levels were further determined in 53 bladder tumors (28 superficial and 25 invasive). The results suggest a miR-21:miR-205 expression ratio as a biomarker for distinguishing between invasive and noninvasive bladder tumors with high sensitivity and specificity. miRNA expression was analyzed in 27 bladder specimens (25 urothelial carcinomas and two normal mucosa) (Gottardo et al. 2007). Human miRNAs hasmiR-223, has-miR-26b, has-miR-221, has-miR-103-1, has-miR-185, has-miR-23b, has-miR-203, has-miR-17-5p, has-miR-23a, and has-miR-205 were significantly up-regulated in bladder cancers compared to normal bladder mucosa.


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Clear Cell Renal Cell Carcinoma A genome-wide expression profiling of miRNAs using microarray analysis and qRT-PCR was performed in clear cell renal cell carcinoma (ccRCC), with matched malignant and non-malignant tissue samples from two independent sets of 12 and 72 ccRCCs. The microarray-based experiments identified 13 overexpressed and 20 down-regulated miRNAs in malignant samples. Expression in ccRCC tissue samples compared to non-malignant samples measured by RT-PCR was increased on average by 2.7- to 23-fold for the miR-16, miR-452*, miR-224, miR-155, and miR-210, but decreased by 4.8- to 138-fold for miR-200b, miR-363, miR-429, miR-200c, miR-514, and miR-141. No significant associations between these differentially expressed miRNAs and the clinico-pathological factors tumor stage, grade, and survival rate were found. Nevertheless, malignant and non-malignant tissue could clearly be differentiated by their miRNA profiles. A combination of miR-141 and miR-155 resulted in a 97% overall correct classification of samples.

Endometrioid Adenocarcinoma The miRNA expression profile was studied in ten pairs of endometrioid adenocarcinoma and adjacent nontumorous endometrium using human miRNA microarray (Wu et al. 2009a, b). Seventeen miRNAs exhibited higher expression and six miRNAs exhibited lower expression in endometrioid adenocarcinoma samples than those in the nontumorous samples in the microarray. Of those, the miR-205, miR-449, and miR-429 were greatly enriched; in contrast, the miR-204, miR-99b, and miR-193b were greatly down-regulated in adenocarcinoma tissues. The expressions of these six miRNAs were validated using real time reverse transcription-PCR.

Head and Neck Cancer Head and neck cancer is the term given to a variety of malignant tumors that develop in the oral cavity, larynx, pharynx, and salivary glands and are predominantly squamous cell carcinomas. Molecular signatures using gene expression analysis have been identified that describe these tumors by location. However, head and neck squamous cell carcinoma (HNSCC) of the oral cavity is noted for its heterogeneity and has defied substantive molecular classification. In contrast to gene expression analysis of tumors, a relatively small number of miRNAs can be used to classify tumors and thus less heterogeneity should exist in miRNA expression profiles of various HNSCCs. Ramdas et al. (2009) studied miRNA expression profiles in HNSCC and adjacent normal tissue. They found that several miRNAs were determined to be differentially regulated in the HNSCC samples when compared with their normal tissue counterparts: miR-7, miR-15b, miR-21, miR-25, miR-34b, miR-93, miR-155, miR-181a, miR-181c, miR-182, miR-185,

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and let-7f increased by >2-folds, and miR-125a and miR-125b decreased by >40%. Quantitative RT-PCR was used to validate the expression of miR-21, miR-155, miR-103, miR-107, miR-93, miR-23b, miR-125b, and let7i miRNAs in both tumor and adjacent normal tissue samples.

Retinoblastoma Differential expression miRNAs in human retinoblastoma tissues was analyzed by miRNA microarray in conjunction with Northern blot analysis and in situ hybridization. A group of miRNAs were identified as highly expressed in retinoblastoma, including hsa-miR-494, hsa-let-7e, hsa-miR-513-1, hsa-miR-513-2, hsa-miR518c*, hsa-miR-129-1, hsa-miR-129-2, hsa-miR-198, hsa-miR-492, hsa-miR-498, hsa-miR-320, hsa-miR-503, and hsa-miR-373*.

Metastasis The spread and growth of cells from a primary tumor site (known as metastasis) is the most common cause of death for cancer patients and may occur through organ damage caused by growing lesions, paraneoplastic syndromes, or treatment complications. At the cellular level, the early stages of metastasis are characterized by the loss of contact with neighboring cells and an increase in invasive capacity. For a metastatic lesion to arise, tumor cells must disseminate by intravasating into the blood or lymphatic system. This requires the breaking of local cellcell contacts and invasion into the surrounding stroma and may be enhanced by neo-angiogenesis into the primary tumor site which is a pre-requisite for continued tumor growth. Several miRNAs have been linked to merastasis. miRNA profiling of human breast cancer MDA-MB-231 cells that were selected for being highly metastatic to the bone or lung, in comparison with the parental unselected cell population has identified three miRNAs (miR-335, miR-126, and miR-206) that have lower expression in both metastatic cell lines and in the metastases that developed after the injection of primary human malignant cells into mice (Tavazoie et al. 2008). Importantly, breast cancer patients with tumors expressing decreased levels of miR-335, miR-206, and miR-126 had a shorter time to metastatic relapse. Inhibition of miR-335 in non-metastatic cells was sufficient to increase metastasis to the lung. Overexpression of miR-335 also decreased the migration and invasion of cells in vitro. Another study identified miRNAs of interest to cancer on the basis of their differential expression between normal mammary tissue and primary breast carcinoma using microarray profiling data. Among these, Ma et al. identified miR-10b as being highly expressed only in metastatic cells (Ma et al. 2007). Inhibition of miR10b decreased invasion in matrigel, while miR-10b overexpression is sufficient to promote cell motility and invasion in otherwise non-invasive cell lines and can


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drive the metastasis of these normally non-metastatic cell lines when they are injected into mouse mammary fat pads. The overexpression of miR-373 and miR-520c increased in vitro invasion through matrigel and increased in vivo metastasis to both bone and lung when MCF7 cells overexpressing these miRNAs were injected into the tail veins of mice. Conversely, inhibition of miR-373 was sufficient to inhibit in vitro migration of otherwise invasive MDA-MB-435 and HCT-15 cells. miR-21 is perhaps the most frequently reported miRNA up-regulated in tumors, with several studies correlating miR-21 overexpression with increased metastasis (Roldo et al. 2006; Slaby et al. 2007). Inhibition of miR-21 in MDA-MB-231 cells was found to decrease invasion in vitro and decrease lung metastasis of cells injected into the tail vein of mice (Zhu et al. 2008). Inhibition of miR-21 also decreased intravasation across the chorioallantoic membrane and reduced lung metastases in chicken embryos (Asangani et al. 2008). In human breast and ovarian tumors, a strong correlation exists between miR200 and E-cadherin expression (Gregory et al. 2008; Park et al. 2008). Consistent with the pro-invasive properties associated with EMT and the inhibition of EMT by miR-200, inhibition of miR-200 promotes cell migration (Gregory et al. 2008) while miR-200 expression reduces migration (Park and Tang 2009). miR-205 has also been implicated in the maintenance of mouse mammary epithelial progenitor cells (Ibarra et al. 2007) and therefore may have a role in cancer-associated stem cells with which EMT has been recently linked (Mani et al. 2008). Altered miR-205 expression has also been reported in various cancers (Feber et al. 2008; Gottardo et al. 2007; Sempere et al. 2007; Iorio et al. 2007; Volinia et al. 2006).

1.2.3.2

Cardiovascular Diseases

Cardiac Hypertrophy and Heart Failure In response to stress (such as hemodynamic alterations associated with myocardial infarction, hypertension, aortic stenosis, valvular dysfunction, etc.), the adult heart undergoes remodeling process and hypertrophic growth to adapt to altered workloads and to compensate for the impaired cardiac function. Hypertrophic growth manifests enlargement of cardiomyocyte size and enhancement of protein synthesis through the activation of intracellular signaling pathways and transcriptional mediators in cardiac myocytes. The process is characterized by a reprogramming of cardiac gene expression and the activation of “fetal” cardiac genes (McKinsey and Olson 2005). Recent studies revealed an important role for specific miRNAs in the control of hypertrophic growth and chamber remodeling of the heart and point to miRNAs as potential therapeutic targets in heart disease. The first common finding is that an array of miRNAs is significantly altered in their expression, some up- and some down-regulated. The second common finding

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is that single miRNAs can critically determine the progression of cardiac hypertrophy. For example, Olson’s group reported >12 miRNAs that are up- or downregulated in cardiac tissue from mice in response to transverse aortic constriction (TAC) or expression of activated calcineurin, stimuli that induce pathological cardiac remodeling (van Rooij et al. 2006). Many of these miRNAs were found similarly regulated in failing human hearts. Forced overexpression of stress-inducible miRNAs induced hypertrophy in cultured cardiomyocytes. Particularly, overexpression of miR-195 alone, which is up-regulated during cardiac hypertrophy, is sufficient to induce pathological cardiac growth and heart failure in transgenic mice. The same group later found that miR-208, encoded by an intron of the alpha myosin heavy chain (aMHC) gene, is required for cardiomyocyte hypertrophy, fibrosis, and expression of aMHC in response to stress and hypothyroidism (van Rooij et al. 2007). The study showed that miR-208 mutant mice failed to undergo stress-induced cardiac remodeling, hypertrophic growth, and bMHC up-regulation, whereas transgenic expression of miR-208 was sufficient to induce bMHC. Abdellatif’s group reported an array of miRNAs that are differentially and temporally regulated during cardiac hypertrophy (Sayed et al. 2007). They found that miR-1 was singularly down-regulated as early as day 1, persisting through day 7, after TAC-induced hypertrophy in a mouse model. A study from Condorelli’s group focuses on the role of miR-133 and miR-1 in cardiac hypertrophy with three murine models: TAC mice, transgenic mice with selective cardiac overexpression of a constitutively active mutant of the Akt kinase, and human tissues from patients with cardiac hypertrophy (Care` et al. 2007). They showed that cardiac hypertrophy in all three models results in reduced expression levels of both miR-133 and miR-1 in the left ventricle. In vitro overexpression of miR-133 or miR-1 inhibits cardiac hypertrophy. In contrast, suppression of miR133 induces hypertrophy, which is more pronounced than that after stimulation with conventional inducers of hypertrophy. In vivo inhibition of miR-133 by a single infusion of an anti-miRNA antisense oligonucleotide (AMO) against miR-133 causes marked and sustained cardiac hypertrophy. Identified 19 deregulated miRNAs in hypertrophic mouse hearts after aortic banding. Knockdown of miR-21 expression via AMO-mediated depletion has a significant negative effect on cardiomyocyte hypertrophy induced by TAC in mice or by angiotensin II or phenylephrine in cultured neonatal cardiomyocytes. Consistently, another independent group identified 17 miRNAs up-regulated and three miRNAs down-regulated in TAC mice, and seven up-regulated and four downregulated in phenylephrine-induced hypertrophy of neonatal cardiomyocytes. They further showed that inhibition of endogenous miR-21 or miR-18b that are most robustly up-regulated augments hypertrophic growth, while introduction of either of these two miRNAs into cardiomyocytes represses cardiomyocyte hypertrophy (Tatsuguchi et al. 2007). A study directed to the human heart identified 67 significantly up-regulated miRNAs and 43 significantly down-regulated miRNAs in failing left ventricles


33

versus normal hearts (Thum et al. 2007). Interestingly, 86.6% of induced miRNAs and 83.7% of repressed miRNAs are regulated in the same direction in fetal and failing heart tissues compared with healthy heart tissues, consistent with the activation of “fetal” cardiac genes in heart failure. Collectively, with respect to hypertrophy, it is evident that in addition to the muscle-specific miRNAs miR-1, miR-133, and miR-208, other miRNAs, including miR-195, miR-21, miR-18b, also play an important role. It appears that multiple miRNAs are involved in cardiac hypertrophy and each of them can independently determine the pathological process. The most consistent changes reported by these studies using microarray are up-regulation of miR-21 (6 of 6 studies), miR-23a (4 of 6), miR-125b (5 of 6), and miR-214 (4 of 6), and down-regulation of miR-150 (5 of 6 studies) and miR-30 (5 of 6) (Wang et al. 2008; Yang et al. 2008a; Barringhaus and Zamore 2009).

Myocardial Ischemia/Reperfusion Injuries It is estimated that >50% of the death due to cardiovascular disease can be attributed to ischemic heart disease leading to myocardial infarction, a syndrome characterized by insufficient blood supply to the myocardium. Despite advances in treatment of myocardial infarction through removal of the occlusion, morbidity and mortality remain substantial, with about 5–6% of patients having a subsequent cardiovascular event within 30 days. The abrupt reperfusion of ischemic myocardium can itself cause tissue damage, a phenomenon termed myocardial reperfusion injury. We first described up-regulation of miR-1 in a rat model of acute myocardial ischemia and in ventricular samples of patients with coronary artery disease (Yang et al. 2007). This up-regulation of miR-1 results in significant cardiac arrhythmogenisis. Shan et al. (2009) confirmed our observation in a similar model but with prolonged time of infarction (1–4 weeks of coronary artery occlusion) and further established the role of miR-1 in apoptotic cell death. The study reported by Roy et al. (2009) revealed that in myocardial tissue following 2 and 7 days of ischemia/ reperfusion subjected to miRNA expression profiling and quantification, using a Bioarray system that screens for human-, mice-, rat-, and Ambi-miRNAs, 13 miRNAs were up-regulated on day 2 post ischemia/reperfusion, nine miRNAs were up-regulated on day 7, and 6 miRs were down-regulated on day 7. The up-regulated miRNAs include miR-21, miR-214, miR379, miR-146b, miR142-5p, miR-15b, miR-17-5p, miR-20b, and miR-106a and the down-regulated miRNAs include miR-150, miR-204, and miR-499. It is unclear yet whether these affected miRNAs are involved in the pathological conditions, what influences they produce, damaging or protective, and how they are acting under this setting. Interesting to note is that some of the miRNAs demonstrated the opposite directions of changes in their expression between ischemic myocardium and hypertrophic hearts. For example, miR-1, let-7, miR-181b, miR-29a, and miR30a/e, which are up-regulated in ischemic myocardium, are down-regulated in hypertrophy. Similarly, miR-208, miR-214, miR-320, and miR-351, which are

34


down-regulated in ischemic myocardium, are up-regulated in hypertrophy. This fact further reinforces the notion that different pathological conditions have different expression profiles. Earlier, we discovered that miR-1 promotes apoptotic cell death induced by oxidative stress (Xu et al. 2007a, b). This observation was later reproduced by two groups. Yu’s laboratory found that glucose induces apoptosis of cardiomyocytes via miR-1 that targets insulin-like growth factor 1 (IGF-1). Subsequently, the same group further observed that miR-1 and miR-206 expression were significantly increased, and IGF-1 protein was markedly reduced without obvious change of its mRNA level after myocardial infarction induction. Position 175-196 of rat IGF-1 30 UTR was identified to be required for efficient down-regulation by miR-1/miR206. In the serum withdrawal and hypoxic conditions, caspase-3 activity and mitochondrial potential were significantly increased in H9C2-miR-1 cells compared with the control group, respectively. These results indicate that miR-1 and miR-206 are involved in apoptotic cell death in myocardial infarction by posttranscriptional repression of IGF-1 (Shan et al. 2009). More recently, Tang et al. (2009a, b) reported that miR-1 is closely related with ischemia/reperfusion injury in a rat model. In vitro, the level of miR-1 was dramatically increased in response to oxidative stress. Overexpression of miR-1 facilitated H2O2-induced apoptosis in cardiomyocytes. Inhibition of miR-1 by antisense inhibitory oligonucleotides caused marked resistance to H2O2. Another miRNA miR-320 was also found to be involved in the regulation of cardiac ischemia/reperfusion (I/R) injury (Ren et al. 2009). The authors found that only miR-320 expression was significantly decreased in the hearts on I/R in vivo and ex vivo. Gain-of-function and loss-of-function approaches were employed in cultured adult rat cardiomyocytes to investigate the functional roles of miR-320. Overexpression of miR-320 enhanced cardiomyocyte death and apoptosis, whereas knockdown was cytoprotective, on simulated I/R. Furthermore, transgenic mice with cardiac-specific overexpression of miR-320 revealed an increased extent of apoptosis and infarction size in the hearts on I/R in vivo and ex vivo relative to the wild-type controls. Conversely, in vivo treatment with antagomir-320 reduced infarction size relative to the administration of mutant antagomir-320 and saline controls. miR-320 produced antithetical regulation of Hsp20 (Ren et al. 2009).

Preconditioning Protection to Ischemic Injuries Mice subjected to cytoprotective heat-shock (HS) showed a significant increase of miR-1, miR-21, and miR-24 in the heart. miRNAs isolated from HS mice and injected into non-HS mice significantly reduced infarct size after ischemia/reperfusion (I/R) injury, which was associated with the inhibition of pro-apoptotic genes and increase in anti-apoptotic genes. Chemically synthesized miR-21 also reduced infarct size, whereas a miR-21 inhibitor abolished this effect. Overall, this study provided evidence for the potential role of endogenously synthesized miRNAs in cardioprotection following I/R injury (Yin et al. 2008).


35

Yin et al. (2009) examined their hypothesis that miRNAs induced after ischemic preconditioning (IPC) in the heart may create a preconditioned phenotype through up-regulating proteins including endothelial nitric oxide synthase (eNOS)/inducible nitric oxide synthase (iNOS) and heat shock protein (HSP)70, which are implicated in the late-phase protection of IPC. miRNAs were extracted from hearts of ICR mice following IPC. The purified miRNAs were injected in vivo into the left ventricular wall of mice, and, 48 h later, the hearts were subjected to regional ischemia/reperfusion injury by left anterior descending artery ligation for 30 min followed by reperfusion for 24 h. IPC caused no changes in miR-23b and miR-483 whereas miR-1, miR-21,and miR-24 were significantly increased. The IPC-miRNA treatment caused an increase in eNOS mRNA and protein, whereas iNOS was not changed. HSF-1 (heat shock transcription factor 1) and HSP70 were also increased with IPC-miRNA treatment versus control. Moreover, injection of IPC-miRNA protected the hearts against ischemia/reperfusion injury, as shown by a reduction of infarct size as compared with saline or non-IPC miRNA-treated control.

Vascular Angiogenesis Reactive oxygen species (ROS), such as superoxide and hydrogen peroxide, are involved in the pathogenesis of many vascular diseases by modulating expression of a large number of genes related to vascular cell differentiation, proliferation, migration, and apoptosis. In this respect, increased ROS are associated with a variety of vascular disorders such as atherosclerosis, hypertension, restenosis after angioplasty or bypass, diabetic vascular complications, transplantation arteriopathy, and vascular aneurysm. ROS-mediated gene expression regulation has recently been extensively studied at epigenetic and transcriptional levels. Exposure of vascular cells to ROS modulates oxidation-sensitive signaling pathways and transcription factors that could be an important mechanism responsible for ROSmediated expression changes of multiple genes. In the study reported by Lin et al. (2009), 143 miRNAs out of the 238 miRNAs in the microarray were found expressed in rat vascular smooth muscle cell (RVSMCs). After treatment with H2O2 for 6 h, 57 miRNAs were highly expressed and deregulated. These include up-regulation of miR-21, miR-15b, miR-10b, miR-18, miR-20a/b, miR-30b/c/d, miR-195, and let-7b/d/f/I and down-regulation of miR-29b, miR-143, miR-145, miR-328, miR214, etc.

1.2.3.3

Neuronal Diseases

Several studies have implicated miRNAs in diseases of the CNS. For example, a mutation in the target site of miR-189 in the human SLITRK1 gene has been shown to be associated with Tourette’s syndrome (Abelson et al. 2005), while another study has reported altered miRNA profiles in the prefrontal cortex of patients with schizophrenia and schizoaffective disorder (Perkins et al. 2007).

36


Saba et al. (2008) used microarrays and RT-PCR to profile miRNA expression changes in the brains of mice infected with mouse-adapted scrapie. Fifteen miRNAs were found de-regulated during the disease processes; miR-342-3p, miR-320, let-7b, miR-328, miR-128, miR-139-5p, and miR-146a were over 2.5-fold up-regulated and miR-338-3p and miR-337-3p were over 2.5 fold down-regulated. Computational analysis predicted numerous potential gene targets of these miRNAs, including 119 genes previously determined to be also de-regulated in mouse scrapie. In particular, a correlation between miRNA expression and putative gene targets involved in intracellular protein-degradation pathways and signaling pathways related to cell death, synapse function, and neurogenesis was identified.

1.2.3.4

Human Immunodeficiency Virus Type 1 (HIV-1) Seropositive Individuals

Houzet et al. (2008) profiled the miRNA expression in peripheral blood mononuclear cells (PBMCs) from 36 HIV-1 seropositive individuals and 12 normal controls. The HIV-1 patients were grouped into four classes: Class I with high CD4þ T cell count and low viral load, Class II with high CD4þ T cell count and high viral load, Class III with low CD4þ T cell count and low viral load, and Class IV with low CD4þ T cell count and high viral load. The expression of 327 well-characterized human cellular miRNAs was analyzed using miRNA microarrays. The data revealed the following two points. (1) miRNA expression is deregulated in HIV infected patients and HIV-1 infection generally resulted in the down regulation of most human miRNAs in vivo. Fifty-nine miRNAs were down regulated while three were up regulated when compared to normal PBMCs. Some polycistronic miRNA clusters such as miR-451 and miR-144; and miR-23a, miR-27a, and miR-24 were down regulated simultaneously. (2) The down-regulation of 14 mRNAs (including miR-19a, miR-20b, miR-30a/c/e, miR-101, miR-155, and miR-146b) was specific to class IV, but was absent from class I, II, or III; the changes in four other miRNAs (miR-143, miR-199a, miR30e-3p, miR-335) were unique to class I, but not observed in class II, III, or IV. Eight other miRNAs were changed in both class I and IV patients, but not in class II or III patients; while a further eight miRNAs (let-7a, miR-1, miR-106b, miR-20a, miR-25, miR-29a, miR-34b, and miR-520b) were changed in class I, II, and IV patients, but were absent from class III patients. Lastly, 12 miRNA changes were present in all four classes of patients. These patterns suggest class-specific “signatures” that plausibly correlate stage-specific miRNA alterations with the in vivo course of HIV-1 infection.

1.2.3.5

Human Non-obstructive Azoospermia

Infertility is a worldwide reproductive health problem which affects 10–15% of couples. Half of the cases are due to male factors, and about 60–75% of male infertility cases are idiopathic, as the molecular mechanisms underlying the defects

1.3 miRNAs as Biomarkers for Human Disease

37

remain unknown. A significant proportion of idiopathic male infertility is accompanied by severe oligozoospermia or azoospermia. Altered miRNA expression in patients with non-obstructive azoospermia has been documented by Lian et al. (2009). In this study, 154 miRNAs were found differentially down-regulated and 19 up-regulated in the testes of non-obstructive azoospermia patients, with initial microarray screening followed by qRT-PCR verification. Around 7.8% (13 out of 154) down-regulated miRNAs belong to the testicular miRNAs: miR-19a, miR20b, miR-29c, miR-30a*, miR-30d*, miR-34b*, miR-92a, miR-181a, miR-449a, miR-652, let-7f, let-7f-2* and let-7i* (Ro et al. 2007). Several down-regulated miRNA clusters in patients with non-obstructive azoospermia were identified, such as the oncogenic potential of the miR-17-92 cluster and miR-371,2,3 cluster.

1.2.3.6

Other Diseases

Hepatitis Using real-time polymerase chain reaction, Ura et al. (2009) measured the expression of 188 miRNAs in liver tissues obtained from 12 patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) and 14 patients with hepatitis C virus (HCV)-related HCC, including background liver tissues and normal liver tissues obtained from nine patients. Global gene expression in the same tissues was analyzed via complementary DNA microarray to examine whether the differentially expressed miRNAs could regulate their target genes. Their data revealed two types of miRNA, one associated with HBV and HCV infections and the other with the stage of liver disease. Out of the 31 miRNAs associated with disease state (HCC vs. chronic hepatitis), 15 (miR-17-3p, miR30a-5p, miR-30e, miR-92, miR-99a, miR-122a, miR-125b, miR-130a, miR-139, miR-187, miR-199a, miR-199a*, miR-200a, miR-200b, and miR-326) are downregulated in HCC, which promote cancer-associated pathways such as cell cycle, adhesion, proteolysis, transcription, and translation; six miRNAs are up-regulated in HCC (miR-21, miR-98, miR-183, miR-221, miR-222, and miR-301), which repress the anti-tumor immune response.

1.3

miRNAs as Biomarkers for Human Disease

The elucidation of miRNA transcriptomes between diseased and normal tissues or between different disease types, stages and grades, gives the chance to identify the miRNAs most probably involved in human disease and to establish new diagnostic and prognostic markers. 1. Even with much less degree of freedom than mRNAs, miRNA expression profiles reflect the developmental lineage and differentiation state of cells and

38

2.

3.

4.

5.

6. 7.


successfully classify poorly differentiated tumors that could not have definitive diagnosis by histopathology, while the classification based upon the mRNA profiles was highly inaccurate. A very promising diagnostic strategy could arise from miRNAs if they are found in serum and can be detected by RT-PCR. Recent studies clearly indicate that miRNAs have unusually high stability in formalin-fixed and paraffin-embedded tissues and can remain intact in plasma and serum as well. Indeed, while expression patterns of miRNAs in tissue specimens have been well regarded as better biomarkers of human cancer, being characteristic of tumor type, tumor grade, and developmental origin, most recently, circulating miRNAs have also been revealed to be the stable blood-based markers for cancer detection. This is presumably because miRNAs are shorter than mRNAs, and therefore more resistant to ribonuclease degradation. Different diseases have distinct miRNA transcriptomes. The cancer cluster of miRNAs is clearly separated from the cardiovascular disease cluster of miRNAs. Studies indicate that all cancers are connected together by miRNA profiles, suggesting that various cancers may share similar associations at the miRNA level, in which some strong onco-miRNAs or miRNA tumor suppressors may play key roles. In a remarkable study, Lu et al. (2005) showed that expression data of only 217 miRNAs performed better at identifying cancer types than analysis of 16,000 mRNAs. They concluded that miRNAs might help detecting cancer better than other strategies presently available because miRNAs are only several hundreds, compared to tens of thousands of mRNAs and proteins. This can also partly be attributed to the fact that miRNA expression tends to be very strictly defined in time and space (Xi et al. 2006). This result revealed a potential correlation between miRNA tissue specificity and disease, which may be of value in predicting specific disease-related miRNAs by combining the miRNA tissue specificity values. Thus, if a disease occurs specifically in a given tissue, the miRNAs specifically expressed in that tissue will have a great potential to be related to that disease. Disease-associated miRNAs show various dysfunctions, such as mutation, upregulation, deletion, and down-regulation. Finally, miRNA analysis requires no expensive and time-consuming detection strategies using antibodies or mass spectrometry.

1.4

Methods for Analyzing miRNAs Expression

Owing to the uniqueness of miRNAs distinct from protein-coding mRNAs, there are differences in the approaches to detect and quantify miRNAs and mRNAs: (1) The extremely small size of miRNAs renders most conventional biological amplification tools ineffective because of the inability of even smaller primers/promoters (8- to10-nt) to bind on such small miRNA templates. For example, the regular

1.4 Methods for Analyzing miRNAs Expression

39

RT-PCR can only be used to quantify miRNA precursors rather than the mature miRNAs. (2) The close similarities among family members of miRNAs have presented challenges for developing miRNA-specific detection assays. (3) Small RNAs are less efficiently precipitated in ethanol and for this reason during the isolation by standard Trizol protocol of the RNA, resuspension in ethanol should be avoided. (4) miRNAs seem to be more stable than longer RNAs, for example in degraded samples it is still possible to obtain readable miRNA expression data. Moreover, miRNAs have a higher stability compared to mRNAs in samples obtained from formalin-fixed paraffin-embedded tissues or in serum.

1.4.1

Ideal Methods for miRNA Detection

The ideal miRNA profiling method should fulfill several requirements: 1. Sensitive enough to determine miRNA profiles even with small amounts of starting material 2. Specific enough to reproducibly detect a 1-nt difference between miRNAs 3. Able to provide quantitative analysis of miRNA levels 4. Capable of processing multiple samples in parallel 5. Easy to perform and not require equipment or reagents not readily available in a conventional molecular biology laboratory (Takada and Mano 2007)

1.4.2

Classification of Methods for miRNA Detection

To date, several decades of methods have been developed for miRNA detection. Currently available methods can be classified into following categories: 1. Based on the mechanism of miRNA capturing: (a) Hybridization-based techniques (e.g., Northern blots, in situ hybridization, RT-PCR, and microarrays). (b) Amplification-based techniques (e.g., real-time quantitative PCR; gold nanoparticle-initiated silver enhancement). (c) Cloning-based techniques (e.g., miRAGE). 2. Based on throughput of miRNA profiling: (a) Low throughput methods (e.g., Northern blots, in situ hybridization). (b) Medium throughput methods (e.g., multiplex RT-PCR, miRAGE). (c) High throughput methods (e.g., microarrays). 3. Based on quantification: (a) Quantitative (e.g., real-time quantitative PCR). (b) Semi-quantitative (e.g., Northern blots). (c) Non-quantitative (e.g., Northern blots, in situ hybridization).

40


4. Based on miRNA capture probes: (a) One-probe assays (e.g., Northern blots, in situ hybridization, and microarrays). (b) Two-probe assays or sandwich-type assays (e.g., gold nanoparticle-based assays and enzyme-amplified assays). 5. Based on read-out format: (a) Optical signal detection (including a variety of biochemical and chemical ligation-based techniques and PCR-based assays that use colorimetry, fluorescence, and bioluminescence). (b) Electrical signal (e.g., polyaniline nanowire technique; electrocatalytic nanoparticle tags technique). 6. Based on miRNA labeling: (a) Fluorescence labeling (e.g., Taqman real-time RT-PCR). (b) Luminescence labeling (e.g., electrocatalytic moiety labeling technique). (c) Non-labeling (e.g., electrocatalytic moiety labeling technique). These above methods have all achieved a certain level of success and have been successfully applied to generate miRNA transcriptomes for our understanding of the biological importance of miRNAs in various pathophysiological settings as described in the earlier sections of this chapter, even though none of these methods is perfect and has inherent limitations. Some methods rely on expensive equipment and an advanced read-out system, which might limit their application.

1.4.3

Brief Introduction to the Currently Available miRNA Detection Methods

Numerous techniques for detecting miRNAs have been developed including a variety of microarray-based (e.g., Krichevsky et al. 2003; Barad et al. 2004; Calin et al. 2004a; Liu et al. 2004a, b; Nelson et al. 2004; Shingara et al. 2005) and PCRbased approaches (Schmittgen et al. 2004; Chen et al. 2005; Jiang et al. 2005), padlock and rolling circle amplification (Jonstrup et al. 2006), an Invader assay (Allawi et al. 2004), an ELISA-based assay (Mora and Getts 2006), bead-based assays (Lu et al. 2005), single molecule detection (Neely et al. 2006), a splinted ligation strategy (Maroney et al. 2007), SAGE-based miRAGE (Cummins et al. 2006), RNA-primed array-based Klenow enzyme (RAKE) assay (Nelson et al. 2004, 2006), gold nanoparticle-based assays (Yang et al. 2008b), gold nanowire method (Fan et al. 2007), and signal amplifying ribozymes (Hartig et al. 2004) (see Table 1.2 for comparison of different approaches for miRNA detection). On the basis of published studies to date, the most widely used strategies for miRNA detection and profiling are microarray-based and PCR-based assays (Chen et al. 2005; Thomson et al. 2007). To date, the most straightforward and widely used assay for small RNA detection has been traditional Northern blotting. When the first miRNAs were described, Northern blotting was used to detect these small RNAs. To date, Northern blot remains the gold standard of miRNA

In situ hybridization (ISH)

Northern blot (NB)

Non-quantitative detection of miRNA expression

Validating miRNA signature highlighted by microarray

l

l

Good specificity

The “gold standard” of miRNA detection

l

l

Less reagent-, time-, and laborconsuming l Semi-quantitative

l

Rapid, computationalized analysis l Less expensive per miRNA

l

l

l

Reagent- and time-consuming

Low sensitivity, requiring large amounts of starting RNA

Low specificity (can be improved)

l

l

Low sensitivity

l

Limitation l Non-quantitative

l

Cellular and subcellular localization of miRNAs

l

With resolution at the single cell level or even at subcellular levels when using nonradioactive probes

l

Non-quantitative

May need to handle radioactive materials l Non-quantitative l Slow analysis speed l Unlikely to be used as a routine method for diagnostic purposes l Direct detection of miRNA in l Straightforward visualization of l Slow analysis speed living cells or fixed, embedded signals for miRNA under tissues detection

Differential expression of miRNAs

l

Table 1.2 Comparison of various approaches for miRNA expression detection Application Advantage l High-throughput miRNA l High-throughput miRNA miRNA profiling profiling Microarray

l

(continued )

Specificity and sensitivity can be improved by LNA modification of probes

Specificity and sensitivity can be improved by LNA modification of probes l DIG-labeling of probes can avoid radioactive materials

l

Remarks l Specificity and sensitivity can be improved by LNA modification of probes

1.4 Methods for Analyzing miRNAs Expression 41

Poly(A)-Tailed Universal Reverse Transcription

miR-Q RT-PCR

Stem-loop Realtime RT-PCR (qRT-PCR)


Can be used extensively for clinical diagnosis l Direct detection of miRNA in total RNA extracted from cell lines or tissues

l

Direct detection of miRNA in total RNA extracted from cell lines or tissues l Validating miRNA signature highlighted by microarray

l

Use for quantification of miRNAs between RNA samples of different statuses, locations, ages, genders, etc. l Can be used extensively for clinical diagnosis

l

l

Table 1.2 (continued) Application l Dynamic changes of miRNA expression

Quantitative

l

l

Advanced discriminative power and sensitivity

Providing a linearity of up to eight orders of magnitude detecting as low as 0.2 fM miRNA molecules l Easy to perform

l

Can be multiplex PCR for medium-throughput miRNA profiling l Advanced discriminative power and sensitivity

l

Proven specificity, which is ensured by using TaqMan to distinguish 1-nt difference

l

Advantage l Can be performed with morphologically preserved tissues sections or cell preparations l Highest sensitivity: requiring minute amounts of starting RNA Slow analysis speed

High cost per miRNA

l

l

l

Specificity may be inherently limited by the use of only one miRNA-specific primer and the use of a universal primer

Low-throughput

Only working with SYBR Green that can limit its specificity

Relying on commercial companies to provide TaqMan probes l Low-throughput

l

l

l

Limitation

l

Specificity and sensitivity can be further improved by LNA modification of probes

Remarks

42 1 Detection, Profiling, and Quantification of miRNA Expression

miRNA cloning

miRNA Amplification Profiling (mRAP)

Direct detection of miRNA in total RNA extracted from cell lines or tissues

l

Can lead to discovery of new miRNAs l Use for differential expression profilings of miRNAs between RNA samples of different

l

Can lead to discovery of new miRNAs l May be used for high-throughput profiling

l

l

Can be used extensively for clinical diagnosis

l

l

l

Advantage of identifying new miRNAs

Can be multiplex PCR for highthroughput miRNA profiling

Can be multiplex PCR for medium-throughput miRNA profiling l Combining cloning for new miRNA discovery, highthroughput profiling, and quantification of miRNA levels into one l High sensitivity and specificty

Proven specificity, which is ensured by using TaqMan l Quantitative

Highest sensitivity: requiring minute amounts of starting RNA

l

l

Relatively simple and convenient

l

Validating miRNA signature highlighted by microarray l Medium-throughput miRNA profiling

l

Validating miRNA signature highlighted by microarray l Can be used for clinical diagnosis Multiplexing RT- l Direct detection of miRNA in total RNA extracted from cell PCR lines or tissues

l

Slow analysis speed

High cost per miRNA

l

Complicated procedures with the need for enrichment of short RNAs by fractionation and of ligation of adaptors

May not suitable for clinical use as a diagnostic tool l Labor intensive

l

l

l

Complicated multiple steps

Relying on commercial companies to provide TaqMan probes

l

l

Slow analysis speed

High cost per miRNA

Low-throughput

l

l

l

l

(continued )

Specificity and sensitivity can be further improved by LNA modification of probes

1.4 Methods for Analyzing miRNAs Expression 43

NanoparticleAmplified SPR Imaging

Electrocatalytic Nanoparticle Tags


Medium-throughput miRNA profiling

l

l

l

Use for quantification and profiling of miRNAs between RNA samples of different statuses, locations, ages, genders, etc.

Direct detection of miRNA in total RNA extracted from cell lines or tissues l Validating miRNA signature highlighted by microarray

l

Direct detection of miRNA in total RNA extracted from cell lines or tissues

l

l

Limitation

Requirement of a large amount (1 mg) total RNA as a starting material l Unlikely be used extensively for clinical diagnosis l An opportunity for the lowl Limited application due to the density electrochemical array requirement for multiple in miRNA expression profiling electrochemical detection instruments l This method allows for as low as l Unlikely to be used extensively 5 ng total RNA for a successful for clinical diagnosis miRNA detection l Capable of identifying miRNAs with 3. Moreover, the study of some miRNAs, such as let-7f might require more than 30 ng/well. The technique has been validated with tissue-specific expression of miR-1 in heart tissue, miR-122 in liver, and miR-124a in brain [Mora & Getts 2006]. The smallest discernible analytical signal was determined according to the International Union of Pure and Applied Chemistry’s definition (Long and Winefordner 1983) to be 0.048 absorbance units, which corresponds to 4.9 pg oligonucleotides/ well, for the control oligonucleotides under more stringent conditions. The same determination was done using LMW total RNA from rat liver and brain, and the smallest discernible analytical signal corresponds to 2.150 and 0.909 ng, respectively, using the Microcon1 YM-100 microconcentrator-enriched RNA/well (Mora and Getts 2006).

References Connors TD, Burn TC, VanRaay, Germino GG, Klinger KW, Laudes GM (1997) Evaluation of DNA sequencing ambiguities using tetramethylammonium chloride hybridization conditions. BioTechniques 22:1088–1090 Goff LA, Yang M, Bowers J, Getts RC, Padgett RW, Hart RP (2005) Rational probe optimization and enhanced detection strategy for microRNAs using microarrays. RNA Biol 2:e9–e16 Long GL, Winefordner JD (1983) Limit of detection a closer look at the IUPAC definition. Anal Chem 55:712A–724A Mora JR, Getts RC (2006) Enzymatic microRNA detection in microtiter plates with DNA dendrimers. Biotechniques 41:420–424 Nilsen TW, Grayzel J, Prensky W (1997) Dendritic nucleic acid structures. J Theor Biol 187: 273–284 Stears RL, Getts RC, Gullans SR (2000) A novel, sensitive detection system for high-density microarray using dendrimer technology. Physiol Genomics 3:93–99 Wood WI, Gitschier J, Lasky LA, Lawn RM (1985) Base composition-independent hybridization in tetramethylammonium chloride: a method for oligonucleotide screening of highly complex gene libraries. Proc Natl Acad Sci USA 82:1585–1588

Chapter 20

Surface-Enhanced Raman Spectroscopy Method

Abstract Surface-enhanced Raman scattering (SERS) in combination with advanced multivariate methods for pattern recognition has been used for rapid, sensitive, and accurate identification of miRNAs. The strength of the SERS-based sensor is its sensitivity to detect extremely low levels of analyte and specificity to provide the molecular fingerprint of the analyte. The SERS spectra of related and unrelated miRNAs can be detected in near-real time; that detection is sequence dependent, and that SERS spectra can be used to classify miRNA patterns with high accuracy. This technique requires oblique-angle deposition (OAD) fabrication of nanostructured SERS substrate, spotting of RNA or miRNA onto the SERS substrate, and measurement of SERS spectra, followed by classification of the SERS spectra using partial least squares discriminate analysis (PLS–DA). The first application of this technique to miRNA profiling was made recently by Driskell et al. from the Department of Infectious Diseases, Center for Disease Intervention, University of Georgia (AHRC, USA) (Biosens Bioelectron 24:923–928, 2008). This technology is well suited for routine miRNA expression profiling in clinic laboratory. However, the SERS technique requires sophisticated read-out system, complicated analysis skill, and convoluted data interpretation and verification, which requires highly trained professionals to handle.

20.1

Introduction

Raman Spectroscopy is a powerful technique used to investigate the chemical states of the bonds in carbon materials. Surface-enhanced Raman spectroscopy (SERS), which enables the collection of Raman signals from a single molecule, makes the technique more useful for analysis of molecules deposited onto metal surfaces. SERS has become a powerful technique for analyzing biological samples as it can rapidly and nondestructively provide chemical and, in some cases, structural information about molecules in aqueous environments. In the Raman scattering process, both visible and near-infrared (NIR) wavelengths of light can be used to Z. Wang and B. Yang, MicroRNA Expression Detection Methods, DOI 10.1007/978-3-642-04928-6_20, # Springer-Verlag Berlin Heidelberg 2010

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induce polarization of Raman-active molecules, leading to inelastic light scattering that yields specific molecular vibrational information. SERS is a surface-sensitive technique that results in the enhancement of Raman scattering by molecules adsorbed on rough metal surfaces. The enhancement factor can be as much as 1014–1015, which allows the technique to be sensitive enough to detect single molecules. Surface-enhanced Raman scattering allows for the detection of molecules attached to the surface of a single metallic nanoparticle, typically a gold or silver nanoparticle. Existing SERS nanoparticles, also referred to as nanotags, generally include the metallic nanoparticle having a reporter molecule in close ˚ ), which produces a strong Raman signal proximity thereto (typically less than 50 A because of a surface-enhanced effect. Bringing reporter molecules in close proximity to the metal surfaces is typically achieved by adsorption of the Raman-active molecule onto suitably roughened metal nanoparticles, e.g., gold, silver, copper, or other free electron metals. The characteristic signal of the reporter molecule is used to determine the presence and amount of the SERS nanoparticles. Consequently, SERS nanoparticles have utility as spectroscopic and optical tags and are often used in assays. The development of surface enhancement has enabled Raman scattering to be an effective tool for qualitative as well as quantitative measurements with high sensitivity and specificity. Recent advances have led to many novel applications of SERS for biological analyses, resulting in new insights for biochemistry and molecular biology, the detection of biological warfare agents, and medical diagnostics for cancer, diabetes, and other diseases. This chapter highlights many of these recent investigations and provides a brief outlook in order to assess possible future directions of SERS as a bioanalytical tool. SERS in combination with advanced multivariate methods for pattern recognition has been used for rapid, sensitive, and accurate identification of miRNAs (Driskell et al. 2008). This technique requires oblique-angle deposition (OAD) fabrication of nanostructured SERS substrate, spotting of RNA or miRNA onto the SERS substrate, and measurement of SERS spectra, followed by classification of the SERS spectra using partial least squares discriminate analysis (PLS–DA). The applicability of this technique to miRNA expression detection is based upon the following results from the study conducted by Driskell et al. (2008). (1) A miRNA sample can be reproducibly spotted onto a nanostructured SERS substrate and the miRNA binds to the surface in the same orientation or distribution of orientations as to yield reproducible vibrational spectra; (2) OAD is a reliable means of producing SERS substrates. The reproducibility of this fabrication technique suggests that these substrates can be used to confidently identify spectral differences among different miRNAs resulting from sequence-dependent structural differences; (3) The SERS spectra are directly related to the miRNA sequence; SERS detection and differentiation is sensitive to differences in miRNA composition, and this is the basis for miRNA classification using the SERS spectra and PLS–DA analysis. In previous studies, a large number of bands (>20) are typically reported for each base between 200 and 1700/cm, with many of the bands overlapping making interpretation difficult. Nonetheless, two bands have been identified with minimal overlap for interpretation of the miRNA spectra reported here. A ring-breathing

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mode for adenine (A) produces an isolated band at 731 cm 1, and ring-breathing modes of both cytosine (C) and uracil (U) provide a band at 793 cm 1 (Shanmukh et al. 2006). It is worth noting that these two bands are not solely responsible for the PLS–DA classification of the miRNA sequences. The spectra clearly display multiple bands of significant miRNA-dependent variance, and this type of bivariate spectral interpretation is insufficient for fully identifying the sequence. The challenge is that the bands are convoluted contributions from the various bases. Thus, methods of multivariate analysis, such as PLS–DA, are required to fully identify the miRNA sequences.

20.2

Protocol

20.2.1 Materials 1. Glass microscope slides 2. 20 nm film of titanium (Ti)

20.2.2 Instruments 1. 2. 3. 4. 5. 6. 7. 8.

Computer-controlled power supply Electron-beam/sputtering evaporation system Renishaw inVia Raman microscope system Concave rubber band algorithm (OPUS, Bruker Optics, Inc., Billerica, MA) PLS Toolbox v4.0 (Eigen Vector Research Inc., Wenatchee, WA) MATLAB environment (v7.2, The Mathworks Inc., Natick, MA) GRAMS A/I spectral software package (Galactic Industries, Nashua, NH) Nine-point, 2nd-order polynomial Savitzky–Golay algorithm

20.2.3 Reagents 1. 2. 3. 4.

Piranha solution (80% sulfuric acid, 20% hydrogen peroxide) RNase-free Milli-Q water Reagents for total RNA isolation Piranha solution: a 3:1 mixture of concentrated sulfuric acid (H2SO4) with hydrogen peroxide (H2O2)

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20.2.4 Procedures The protocols described in this section are essentially the same as reported in the study by Driskell et al. (2008) (see Fig. 20.1).

20.2.4.1

Substrate Preparation

OAD fabrication of aligned silver nanorod arrays is used as SERS substrates the SERS method. 1. Prepare colloidal silver by the aqueous reduction of silver nitrate (10–3 M, 200 mL) with trisodium citrate (1%, 4 mL). 2. Cut glass microscope slides into 1 1 cm portions, clean with hot piranha solution to remove organic residues from substrates (the handling of Piranha solutions requires special protection equipment including: a full face shield, heavy duty rubber gloves (regular Nitrile gloves will not provide sufficient protection), as well as an acid apron to wear on top of the lab coat.) (Seu et al. 2007). 3. Rinse the slides with distilled water. 4. Dry the substrates with N2(g) and load into a custom-designed electron-beam/ sputtering evaporation system (Chaney et al. 2005). 5. Deposit a 20-nm film of Ti as an adhesion layer. 6. Evaporate a film of Ag (500 nm) onto the substrate at an angle normal to the ˚ /s. surface at a rate of 3.0 A 7. Rotate the substrates to 86 C with respect to the surface normal. ˚ /s for 100 min. 8. Allow Ag nanorods to grow at an oblique angle at a rate of 3.0 A Each deposition step is automated using a feedback loop integrated QCM to Prepare colloidal silver by aqueous reduction of silver nitrate

Isolate RNA from tissues or cells

Prepare siver nanorod as SERS substrate

Spot RNA sample onto the substrates

Collect and analyze SERS spectra

Fig. 20.1 Flowchart of the surface-enhanced Raman scattering (SERS) procedures for miRNA expression detection. According to Driskell et al. (2008)

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record the deposition rate and thickness, and a computer-controlled power supply to adjust the e-beam current. As reported elsewhere (Shanmukh et al. 2006), these deposition conditions result in optimal SERS substrates with overall nanorod lengths of ~900 nm, diameters of ~100 nm, and densities of ~13 nanorods/mm2 (Shanmukh et al. 2006).

20.2.4.2

Total RNA Extraction

See Section II for detailed protocols.

20.2.4.3

SERS Measurements

1. Acquire SERS spectra using a Renishaw inVia Raman microscope system. Use a 785-nm near-IR diode laser as the excitation source. Focus the laser into ~115 11 mm spot using a 5 objective. Set the laser power to 10% (the power at the sample surface is ~15 mW). Extend the scan spectra with a spectral range of 400–1,800/cm using a 10-s exposure. 2. Spot RNA sample onto the substrates, 1 mL/miRNA and allow to dry. Each RNA sample should be spotted in duplicate on a single substrate with 3 spectra recorded for each spot for a total of 6 spectra per miRNA/substrate. To ensure substrate-to-substrate reproducibility, each miRNA should be applied to three different substrates. 3. Use DEPC-treated water as a control.

20.2.4.4

Data Analysis

1. In order to evaluate the reproducibility of the method, SERS spectra should be collected from different locations on a single substrate, from different substrates, and for different miRNA sequences. 2. The spectra need to be baseline corrected using a concave rubber band algorithm with 10 iterations and 64 points, and then vector normalized. These steps allow for comparison of Raman band locations and relative peak intensities in the figures. 3. Perform classification and identification of different miRNAs using PLS Toolbox v4.0, operating in the MATLAB environment. Process SERS spectra for statistical analysis by taking the first derivative of each spectrum using a ninepoint, 2nd-order polynomial Savitzky–Golay algorithm followed by normalization to unit vector length. Then mean-center the normalized first derivative spectra and analyze with partial least squares discriminate analysis (PLS–DA). PLS–DA is an established and statistically robust method for objective and blind data classification. Training spectra of known origin should be used to build a

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PLS–DA model with a class reserved for each miRNA sequence. The spectra also need to be analyzed manually to strengthen the PLS–DA interpretation of the SERS results, and to independently validate the PLS–DA methodology. 4. Individually analyze raw SERS spectra using the GRAMS A/I spectral software package by measuring the peak heights for the bands located at 731 and 793 cm 1 and comparing the relative intensities for each sample.

20.3

Application and Limitation

The work presented by Driskell et al. demonstrates the utility of SERS for sensitive and rapid (10 s) detection of miRNA members and family members. The SERS platform based on OAD-fabricated silver nanorod arrays can be used for the detection and classification of miRNAs. The SERS method uses limited volumes of the specimen for miRNA analysis without RNA labeling and/or amplification steps, which are dependent on the intrinsic stability and specificity of the reagents. The SERS spectra of related and unrelated miRNAs can be detected in near-real time, that detection is sequence dependent, and that SERS spectra can be used to classify miRNA patterns with high accuracy. This technology is well suited for routine miRNA expression profiling in clinic laboratory. The SERS technique requires sophisticated read-out system, complicated analysis skill, and convoluted data interpretation and verification, rendering it less practical in most of molecular biology laboratories.

References Chaney SB, Shanmukh S, Zhao Y-P, Dluhy RA (2005) Aligned silver nanorod arrays produce high sensitivity surface-enhanced Raman spectroscopy substrates. Appl Phys Lett 87:31908–31910 Driskell JD, Seto AG, Jones LP, Jokela S, Dluhy RA, Zhao YP, Tripp RA (2008) Rapid microRNA (miRNA) detection and classification via surface-enhanced Raman spectroscopy (SERS). Biosens Bioelectron 24:923–928 Seu KJ, Pandey AP, Haque F, Proctor EA, Ribbe AE, Hovis JS (2007) Effect of surface treatment on diffusion and domain formation in supported lipid bilayers. Biophys J 92:2445–2450 Shanmukh S, Jones L, Driskell J, Zhao Y, Dluhy R, Tripp RA (2006) Rapid and sensitive detection of respiratory virus molecular signatures using a silver nanorod array SERS substrate. Nano Lett 6:2630–2636

Chapter 21

RAKE Assay

Abstract The RNA-primed, array-based Klenow enzyme (RAKE) assay is a new method for high-throughput miRNA detection. It involves the on-slide application of the Klenow fragment of DNA polymerase I to extend unmodified miRNAs hybridized to immobilized DNA probes. RAKE offers unique advantages for specificity over Northern blots or other microarray-based expression profiling platforms. An oligo with a 5’ spacer is covalently linked onto a glass platform. The spacer sequence is followed by a miRNA antisense capture probe with three thymidine residues in between. RNA samples are hybridized to this array. miRNAs in the sample would bind to their specific probe and form a double stranded structure. The addition of exonuclease I will only degrade unbound single stranded oligos. The miRNA that have latched onto its probe will act as a primer. Subsequent PCR will result in the addition of biotin-conjugated dATPs onto the spacer template, which emits an augmented signal without PCR amplification of the original RNA sample. The technique was invented by Mourelatos and colleagues from the Department of Pathology and Laboratory Medicine, School of Medicine, University of Pennsylvania (Philadelphia, Pennsylvania, USA) (Nelson et al. 2006). The RAKE technique was initially applied to studying human cell lines and brain tumors, proving that RAKE assay is a sensitive and specific method for miRNA detection and an ideal approach for rapid expression profiling of all known miRNAs. Later, the same group used RAKE to profile miRNAs from normal human adult and fetal brains and from reactive astrocytosis and oligodendroglial tumors (RNA 12:187–191, 2006).

21.1

Introduction

The RNA-primed, array-based Klenow enzyme (RAKE) assay is a new method for high-throughput miRNA detection. It involves on-slide application of the Klenow fragment of DNA polymerase I to extend unmodified miRNAs hybridized to immobilized DNA probes. RAKE offers unique advantages for specificity over Z. Wang and B. Yang, MicroRNA Expression Detection Methods, DOI 10.1007/978-3-642-04928-6_21, # Springer-Verlag Berlin Heidelberg 2010

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Northern blots or other microarray-based expression profiling platforms. An oligo with a 5’ spacer is covalently linked onto a glass platform. The spacer sequence is followed by a miRNA antisense capture probe with three thymidine residues in between. RNA samples are hybridized to this array. miRNAs in the sample would bind to their specific probe and form a double stranded structure. The addition of exonuclease I will only degrade unbound single stranded oligos. The miRNA that have latched onto its probe will act as a primer. Subsequent PCR will result in the addition of biotin-conjugated dATPs onto the spacer template, which emits an augmented signal without PCR amplification of the original RNA sample (Nelson et al. 2004). RAKE is a new tool with high sensitivity and specificity for miRNA profiling. A major advantage of RAKE is that there is no sample RNA manipulation. Certain possible biases that may be introduced during enzymatic labeling, or during cDNA generation or amplification of the sample RNA before hybridization to the glass microarray, are thus avoided. RAKE allows for rapid and simultaneous detection of all known miRNAs from the same sample. Another advantage of RAKE is the ability to completely automate all steps from sample hybridization to detection. This is achieved by using existing technologies and equipment used for traditional mRNA microarrays, and allows for highly consistent performance. Northern blotting is considered the standard method for miRNA validation and quantification (Ambros et al. 2003); it offers both “quantitative” and “qualitative” information and, unlike a microarray experiment, confirms the length of the hybridized transcripts. In contrast to RAKE, however, Northern blotting is laborious and hence less well suited for high-throughput expression profiling. The RAKE assay also gives unique qualitative data, because the 3’ end of the miRNA ‘primer’ should hybridize specifically to the oligonucleotide ‘template’. For this reason, RAKE can discriminate the exact 3’ end of miRNAs. This is significant because of the many mature miRNAs for which paralogs differ at the 3’ end. These miRNAs, derived from different genes, would be predicted to cross-react adversely in northern blots (and in standard microarray methods using labeled target pools) but not usually on RAKE assay. According to Nelson et al. (2004), the RAKE assay is devised to exploit the known ability of the Klenow enzyme fragment to act as a DNA polymerase using an RNA primer on a DNA oligonucleotide template (Huang and Szostak 1996; Huang and Alsaidi 2003). Earlier studies have demonstrated on-slide enzymatic reactions and primer extension (Nikiforov et al. 1994; Head et al. 1997). However, direct detection of RNA hybridization (using RNA-primed DNA polymerase) has not been reported on a microarray, nor has the special properties of the Klenow enzyme been used in microarray studies. It is also necessary to use exonuclease I, a 3’-5’, single-stranded DNA–specific exonuclease that is highly processive (Brody et al. 1986). It is important to note that the activities of both Klenow enzyme and exonuclease I are independent of the sequence of their substrates (Brody et al. 1986). Systematic bias is therefore not introduced. RNA ligases, in contrast, are prone to bias because the enzyme kinetics change with substrate sequence (Ohtsuka et al. 1997; Romaniuk et al. 1982), producing an inaccurate representation of the miRNAs present in a target pool labeled by RNA ligase methods. The results

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from the study by Nelson et al. (2004) demonstrate sensitivity to the level of 10 pg of target miRNA, which is comparable to that of the Northern blots (Lim et al. 2003).

21.2

Protocol

21.2.1 Materials 1. 2. 3. 4. 5.

Cell culture materials RNA isolation materials 20% urea-PAGE gel 384-well plates (Qiagen) CodeLink slides (Amersham)

21.2.2 Instruments 1. 2. 3. 4. 5.

Storm 860 Phosphorimager (Molecular Dynamics) or an equivalent GeneMachines OmniGrid 100 robot or an equivalent Genepix 4000B laser scanner (Axon) or an equivalent Genepix Pro5.0 software package (Axon) or an equivalent Excel and Genespring 6.2 or equivalents

21.2.3

Reagents

1. MirVana kit designed to isolate low molecular weight RNA (Ambion) 2. Trizol LS reagent (Ambion) 3. 150 mM sodium phosphate buffer (pH8.5; 200 U/ml print buffer) with 0.0005% Sarkosyl 4. 2 and 5 SSC 5. 10% formamide 6. Hybridization buffer 7. Exonuclease I (NEB; fresh buffer at pH7.5; 4 U/mL) 8. 0.05% SDS 9. Exo(–) Klenow (Promega; 0.15 U/ml) 10. DNA polymerase buffer (Promega) 11. biotin-7-dATP (Invitrogen; 4 mM) 12. Streptavidin-conjugated Alexa-fluor-547 (Molecular probes; 15 ng/ml)

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21 RAKE Assay

21.2.4 Procedures The procedures described herein are based on the study reported by Nelson et al. (2004) (See Figs. 21.1 and 21.2).

21.2.4.1

RNA Isolation and Northern Blots

1. Isolate total RNA using MirVana kit designed to isolate low molecular weight RNA. 2. If Northern blot analysis needs to be performed to verify the RAKE data, then the total RNA can be isolated from the cells or tissues using the Trizol LS Triple Thymidines Spacer

Anti-miR-1

5’-GTCGTGACTGGGAATAGCCTGTTTATACATACTTCTTTACATTCCA-3’

3’

3’ TTT TTT

5’

3’

3’

TTT

TTT

TTT Hybridization

TTT

TTT

Glass microarray slide Immobilization

TTT

+

TTT

miR-1

TTT

TTT TTT

Oligo probes

3’

3’

3’

3’

miRNA Capture Oligodeoxynucleotide Probe

EndoI

Endonuclease I

Laser scanning Fluorophore-conjugated streptavidin

Klenow dsDNA/RNA Klenow

XXX

Biotin Conjugation

XXX

PolyI PolyI

TTT

TTT

B

Fluorophore Conjugation

AAA

B

TTT

FS

AAA

B

XXX

Biotin-dATP

FS

AAA

Image Analysis

ssDNA Degradation

Fig. 21.1 Schematic diagram of RNA-primed, array-based Klenow enzyme (RAKE) assay. The sample probe at the top of the figure illustrates the generic structure of the DNA oligonucleotides used on the microarray. The nucleotides at the 5’ half comprise a spacer, which is constant for all the probes, followed by three thymidine nucleotides. The variable portion of each probe is at the 3’ end, which is the antisense sequence of various miRNAs. An RNA sample, containing miRNAs, is hybridized to a glass microarray on which all the DNA probes have been spotted. Next, the slide is washed and exonuclease I is applied to degrade unhybridized (single-stranded) DNA probes. The slide is washed again and the Klenow fragment of DNA polymerase I is applied to catalyze the addition of biotin-conjugated dATPs using the miRNA as a primer and the spotted probe as template. Finally, the biotins are labeled with streptavidin-conjugated fluorophore to specifically highlight spots (here, spot ‘X’) where miRNA hybridization occurred. Modified from Nelson et al. (2004)

21.2 Protocol

285 Select miRNAs of your interests

Synthesize miRNA capture probes


Immobilization of probes onto a glasss microarray slide

Hybridization of miRNAs and probes to form miRNA/DNA duplex on the microarray slide

Digest non-hybridized single-stranded probes

Klenow-meidated biotin conjugation

Fluorophore conjugation

Laser scanning

Fig. 21.2 Flowchart of the RAKE assay for miRNA expression detection. According to Nelson et al. (2004)

reagent. Run RNA on 20% urea-PAGE gels, blot, and probe using 5’-end radiolabeled probes against the target miRNAs, as described in detail in Sect. 3. Expose blots on phosphorimager screens overnight and scan signals and quantitate bands using a Storm 860 Phosphorimager.

21.2.4.2

Design of Oligos for Microarray

1. An oligo contains three parts: a 5’ spacer GTCGTGACTGGGAATAGCCTG, three thymidine residues, and a miRNA antisense capture probe. Taking miR-1 as an example, the full oligo sequence then should be 5’-GTCGTGACTGGGAATAGCCTG-TTT-ATACATACTTCTTTACATTCCA-3’ (Fig. 24.1).

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2. Synthesize the oligo in the form of DNA, at 600 pmol on 384-well plates, each containing a 5’-C6-amino modified linker, using the services provided by qualified companies.

21.2.4.3

Microarray Platform

1. Suspend the probes at 40 mM in 150 mM sodium phosphate buffer (pH8.5; 200 U/ml print buffer) with 0.0005% Sarkosyl. 2. Use a GeneMachines OmniGrid 100 robot to print the probes onto CodeLink slides at 30–35% humidity at 24–27 C, so that the oligo is covalently linked onto a glass platform. 3. Each spot element is B120 mm in diameter and the center-to-center spacing is 400 mm. Each glass slide contain six spots (three spatially separated pairs) corresponding to each probe, for a total 1,422 spots including controls.

21.2.4.4 1. 2. 3. 4.

5. 6. 7. 8. 9. 10. 11. 12. 13.

RAKE Protocol

1 min wash in 2 SSC at 25 C 5 min rinse in 5 SSC with 10% formamide at 25 C 3 30 s rinse in 2 SSC at 25 C 18 h target/probe hybridization (35 mL concentrated hybridization buffer, 65 mL small RNA preparation containing 4 mg low molecular weight RNA, and 10 mL plant DNA spike-in solution, which are together heated to 75 C and allowed to cool at room temperature prior to hybridization) at 25 C 3 1 min rinse in 2 SSC at 37 C 3 h incubation with Exonuclease I (4 U/mL) at 27 C 3 1 min rinse in 2 SSC at 27 C 10 min rinse in 2 SSC with 0.05% SDS at 27 C 4 1 min rinse in 2 SSC at 37 C 60 min incubation with Exo(–) Klenow (0.15 U/ml) in 1 DNA polymerase buffer (Promega) with biotin-7-dATP (4 mM) at 27 C 2 1 min rinse in 2 SSC at 25 C 30 min incubation with streptavidin-conjugated Alexa-fluor-547 (15 ng/ml) at 25 C 3 1 min rinse in 2 SSC at 25 C

21.2.4.5

Validation Steps

1. Generate a concentration curve using a synthetic target RNA oligonucleotide (e.g., miR-1) in the background of a complex RNA mixture (e.g., low-molecularweight RNA isolated from HeLa cells, a cell line that does not contain miR-1).

References

21.2.4.6

287

Image Analysis and Data Processing

1. Scan slides using a Genepix 4000B laser scanner (Axon) at a constant power level and sensitivity (550 PMT) using a single color channel (532-nm wavelength). 2. Eliminate nonhybridizing and artifact-associated spots by both visual- and software-guided flags. 3. Measure image intensities as a function of the median of foreground minus background. Normalize negative values to zero. 4. Analyze images using the Genepix Pro5.0 software package (Axon). Use Excel and Genespring 6.2 for further data analysis.

21.3


The RAKE assay is a sensitive, specific technique for assessing DNA and RNA targets, offering unique advantages for specificity over Northern blots or other microarray-based expression profiling platforms, and may have broader applications than for miRNA detection (Nelson et al. 2004). The RAKE assay allows for sensitive, specific and high-throughput miRNA expression profiling. The sensitivity of RAKE is very similar to the sensitivity of the technique reported by Miska et al. (2004). However, in contrast to other microarray techniques, RAKE does not involve the generation of a cDNA library or amplification of the RNA sample and avoids sample RNA manipulation prior to hybridization. RAKE should be superior at discriminating paralogous miRNAs that differ at their 3’ ends, because these other techniques rely solely on hybridization to detect and discriminate between miRNA paralogs (Miska et al. 2004; Liu et al. 2004).

References Ambros V, Bartel B, Bartel DP, Burge CB, Carrington JC, Chen X, Dreyfuss G, Eddy SR, Griffiths-Jones S, Marshall M, Matzke M, Ruvkun G, Tuschl T (2003) A uniform system for microRNA annotation. RNA 9:277–279 Brody RS, Doherty KG, Zimmerman PD (1986) Processivity and kinetics of the reaction of exonuclease I from E. coli with polydeoxyribonucleotides. J Biol Chem 261:7136–7143 Head SR, Rogers YH, Parikh K, Lan G, Anderson S, Goelet P, Boyce-Jacino MT (1997) Nested genetic bit analysis (N-GBA) formutation detection in the p53 tumor suppressor gene. Nucleic Acids Res 25:5065–5071 Huang Z, Alsaidi M (2003) Selective labeling and detection of specific mRNA in a total-RNA sample. Anal Biochem 322:269–274 Huang Z, Szostak JW (1996) A simple method for 3’-labeling of RNA. Nucleic Acids Res 24:4360–4361 Lim LP, Lau NC, Weinstein EG, Abdelhakim A, Yekta S, Rhoades MW, Burge CB, Bartel DP (2003) The microRNAs of Caenorhabditis elegans. Genes Dev 17:991–1008

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Liu CG, Calin GA, Meloon B, Gamliel N, Sevignani C, Ferracin M, Dumitru CD, Shimizu M, Zupo S, Dono M, Alder H, Bullrich F, Negrini M, Croce CM (2004) An oligonucleotide microchip for genome-wide microRNA profiling in human and mouse tissues. Proc Natl Acad Sci USA 101:9740–9744 Miska EA, Alvarez-Saavedra E, Townsend M, Yoshii A, Sestan N, Rakic P, Constantine-Paton M, Horvitz HR (2004) Microarray analysis of microRNA expression in the developing mammalian brain. Genome Biol 5:R68 Nelson PT, Baldwin DA, Kloosterman WP, Kauppinen S, Plasterk RH, Mourelatos Z (2006) RAKE and LNA-ISH reveal microRNA expression and localization in archival human brain. RNA 12:187–191 Nelson PT, Baldwin DA, Scearce LM, Oberholtzer JC, Tobias JW, Mourelatos Z (2004) Microarray-based, high-throughput gene expression profiling of microRNAs. Nat Methods 1:106–107 Nikiforov TT, Rendle RB, Goelet P, Rogers YH, Kotewicz ML, Anderson S, Trainor GL, Knapp MR (1994) Genetic Bit Analysis: a solid phase method for typing single nucleotide polymorphisms. Nucleic Acids Res 22:4167–4175 Ohtsuka E, Nishikawa S, Fukumoto R, Tanaka S, Markham AF (1997) Joining of synthetic ribotrinucleotides with defined sequences catalyzed by T4 RNA ligase. Eur J Biochem 81:285–291 Romaniuk E, McLaughlin LW, Neilson T, Romaniuk PJ (1982) The effect of acceptor oligoribonucleotide sequence on the T4 RNA ligase reaction. Eur J Biochem 125:639–643

Chapter 22

Bead-Based Flow Cytometric miRNA Expression Profiling

Abstract The bead-based flow cytometric miRNA expression profiling method involves coupling of miRNA capture probes to carboxylated 5-micron polystyrene beads, RT-PCR amplification of miRNAs, hybridization of miRNAs to the capture beads, staining with streptavidin-phycoerythrin, and and final read out using a flow cytometer to measure bead color (denoting miRNA identity) and phycoerythrin intensity (denoting miRNA abundance). The technique was invented by Lu et al in Broad Institute of MIT and Harvard (Cambridge, Massachusetts, USA) (Lu et al. 2005). The bead-based method has been applied to conduct a systematic expression analysis of 217 mammalian miRNAs from 334 samples, including multiple human cancers. The data proved it is able to distinguish tumors of different developmental origin, to distinguish tumors from normal tissues, and to distinguish tumors of different stages. The technique is feasible, and has the attractive properties of improved accuracy, high speed and low cost. Moreover, it is an efficient detection platform for high throughput miRNA profiling in a quantitative manner. The beadbased miRNA detection method is also easy to implement in a routine clinical setting.

22.1

Introduction

The bead-based flow cytometric miRNA expression profiling method takes the advantages of bead-anchored hybridization. The procedures involve several straightforward steps: (1) Coupling of oligonucleotide-capture probes complementary to miRNAs of interest to carboxylated 5-micron polystyrene beads impregnated with variable mixtures of two fluorescent dyes (that can yield up to 100 colors), each representing a single miRNA; (2) Adaptor ligations using both the 5’-phosphate and the 3’-hydroxyl groups of miRNAs, (3) RT-PCR amplification of miRNAs using a common biotinylated primer; (4) Hybridization to the capture beads; and (5) Staining with streptavidin-phycoerythrin; and (6) Analyze the beads

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22 Bead-Based Flow Cytometric miRNA Expression Profiling

using a flow cytometer capable of measuring bead color (denoting miRNA identity) and phycoerythrin intensity (denoting miRNA abundance). Bead-based hybridization is superior to glass microarray hybridization in which it is more closely approximate hybridization in solution. In addition, the bead method has a linear relationship for detection over a hundred-fold range of expression. The method meets the requirements of both high throughput and quantitativeness.

22.2

Protocol

22.2.1 Materials 1. 2. 3. 4. 5.

MAP beads (Luminex Corporation) 96-well plates All materials for cell culture All materials for RNA exaction All materials for RT-PCR

22.2.2 Instruments 1. Thermocycler 2. Flow cytometer, Luminex 100IS machine

22.2.3 Reagents 1. TE buffer (10 mM Tris–HCl pH8.0, 1 mM EDTA) 2. T4 RNA ligase (Amersham Biosciences) 3. 1.5 TMAC (4.5 M tetramethylammonium chloride, 0.15% sarkosyl, 75 mM Tris–HCl pH 8.0, 6 mM EDTA) 4. Streptavidin-phycoerythrin (Molecular Probes) 5. All reagents for cell culture 6. All reagents for RNA exaction 7. All reagents for RT-PCR

22.2.4 Procedures The procedures described herein are based on the study reported by Lu et al. (2007) (See Fig. 22.1).

22.2 Protocol

291 Isolate RNA from tissues or cells

Synthesize Adaptor -specific primer

Synthesize Adaptor probe

Ligation of adaptor probe to 3' and 5' ends of RNAs using T4 ligase

Revser transcription

Select miRNAs of your interests

PCR

Synthesize miRNA capture probes

Precipitate PCR products

Conjugate the capturee probes to carboxylated xMAP beads in 96-well plates

Hybridization

Measure median fluorescence intensity

Computational analysis

Fig. 22.1 Flowchart of the bead-based flow cytometric miRNA expression profiling method for miRNA expression detection. According to Lu et al. (2007)

22.2.4.1

miRNA Labeling

1. Prepare total RNA samples from tissues or cells from control and diseased subjects. 2. Use two synthetic pre-labeling-control RNA oligonucleotides (5’-pCAGUCAGUCAGUCAGUCAGUCAG-3’, and 5’-pGACCUCCAUGUAAACGUACAA-3’) to control for target preparation efficiency. 3. Spike them each at 3 fmoles per mg total RNA. Small RNAs (18–26 nucleotides) are recovered from 1–10 mg total RNA through denaturing polyacrylamide gel purification. 4. Adaptor-ligate small RNAs sequentially on the 3’-end and 5’-end using T4 RNA ligase. 5. After reverse-transcription using adaptor-specific primers, PCR-amplify the products (95 C for 40 s; 50 C for 30 s; 72 C for 30 s; 18 cycles for 10 mg starting

292


total RNA) using a 3’-primer 5’-TACTGGAATTCGCGGTTA-3’ and 5’ primer 5’-biotin-CAACGGAATTCCTCACTAAA-3’ (IDT). 6. Precipitate the PCR products and dissolve in 66 mL TE buffer containing two biotinylated post-labeling-control oligonucleotides (100 fmoles of FVR506, 25 fmoles PTG20210).

22.2.4.2

Design of miRNA Capture Probes

1. Obtain sequences for the miRNAs of interest from the miRNA Registry (microrna. sanger.ac.uk). 2. Design the miRNA capture oligonucleotides probes exactly antisense to the selected miRNAs for study with a 6-carbon linker at 5’end. Synthesize the antisense oligos using the service provided by IDT (Integrated DNA Technologies, Coralville, IA, USA).

22.2.4.3

Bead-Based Detection

1. Conjugate the capture probes to carboxylated xMAP beads in 96-well plates, following the manufacturer’s protocol. For each probe set, mix 3 mL of every probe–bead conjugate into 1 mL of 1.5 TMAC. 2. Hybridize the samples in a 96-well plate, with two mock PCR samples (using water as template) in each plate as a background control. Hybridization should be carried out overnight at 50 C with 33 mL of the bead mixture and 15 mL of labeled material. 3. Spin down beads, resuspend in 1 TMAC containing 10 mg/mL streptavidinphycoerythrin and incubate at 50 C for 10 min before data acquisition. 4. Use Luminex 100IS machine to measure median fluorescence intensity values.

22.2.4.4

Computational Analyses

1. To eliminate bead-specific background, subtract the average readings of that particular bead in the two-embedded mock-PCR samples in each plate. Scale profiling data according to the post-labeling-controls and then the pre-labelingcontrols, in order to normalize readings from different probe/bead sets for the same sample, and to normalize for the labeling efficiency, respectively. Scaling should be done in two steps: (1) well-to-well scaling – scale the reading from each well such that the total of the two post-labeling controls, in that well, become the median value based on a pilot study and (2) sample scaling – scale the normalized readings such that the total of the six pre-labeling controls in each sample reach the median value based on a pilot study. 2. Set a threshold of data at 32 and log2-transformed.

22.3 Application and Limitation

293

3. Before clustering, filter data to eliminate genes with expression lower than 7.25 (on a log2 scale) in all samples. A miRNA is regarded as “not expressed” or “not detectible”, if in none of the samples, that particular miRNA has an expression value above a minimal cutoff. This cutoff value is determined based on noise analyses of target preparation and bead detection. Any feature that is not expressed under this criterion will be filtered out before clustering. 4. Perform hierarchical clustering with average linkage and Pearson correlation. 5. Next, center all features and normalize to a mean of 0 and a standard deviation of 1. Perform k-nearest-neighbor classification of normal versus disease samples with k ¼ 3 in the selected feature space using euclidean distance measure. 6. Quality control should be performed as part of the preprocessing by requiring that the reading from each control probe exceeds some minimal probe-specific threshold. These thresholds are determined by identifying a natural lower cutoff, i.e., a dip, in the distribution of each control probe. The cutoff values should be chosen based on a set of samples in a pilot study. The lower post-control should be greater than 500 and the higher post-control must exceed 2,450. The lower and higher pre-controls should exceed 1,400 and 2,000 respectively (after wellto-well scaling).

22.3


Utilizing the bead-based flow cytometric miRNA expression profiling method, Lu et al. (2007) carried out a systematic expression analysis of 217 mammalian miRNAs from 334 samples, including multiple human cancers. They observed a general down regulation of miRNAs in tumors compared with normal tissues and were able to successfully classify poorly differentiated tumors using miRNA expression profiles. In particular, the data from the bead-based miRNA profiling is able to distinguish tumors of different developmental origin, to distinguish tumors from normal tissues, and to distinguish tumors of different stages. By comparison, messenger RNA (mRNA) profiles were found highly inaccurate when applied to the same samples. These findings highlight the potential of miRNA profiling as a better diagnostic tool for cancer. Their observation that miRNA expression seems globally higher in normal tissues compared with tumors led us to the hypothesis that global miRNA expression reflects the state of cellular differentiation. These experiments support the hypothesis that global changes in miRNA expression are associated with differentiation, the abrogation of which is a hallmark of all human cancers. These findings are also consistent with the recent observation that mouse embryonic stem cells lacking Dicer, an enzyme required for miRNA maturation, fail to differentiate normally. miRNAs can function to prevent cell division and drive terminal differentiation. An implication of this hypothesis is that down regulation of some miRNAs might play a causal role in the generation or maintenance of tumors.

294


Their results demonstrate that the bead-based miRNA detection is feasible, and has the attractive properties of improved accuracy, high speed and low cost. Moreover, it is an efficient detection platform for high throughput miRNA profiling in a quantitative manner. The bead-based miRNA detection method is also easy to implement in a routine clinical setting.

References Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D, Sweet-Cordero A, Ebert BL, Mak RH, Ferrando AA, Downing JR, Jacks T, Horvitz HR, Golub TR (2005) MicroRNA expression profiles classify human cancers. Nature 435:745–746

Chapter 23

Bioluminescence miRNA Detection Method

Abstract The Bioluminescence miRNA detection technology is a competitive solid-phase hybridization-based method using the bioluminescent protein Renilla luciferase (Rluc) as a label for detection and quantification of miRNAs. The method is an alternative ELISA for the detection of target miRNAs using the free synthetic miRNAs and Rluc-labeled miRNAs that compete to bind to an immobilized antimiRNA probe. The technology was developed by Cissell et al. (Anal Chem 80:2319–2325, 2008) (Department of Chemistry and Chemical Biology, Indiana University Purdue University Indianapolis; Anal Chem 80:2319–2325, 2008). It is rapid, requiring a microplate format, a total assay time of 1.5 h without the need for sample PCR amplification, and it is a highly sensitive method for miRNA detection with a detection limit of 1 fmol. The assay offers the advantage of parallel analysis in a 96-well microtiter plate and makes it suitable for application in clinical diagnostics and drug discovery. Moreover, the high signal-to-noise ratio afforded by bioluminescence and minimal sample preparation of the technology, enhances the suitability of this method for adapting to miniaturized analytical platforms providing for high sample throughput. This technology has been applied for the determination of miR-21 in both human breast adenocarcinoma MCF-7 and nontumorigenic epithelial MCF-10A cellular extracts (Anal Chem 80:2319–2325, 2008).

23.1

Introduction

The Bioluminescence miRNA detection technology is a competitive solid-phase hybridization-based method using the bioluminescent protein Renilla luciferase (Rluc) as a label for detection and quantification of miRNAs. The method is an alternative ELISA for the detection of target miRNAs using the free synthetic miRNAs and Rluc-labeled miRNAs that compete to bind to an immobilized antimiRNA probe (Fig. 23.1). Rluc, a small 38-kDa protein which is usually employed as a reporter for gene expression analysis, can catalyze the oxidative decarboxylation of coelenterazine in Z. Wang and B. Yang, MicroRNA Expression Detection Methods, DOI 10.1007/978-3-642-04928-6_23, # Springer-Verlag Berlin Heidelberg 2010

295

296

23 Bioluminescence miRNA Detection Method

Anti-miR-1

Biotin

5’-ATACATACTTCTTTACATTCCA-Biotin

NH2

miR-1

5’-UGGAAUGUAAAGAAGUAUGUAU-NH2

miR-1

5’-UGGAAUGUAAAGAAGUAUGUAU-3’

Sulfo-SMCC

Rluc

5’-UGGAAUGUAAAGAAGUAUGUAU-NH2-Rluc

Compete for binding 5’-UGGAAUGUAAAGAAGUAUGUAU-3’ 5’-ATACATACTTCTTTACATTCCA-Biotin

Neutravidin-coated microtiter plate

miR-1

5’-UGGAAUGUAAAGAAGUAUGUAU-NH2-Rluc



Immobilize onto plate

Reduced Rluc

Coelenterazine

Measure luminescence

Fig. 23.1 Schematic representation of the Bioluminescence-based miRNA detection technology using miR-1 as an example. Modified from Cissell et al. (2008)

the presence of molecular oxygen to coelenteramide (Hart et al. 1979; Matthews et al. 1977a, b; Srikantha et al. 1996). This process leads to emission of light that follows glow-type kinetics and has an emission wavelength a maximum of 485 nm (Matthews et al. 1977a). Several coelenterazine analogs have been synthesized with different properties in terms of emission wavelength, quantum efficiency, and cell permeability, thus enhancing the applications of Rluc (Matthews et al. 1977b). Since light generation occurs due to a chemical reaction, there is no requirement for external excitation light providing for the detection of Rluc activity with a high signal-to-noise ratio yielding a detection limit in the attomolezeptomole range. Additionally, due to availability of the Rluc gene for cloning, the protein can be reproducibly produced in unlimited amounts and fused to any desired molecule.

23.2

Protocol

23.2.1 Materials 1. 96-well neutravidin-coated white microtiter plates 2. High-performance nickel-bound Sepharose column 3. Plasmid phRl-CMV (Promega, WI)

23.2 Protocol

4. 5. 6. 7.

297

Plasmid pRSetB (Invitrogen) Escherichia coli cells strain 2566 (NEB Biolabs) All materials for cell culture All materials for RNA exaction

23.2.2 Instruments 1. 2. 3. 4. 5.

Fisher Scientific orbital shaker Beckman J2-MI centrifuge (Palo Alto, CA) Varian Cary Eclipse fluorescence spectrophotometer (Palo Alto, CA) Genios workstation luminometer (Tecan, NC) Perkin-Elmer UV/vis/NIR Lambda spectrophotometer

23.2.3 Reagents 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14.

15. 16. 17. 18.

Luria Bertani (LB) broth Agar Ampicillin Bovine serum albumin (BSA) Coomasie Brilliant Blue R250 stain High performance Ni+2-Sepharose beads Coelenterazine Chemically synthesized oligonucleotide probes Sulfosuccinimidyl 4-(N-maleimidomethyl)cyclohexane-1-carboxylate (sulfoSMCC) Trizol reagent Trypsin-EDTA DEPC Buffer A: [10 mM HEPES, pH7.9 containing 1.5 mM MgCl2, 10 mM KCl, and 0.5 mM DTT] Buffer B: [20 mM HEPES, pH7.9 containing 25% (v/v) glycerol, 0.42 M NaCl, 1.5 mM MgCl2, 0.2 mM EDTA, 0.5 mM phenylmethylsulfonyl fluoride (PMSF), 0.5 mM DTT, and 0.1% Nonidet P-40] Buffer C: [20 mM sodium phosphate, pH7.5 containing 20% (v/v) glycerol, 0.1 M KCl, 0.2 mM EDTA, 0.5 mM PMSF, and 0.5 mM DTT] Binding buffer: [100 mM potassium phosphate buffer containing 250 mM NaCl, 0.6 mM NaN3, and 20 mM imidazole, pH7.6] Wash buffer: [100 mM phosphate buffer containing 250 mM NaCl, 1 mM EDTA, and 0.5% BSA, pH7.4] Elution buffer: [100 mM potassium phosphate buffer containing 250 mM NaCl, 0.6 mM NaN3, and 100 mM imidazole, pH7.6]

298


19. Rluc buffer: [100 mM phosphate buffer containing 250 mM NaCl, 1 mMEDTA, and 0.1% BSA, pH7.4] 20. 100 mM potassium phosphate buffer containing 100 mM NaCl, pH7.4 21. Binding buffer: [100 mM potassium phosphate buffer containing 250 mM NaCl, 0.6 mM NaN3, and 20 mM imidazole, pH7.6] 22. Wash buffer I: [100 mM potassium phosphate buffer containing 250 mM NaCl, 0.6 mM NaN3, and 50 mM imidazole, pH7.6] 23. Dialysis buffer: [100 mM potassium phosphate containing 250 mM NaCl, 0.1% BSA, 0.6 mM NaN3 pH7.4] 24. Wash buffer II: [100 mM phosphate buffer containing 250 mM NaCl, 1 mM EDTA, and 0.5% BSA, pH7.4] 25. All reagents for cell culture 26. All reagents for RNA exaction

23.2.4 Procedures The protocols described in this section are essentially the same as reported in the study by Cissell et al. (2008) (See Figs. 23.1 and 23.2). Construct Rluc (Renilla luciferase ) plasmid

Amplify and purify the Rluc plasmid


Select miRNAs of your interests

Synthesize the selected miRNAs with an amino modification at its 3' end

Synthesize biotinylated anti-miRNA probes

Reduce the Rluc plasmid using TCEP

miRNA-Rluc conjugate

Hybridization (miRNAs in the RNA sample compete with the anti-miRNA probes for binding miRNA-Rluc conjugate)

Measure luminescence with a microplate reader

Fig. 23.2 Flowchart of the Bioluminescence method for miRNA expression detection. According to Cissell et al. (2008)

23.2 Protocol

23.2.4.1

299

Rluc Plasmid Construction

1. Isolate the Rluc gene from the plasmid phRl-CMV using polymerase chain reaction (PCR) employing primers (Rlucfor-50 GGTGGTGGATCCGATGGC TTCCAAGGTGTACGACCCCGAG30 , RlucRev-50 GGTGGTGAATTCTTAC TGCTCGTTCTTCAGCACGCGCTCC30 ). 2. Digest the Rluc gene and the plasmid pRsetB with restriction enzymes BamHI and EcoRI and subcloned into pRSetB to construct the plasmid pSKD2. 3. Transform the ligated plasmid into Escherichia coli cells strain 2566. 4. Perform Gene sequencing and restriction analysis to confirm the presence of the gene for Rluc.

23.2.4.2

Rluc Expression

1. Grow the cells containing the plasmid pSKD2 in LB broth containing ampicillin (100 mg/ml) at 37 C to an optical density of 0.5 measured at 600 nm. 2. Induce protein expression using IPTG (0.5 mM final concentration), and grow the cells for an additional 3 h at 37 C. 3. Harvest the cells by centrifugation at 16 C, 8,600 g, for 15 min and sonicate for 5 min with a 20 s on and 20 s off cycle. 4. Obtain the crude protein by centrifugation at 16 C, 8600 g, for 15 min.

23.2.4.3

Rluc Purification

1. Prepare high-performance nickel-bound Sepharose column by washing with distilled water followed by equilibration with the Binding buffer. 2. Load the crude Rluc protein in the Binding buffer onto the nickel-bound Sepharose column and incubate overnight at 4 C. 3. Wash the column with 20 mL of the Binding buffer, followed by 20 mL of Wash buffer. 4. Elute the purified Rluc with 5 mL of the Elution buffer. Collect the eluted fractions that show luminescent activity, and determine the purity by SDSPAGE using Coomassie staining solution. 5. Determine protein concentration by measuring absorbance at 280 nm employing an extinction coefficient of 62 520/M/cm.

23.2.4.4

Labeling of Oligonucleotide Probes

1. Obtain the sequence of a miRNA of interest from the miRNA Registry (microrna.sanger.ac.uk).

300


2. Design a “miRNA” oligonucleotide probe that has the same sequence as the selected miRNA but contains an amino modification at its 30 end (50 -miRNANH2-30 ). 3. Incubate this “miRNA” oligonucleotide probe with equimoles of sulfo-SMCC (1:1) at room temperature for 30 min. 4. Add TCEP to the purified Rluc descibed in 23.2.4.3 to reduce the Rluc. 5. Then add the reduced Rluc to the probe mixture at a ratio of Rluc to probe 1:5. 6. Incubate for 30 min at room temperature, and add BSA to the reaction mix to achieve a final amount of 0.1% BSA. 7. Remove the unconjugated probe and sulfo-SMCC using the Dialysis buffer to eliminate interference from unlabeled oligonucleotide. Removal of unconjugated probe is also necessary for the determination of concentration of miRNA-Rluc concentration. 8. Determine the concentration of miRNA-Rluc conjugate spectrophotometrically at 260 nm [48]. 9. Place a volume of 200 mL of miRNA-Rluc probe into a microtiter plate, and obtain the emission scan of the protein after the addition of 0.5 mL of coelenterazine (1 mg/mL).

23.2.4.5

Hybridization Study

1. Design an antisense oligonucleotide probe complementary to the selected miRNA containing a biotin moiety at the 30 end (50 -anti-miRNA-biotin-30 ). 2. Prepare biotinylated anti-miRNA probe a 20 pM/mL concentration of in Rluc buffer. 3. Make a serial dilution of the miRNA-Rluc probe using the Rluc buffer. 4. Mix 50 mL anti-miRNA-biotin probe and 50 mL of varying concentrations of miRNA-Rluc probe and incubate at 37 C for 30 min. 5. At the end of 30 min, place the probe mixture in a neutravidin-coated microtiter plate prewashed three times with the Wash buffer II and incubate with shaking at room temperature for 1 h, followed by three washes using the Wash buffer II. 6. Next, add 200 mL Rluc buffer and 0.5 mL coelenterazine to the plate, and measure luminescence with a 100-ms integration time.

23.2.4.6

Standard Curve of miRNA

1. Incubate a mixture of anti-miRNA-biotin probe (50 mL, 20 pM), miRNA-Rluc probe (25 mL, 20 pM), and 25 mL of varying concentrations of unlabeled miRNA DNA probe at 37 C for 30 min. 2. Next, add the mixture to the neutravidin-coated plate and perform the immobilization step at room temperature for 1 h shaking it well. 3. After 3-times of the wash step, add 200 mL Rluc buffer and 0.5 mL coelenterazine to the wells and measure luminescence.


301

4. Generate a dose-response curve for the miRNA in the form of DNA by plotting luminescence intensity against the concentration of that miRNA. 5. Generate a dose-response curve using miRNA in the form of RNA.

23.2.4.7

Quantification of miRNA

1. Detach adherent cells by trypsin treatment and wash with autoclaved and DEPCtreated 100 mM potassium phosphate buffer containing 100 mM NaCl, pH7.4. 2. Prepare the cellular extract using previously published protocol. Wash the cell pellet with the Buffer A, resuspend the cells in the Buffer B, and incubate on ice for 15 min. 3. Vortex the cell suspension and centrifuge for 10 min at 4 C. 4. Dilute the supernatant with the Buffer C and extract the total RNA using Trizol reagent, followed by chloroform/2-propanol precipitation. 5. Use a total of 10 mg of isolated RNA in the assay to determine the amount of the miRNA of interest by generating a dose-response curve using standard amounts of that synthetic miRNA.

23.3


The Bioluminescence miRNA detection technology employs Rluc as a direct label for hybridization assays; the bioluminescence emission, small size and requirement for the addition of only coelenterazine make Rluc an efficient label for hybridization assays. It is rapid, requiring a microplate format a total assay time of 1.5 h without the need for sample PCR amplification, and it is a highly sensitive method for miRNA detection with a detection limit of 1 fM. The assay offers the advantage of parallel analysis in a 96-well microtiter plate and makes it suitable for application in clinical diagnostics and drug discovery. Moreover, the high signal-to-noise ratio afforded by bioluminescence and minimal sample preparation of the technology enhances the suitability of this method for adapting to miniaturized analytical platforms providing for high sample throughput. Furthermore, recent findings in miRNAs research area indicate that miRNAs can be isolated from biofluids such as serum, plasma, saliva, and urine (Tricoli and Jacobson 2007). The assay developed in this work can be easily applied to the determination of miRNAs in these sample matrixes if desired since bioluminescent proteins have been employed previously in saliva and blood analysis of physiological molecules (Desai et al. 2002; Mirasoli et al. 2002). This technology has been applied for the determination of miR-21 in both human breast adenocarcinoma MCF-7 and nontumorigenic epithelial MCF-10A cellular extracts (Cissell et al. 2008). The method allows sensitive, accurate, and precise measurement of miR-21 in vitro as well as in cells and is able to discriminate levels

302


of miR-21 in cancerous versus noncancerous cells, which can be a significant advantage in early cancer diagnosis.

References Cissell KA, Rahimi Y, Shrestha S, Hunt EA, Deo SK (2008) Bioluminescence-based detection of microRNA, miR21 in breast cancer cells. Anal Chem 80:2319–2325 Desai UA, Deo SK, Hyland KV, Poon M, Daunert S (2002) Determination of prostacyclin in plasma through a bioluminescent immunoassay for 6-keto-prostaglandin F1alpha: implication of dosage in patients with primary pulmonary hypertension. Anal Chem 74:3892–3898 Hart RC, Matthews JC, Hori K, Cormier MJ (1979) Renilla reniformis bioluminescence: luciferase-catalyzed production of nonradiating excited states from luciferin analogues and elucidation of the excited state species involved in energy transfer to Renilla green fluorescent protein. Biochemistry 18:2204–2210 Matthews JC, Hori K, Cormier MJ (1977a) Purification and properties of Renilla reniformis luciferase. Biochemistry 16:85–91 Matthews JC, Hori K, Cormier MJ (1977b) Substrate and substrate analogue binding properties of Renilla luciferase. Biochemistry 16:5217–5220 Mirasoli M, Deo SK, Lewis JC, Roda A, Daunert S (2002) Bioluminescence immunoassay for cortisol using recombinant aequorin as a label. Anal Biochem 306:204–211 Srikantha T, Klapach A, Lorenz WW, Tsai LK, Laughlin LA, Gorman JA, Soll DR (1996) The sea pansy Renilla reniformis luciferase serves as a sensitive bioluminescent reporter for differential gene expression in Candida albicans. J Bacteriol 178:121–129 Tricoli JV, Jacobson JW (2007) MicroRNA: potential for cancer detection, diagnosis, and prognosis. Cancer Res 67:4553–4555

Chapter 24

Molecular Beacon Method

Abstract Molecular beacons are single-stranded oligonucleotide hybridization probes with a stem-and-loop structure that recognize and report the presence of specific nucleic acids in homogeneous solutions (Tyagi et al. 1996). The loop contains a probe sequence that is complementary to a target sequence, and the stem is formed by the annealing of complementary arm sequences that are located on either side of the probe sequence. Paiboonskuwong and Kato (Nucleic Acids Symp Ser (Oxf) 50:327–328, 2006) from the Research Institute for Cell Engineering, National Institute of Advanced Industrial Science and Technology (AIST; Tsukuba Science City, Japan) modified the molecular beacon approach with an aim to detect miRNAs in the mature form. The probe is modified so that when hybridizing with either pre-miRNA or pri-miRNA, its fluorescence is quenched by the guanine in the sequences of pre-miRNA or pri-miRNA complementarily to the probe. Hybridization of the probe with a mature miRNA which has no complementary guanine can result in fluorescent emission. In this way, the modified molecular beacon method can distinguish the mature from the precursor miRNAs.

24.1

Introduction

Molecular beacons are single-stranded oligonucleotide hybridization probes with a stem-and-loop structure that recognize and report the presence of specific nucleic acids in homogeneous solutions (Tyagi and Kramer 1996; Marras 2006; Marras et al. 2006; Bratu 2006; Broude 2002). The loop contains a probe sequence that is complementary to a target sequence, and the stem is formed by the annealing of complementary arm sequences that are located on either side of the probe sequence. A fluorophore is covalently linked to the end of one arm and a quencher is covalently linked to the end of the other arm. Molecular beacons do not fluoresce when they are free in solution or in the absence of targets, because the stem places


303

304

24 Molecular Beacon Method

the fluorophore so close to the nonfluorescent quencher that they transiently share electrons, eliminating the ability of the fluorophore to fluoresce. However, when they hybridize to a nucleic acid strand containing a target sequence, they form a probe-target hybrid that is longer and more stable than the stem hybrid. Consequently, the molecular beacons undergo a spontaneous conformational reorganization that forces the stem hybrid to dissociate and the fluorophore and the quencher to move away from each other, restoring fluorescence (Figs. 24.1 and 24.2). The rigidity and length of the probe-target hybrid precludes the simultaneous existence of the stem hybrid. Only perfectly complementary targets elicit this response, as hybridization does not occur when the target contains a mismatched nucleotide or a deletion (Kramer http://www.molecular-beacons.org/Introduction.html). Molecular beacons can be synthesized that possess differently colored fluorophores, enabling assays to be carried out that simultaneously detect different targets

Probe

a

miRNA miRNA

+

Probe

Q

F

Q F Quenched

F G

Pre-miRNA

b

Probe

Pre-miRNA

Probe

F G Probe

G F Quenched

Mature miRNA

Mature miRNA

G

Fig. 24.1 Schematic depiction of molecular beacon probe and the mechanism for miRNA detection. The 3’ terminal cytosine of of a molecular beacon probe is covalently linked to BODIPY1 FL (F), which is quenched by the adjacent guinine base (G) through base-pairing. In this inactivated state, molecular beacons do not fluoresce. (a) When they hybridize to a nucleic acid strand containing a target sequence, they form a probe-target hybrid that is longer and more stable than the stem hybrid. Consequently, the molecular beacons undergo a spontaneous conformational reorganization that forces the stem hybrid to dissociate and the fluorophore and the quencher to move away from each other, restoring fluorescence. (b) When the probe hybridizdes with the precursor miRNA (pre-miRNA), it stays inactivated due to the quench by the guanine base in the pre-miRNA. However, when a molecular beacon probe is hybridized with a mature miRNA that does not have guanine bases at the position to basepair with the cytosine in the probe, then it fluoresces. Modified from Paiboonskuwong and Kato (2006)

24.1 Introduction

305 Select miRNAs of your interests

Design and synthesize stem-loop molecular beacon probes


Label the probes with fluorophore BODIPY® FL at 3'end

Hybridization of miRNAs and probes

Detect fluorescence and analyze data

Fig. 24.2 Flowchart of the Molecular Beacon method for miRNA expression detection. According to Paiboonskuwong and Kato (2006)

in the same reaction (Vet and Marras 2004; Kramer http://www.molecular-beacons. org/Introduction.html). For example, multiplex assays can contain a number of different primer sets, each set enabling the amplification of a unique gene sequence from a different pathogenic agent, and a corresponding number of molecular beacons can be present, each containing a probe sequence specific for one of the amplicons, and each labeled with a fluorophore of a different color. The color of the resulting fluorescence, if any, identifies the pathogenic agent in the sample, and the number of amplification cycles required to generate detectable fluorescence provides a quantitative measure of the number of target organisms present. Molecular beacons are extraordinarily specific. They easily discriminate target sequences that differ from one another by a single nucleotide substitution (Tyagi et al. 1998; Marras et al. 1999; Kramer http://www.molecular-beacons.org/ Introduction.html). The reason that molecular beacons are so “finicky” is that they can exist in two different stable physical states. In one state, the molecular beacons are hybridized to their targets, and energy is stored in the probe-target helix. In the second state, the molecular beacons are free in solution, and energy is stored in their stem helix. Molecular beacons are designed so that their probe sequence is just long enough for a perfectly complementary probe-target hybrid to be more stable than the stem hybrid. Consequently, the molecular beacons spontaneously form fluorescent probe-target hybrids. However, if as little as a single nucleotide in the target is not complementary to the probe sequence of the molecular beacon, the probetarget helix would be less stable. In this situation, the stem helix of the molecular beacon is more stable than the mismatched probe-target helix, and the molecular

306


beacons remain unhybridized. Thus, molecular beacons can be thought of as “molecular switches” that are on their targets and brightly fluorescent when the targets are perfectly complementary to the probe, but remain off the targets and dark if the targets contain a mutation (Kramer http://www.molecular-beacons.org/ Introduction.html). The probes are particularly suited for monitoring the synthesis of specific nucleic acids in real/actual time. When used in nucleic acid amplification assays, gene detection is homogeneous and sensitive, and can be carried out in a sealed tube. When introduced into living cells, these probes should enable the origin, movement, and fate of specific mRNAs to be traced. This provides a novel nonradioactive method for detecting specific sequences of nucleic acids. They are useful in situations where it is either not possible or desirable to isolate the probe-target hybrids from an excess of the hybridization probes (Kramer http://www.molecularbeacons.org/Introduction.html). Molecular beacons can be used as amplicon detector probes in diagnostic assays. As nonhybridized molecular beacons are dark, it is not necessary to isolate the probe-target hybrids to determine the number of amplicons synthesized during an assay. Molecular beacons are added to the assay mixture before carrying out gene amplification and fluorescence is measured in real/actual time. The assay tube remains sealed. Consequently, the amplicons cannot escape to contaminate untested samples. Furthermore, the use of molecular beacons provides an additional level of specificity. Since it is very unlikely that false amplicons or primer-dimers possess target sequences for the molecular beacons, the generation of fluorescence is exclusively due to the synthesis of the intended amplicons (Kramer http://www. molecular-beacons.org/Introduction.html). Paiboonskuwong and Kato used the molecular beacon approach with a small, elegant modification to detect miRNAs in the mature form (Paiboonskuwong and Kato 2006). The probe is modified so that when hybridizing with either pre-miRNA or pri-miRNA, its fluorescence is quenched by the guanine in the sequences of premiRNA or pri-miRNA complementarily to the probe. Hybridization of the probe with a mature miRNA which has no complementary guanine can result in fluorescent emission. In this way, the modified molecular beacon method can distinguish the mature from the precursor miRNAs (Fig. 24.1).

24.2

Protocol

24.2.1 Materials 1. Zuker DNA folding program (available on the internet at http://frontend.bioinfo. rpi.edu/applications/mfold/cgi-bin/dna-form1.cgi) 2. Beacon Designer software (available from Premier Biosoft International at www.premierbiosoft.com)

24.2 Protocol

307

24.2.2 Instruments 1. Real-time PCR system 750 (Applied Biosystems) or an equivalent 2. Any instrument able to detect fluorescence

24.2.3 Reagents 1. Reverse transcription reaction kit 2. PCR reaction kit 3. RNA isolation kit and reagents

24.2.4 Procedures The protocols described in this section are essentially the same as reported in the study by Kramer posted in the website http://www.molecular-beacons.org/PA_ design.html (see Figs. 24.1 and 24.2). 24.2.4.1

Design of Molecular Beacon Probes

According to Tyagi (Tyagi and Kramer 1996; Marras et al. 2003), in order to design molecular beacons that function optimally under a given set of assay conditions, it is important to understand how their fluorescence changes with temperature in the presence and in the absence of their targets. As shown by the green fluorescence versus temperature trace below, at lower temperatures molecular beacons exist in a closed state, the fluorophore and the quencher are held in close proximity to each other by the hairpin stem, and there is no fluorescence. However, at high temperatures the helical order of the stem gives way to a random-coil configuration, separating the fluorophore from the quencher and restoring fluorescence. The temperature at which the stem melts depends upon the GC content and the length of the stem sequence. If a target is added to a solution containing a molecular beacon at temperatures below the melting temperature of its stem, the molecular beacon spontaneously binds to its target, dissociating the stem, and turning on its fluorescence. How the fluorescence of the probe-target hybrid varies with the temperature is indicated by the red fluorescence versus temperature trace. At low temperatures, the probe-target hybrid remains brightly fluorescent, but as the temperature is raised the probe dissociates from the target and tends to return to its hairpin state, diminishing the fluorescence significantly. The temperature, at which the probe-target hybrid melts apart, depends upon the GC content and the length of the probe sequence. The longer the probe and the higher its GC content, the higher the melting temperature of the probe-target hybrid. It is important to note that the probe-target hybrid melting temperature can be adjusted independently

308


from the melting temperature of the stem by selecting a target region of appropriate length. The fluorescence versus temperature profiles of the molecular beacon that were used in this example indicate that the molecular beacon is suitable for assays that are performed below 55˚C, because below 55˚C the free molecular beacons remain dark, yet the probe-target hybrids form spontaneously and are stable. Molecular beacons can also be designed with the help of a dedicated software package called “Beacon Designer”, which is available from Premier Biosoft International (www.premierbiosoft.com). In addition, companies, such as NYtor (www. nytor.nl), are specialized in designing real-time PCR assays that utilize molecular beacons for the detection of single nucleotide polymorphisms and high-throughput multiplex diagnostic assays. 1. The process of molecular beacon design begins with the selection of the probe sequence or selection of target miRNAs of interest. If you are designing molecular beacons to detect the synthesis of products during polymerase chain reactions, you can select any region within the amplicon that is outside the primer binding sites. The probe sequence of the molecular beacon should be so long that at the annealing temperature of the PCR it is able to bind to its target. In order to discriminate between amplicons that differ from one another by as little as a single nucleotide substitution, the length of the probe sequence should be such that it dissociates from its target at temperatures 7–10 C higher than the annealing temperature of the PCR. If, on the other hand, single-nucleotide allele discrimination is not desired, longer and more stable probes can be chosen. The melting temperature of the probe-target hybrid can be predicted using the “percent-GC” rule or “nearest neighbor” rules (available in most probe or primer design software packages). The prediction should be made for the probe sequence alone before choosing the stem sequences. In practice, the length of the probe sequence usually falls in the range between 15 and 30 nucleotides [Kramer http://www.molecular-beacons.org/PA_design.html]. 2. A molecular beacon probe is a stem-loop structure. The loop portion is a probe sequence that is complementary to the predetermined sequence in the target miRNA. For example, miR-1 has the sequence of 5’-UGGAAUGUAAAGAAGUAUGUAU-3’. Accordingly, the loop portion of a molecular beacon probe should be: 5’-AUACAUACUUCUUUACAUUCCA-3’. 3. After selecting the probe sequence, two complementary arm sequences are added on either side of the probe sequence. The sequence of 3’-stem should be designed to be complementary to the sequences of pri-miRNA or pre-miRNA but not that of mature miRNA (Fig. 24.1). We therefore have for miR-1: 5’gcacgAUACAUACUUCUUUACAUUCCAcgugc-3’. 4. The cytosine base at one end of the probe is covalently linked with BODIPY1 FL (Molecular Probes). The fluorescent emission from the probe modified with BODIPY1 FL should be diminished after hybridization with the adjacent guanine base. The length and the GC content of the stem sequence is designed in such a way that at the annealing temperature of the PCR, and in the absence of the target,

24.2 Protocol

309

the molecular beacons remain closed and non-fluorescent. This is ensured by choosing a stem that melts 7–10 C higher than the annealing temperature of the PCR. Usually the stems are 5–7 basepairs long and have a very high GC content (75–100%). The melting temperature of the stem cannot be predicted by the percent-GC rule, since the stem is created by intramolecular hybridization. In general, 5 basepair-long GC-rich stems will melt between 55 and 60 C, 6 basepair-long GC-rich stems will melt between 60 and 65 C, and 7-basepair long GC-rich stems will melt between 65 and 70 C. Although any arbitrary sequence can be used in designing the stems, don’t use guanosine residues near the end to which the fluorophore is attached (instead, use them at the end where the quencher is attached), as guanosine residues tend to quench the fluorophore. Longer stems can be used to enhance the specificity of the molecular beacons (Kramer http://www.molecular-beacons.org/PA_design.html). 5. It is important that the conformation assumed by the free molecular beacons be the intended hairpin structure, rather than other structures that either do not place the fluorophore in the immediate vicinity of the quencher, or that form longer stems than intended. The former will cause high background signals, and the latter will make the molecular beacons sluggish in binding to the targets. A folding of the selected sequence by the Zuker DNA folding program will reveal such problems. If unexpected secondary structures result from the choice of the stem sequence, a different stem sequence can be chosen. If, on the other hand, unexpected secondary structures arise from the identity of the probe sequence, the frame of the probe can be moved along the target sequence to obtain a probe sequence that is not self-complementary. Small stems within the probe’s hairpin loop that are 2- to 3-nucleotides long do not adversely affect the performance of molecular beacons. 6. As with PCR primers, the sequence of the molecular beacon should be compared with the sequences of the primers, using a primer design software program to make sure that there are no regions of substantial complementarities that may cause the molecular beacon to bind to one of the primers, causing primer extension. Also, the primers that are used should be designed to produce a relatively short amplicon. In general, the amplicons should be less than 150basepairs long. Molecular beacons are internal probes that must compete with the other strands of the amplicon for binding to the strand that contains their target sequence. Having a shorter amplicon allows the molecular beacons to compete more efficiently, and therefore produces stronger fluorescence signals during real-time PCR. In addition, smaller amplicons result in more efficient amplification. And finally, the magnitude of the molecular beacon signal can be increased by performing asymmetric PCR, in which the primer that makes the strand that is complementary to the molecular beacon is present at a slightly higher concentration than the other primer. 7. A 3’ terminus of the probe is labeled with 4,4-defluoro-5,7-dimethyl-4-bora3a,4a-diaza-s-indacene-3-propionic acid (BODIPY1 FL), whose fluorescence is quenched by the adjacent guanine base(s).

310

24.2.4.2


Total RNA Extraction

See Section II for detailed protocols.

24.2.4.3

Detect Fluorescence

See Fig. 24.2

24.3


Molecular beacons have three key properties that enable the design of new and powerful diagnostic assays: (1) they only fluoresce when bound to their targets, (2) they can be labeled with a fluorophore of any desired color, and (3) they are so specific that they easily discriminate single-nucleotide differences in miRNAs. Now that a number of new and versatile spectrofluorometric thermal cyclers are available to clinical diagnostic and research laboratories, assays that simultaneously utilize as many as seven differently colored molecular beacons can be designed. This enables cost-efficient multiplex assays to be developed for miRNA expression profiling. Molecular beacons are also ideal probes for use in diagnostic assays designed for genetic screening and SNP detection for miRNAs. Molecular beacon probes in conjunction with real-time RT-PCR provide quantitative analysis of miRNA expression in a cell. The method is straightforward and simple to perform.

References Bratu DP (2006) Molecular beacons: fluorescent probes for detection of endogenous mRNAs in living cells. Methods Mol Biol 319:1–14 Broude NE (2002) Stem-loop oligonucleotides: a robust tool for molecular biology and biotechnology. Trends Biotechnol 20:249–256 Marras SAE (2006) Selection of fluorophore and quencher pairs for fluorescent nucleic acid hybridization probes. Methods Mol Biol 335:3–16 Marras SAE, Kramer FR, and Tyagi S (1999) Multiplex detection of single-nucleotide variations using molecular beacons. Genet Anal 14:151–156 Marras SAE, Kramer FR, and Tyagi S (2003) Genotyping single nucleotide polymorphisms with molecular beacons. In Kwok, P. Y. (ed.), Single nucleotide polymorphisms: methods and protocols. The Humana Press Inc., Totowa, NJ, Vol. 212, pp. 111–128 Marras SAE, Tyagi S, and Kramer FR (2006) Real-time assays with molecular beacons and other fluorescent nucleic acid hybridization probes. Clin Chim Acta 363:48–60 Paiboonskuwong K, Kato Y (2006) Detection of the mature, but not precursor, RNA using a fluorescent DNA probe. Nucleic Acids Symp Ser (Oxf) 50:327–328

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Tyagi S, Kramer FR (1996) Molecular beacons: probes that fluoresce upon hybridization. Nat Biotechnol 14:303–308 Tyagi S, Bratu DP, and Kramer FR (1998) Multicolor molecular beacons for allele discrimination. Nat Biotechnol 16:49–53 Vet JAM, Marras SAE (2004) Design and optimization of molecular beacon real-time polymerase chain reaction assays. In Herdewijn, P. (ed.), Oligonucleotide synthesis: Methods and Applications. Humana Press, Totowa, NJ, Vol. 288, pp. 273–290

Chapter 25

Ribozyme Method

Abstract The hairpin ribozyme belongs to a family of small catalytic RNAs that cleave the RNA substrates in a reversible reaction, generating 20,30-cyclic phosphate and 50-hydroxyl termini. The ribozyme method takes advantage of their ability to perform RNA-cleavage reactions under multiple turnovers and their potential to be regulated by external oligonucleotides. The rational and straightforward design of the hairpin ribozymes can be sequence-specifically induced by external oligonucleotides and cleave a short RNA substrate labeled with a fluorophor at the 30 -end and a quencher at the 50 -end, as a function of the presence or absence of a miRNA effector. This design enables real-time monitoring of ribozyme activity via FRET read-out. This technique was first introduced to detect miRNAs by Hartig et al., Kekule´ Institut fu¨r Organische Chemie und Biochemie, Universita¨t Bonn, Bonn, Germany (J Am Chem Soc 126:722–723, 2004). The ribozyme method has been tested with nine examples of ribozymes, which are activated by nine different miRNAs from Drosophila. Due to intrinsic signal amplification, the sensitivity of ribozyme method for the detection of miRNAs is increased at least an order of magnitude compared to that of standard molecular beacons. These probes may be useful in applications that require direct detection of nucleic acids within their natural environment.

25.1

Introduction

Ribozymes are small and versatile catalytic RNA molecules that cleave RNAs at specific sites in a Watson–Crick-based specific manner. The rapidly developing field of RNA catalysts is of current interest not only because of their intrinsic catalytic properties but also because of their potential utility as therapeutic agents and specific regulators of gene expression (Rossi and Sarver 1990; Sarver et al. 1990; Erickson and Izant 1992; Rossi 1995; Eckstein and Lilley 1996; Turner 1997; Krupp and Gaur 2000; Sioud 2004).


313

314

25 Ribozyme Method

Two types of ribozymes, hammerhead and hairpin ribozymes according to their tertiary structure, have been extensively investigated and proven to be effective in repressing the expression of various genes (Kawasaki et al. 2004; Akashi et al. 2005; Doherty and Doudna 2001; Lilley 2005). The hammerhead ribozyme consists of two binding regions (stems I and III), which are complementary to the target mRNA sequences flanking the cleave site, and a catalytic core region that includes a mutually complementary stem II (Doherty and Doudna 2001). The target mRNA, on the other hand, requires NUX triplet (N: any base; X: any base except for G) to be present as a substrate of the ribozyme (Shimayama et al. 1995). The hairpin ribozyme belongs to the family of small catalytic RNAs that cleave RNA substrates in a reversible reaction that generates 20,30-cyclic phosphate and 50-hydroxyl termini. The ribozyme method takes advantage of their ability to perform RNA-cleavage reactions under multiple turnover and their potential to be regulated by external oligonucleotides. The rational and straightforward design of hairpin ribozymes can be sequence-specifically induced by external

c g ug c UU C CA U UC GU U A AG C C A U A A UUU UGU Active A UC AA U G CA AC AA Q F C C AU G 3’-AUC CGUGC AC UAU U 5’-UAG A gcacg A UG A A UA AG

Inactive

C

CA Q F C C 3’-AUC CGUGC-5’ 5’-UAGAGAAgcacg |||||||||||| Anti-miRNA AUCUCUUcgugc DA-HR

miRNA

B

Pseudo half-knot formation

B

Fig. 25.1 Schematic illustration of a hairpin ribozyme responsive to a target miRNA. A hairpin ribozyme consists of three domains: domains A, B, and C. Domain C contains a region complementary to the target miRNA (anti-miRNA region in red) and a region that can hybridize to domain A (DA-HR in purple). Normally, domain C is inserted between domains A and B, preventing A and B from docking and rendering the ribozyme inactive (left). When the target miRNA (red) binds to its complementary region in domain C, the catalytic activity of ribozyme is activated to cleave the fluorophor (F)- and quencher (Q)-labeled substrate (Modified from Hartig et al. 2004)

25.2 Protocol

315

oligonucleotides and cleave a short RNA substrate labeled with a fluorophor at the 30 -end and a quencher at the 50 -end, as a function of the presence or absence of a miRNA effector (Fig. 25.1). This design enables real-time monitoring of ribozyme activity via FRET read-out (Jenne et al. 1999, 2001; Hartig et al. 2002, 2004; Vitiello et al. 2000). Catalysis of RNA cleavage by hairpin ribozyme depends on its conformational flexibility during the docking of two helical domains, A and B (Fedor 2000; Rupert and Ferre´-D’Amare´ 2001). On the basis of this mechanism, variants of the hairpin ribozyme can be induced by external effector oligonucleotides (e.g., miRNAs) that interfere with the docking process. This is done by incorporating domain C, which is complementary to the target miRNA, and also contains a short sequence that can partially pair with domain A, thus rendering the ribozyme inactive. When the complementary target sequence is added, it hybridizes with domain C and forms a pseudo-half-knot structure (Ecker et al. 1992). Domains A and B can dock again, resulting in the cleavage of the substrate and the generation of a fluorescence signal (Komatsu et al. 2002; Vaish et al. 2003). Due to intrinsic signal amplification, their sensitivity is increased at least an order of magnitude compared to standard molecular beacons. Properly designed ribozymes exhibit very low cleavage activity in the absence of the corresponding miRNA and are activated when it is added. Significant increase in fluorescence should be well detectable at miRNA concentrations as low as 5 nM, corresponding to a detection limit of 50 fmol miRNA in the reaction mixture (Hartig et al. 2004).

25.2

Protocol

25.2.1 Materials 1. 384-well-plates (Corning)

25.2.2 Instruments 1. Fluorscan (Ascent FL) or an equivalent 2. NanoDrop spectrophotometer

25.2.3 Reagents 1. MgCl2 2. MAXIscript1 T7 Kit (Ambion) 3. Trizol Reagent (Invitrogen, Carlsbad, CA)

316

4. 5. 6. 7. 8.

25 Ribozyme Method

mirVana miRNA Isolation Kit (Ambion) Phenol/chloroform DNase I (Invitrogen) Absolute ethanol DMSO (Invitrogen)

25.2.4 Procedures The protocols described in this section are essentially the same as reported in the study by Hartig et al. in 2004 (see Figs. 25.1 and 25.2). A hairpin ribozyme contains two domains (A and B), with each domain containing an unpaired loop and two basepaired helices (H1-H2 or H3-H4). Thus, a hairpin ribozyme has two loops (loops A and B) and four stems (H1–H4), with the reactive

Domain C 5’-UAGAGAAgcacgAUACAUACUUCUUUACAUUCCAcgugcUUCUCUA-3’ Anti-miR-1 (or other miRNAs) DA-HR

5’-UAGAGAAgcacg |||||||||||| Anti-miR-1 3’-AUCUCUUcgugc DA-HR

+

Ribozyme substrate 5’-(Q)-CGUGCCCACCUA-(F)-3’

miR-1 5’-UGGAAUGUAAAGAAGUAUGUAU-3’

AC C C (F)-AUC CGUGC-(Q) 5’-UAGAGAAgcacgAUACAUACUUCUUUACAUUCCAcgugcUUCUCUA-3’ 3’-UAUGUAUGAAGAAAUGUAAGGU-5’

Fig. 25.2 Schematic illustration of the design of domain C and ribozyme substrate using miR-1 as a target example. The sequences of domain C and ribozyme are shown. Domain C contains a region complementary to miR-1 (anti-miR-1 region in red), a region that can hybridize to domain A (DA-HR in purple), and a linker sequence that is partially complementary to the ribozyme substrate. The ribozyme substrate contains from 50 - to 30 -end a region partially complementary to the linker sequence in domain C, an uncomplimentary region (CCAC), and a region complementary to the domain A-binding site in domain C

25.2 Protocol

317

phosphodiester located within loop A (Feldstein et al. 1989; Hampel and Tritz 1989; Haseloff and Gerlach 1989). Assembly of the functional structure imposes no obvious constraints on the maximum length of H1 or H4. And H2 and H4 have strictly four and five basepairs, respectively. Domain C contains a region complementary to the target miRNA, and a region that can hybridize to domain A of the ribozyme, preventing A and B from docking and rendering the ribozyme inactive. The ribozyme cleaves the fluorophor (F)- and quencher (Q)-labeled substrate when the miRNA for detection binds to its complementary region in domain C (Fig. 25.1).

25.2.4.1

Design of Ribozyme Domain B and Domain C

1. Obtain sequences for the miRNAs of interest from the miRNA Registry (microrna.sanger.ac.uk). 2. Design a hairpin ribozyme domain B for the selected miRNA using the computer software pcFOLD or other compatible softwares. 3. Domain C, a stem-loop structure, should contain a miRNA-hybridizing region, domain A-hybridizing region, and 30 - and 50 -flanking regions. Design an oligonucleotide fragment antisense to the selected miRNA for the detection of a miRNA-hybridizing region. 4. Add linker sequences “gcacg” to the 50 -end and “cgugc” to the 30 -end of the miRNA-hybridizing region, respectively. 5. Add a sequence “UAGAGAA” upstream (to the 50 -end) and a sequence UUCUCUA (the domain A-hybridizing region) downstream (to the 30 -end), the above fragment. Considering domain C for miR-1 as an example, we have: 50 -UAGAGAAgcacgAUACAUACUUCUUUACAUUCCAcgugcUUCUCUA-30 . Note that the region UAGAGAAgcacg is complementary to cgugcUUCUCUA, which forms a stem leaving miR-1 sequence in a loop structure (Fig. 25.2). 6. Connect domains C and B, with domain C upstream of domain B.

25.2.4.2

Synthesis of Ribozyme

1. Attach the sequence of T7 promoter to the 50 -end of the above-designed ribozyme to form a template for in vitro transcription. Synthesize the T7-ribozyme fragment in the form of DNA using the service provided by IDT1 (Integrated DNA Technologies, Coralville, IA, USA) or PCR amplify the fragment. 2. Perform in vitro transcription using T7-ribozyme fragment as a template and MAXIscript1 T7 Kit (Ambion) according to the manufacture’s instruction. 3. Digest the template with RNAse A free DNase I and purify the ribozyme. 4. Dissolve the ribozyme in DEPC H2O and measure the concentration using a NanoDrop spectrophotometer.

318

25.2.4.3

25 Ribozyme Method

Designing of Ribozyme Domain A Substrate

1. A ribozyme substrate should contain in its 50 -end a region partially complementary to the 50 -end linker sequence (gcacg) in domain C and in its 30 -end region partially complementary to the most 50 -end 3 nts in domain C. The middle region should contain 4 nts that are not complementary to the domain A-hybridizing region in domain C. Considering domain C for miR-1 as an example, we have: 50 -AUGUAUCCACCUA-30 , in which the bold letters represent the regions complementary to the linker and the domain A-hybridizing region, respectively (Fig. 25.2). 2. Label the substrate sequence with a quencher at the 50 -end and a fluorophor at the 30 -end using the service provided by Dharmacon (Lafayette, CO).

25.2.4.4

Ribozyme Reaction

1. Mix ribozyme and miRNA in 50 mM Tris/HCl (pH 7.5), 30 mM MgCl2 and 200 nM (20-fold excess over ribozyme) of labeled ribozyme substrate in 384-well-plates in a volume of 50 mL. Incubate for 10 min at 37 C. 2. Start the cleavage reaction by adding MgCl2. 3. Monitor the cleavage reaction by measuring the fluorescence of the FAMgroup, with excitation at 485 nm and emission at 518 nm using Fluorscan (Ascent FL).

25.3


The catalytic activity increases in a concentration-dependent manner. The ribozyme method has been tested with nine examples of ribozymes, activated by nine different miRNAs from Drosophila: iHP-miR-1, iHP-miR-2, iHP-miR-4, iHPmiR-5, iHP-mi-R7, iHP-miR-10, iHP-miR-34, iHP-miR-79, and iHP-let-7 (Hartig et al. 2004). Their results demonstrated that the hairpin ribozymes can be designed in a rational and straightforward manner that can be induced by external oligonucleotides sequence-specifically. This design enables real-time monitoring of ribozyme activity via FRET read-out. Due to intrinsic signal amplification, their sensitivity is increased at least an order of magnitude compared to that of standard molecular beacons. These probes may be useful in applications that require direct detection of nucleic acids within their natural environment. When ribozymes work inside the cells, they must be internalized into individual cells and access the target mRNA. However, cellular uptake of ribozymes and other naked nucleic acids is usually inefficient, due to their charged composition and large molecular size. To overcome this problem, liposomes and charged lipids are commonly used as delivery systems for ribozymes. Complexes of nucleic acids

References

319

with cationic lipids are usually internalized into the cells by endocytosis. For application of ribozymes in vivo, chemically synthesized ribozymes can be directly administered, or a plasmid vector encoding ribozyme genes can be introduced into the cells, where ribozymes can be transcribed by transcriptional factors in the host. Since naked nucleic acids are rapidly degraded by nucleases in cells, especially in the gastrointestinal tract and blood, ribozymes synthesized in vitro should be protected by chemical modification, such as thio-modification or alkylation at the 20 position of the ribose ring.

References Akashi H, Matsumoto S, Taira K (2005) Gene discovery by ribozyme and siRNA libraries. Nat Rev Mol Cell Biol 6:413–422 Doherty EA, Doudna JA (2001) Ribozyme structures and mechanisms. Annu Rev Biophys Biomol Struct 30:457–475 Ecker DJ, Vickers TA, Bruice TW, Freier SM, Jenison RD, Manoharan M, Zounes M (1992) Pseudo–half-knot formation with RNA. Science 257:958–961 Erickson RP, Izant J, eds (1992) Gene Regulation: Biology of Antisense RNA and DNA. RavenPress, New York Eckstein F, Lilley DMJ, eds (1996) Nucleic Acids and Moleculad Biology: Catalytic RNA Vol. 10, Spling-Verlag, Berlin Fedor MJ (2000) Structure and function of the hairpin ribozyme. J Mol Biol 297:269–291 Feldstein PA, Buzayan JM, Bruening G (1989) Two sequences participating in the autolytic processing of satellite tobacco ringspot virus complementary RNA. Gene 82:53–61 Hampel A, Tritz R (1989) RNA catalytic properties of the minimum (-)sTRSV sequence. Biochemistry 28:4929–4933 Hartig JS, Gru¨ne I, Najafi-Shoushtari SH, Famulok M (2004) Sequence-specific detection of MicroRNAs by signal-amplifying ribozymes. J Am Chem Soc 126:722–723 Hartig JS, Najafi-Shoushtari SH, Gru¨ne I, Yan A, Ellington AD, Famulok M (2002) Proteindependent ribozymes report molecular interactions in real time. Nat Biotechnol 20:717–722 Haseloff J, Gerlach WL (1989) Sequences required for self-catalysed cleavage of the satellite RNA of tobacco ringspot virus. Gene 82:43–52 Jenne A, Gmelin W, Raffler N, Famulok M (1999) Real-time characterization of ribozymes by fluorescence resonance energy transfer (FRET). Angew Chem Int Ed 38:1300–1303 Jenne A, Hartig JS, Piganeau N, Tauer A, Samarsky DA, Green MR, Davies J, Famulok M (2001) Rapid identification and characterization of hammerhead-ribozyme inhibitors using fluorescence-based technology. Nat Biotechnol 19:56–61 Kawasaki H, Wadhwa R, Taira K. (2004) World of small RNAs: from ribozymes to siRNA and miRNA. Differentiation 72:58–64 Komatsu Y, Nobuoka K, Karino-Abe N, Matsuda A, Ohtsuka E (2002) In vitro selection of hairpin ribozymes activated with short oligonucleotides. Biochemistry 41:9090–9098 Krupp G, Gaur RK, eds (2000) Ribozyme, Biochemistry and Biotechnology. Lilley DM (2005) Structure, folding and mechanisms of ribozymes. Curr Opin Struct Biol 15:313–323 Rossi JJ (1995) Controlled, targeted, intracellular expression of ribozymes: progress and problems. Trends Biotechnol 13:301–306 Rossi JJ, Sarver N (1990) RNA enzymes (ribozymes) as antiviral therapeutic agents. Trends Biotechnol 8:179–183

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25 Ribozyme Method

Rupert PB, Ferre´-D’Amare´ AR (2001) Crystal structure of a hairpin ribozyme-inhibitor complex with implications for catalysis. Nature 410:780–786 Sarver N, Cantin EM, Chang PS, Zaida JA, Ladne PA, Stephenes DA, Rossi JJ (1990) Ribozymes as potential anti-HIV-1 therapeutic agents. Science 247:1222–1225 Shimayama T, Nishikawa S, Taira K (1995) Generality of the NUX rule: kinetic analysis of the results of systematic mutations in the trinucleotide at the cleavage site of hammerhead ribozymes. Biochemistry 34:3649–3654 Sioud M, ed (2004) Methods in Molecular Biology: Ribozymesand siRNA Protocols. Vol. 252, Humana Press, Totowa Turner PC, ed (1997) Methods in Molecular Biology: Ribozyme Protocols. Vol. 74, Humana Press, Totowa Vaish NK, Jadhav VR, Kossen K, Pasko C, Andrews LE, McSwiggen JA, Polisky B, Seiwert SD (2003) Zeptomole detection of a viral nucleic acid using a target-activated ribozyme. RNA 9:1058–1072 Vitiello D, Pecchia DB, Burke JM. Intracellular ribozyme-catalyzed trans-cleavage of RNA monitored by fluorescence resonance energy transfer. RNA. 2000;6:628–637

Chapter 26

Electrocatalytic Moiety Labeling Technique for High-Sensitivity miRNA Expression Analysis

Abstract Chemical labeling of miRNAs for electrochemical assay has become a useful approach for miRNA detection. It is believed that due to the extremely small size of miRNAs, direct labeling on miRNAs may be more advantageous. Recently, Gao and Yu from the Institute of Microelectronics (Singapore) presented a novel labeling procedure that utilizes a chemical ligation to directly label miRNA with a redox active and catalytic moiety (Biosens Bioelectron 22:933–940, 2007). The miRNA is labeled in the total RNA mixture in a one-step non-enzymatic reaction under very mild conditions with a redox active and electrocatalytic moiety, Ru (PD)2Cl2 (PD ¼ 1,10-phenanthroline-5,6-dione), through coordinative bonds with purine bases in the miRNA molecule. The excellent electrocatalytic activity of the Ru(PD)2Cl2 towards the oxidation of hydrazine makes it possible to conduct ultrasensitive miRNA detection. Under optimized experimental conditions, the assay allows the detection of miRNAs in the range of 0.50–400 pM with a detection limit of 0.20 pM in 2.5 microl (0.50 amole). miRNA quantitation is, therefore, performed in as little as 10 ng of total RNA, providing a handy platform for miRNA expression analysis. The amplification from the electrocatalytic oxidation of hydrazine greatly enhances the detectability of the assay, thereby lowering the detection limit to 0.2 pM. The electrochemical miRNA assay described here is rapid, ultrasensitive, non-radioactive, and is able to directly detect miRNA without involving biological ligation. The method allows us to identify miRNAs with less than twofold difference in expression levels under two conditions.

26.1

Introduction

Chemical labeling of miRNAs for electrochemical assay has become a useful approach for miRNA detection. It is believed that due to the extremely small size of miRNAs, direct labeling on miRNAs may be more advantageous. Recently, Babak and co-workers proposed a cisplatin-based chemical labeling procedure for miRNAs (Babak et al. 2004). One binding site of cisplatin is covalently bound to a Z. Wang and B. Yang, MicroRNA Expression Detection Methods, DOI 10.1007/978-3-642-04928-6_26, # Springer-Verlag Berlin Heidelberg 2010

321

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26 Electrocatalytic Moiety Labeling Technique

fluorophore and the other site is a labile nitrate ligand. Incubation in an aqueous solution with miRNAs at elevated temperatures results in a ligand exchange between the labile nitrate of the cisplatin complex and the more strongly coordinating purine moiety, forming a new complex between cisplatin and the N7 position of G base. The miRNA was, therefore, directly labeled with the cisplatin–fluorophore conjugate through a coordinative bond with G base in miRNA. Another direct labeling procedure at the 3’ end was recently developed by Liang et al. in which miRNAs were first tagged with biotin. After the introduction of quantum dots to the hybridized miRNAs through a reaction with quantum dots–avidin conjugates, the miRNAs were detected fluorescently with a dynamic range from 156 pM to 20 nM (Liang et al. 2005). Thomson’s group used T4 RNA ligase to couple the 3’ end of miRNA to a fluorophore-tagged nucleotide (Thomson et al. 2004). Overall, these direct ligation procedures do not offer the expected sensitivity for miRNA expression analysis. To further enhance the sensitivity and lower the detection limit, Gao and Yu proposed that a chemical or biological amplification scheme must be employed in the direct ligation procedures. It has been demonstrated that the sensitivity of the amplified electrochemical detection of nucleic acids is comparable to that of PCR-based fluorescent assays (Zhang et al. 2003; Xie et al. 2004a, b; Piunno and Krull 2005). Recently, Gao and Yu presented a novel labeling procedure that utilizes a chemical ligation to directly label miRNA with a redox active and catalytic moiety (Gao and Yu 2007). The miRNA is labeled in total RNA mixture in a one-step non-enzymatic reaction under very mild conditions with a redox active and electrocatalytic moiety, Ru(PD)2Cl2 (PD ¼ 1,10-phenanthroline-5,6-dione), through coordinative bonds with purine bases in the miRNA molecule. The excellent electrocatalytic activity of the Ru(PD)2Cl2 towards the oxidation of hydrazine makes it possible to conduct ultrasensitive miRNA detection. Under optimized experimental conditions, the assay allows the detection of miRNAs in the range of 0.50–400 pM, with a detection limit of 0.20 pM in 2.5 microl (0.50 amole). miRNA quantitation is, therefore, performed in as little as 10 ng of total RNA, providing a much-needed platform for miRNA expression analysis. The amplification from the electrocatalytic oxidation of hydrazine greatly enhances the detectability of the assay, thereby lowering the detection limit to 0.2 pM (Fig. 26.1).

26.2

Protocol

26.2.1 Materials 1. Indium tin oxide (ITO)-coated glass slides (Delta Technologies Limited, Stillwater, MN) 2. Montage spin column YM-50 column (Millipore Corporation)

26.2 Protocol

323

miRNA miRNA capture probes

+

ITO-coated glass slide

Immobilization

Hybridization

hydrazine

N2 Electrochemical Measurement to Detect miRNAs

Electrocatalysis

Fig. 26.1 Diagram illustrating the principle of electrocatalytic moiety labeling technique for highsensitivity miRNA expression analysis. Modified from Gao and Yu (2007)

3. Non-leak Ag/AgCl (3.0 M NaCl) reference electrode (Cypress Systems, Lawrence, KS) 4. Platinum wire counter electrode

26.2.2 Instruments 1. CH Instruments model 660A electrochemical workstation (CH Instruments, Austin, TX) 2. Conventional three-electrode system, consisting of an ITO working electrode, was used in all electrochemical measurements 3. Finnigan/MAT LCQ Mass Spectrometer (ThermoFinnigan, San Jose, CA) 4. Elan DRC II ICP-MS spectrometer (PerkinElmer, Wellesley, MA) 5. V-570 UV/VIS/NIR spectrophotometer (JASCO Corp., Japan) 6. UV–vis spectrophotometry 7. An environmental chamber

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26.2.3 Reagents 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14.

RuCl3 A phosphate buffered-saline (PBS, pH8.0): [0.15 M NaCl, 20 mM phosphate] Diethyl pyrocarbonate RNaseZap (Ambion, TX) 0.1 M pH6.0 acetate buffer 3 M KCl Trizol Reagent (Invitrogen, Carlsbad, CA) mirVana miRNA Isolation Kit (Ambion) Phenol/chloroform DNase I (Invitrogen) Absolute ethanol 0.1% SDS Sodium borohydride solution. 11-Aminoundecanoic acid (AUA, > 99%)

26.2.4 Procedures The protocols described in this section are essentially the same as reported in the study by Gao and Yu in 2007.

26.2.4.1

Synthesis of Ru(PD)2Cl2

Synthesize Ru(PD)2Cl2 (PD ¼ 1,10-phenanthroline-5,6-dione) according to the procedures described in literature (Goss and Abrunã 1985; Rivera et al 1994).

26.2.4.2

Total RNA Extraction and Labeling

1. Extract total RNA from cultured cells or tissues using TRIzol reagent according to the manufacturer’s recommended protocol 2. Enrich miRNAs in the total RNA using a Montage spin column YM-50 column 3. Determine the RNA concentration by UV–vis spectrophotometry. Typically, 1.0 mg of total RNA is used in each of the labeling reactions 4. Add 20 mL of 0.25 mM Ru(PD)2Cl2 in 0.1 M pH 6.0 acetate buffer to 5 mL of total RNA solution 5. Incubate the mixture for 30–40 min at 80 C and cool on ice 6. Add 5 mL of 3 M KCl into the labeled RNA sample and store at 20 C Since the labeling process is only effective to G and A bases, the label/base ratio is normally in the range of 1/3 to 1/4, depending on the sequence of

26.2 Protocol

325

individual miRNA molecule. Theoretically, if this ratio remains unchanged for all miRNAs, the same current sensitivity per base should be obtained for all miRNAs. At the same molar concentration, the sensitivity should be roughly proportional to the number of base in the miRNA, but this trend was not observed in our experiments. It is noteworthy that the sensitivity per base is dependent on miRNA sequence and (G þ A) content. However, no straightforward relation between (G þ A) content and current sensitivity was observed. This is probably due to the fact that G and A are not evenly distributed. Owing to the steric hindrance and three-dimensional packing of the miRNA molecules on the electrode surface, it would be impossible to label some of the G and A bases when they are in a cluster; hence, a low labeling efficiency is expected. For example, the (G þ A) content (78%) in miR-320 is more than doubled as compared to that of miR-92, but the sensitivity for miR-320 was merely 35% higher than that of miRr-92 (Gao and Yu 2007).

26.2.4.3

Design of miRNA Capture Oligonucleotide Probes

1. Obtain sequences for miRNA detection from the miRNA Registry (microrna. sanger.ac.uk) 2. Design miRNA capture oligonucleotide probes exactly antisense to the selected miRNAs 3. Synthesize and aldehyde-modify the probes by Invitrogen (Carlsbad, CA)

26.2.4.4

Electrode Preparation and Capture Probes Immobilization

1. Silanize the ITO electrodes with the bifunctional reagent 1,12-dodecanedicarboxylic acid (DDCA) to form a carboxylic acid-terminated monolayer (Hedges et al. 2004; Gao and Tansil 2005). 2. Denature aldehyde-modified capture probes for 10 min at 90 C and dilute to a concentration of 0.5 mM in 0.1 M acetate buffer (pH6.0). 3. Dispense a 25 mL aliquot of the capture probes solution onto the silanized electrode and incubate for 2–3 h at 20 C in an environmental chamber. 4. After incubation, rinse the electrode successively with 0.1% SDS and water. 5. Conduct reduction of the imines by a 5 min incubation of the ITO electrode in a 2.5 mg/ml sodium borohydride solution made of PBS/ethanol (3/1). 6. Soak the electrode in vigorously stirred hot water (9095 C) for 2 min, copiously rinse with water and blow dry with a stream of nitrogen. 7. Immerse the capture probe-coated electrode in an ethanolic solution of 2.0 mg/ ml AUA for 3–5 h. 8. Rinse off unreacted AUA molecules and wash the electrode by immersion in stirred ethanol for 10 min, followed by a thorough rinsing with ethanol and water. The surface density of the immobilized capture probes should be around (6.0–8.5) 1012 mol/cm2.

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The amount of capture probes immobilized on the electrode surface and hybridization efficiency determine the amount of target miRNA bound to the surface and thereby the amount of labels. However, in the model proposed by Gao and Yu (2007), multiple Ru(PD)2Clþ labels on a single miRNA strand greatly increases the label loading, proportionally increasing the response from electrocatalytic oxidation, and hence the sensitivity and detection limit of the miRNA assay is substantially improved.

26.2.4.5

miRNA Assay

1. Place the treated electrode in a moisture saturated environmental chamber maintained at 30 C 2. Uniformly spread a 2.5 mL aliquot of hybridization solution, containing the desired amount of labeled miRNA, onto the electrode 3. Rinse the electrode thoroughly with a blank hybridization solution at 30 C after a 60-min hybridization period 4. Measure the hydrazine electrooxidation current amperometrically at 0.1 V in vigorously stirred PBS containing 5 mM hydrazine 5. At low miRNA concentrations, smoothing need to be applied after each amperometric measurement to remove random noise and electromagnetic interference 6. Electrochemical experiments should be carried out using a CH Instruments model 660A electrochemical workstation 7. Utilize a conventional three-electrode system (consisting of an ITO working electrode, a non-leak Ag/AgCl (3 M NaCl) reference electrode, and a platinum wire counter electrode) in all electrochemical measurements. All potentials should be referred to the Ag/AgCl electrode 8. Perform electrospray ionization mass spectrometric (ESI-MS) experiments with a Finnigan/MAT LCQ Mass Spectrometer 9. Conduct inductively coupled plasma mass spectrometry (ICP–MS) with an Elan DRC II ICP–MS spectrometer 10. Record UV–vis spectra on a V-570 UV/VIS/NIR spectrophotometer 11. Carry out all experiments at room temperature, unless otherwise stated

26.2.4.6

Calibration Curves for miRNAs

1. For quantitative detection of miRNAs, it is necessary to establish a standard curve for each of the miRNAs in question. Synthesize miRNAs using the services provided by companies. 2. Solutions of different concentrations of labeled miRNAs, ranging from 0.10 to 1,000 pM, should be tested, following the procedures described above. 3. For the control experiments, non-complementary capture probes were used in the electrode preparation (Fig. 26.2).


327

Silanize the ITO electrodes Select a miRNA of interest Preparation of tissue or cells total RNA samples

Design and synthesize RNA labelling with Ru(PD)2Cl2 of miRNA capture probes

Coat electrodes with miRNA capture probes

Immobilize electrodes on indium tin oxide (ITO) coated glass slides

Hybridization of labelled miRNAs with miRNA capture probes

Electrooxidation with hydrazine

Electrocatalysis

Electrochemical measurements

Fig. 26.2 Flowchart of the electrocatalytic moiety labeling technique for miRNA expression detection. According to Gao and Yu (2007)

26.3


The Electrocatalytic Moiety Labeling technique for miRNA detection possesses several advantages. 1. The electrochemical miRNA assay described here is rapid, ultrasensitive, nonradioactive and is able to directly detect miRNA without involving biological ligation. 2. By employing Ru(PD)2Cl2, miRNA is directly labeled with redox and electrocatalytic moieties under very mild conditions. 3. Specific miRNAs are detected amperometrically at sub-picomolar levels with high specificity. This electrochemical miRNA assay is easily extendable to a

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low-density array of 50–100 electrodes. The relatively limited number of miRNA offers excellent opportunity for low-density electrochemical arrays in miRNA assays. The advantages of low-density electrochemical biosensor arrays are: (a) more cost-effective than optical biosensor arrays; (b) ultrasensitive when coupled with catalysis; (c) rapid, direct, turbid, and light absorbing-tolerant detection; and (d) portable, robust, low cost, and easy-to-handle electrical components suitable for field tests and homecare use. Such a tool would be of great scientific value and may open the door to routine miRNA expression profiling and molecular diagnostics. 4. The method allows us to identify miRNAs with less than two-fold difference in expression levels under two conditions. The major drawbacks of this technique are the requirement of many instruments and chemical synthesis of reagents, which are not frequently seen in a regular molecular biology laboratory. And since the labeling process is only effective to G and A bases, the labeling intensity of a miRNA will depend on the GA content and the clustering of the GAs owing to the steric hindrance and three-dimensional packing of the miRNA molecules on the electrode surface. This property makes quantitative analysis of miRNAs difficult.

References Babak T, Zhang W, Morris Q, Blencowe BJ, Hughes TR (2004) Probing microRNAs with microarrays: tissue specificity and functional inference. RNA 10:1813–1819 Gao Z, Tansil NC (2005) An ultrasensitive photoelectrochemical nucleic acid biosensor. Nucleic Acids Res 33:e123 Gao Z, Yu YH (2007) Direct labeling microRNA with an electrocatalytic moiety and its application in ultrasensitive microRNA assays. Biosens Bioelectron 22:933–940 Goss CA, Abrunã HD (1985) Inorg Chem 24:4263 Hedges DH, Richardson DJ, Russell DA (2004) Electrochemical control of protein monolayers at indium tin oxide surfaces for the reagentless optical biosensing of nitric oxide. Langmuir 20:1901–1908 Liang RQ, Li W, Li Y, Tan CY, Li JX, Jin YX, Ruan KC (2005) An oligonucleotide microarray for microRNA expression analysis based on labeling RNA with quantum dot and nanogold probe. Nucleic Acids Res 33:e17 Piunno PA, Krull UJ (2005) Trends in the development of nucleic acid biosensors for medical diagnostics. Anal Bioanal Chem 381:1004–1011 Rivera N, Coloń Y, Guadalupe AR (1994) Bioelectrochem Bioenerg 34:169 Thomson JM, Parker J, Perou CM, Hammond SM (2004) A custom microarray platform for analysis of microRNA gene expression. Nat Methods 1:47–53 Xie H, Yu YH, Xie F, Lao YZ, Gao Z (2004a) A nucleic acid biosensor for gene expression analysis in nanograms of mRNA. Anal Chem 76:4023–4029 Xie H, Zhang C, Gao Z (2004b) Amperometric detection of nucleic acid at femtomolar levels with a nucleic acid/electrochemical activator bilayer on gold electrode. Anal Chem 76:1611–1617 Zhang Y, Kim HH, Heller A (2003) Enzyme-amplified amperometric detection of 3000 copies of DNA in a 10-microL droplet at 0.5 fM concentration. Anal Chem 75:3267–3269

Part IX

Circulating miRNA Detection Methods

Chapter 27

Serum and Plasma miRNA Detection

Abstract Blood-based miRNA detection is not a method but an approach. This approach relies on the facts that (1) miRNAs are present in the serum and plasma of humans and animals such as mice, rats, bovine fetuses, calves, and horses; (2) miRNAs are present in human plasma in a remarkably stable form that is protected from endogenous RNase activity; and (3) the levels of miRNAs in serum are reproducible and consistent among individuals of the same species. The bloodbased miRNA detection enables the development of miRNAs as novel biomarkers for diagnosis and prognosis of human diseases and of noninvasive diagnostic and prognostic approaches as practical tools in clinical use. In theory, most of the miRNA detection methods described above can be used to detect serum and plasma miRNAs. The feasibility of blood-based miRNA detection has recently been verified by several groups with the studies documented by Chen et al. (Cell Res 18:997–1006, 2008) and Mitchell et al. (Proc Natl Acad Sci USA 105:10513– 10518, 2008) representing the first successful examples of such efforts. These studies clearly indicate the value of blood-based miRNA detection as an invaluable approach for identifying aberrantly expressed miRNAs under specific pathological conditions and for realizing these miRNAs as novel biomarkers for diagnosis and prognosis of human diseases.

27.1

Introduction

Besides being recognized as key molecules in intracellular regulatory networks for gene expression, the spectra and levels of some miRNAs are emerging as biomarkers for various pathological conditions (Waldman and Terzic 2008; Lee and Dutta 2008; Dillhoff et al. 2008). Recent findings suggest that circulating miRNAs may be plasma biomarkers for the diagnosis of the lung (Chen et al. 2008), colorectal (Chen et al. 2008), and prostate cancers (Mitchell et al. 2008). Indeed, miRNAs are present in the serum and plasma of humans and animals such as mice, rats, bovine fetuses, calves, and horses (Ai et al. 2009; Chen et al. 2008; Hunter et al. 2008; Mitchell et al. 2008; Chim et al. 2008; Gilad et al. 2008; Lawrie et al. 2008; Z. Wang and B. Yang, MicroRNA Expression Detection Methods, DOI 10.1007/978-3-642-04928-6_27, # Springer-Verlag Berlin Heidelberg 2010

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27 Serum and Plasma miRNA Detection

Resnick et al. 2009; Taylor and Gercel-Taylor 2008; Wang et al. 2009). miRNAs are present in human plasma in a remarkably stable form that is protected from endogenous RNase activity. The levels of miRNAs in serum are reproducible and consistent among individuals of the same species. Strikingly, Gilad et al. (2008) confirmed that miRNAs are also detectable in other body fluids, such as urine, saliva, amniotic fluid, and pleural fluid. Of note, serum and urine display different miRNA abundance profiles as might be expected for two dissimilar biological fluids, further supporting the hypothesis that bodily fluid microRNA profiles reflect physiology. Challenges for developing protein-based biomarkers from body fluids, such as plasma, serum, and urine, include the complexity of protein composition, the assorted posttranslational modifications, the low abundance of proteins of interest, the difficulty of developing suitable high-affinity capture agents, the complexities of proteolysis and protein denaturation, and potentially complex assay methods (Cowan and Vera 2008; Ebert et al. 2006). All of these make the discovery and development of protein-based biomarkers with proper specificity, sensitivity, and predictive value an expensive, time consuming, and difficult task. The biological function of circulating miRNA is largely unknown; however, unlike proteins, there are far fewer known miRNA species, so obtaining a complete profile is relatively easy. Currently, there are 866 known miRNAs for human and 627 for mouse (based on the latest miRBase release 12.0 http://microrna.sanger.ac.uk/sequences/index. shtml) compared with perhaps a million or more serum proteins, including various processing variants and posttranslationally modified proteins. mRNA must be translated into protein to have a biological effect whereas miRNAs are themselves the active moiety, often influencing the expression of multiple other genes, and thus likely reflect altered physiology more directly. In addition, miRNAs do not have known post-processing modifications, and with their size, their chemical composition is much less complex than most other biological molecules. Detecting specific miRNA species, although somewhat challenging, is inherently a much easier task than detecting proteins. A synthetic complementary oligonucleotide should deliver sufficient specificity in most cases, and a standard PCR assay can be used to increase the detection sensitivity. It has also been demonstrated that the circulating miRNAs are stable and can be reliably extracted and assayed in either serum or plasma (Mitchell et al. 2008). Both real-time RT-PCR and microarray methods have been applied to detect blood miRNAs. Theoretically speaking, most of the methodologies described in earlier chapters can be applied for miRNA expression profiling and detection.

27.2

Protocol

27.2.1 Materials 1. 18 gage needle 2. 2 mL syringe

27.2 Protocol

333

3. 2 mL microfuge tubes 4. TaqMan Array Human MicroRNA Panel (v.1, Applied Biosystems, Foster City, CA)

27.2.2 Instruments 1. Surgical blade 2. Centrifuge 3. Nanodrop instrument (ND-1000 Spectrophotometer, NanoDrop Technologies) or an equivalent 4. Agilent1 2100 bioanalyzer (Agilent Technologies) or an equivalent 5. A Real-Time PCR System 6. ABI Prism 7900HT Sequence detection system (Applied Biosystems) 7. ThermoScientific NanoDrop1000 (Thermo Fisher Scientific, Inc., Waltham, MA) or an equivalent

27.2.3 Reagents 1. RNAlater1 Tissue Collection:RNA Stabilization Solution (Ambion, Austin TX) 2. Trizol Reagent (Invitrogen, Carlsbad, CA) 3. mirVana miRNA Isolation Kit (Ambion) 4. Ambion Mouse RiboPureTM -Blood RNA Isolation kit (AB cat #AM1951) 5. Phenol/chloroform 6. DNase I (Invitrogen) 7. Absolute ethanol 8. TaqMan1 MicroRNA Assays (Applied Biosystems) 9. AB TaqMan1 microRNA Reverse Transcription Kit (AB cat #4366597) 10. DMSO (Invitrogen) 11. TaqMan Universal PCR Master Mix (Applied Biosystems) 12. Tri-Reagent BD (Molecular Research Center, Inc., Cincinnati, OH)

27.2.4 Procedures The protocols described in this section are essentially the same as reported in the studies by Chen et al. (2008) and by Mitchell et al. (2008).

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27.2.4.1


Blood Collection

Mouse Blood 1. To perform cardiac puncture, euthanize mice using carbon dioxide. Then access the ventricle with an 18 gage needle and aspirate 400–500 mL blood into a 2 mL syringe. Discharge the blood immediately into a 2 mL microfuge tube preloaded with 1.3 mL RNAlater1 Tissue Collection:RNA Stabilization Solution, mix by inversion, and store at 20 C. 2. To perform blood collection by tail vein, place mice under a heat lamp for 5 min, then make a small peripheral tail incision. Collect 2–10 drops of blood directly into a 2 mL microfuge tube preloaded with 1.3 ml RNALater1 Solution, mix by inversion, and store at 20 C. An average drop of blood approximates 24 mL (Fan et al. 2008).

Human Blood 1. Obtain written consent and ethics permission from blood donors 2. Separate whole blood into serum and cellular fractions within 2 h after blood has been derived, by clotting blood sample 3. Freeze cellular fractions immediately in liquid nitrogen and store sera at 80 C (Chen et al. 2008)

27.2.4.2

Separation of Plasma and Blood Cells

1. To harvest cell-free plasma, centrifuge blood samples twice at 4 C: first at 1,600 g for 10 min and collect the supernatant, followed by a second centrifuge of the supernatant at 16,000 g for 10 min to remove blood cells. 2. To harvest blood cells (including leukocytes and erythrocytes), centrifuge the blood cells obtained in the first centrifugation at 2,300 g for 5 min to remove residual plasma (Chim et al. 2008).

27.2.4.3

RNA Extraction

Whole Blood RNA 1. The Ambion Mouse RiboPureTM -Blood RNA Isolation kit can be used for extraction of RNA. Centrifuge samples and remove the RNAlater1 Solution prior to disruption of the blood pellet in a guanidinium-based lysis solution, followed by organic extraction and purification of the total RNA fraction (including small RNA) by solid phase extraction onto a silica matrix.

27.2 Protocol

335

2. Use the Alternative Protocol described in the kit instruction manual for samples less than 250 ml from the mouse tail vein. 3. Determine RNA yields by UV absorbance using a Nanodrop instrument and examine the intactness on an Agilent1 2100 bioanalyzer. 4. Dilute the cardiac puncture samples 1:10 before running (Fan et al. 2008).

Serum RNA 1. Extract RNA from 250 mL of serum using the Tri-Reagent BD as described by the manufacturer 2. Or use Trizol Reagent and add three steps of phenol/chloroform purification since serum is full of proteins. In general, the yield is expected to be around 5–10 mg RNA/50 mL serum (Chen et al. 2008) 3. Or use the mirVana miRNA Isolation Kit (Ambion) 4. To minimize DNA contamination, treat the eluted RNA preparation with DNase I 5. Assess RNA quality with the ThermoScientific NanoDrop1000 (Resnick et al. 2009)

Blood Cell RNA 1. Isolate blood cell total RNA using Trizol, according to the manufacturer’s instructions.

27.2.4.4

miRNA Profiling and Quantification

1. Reverse transcription reactions are carried out for 65 min using the AB TaqMan1 microRNA Reverse Transcription Kit which includes M-MLV reverse transcriptase. 2. Conduct miRNA analysis using the mirVana qRT-PCR primer sets or the TaqMan1 MicroRNA Assays. The mirVana qRT-PCR assays use 10 ng of input total RNA that is analyzed using target-specific primers for reverse transcription with M-MLV reverse transcriptase, followed by PCR amplification with a pair of miRNA target-specific primers and detection with SYBR1 Green I nucleic acid gel stain 10,000 concentrate in DMSO. Melting curve analysis is carried out for each target to assess amplification specificity; for some targets, nonspecific amplification is observed in the no-template negative controls, which cannot be discriminated by melt-curve analysis. 3. The TaqMan MicroRNA Assays use 10 ng of input total RNA with miRNAtarget-specific reverse transcription primers and target-specific internal hybridization probes (“TaqMan probes”), and are run in 96-well or 384-well formats. qRT-PCR assays of similar design need to be carried out for constitutively

336


expressed small RNAs of similar size to miRNAs (e.g., snoRNAs) and used for normalization of input RNA amount (analogous to use of constitutive mRNAs such as GAPDH for normalization of protein-coding genes). 4. Amplification reactions consist of a hold of 10 min at 95 C and 40 cycles (15 s/ 95 C, 60 s/60 C) on an Applied Biosystems 7900HT Real-Time PCR System and require about 1.5 h to complete. The assays should be carried out in duplicate or triplicate and the geometric average Ct value is used to calculate relative expression for each data point. Within each experiment, the endogenous control that has the highest Ct should be set as the baseline, and the Ct between the baseline and the Ct of the small RNA control in each sample should be used as a normalization factor that is added to the raw Ct for each sample. Normalized Ct values larger than 35 were reported as 35 (Fan et al. 2008) (Fig. 27.1).

Collect blood sample from mice by cardiac puncture or via tail vein

Collect blood sample from human donors

Two centrifuges to separate plasma and blood cells in the presence of anti-coagulant

Collect supernatant as plasma

Let blood sample coagulate without anti-coagulant

Centrifuge again

Remove supernatant to collect blood cells

Collect fluid as serum

Isolate total RNA from various blood components using Tri-Reagent BD

Real-time RT-PCR to detect circulating miRNAs

Fig. 27.1 Flowchart of the use of blood samples for miRNA expression detection. According to Chen et al. (2008), Mitchell et al. (2008)

References

27.3

337


The diagnostic and prognostic utility of circulating RNAs in both benign and malignant conditions has recently been revealed. Placental-associated circulating miRNAs correlate with pregnancy progression (Chim et al. 2008). In malignant states, circulating mRNAs in renal cell carcinoma patients (Feng et al. 2008) as well as miRNAs from the serum of patients with diffuse large B cell lymphoma (Lawrie et al. 2008) have been shown to be stable and highly predictive of malignancy as well as survival. miRNAs originating from human prostate cancer xenografts enter the circulation, are readily measured in plasma, and can robustly distinguish xenografted mice from controls. This concept extends to cancer in humans, where serum levels of miR-141 (a miRNA expressed in prostate cancer) can distinguish patients with prostate cancer from healthy controls (Mitchell et al. 2008). It has been demonstrated that the miRNA signature of circulating tumor exosomes of ovarian cancer patients demonstrates high correlation with miRNA expression of the primary tumor (Taylor and Gercel-Taylor 2008). Resnick et al. (2009) described miRNA extraction from the serum of ovarian cancer patients, the differential expression of a number of these miRNAs between patients and healthy controls as well as a novel real-time PCR microarray detection method (Resnick et al. 2009). Wang et al. (2009) recently established specific circulating microRNAs as sensitive and informative biomarkers for drug-induced liver injury. More recently, Ai et al. (2009) described a study designed to establish circulating miR-1 as a novel biomarker for acute myocardial infarction. miR-1 level was found significantly higher in both whole blood and plasma from patients with acute myocardial infarction relative to healthy subjects and the level was dropped to normal on discharge following medication. Increased circulating miR-1 was not associated with age, gender, blood pressure as well as diabetes mellitus, or established biomarkers. However, miR-1 level was well correlated with the width of QRS complex in ECG, consistent with the ability of miR-1 to slow cardiac conduction and promotes arrhythmogenesis (Yang et al. 2007, 2008; Wang et al. 2008). Use of blood samples (serum, plasma, or blood cells) for miRNA profiling and detection holds a promising future for using circulating miRNAs as biomarkers for diagnosis and prognaosis of human diseases. With the recent rapid development in this field, the age of miRNA-based biomarkers is soon to come.

References Ai J, Zhang R, Pu J, Li Y, Lu Y, Jiao J, Li K, Yu B, Li Z, Wang R, Wang L, Li Q, Wang N, Shan H, Yang B (2009) Circulating microRNA-1 as a novel biomarker for acute myocardial infarction. Cardiovasc Res (in revision) Chen X, Ba Y, Ma L, Cai X, Yin Y, Wang K, Guo J, Zhang Y, Chen J, Guo X, Li Q, Li X, Wang W, Zhang Y, Wang J, Jiang X, Xiang Y, Xu C, Zheng P, Zhang J, Li R, Zhang H, Shang X, Gong T, Ning G, Wang J, Zen K, Zhang J, Zhang CY (2008) Characterization of

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microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases. Cell Res 18:997–1006 Chim SS, Shing TK, Hung EC, Leung TY, Lau TK, Chiu RW, Lo YM (2008) Detection and characterization of placental microRNAs in maternal plasma. Clin Chem 54:482–490 Cowan ML, Vera J (2008) Proteomics: Advances in biomarker discovery. Exp Rev Proteomics 5:21–23 Dillhoff M, Wojcik SE, Bloomston M (2008) MicroRNAs in solid tumors. J Surg Res, in press Ebert MP, Korc M, Malfertheiner P, Rocken C (2006) Advances, challenges, and limitations in serum-proteome-based cancer diagnosis. J Proteome Res 5:19–25 Fan AC, Goldrick MM, Ho J, Liang Y, Bachireddy P, Felsher DW (2008) A quantitative PCR method to detect blood microRNAs associated with tumorigenesis in transgenic mice. Mol Cancer 7:74 Feng G, Li G, Gentil-Perret A, Tostain J, Genin C (2008) Elevated serumcirculating RNA in patients with conventional renal cell cancer. Anticancer Res 28:321–326 Gilad S, Meiri E, Yogev Y, Benjamin S, Lebanony D, Yerushalmi N, Benjamin H, Kushnir M, Cholakh H, Melamed N, Bentwich Z, Hod M, Goren Y, Chajut A (2008) Serum microRNAs are promising novel biomarkers. PLoS ONE 3:e3148 Hunter MP, Ismail N, Zhang X, Aguda BD, Lee EJ, Yu L, Xiao T, Schafer J, Lee ML, Schmittgen TD, Nana-Sinkam SP, Jarjoura D, Marsh CB (2008) Detection of microRNA expression in human peripheral blood microvesicles. PLoS ONE 3:e3694 Lawrie CH, Gal S, Dunlop HM, Pushkaran B, Liggins AP, Pulford K, Banham AH, Pezzella F, Boultwood J, Wainscoat JS, Hatton CS, Harris AL (2008) Detection of elevated levels of tumourassociated microRNAs in serum of patients with diffuse large B-cell lymphoma. Br J Haematol 141:672–675 Lee YS, Dutta A (2008) MicroRNAs in cancer. Annu Rev Pathol 4:199–227 Mitchell PS, Parkin RK, Kroh EM, Fritz BR, Wyman SK, Pogosova-Agadjanyan EL, Peterson A, Noteboom J, O’Briant KC, Allen A, Lin DW, Urban N, Drescher CW, Knudsen BS, Stirewalt DL, Gentleman R, Vessella RL, Nelson PS, Martin DB, Tewari M (2008) Circulating microRNAs as stable blood-based markers for cancer detection. Proc Natl Acad Sci USA 105:10513–10518 Resnick KE, Alder H, Hagan JP, Richardson DL, Croce CM, Cohn DE (2009) The detection of differentially expressed microRNAs from the serum of ovarian cancer patients using a novel real-time PCR platform. Gynecol Oncol 112:55–59 Taylor DD, Gercel-Taylor C (2008) MicroRNA signatures of tumor-derived exosomes as diagnostic biomarkers of ovarian cancer. Gynecol Oncol 110:13–21 Waldman SA, Terzic A (2008) MicroRNA signatures as diagnostic and therapeutic targets. Clin Chem 54:943–944 Wang K, Zhang S, Marzolf B, Troisch P, Brightman A, Hu Z, Hood LE, Galas DJ (2009) Circulating microRNAs, potential biomarkers for drug-induced liver injury. Proc Natl Acad Sci USA 106:4402–4407 Wang Z, Luo X, Lu Y, Yang B (2008) miRNAs at the heart of the matter. J Mol Med 86:771–783 Yang B, Lin H, Xiao J, Lu Y, Luo X, Li B, Zhang Y, Xu C, Bai Y, Wang H, Chen G, Wang Z (2007) The muscle-specific microRNA miR-1 causes cardiac arrhythmias by targeting GJA1 and KCNJ2 genes. Nat Med 13:486–491 Yang B, Lu Y, Wang Z (2008) Control of cardiac excitability by microRNAs. Cardiovasc Res 79:571–580

Chapter 28

miRNA Detection from Peripheral Blood Microvesicles

Abstract Microvesicles are small exosomes/vesicles of endocytic origin released by normal healthy or damaged cell types or activated platelets. These cell particles are enriched in bioactive molecules and contain nucleic acid and/or protein, playing a role in growth, differentiation, cancer progression, and cell signal transduction. Changes of microvesicles concentration have been noticed under several pathological conditions. The miRNAs expressed in the microvesicles from the blood may play a role in homeostasis, and the miRNA expression profiles may also reflect certain pathophysiological conditions. The first efforts to measure miRNA expression in microvesicles were made by Valadi and colleagues (Nat Cell Biol 9:654–659, 2007) from the Department of Internal Medicine and Department of Respiratory Medicine and Allergology, the Sahlgrenska Academy, Go¨teborg University (Sweden) and by Hunter et al. (PLoS ONE 3:e3694, 2008) from the Division of Pulmonary, Allergy, Critical Care, Sleep Medicine, College of Medicine, The Ohio State University (Columbus, OH, USA).

28.1

Introduction

It has recently been recognized that microvesicles play an important role in genetic exchange of mRNA and miRNA between cells (Valadi et al. 2007). Microvesicles are small exosomes/vesicles (from 50 nm to 1 mm) of endocytic origin released by normal healthy or damaged cell types or activated platelets (Wieckowski and Whiteside 2006; Ratajczak et al. 2006b; Valenti et al. 2007). They can be considered as shed from the plasma membrane into the extracellular environment to facilitate communication between cells. These cell particles also play a role in growth, differentiation, and cancer progression (Ratajczak et al. 2006a). Microvesicles are enriched in bioactive molecules and contain nucleic acid and/or protein. Concentration of microvesicle in the peripheral blood of healthy individuals is estimated to be in the range of 5–50 mg/mL. In the peripheral blood, two-thirds of microvesicles are derived from platelets; a small percentage of microvesicles are derived Z. Wang and B. Yang, MicroRNA Expression Detection Methods, DOI 10.1007/978-3-642-04928-6_28, # Springer-Verlag Berlin Heidelberg 2010

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from endothelial cells (Hunter et al. 2008). Elevation of endothelial-derived microvesicles has been reported in patients with pulmonary arterial hypertension (Bakouboula et al. 2008). Platelet-derived microvesicles play a role in angiogenesis and the metastatic spread of cancers, such as lung cancer (Janowska-Wieczorek et al. 2005), and in immune response upon the regulation of gene expression in hematopoietic, endothelial, and monocytic cells (Setzer et al. 2006; Majka et al. 2007). Notably, platelet-derived microvesicle subpopulations are increased in patients with sepsis (Janiszewski et al. 2004; Meziani et al. 2008), whereas patients with pulmonary arterial hypertension have increased endothelial-derived microvesicles (Bakouboula et al. 2008). Intriguingly, Valadi and colleagues reported that vesicles released from human and murine mast cell lines contain over 1,200 mRNA and ~121 miRNA molecules (Valadi et al. 2007). Hunter et al. (2008) defined miRNA expression profile in circulating plasma microvesicles of normal human subjects, providing a basis for future studies to determine the predictive role of peripheral blood miRNA signatures in human diseases. Hierarchical clustering of the data sets indicated significant differences in miRNA expression between peripheral blood mononuclear cells (PBMC) and plasma microvesicles, with 33 and 4 miRNAs significantly differentially expressed in the plasma microvesicles and mononuclear cells, respectively. The majority of the miRNAs expressed in the microvesicles from the blood were predicted to regulate cellular differentiation of blood cells and metabolic pathways and a few miRNAs were predicted to be important modulators of immune function (Hunter et al. 2008). The detection of tissue specific miRNAs and microvesicles in the peripheral blood may become a frequent event upon tissue damage.

28.2

Protocol

28.2.1 Materials 1. 2. 3. 4. 5.

19-gage needle Syringe (50 cc) EDTA tubes Ficoll-hypaque (d = 1.077) (Mediatech, Inc.) 2 micron bead standards (BD Biosciences)

28.2.2 Instruments 1. Agilent 2100 Bioanalyzer (Agilent Technologies, Inc, Santa Clara, CA) or an equivalent 2. Applied Biosystems 7900HT real-time PCR instrument or an equivalent

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341

3. Liquid-handling robots 4. Zymak Twister robot 5. BD Aria flow cytometer (BD Biosciences)

28.2.3 Reagents 1. 2. 3. 4. 5.

Sterile low endotoxin PBS (Mediatech, Inc. Manassas, VA) PBS 3.8% sodium citrate tubes 1 mM PGE1 (Sigma-Aldrich, St. Louis, MO) Tyrodes buffer: [138 mM NaCl, 2.9 mM KCl, 12 mM NaHCO3, 0.36 mM NaHPO4, 5.5 mM glucose, 1.8 mM CaCl2, and 0.49 mM MgCl2, pH 6.5 or pH7.4] 6. Trizol (Invitrogen, Carlsbad, CA) 7. Stem-looped primers (Mega Plex kit, Applied Biosystems, Foster City, CA)

28.2.4 Procedures The protocols described in this section are essentially the same as reported in the study by Hunter et al. (2008).

28.2.4.1

Blood Collection

1. Collect peripheral blood: discard the first 2 cc, then draw 40 cc of blood through a 19-gage needle in EDTA tubes from diseased and healthy subjects following informed consent, between morning and early afternoon. 2. Dilute the peripheral blood 1:1 with sterile low endotoxin PBS, layer over ficollhypaque (d = 1.077), and centrifuge at 1,000 g. 3. Wash the mononuclear cell fraction once in PBS.

28.2.4.2

Microvesicle Isolation

1. To purify the microvesicles from the plasma, concentrate the vesicles by centrifugation at 160,000 g for 1 hr at 4 C (Nieuwland et al. 2000). 2. To isolate platelets, collect blood from donors in 3.8% sodium citrate tubes in 1:9 volume ratio. 3. Following centrifugation of the blood at 1000 g for 15 min at room temperature, incubate the platelet rich plasma with 1 mM PGE1.

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4. Centrifuge again and wash the platelets twice by resuspending the platelet pellet in Tyrodes buffer (pH6.5) and centrifuged again. 5. Then wash the platelets one additional time in Tyrodes buffer (pH7.4) (Hunter et al. 2008; Walkowiak et al. 2000).

28.2.4.3

RNA Extraction

1. Isolate total RNA by Trizol extraction method 2. To increase the yield of small RNAs, precipitate the RNA overnight 3. Determine RNA concentration and RNA integrity by capillary electrophoresis on an Agilent 2100 Bioanalyzer or a Nanodrop 4. For RNA isolated from mononuclear cells, only a RNA integrity number (RIN) 9 should be used along with its matched plasma sample for profiling

28.2.4.4

miRNA Profiling by Quantive PCR

1. Perform real-time PCR using an Applied Biosystems 7900HT real-time PCR instrument (or an equivalent) equipped with a 384 well reaction plate. 2. Convert RNA (500 ng) to cDNA by priming with a mixture of looped primers using previously published reverse transcription conditions as described in Section V. 3. Primers to the internal controls, small nucleolar (sno)RNA U38B, snoRNA U43, small nuclear (sn)RNA U6 as well as 18 S and 5 S rRNA should be included in the mix of primers. 4. Use Liquid-handling robots and the Zymak Twister robot to increase throughput and reduce error.

28.2.4.5

Flow Cytometry

1. Directly immunostain peripheral blood microvesicles from plasma without concentration by centrifugation 2. To confirm that the microvesicles were the correct size, set flow cytometry gates using 2 micron bead standards 3. Analyze the samples on BD Aria flow cytometer or an equivalent. Data can be expressed as percent of gated events

28.2.4.6

Other Analyses

1. Since Ct scores of real-time RT-PCR >35 are considered non-specific (Schmittgen et al. 2008), miRNAs in which 80% of individual observations have a raw Ct

References

343

score >35 should not be included in the final data analysis. However, based on our experience, Ct value >30 can be considered as null expression. 2. To reduce the bias caused by the use of an arbitrary miRNA as a normalization correction factor, the miRNAs should be compared between plasma microvesicles and PBMC based on their relative expression to the overall miRNA expression on each array using median normalization analysis (Wang et al. 2007). 3. To test the difference of specific miRNA between plasma microvesicles and PBMC, linear mixed models should be used and p-values should be generated from the model based on the estimated difference and sample variation. miRNAs should be subjected to hierarchical clustering using Euclidean distance based on their relative mean expression. miRNAs should also be ranked based on their raw Ct score for each plasma microvesicles and PBMC (Hunter et al. 2008).

28.3


In the study reported by Hunter et al. (2008), the miRNAs circulating in plasma microvesicles, platelets, and PBMC of normal human volunteers in the peripheral blood were detected and analyzed. They characterized peripheral blood miRNA patterns in healthy humans, and found significant differences in miRNA expression between plasma microvesicles, platelets, and PBMC. The data indicate that the miRNAs are also contained in plasma microvesicles, and these microvesiclecontaining miRNAs on target cell mRNA expression in their target cells influence homeostasis. The detection of tumors exosomes (microvesicles) in the peripheral blood has been found to contain miRNAs (Taylor and Gercel-Taylor 2008). It has been found that the internal controls (18 S, 5 S, snoRNA U38B, snoRNA U43, and snRNA U6) were highly variable in the plasma microvesicles and were significantly different in plasma microvesicles versus PBMC (Hunter et al. 2008). Careful selection of reasonable controls therefore becomes a significant factor influencing the quantification of microvesicle-containing miRNAs.

References Bakouboula B, Morel O, Faure A, Zobairi F, Jesel L, Trinh A, Zupan M, Canuet M, Grunebaum L, Brunette A, Desprez D, Chabot F, Weitzenblum E, Freyssinet JM, Chaouat A, Toti F (2008) Procoagulant membrane microparticles correlate with the severity of pulmonary arterial hypertension. Am J Respir Crit Care Med 177: 536–543 Hunter MP, Ismail N, Zhang X, Aguda BD, Lee EJ, Yu L, Xiao T, Schafer J, Lee ML, Schmittgen TD, Nana-Sinkam SP, Jarjoura D, Marsh CB (2008) Detection of microRNA expression in human peripheral blood microvesicles. PLoS ONE 3:e3694

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Janiszewski M, Do Carmo AO, Pedro MA, Silva E, Knobel E, Laurindo FR (2004) Plateletderived exosomes of septic individuals possess proapoptotic NAD(P)H oxidase activity: A novel vascular redox pathway. Crit Care Med 32:818–825 Janowska-Wieczorek A, Wysoczynski M, Kijowski J, Marquez-Curtis L, Machalinski B, Ratajczak J, Ratajczak MZ (2005) Microvesicles derived from activated platelets induce metastasis and angiogenesis in lung cancer. Int J Cancer 113:752–760 Majka M, Kijowski J, Lesko E, Goz´dizk J, Zupanska B, Ratajczak MZ (2007) Evidence that platelet-derived microvesicles may transfer platelet-specific immunoreactive antigens to the surface of endothelial cells and CD34+ hematopoietic stem/progenitor cells–implication for the pathogenesis of immune thrombocytopenias. Folia Histochem Cytobiol 45:27–32 Meziani F, Tesse A, Andriantsitohaina R (2008) Microparticles are vectors of paradoxical information in vascular cells including the endothelium: role in health and diseases. Pharmacol Rep 60:75–84 Nieuwland R, Berckmans RJ, McGregor S, Boing AN, Romijn FP, Westendorp RG, Hack CE, Sturk A (2000) Cellular origin and procoagulant properties of microparticles in meningococcal sepsis. Blood 95:930–935 Ratajczak J, Miekus K, Kucia M, Zhang J, Reca R, Dvorak P, Ratajczak MZ (2006a) Embryonic stem cell-derived microvesicles reprogram hematopoietic progenitors: evidence for horizontal transfer of mRNA and protein delivery. Leukemia 20:847–856 Ratajczak J, Wysoczynski M, Hayek F, Janowska-Wieczorek A, Ratajczak MZ (2006b) Membrane-derived microvesicles: important and underappreciated mediators of cell-to-cell communication. Leukemia 20: 1487–1495 Schmittgen TD, Lee EJ, Jiang J, Sarkar A, Yang L, Elton TS, Chen C (2008) Real-time PCR quantification of precursor and mature microRNA. Methods 44:31–38 Setzer F, Oberle V, Bla¨ss M, Mo¨ller E, Russwurm S, Deigner HP, Claus RA, Bauer M, Reinhart K, Lo¨sche W (2006) Platelet-derived microvesicles induce differential gene expression in monocytic cells: a DNA microarray study. Platelets 17:571–576 Taylor DD, Gercel-Taylor C (2008) MicroRNA signatures of tumor-derived exosomes as diagnostic biomarkers of ovarian cancer. Gynecol Oncol 110:13–21 Valadi H, Ekstro¨m K, Bossios A, Sjo¨strand M, Lee JJ, Lo¨tvall JO (2007) Exosomemediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat Cell Biol 9:654–659 Valenti R, Huber V, Iero M, Filipazzi P, Parmiani G, Rivoltini L (2007) Tumorreleased microvesicles as vehicles of immunosuppression. Cancer Res 67:2912–2915 Walkowiak B, Kralisz U, Michalec L, Majewska E, Koziolkiewicz W, Ligocka A, Cierniewski CS (2000) Comparison of platelet aggregability and P-selectin surface expression on platelets isolated by different methods. Thromb Res 99:495–502 Wang Y, Zeigler MM, Lam GK, Hunter MG, Eubank TD, Khramtsov VV, Tridandapani S, Sen CK, Marsh CB (2007) The role of the NADPH oxidase complex, p38 MAPK, and Akt in regulating human monocyte/macrophage survival. Am J Respir Cell Mol Biol 36: 68–77 Wieckowski E, Whiteside TL (2006) Human tumor-derived vs dendritic cellderived exosomes have distinct biologic roles and molecular profiles. Immunol Res 36:247–254

Chapter 29

Detection of Placental miRNAs in Maternal Plasma

Abstract The discovery of fetal nucleic acids in the plasma of pregnant women (Lancet 350:485–487, 1997, Nat Rev Genet 8:71–77, 2007; Proc Natl Acad Sci USA 100:4748 –4753, 2003) has led to the development of a number of noninvasive prenatal diagnostic tests. Nucleic acids of placental origin were previously shown to be released into maternal plasma (Proc Natl Acad Sci USA 100:4748– 4753, 2003; Proc Natl Acad Sci USA 102:14753–14758, 2005). The recent work documented by Lo’s group from the Centre for Research into Circulating Fetal Nucleic Acids, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong (Hong Kong SAR, China) (Clin Chem 54:482–490, 2008) clearly demonstrated that placental miRNAs exist in maternal plasma in readily detectable quantities. The successful application of the approach indicates that placental miRNAs represent a novel class of fetal nucleic acid markers in maternal plasma. Because miRNAs are exceptionally stable in plasma, they hold promise as markers in the clinical setting. The measurement of miRNAs in maternal plasma may become a useful and practical strategy for prenatal monitoring and diagnosis.

29.1

Introduction

The discovery of fetal nucleic acids in the plasma of pregnant women (Lo et al. 1997; Lo and Chiu 2007; Ng et al. 2003) has led to the development of a number of noninvasive prenatal diagnostic tests. Nucleic acids of placental origin were previously shown to be released into maternal plasma (Ng et al. 2003; Chim et al. 2005). The recent work documented by Lo and colleagues (Chim et al. 2008) clearly demonstrated that placental miRNAs exist in maternal plasma in readily detectable quantities. By systematically searching a panel of 157 miRNA assays, these authors have identified 17 placental miRNAs as candidate biomarkers for monitoring pregnancy in maternal plasma. Furthermore, they were able to identify four placental miRNAs (miR-141, miR-149, miR-299-5p, and miR-135b) at higher rates in the maternal plasma before delivery than after. Hence, the authors concluded that these Z. Wang and B. Yang, MicroRNA Expression Detection Methods, DOI 10.1007/978-3-642-04928-6_29, # Springer-Verlag Berlin Heidelberg 2010

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29 Detection of Placental miRNAs in Maternal Plasma

miRNA species are associated with pregnancy. Their data lhave also shown that the plasma concentration of a placental miRNA, miR-141, increases as the pregnancy progresses into the third trimester. This increase in miR-141 in maternal plasma may reflect an increase in the size of the placenta or an increased concentration of miR-141 in the third-trimester placenta. The quantification of placental miRNAs in maternal plasma may offer a noninvasive means for monitoring gene regulation in the placenta. Further, this study also revealed that miRNAs themselves are intrinsically more stable in plasma than mRNAs (Chim et al. 2008). The biological significance of placental miRNAs in maternal plasma requires further elucidation. An intriguing possibility is that these small molecules are taken up by cells exposed to the maternal circulation and may modulate gene expression of the maternal compartment. These findings suggest that the detection of circulating fetal miRNAs holds much promise for noninvasive prenatal diagnosis.

29.2

Protocol

29.2.1 Materials 1. Filter with a pore size of 5 mm, 0.45 mm, or 0.22 mm (Millex-GV; Millipore)

29.2.2 Instruments 1. ABI Prism 7300 or 7900 Sequence Detector (Applied Biosystems)

29.2.3 Reagents 1. 2. 3. 4.

EDTA Trizol LS reagent (Invitrogen) mirVana miRNA Isolation Kit (Ambion) TaqMan Array Human MicroRNA Panel v1.0 (Early Access) (Applied Biosystems), which contains 157 TaqMan MicroRNA Assays, including the respective reverse-transcription primers, PCR primers, and TaqMan probe 5. TaqMan MicroRNA Reverse Transcription Kit (Applied Biosystems) 6. TaqMan Universal PCR Master Mix (Applied Biosystems) 7. DNase I (Invitrogen)

29.2 Protocol

347

29.2.4 Procedures The protocols described in this section are essentially the same as reported in the study by Lo’s laboratory (Chim et al. 2008).

29.2.4.1

Maternal Peripheral Blood Sample

1. Collect samples of maternal peripheral blood (12 mL) into tubes containing EDTA. 2. To harvest cell-free plasma, centrifuge the maternal blood samples twice at 4 C. After the first centrifugation at 1600 g for 10 min, centrifuge again the supernatant at 16,000 g for 10 min to remove blood cells (Chiu et al. 2001). 3. To harvest maternal blood cells (including leukocytes and erythrocytes), centrifuge the blood cells obtained in the first centrifugation at 2,300 g for 5 min to remove residual plasma. 4. Add Trizol LS reagent in volumetric ratios of 1:0.8 and 3:1 to the harvested maternal plasma and maternal blood cells, respectively. Specifically, add 0.4 mL of chloroform to 1.6 mL of plasma preserved in 2 mL of Trizol LS reagent. Add 0.24 mL of chloroform to 0.3 mL of processed blood cells preserved in 0.9 mL Trizol LS reagent.

29.2.4.2

RNA Extraction

1. Extract total RNA containing small RNA molecules with the Trizol LS or Trizol reagent and the mirVana miRNA Isolation Kit, according to manufacturer’s protocols. 2. After the chloroform-addition steps and phase separation, mix the aqueous layer with 1.25 volumes of absolute ethanol, load the solution onto the cartridge provided in the mirVana miRNA Isolation Kit, and process the sample. 3. To minimize DNA contamination, treat the eluted RNA preparation with DNase I. For miRNA profiling, further dilute RNA preparations obtained from samples of placentas, maternal blood cells, or postdelivery maternal plasma to 1 mg/L, according to absorbance readings at 260 nm.

29.2.4.3

Real-Time Quantitative RT–PCR Analysis

See Section V for detail.

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29.2.4.4

29 Detection of Placental miRNAs in Maternal Plasma

miRNA Profiling

1. Use the TaqMan Array Human MicroRNA Panel v1.0 for miRNA profiling 2. Use the TaqMan MicroRNA Reverse Transcription Kit for reverse transcription in a 25 mL of total reaction volume with 2.5 mL (2.5 ng) of the total RNA sample 3. Use the TaqMan Universal PCR Master Mix for the PCR

29.2.4.5

Filtration Studies of Placental miRNA and mRNA in Maternal Plasma

1. To ensure that the pregnancy-specific miRNA molecules in maternal plasma are not associated with subcellular particles (Ng et al. 2003), filter samples of maternal plasma through a filter with a pore size of 5 mm, 0.45 mm, or 0.22 mm 2. Extract RNA from the plasma samples with 1 mL of Trizol LS 3. Perform qPCR as for the earlier steps

29.3


Chim et al systematically searched for placental miRNAs in maternal plasma to identify miRNAs that are at high concentrations in placentas compared with maternal blood cells and then investigated the stability and filterability of this novel class of pregnancy-associated markers in maternal plasma (Chim et al. 2008). In a panel of TaqMan MicroRNA Assays available for 157 well-established miRNAs, 17 occurred at concentrations >10-fold higher in the placentas than in maternal blood cells and were undetectable in postdelivery maternal plasma. The four most abundant of these placental miRNAs (miR-141, miR-149, miR-299-5p, and miR-135b) were detectable in maternal plasma during pregnancy and showed reduced detection rates in postdelivery plasma. The plasma concentration of miR-141 increased as pregnancy progressed into the third trimester. The successful application of the approach indicates that placental miRNAs represent a novel class of fetal nucleic acid markers in maternal plasma. Because miRNAs are exceptionally stable in plasma, they hold promise as markers in the clinical setting. The measurement of miRNAs in maternal plasma may become a useful and practical strategy for prenatal monitoring and diagnosis. The biological significance of placental miRNAs in maternal plasma requires further elucidation, but an intriguing possibility is that these small molecules are taken up by cells exposed to the maternal circulation and may modulate gene expression of the maternal compartment.

References

349

References Chim SS, Shing TK, Hung EC, Leung TY, Lau TK, Chiu RW, Lo YM (2008) Detection and characterization of placental microRNAs in maternal plasma. Clin Chem 54:482–490 Chim SS, Tong YK, Chiu RW, Lau TK, Leung TN, Chan LY, Oudejans CB, Ding C, Lo YM (2005) Detection of the placental epigenetic signature of the maspin gene in maternal plasma. Proc Natl Acad Sci USA 102:14753–14758 Chiu RWK, Poon LLM, Lau TK, Leung TN, Wong EM, Lo YMD (2001) Effects of bloodprocessing protocols on fetal and total DNA quantification in maternal plasma. Clin Chem 47:1607–1613 Lo YMD, Chiu RWK (2007) Prenatal diagnosis: progress through plasma nucleic acids. Nat Rev Genet 8:71–77 Lo YM, Corbetta N, Chamberlain PF, Rai V, Sargent IL, Redman CW, Wainscoat JS (1997) Presence of fetal DNA in maternal plasma and serum. Lancet 350:485–487 Ng EK, Tsui NB, Lau TK, Leung TN, Chiu RW, Panesar NS, Lit LC, Chan KW, Lo YM (2003) mRNA of placental origin is readily detectable in maternal plasma. Proc Natl Acad Sci USA 100:4748–4753

Part X

Single-cell miRNA Detection Methods

Chapter 30

Quantitative LNA-ELF-FISH Method for miRNA Detection in Single Mammalian Cell

Abstract A method for single-cell miRNA detection has recently been reported, combining the unique recognition properties of locked nucleic acid (LNA) probes with enzyme-labeled fluorescence (ELF) signal amplification. Using this approach, individual miRNAs are identified as bright, photostable fluorescent spots. This technique was developed by Lu and Tsourkas (Nucleic Acids Res doi:10.1093/ nar/gkp482, 2009) from the Department of Bioengineering, University of Pennsylvania School of Engineering and Applied Sciences (Philadelphia, PA, USA) to tackle the problem of cell-to-cell fluctuations in gene expression on phenotypic diversity encountered in the analyses of miRNA expression of cell populations. The LNA-ELF-FISH (fluorescence in situ hybridization) approach has been used to quantify miR-15a in MDA-MB-231 and HeLa cells and miR-155 in MCF-7 cells (Nucleic Acids Res doi:10.1093/nar/gkp482, 2009). The results verified that LNAELF-FISH is a highly sensitive and specific method for miRNA detection at the single molecule level in individual cells. With this technique, single miRNAs could be visualized and counted to yield quantitative information on miRNA expression. LNA-ELF-FISH is extremely simple and yields reproducible data.

30.1

Introduction

Considering recent reports on the complex stochastic nature of gene expression in mammalian cells and the impact of these fluctuations on phenotypic diversity, it is likely that looking at the average miRNA expression of cell populations could result in the loss of important information connecting miRNA expression and cell function. Therefore, it is predicted that insight into the physiologic function of miRNA will require miRNA abundance to be quantified at the single-cell level. Profiling of miRNAs in individual cells is a prerequisite in some instances where there is inherent variability among the cells or because only a few such cells are available for analysis such as the case of early developing embryos. However, because of the


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30 Quantitative LNA-ELF-FISH Method for miRNA Detection

technical difficulties, the methods suitable for single-cell analysis of miRNA expression have been sparse thus far. With the recent introduction of locked nucleic acid (LNA) oligonucleotides as hybridization probes, miRNA-FISH has become a powerful technique for imaging the spatial localization of miRNA at the tissue, cellular, and even subcellular level (Kloosterman et al. 2006; Nelson et al. 2005; Politz et al. 2006; Silahtaroglu et al. 2007; Wienholds et al. 2005). LNA probes exhibit a remarkable affinity for and specificity against RNA targets, allowing for the discrimination of even single-base mismatches (Chou et al. 2005; Johnson et al. 2004; Valoczi et al. 2004; You et al. 2006). Unfortunately, miRNA-FISH generally cannot be used to provide accurate quantitative measures of miRNA expression, but rather is typically limited to providing a qualitative assessment of miRNA localization patterns and tissue distribution. One of the methods for single-cell miRNA detection has recently been reported by Lu and Tsourkas (2009), combining the unique recognition properties of LNA probes with enzyme-labeled fluorescence (ELF) signal amplification (Paragas et al. 1997). ELF is a process whereby cleavage of a pro-luminescent substrate by phosphatase yields a brilliant, yellow-green fluorescent product at the site of enzymatic activity. The ELF precipitate is not only photostable compared to commonly used fluorophores, but also results in labeling that is up to 40 times brighter than signals achieved with probes directly labeled with fluorophores (Paragas et al. 1997). Using this approach, individual miRNAs are identified as bright, photostable fluorescent spots.

30.2

Protocol

30.2.1 Materials 1. Cell culture materials 2. Multi-chambered coverglass slides (Lab-Tek, Nalge Nunc, Rochester, New York, United States)

30.2.2 Instruments 1. Olympus IX81 motorized inverted fluorescence microscope equipped with a back-illuminated EMCCD camera (Andor) 2. X-cite 120 excitation source (EXFO) 3. Sutter excitation and filter wheels 4. IPLab acquisition software 5. AutoQuant plug-in software 6. Particle analysis counter program

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355

30.2.3 Reagents 1. Cy3 dyes (Amersham Biosciences) 2. 4% formaldehyde 3. Pre-hybridization buffer: [25% formamide, 0.05 M EDTA, 4 SSC, 10% dextran sulfate, 1 Denhardt’s solution, 0.5 mg/mL Escherichia coli tRNA, and 0.5 mg/ml RVC) 4. 4 SSC, 2 SSC and 1 SSC 5. PBS 6. ELF 97 mRNA In Situ Hybridization Kit (Molecular Probes, Inc, Eugene, OR, USA) 7. Anti-DIG antibody (Jackson Immuno Research) 8. Post-fixation solution: [2% formaldehyde, 20 mg/mL BSA in 1 PBS] 9. 0.16 M l-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC) (Sigma) 10. 0.2% (w/v) glycine/TBS 11. Wash solution: [0.13 M 1-methylimidazole, 300 mM NaCl, pH8.0 adjusted with HCl] 12. 70% ethanol

30.2.4 Procedures The protocols described in this section are essentially the same as reported in the study by Lu and Tsourkas (2009).

30.2.4.1

Design of LNA-FISH Probes

1. Obtain sequences for the miRNAs of interest from the miRNA Registry (microrna.sanger.ac.uk) 2. Design oligonucleotide probes exactly antisense to the selected target miRNAs. Synthesize the probes from Exiqon with LNA modification and a digoxigenin at the 30 -end

30.2.4.2

Enzyme-Labeled Fluorescence (ELF) Treatment

1. Seed cells onto multi-chambered coverglass slides and incubate under normal growth conditions overnight, reaching 50–70% confluency. 2. Fix the cells with 4% formaldehyde for 30 min at room temperature, wash three times with 1 PBS, and permeabilize at 4 C in 70% ethanol overnight. 3. Perform hybridization with the LNA probe (10 nM) at 20–22 C below the melting temperature of the LNA-FISH probe for 1 h after incubation in the

356

4. 5.

6.

7.


pre-hybridization buffer for 2 h at 60 C. The optimal level of formamide to be used during hybridization and washing for maximal signal-to-background is empirically determined to be 25%. Perform three stringent washes in 4 SSC, 2 SSC, and 1 SSC. After three stringent washes, as described above, treat the cell samples using the ELF 97 mRNA In Situ Hybridization Kit, according to the manufacturer’s instructions. Briefly, (1) incubate the cells in blocking buffer from the ELF 97 mRNA In Situ Hybridization Kit for 1 h at room temperature. Then, (2) add 2 mg/mL anti-DIG antibody in blocking buffer to the cells and incubate at room temperature for 1 h. (3) After three washes in 1 wash buffer, amplify signals in ELF 97 phosphatase substrate working solution for 10–15 min. For signal preservation, wash the cell samples with 1 wash buffer and postfix the cells by incubating the slides in the post-fixation solution for 30 min at room temperature. Counterstain the slides in 1 mg/ mL Hoechst 33342 and mount in mounting solution. Conduct control experiments using the identical procedure as described above, except for a single hybridization step, were performed with the LNA probes (i.e., LNA-ELF-FISH). ELF should be also performed only as deemed necessary.

30.2.4.3

LNA-ELF-FISH with EDC Treatment

1. After having fixed the cells in 4% paraformaldehyde for 30 min at room temperature and permeabilized in 70% ethanol at 4 C overnight, rehydrate the cells and wash three times with 1 PBS 2. To remove residual phosphate from the PBS washes, incubate slides twice for 10 min in a freshly prepared wash solution 3. Add l-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC) to the cells and incubate for 1 h at 25 C 4. Wash the slides in 0.2% (w/v) glycine/TBS and then wash twice in PBS for prehybridization 5. Carry out subsequent LNA hybridization and ELF signal amplifications steps as described above

30.2.4.4

Image Acquisition and Analysis

1. Following in situ hybridization, image cells using an Olympus IX81 motorized inverted fluorescence microscope equipped with a back-illuminated EMCCD camera (Andor), an X-cite 120 excitation source (EXFO), and Sutter excitation and filter wheels. Use a UPLN 60 oilimmersion objective, N.A. 0.9, or an equivalent, for all imaging experiments. Use IPLab acquisition software to acquire the 2D and 3D images.

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357

2. After randomly selecting cells in a field, take a 3D stack viewed image with 0.3 mm increments in the z-direction and a total of 35 sections. 3. After 3D deconvolution of the images in IPLab using AutoQuant plug-in software, a 2D image was constructed in IPLab using a maximum intensity merged image. 4. Open images in ImageJ and process using the following commands: (1) Process ->Sharpen, (2) Image ->type ->8-bit, and (3) Process ->binary ->make binary. 5. Count the total number of isolated signals in ImageJ using the particle analysis counter program (Analyze ->analyze particles) (Fig. 30.1).

Select a miRNA of interest

Synthesize LNA-FISH miRNA capture probes with LNA modification and a 3'digoxigenin

Fix cells with 4% formaldehyde

Hybridization of cells with LNA-FISH probe

Treatment with enzyme-labeled fluorescence (ELF) 97 mRNA In Situ Hybridization Kit

Treatment with anti-DIG antibody

Counterstain the slides in Hoechst 33342 and mount in mounting solution.

Fluorescence microscope for image acquisition and analysis

Fig. 30.1 Flowchart of the LNA-ELF-FISH approach for miRNA expression detection. According to Lu and Tsourkas (2009)

358

30.3



The LNA-ELF-FISH approach has been used to quantify miR-15a in MDA-MB231 and HeLa cells and miR-155 in MCF-7 cells (Lu and Tsourkas 2009). The results verified that LNA-ELF-FISH is a highly sensitive and specific method for miRNA detection at the single molecule level in individual cells; by combining LNA hybridization probes with ELF signal amplification, single miRNAs could be visualized and counted to yield quantitative information on miRNA expression. The dynamic range of this approach spans more than three orders of magnitude (i.e., 1 to ~1,000 miRNAs per cell) directly and through the construction of standardization curves could also yield quantitative measurements on cells with higher miRNA copy numbers (Lu and Tsourkas 2009). LNA-ELF-FISH is extremely simple and yields reproducible data. Further, in contrast to RT-PCR, no cell lysis, miRNA purification, or sample enrichment steps are required and spatial information is retained. One important advantage is the need for only a single hybridization probe, a large savings in cost and elimination of the need to identify a large number of probes with similar melting temperatures (Lu and Tsourkas 2009). In addition, LNA-ELF-FISH includes the long stokes shift and high photostability of the fluorescent precipitate, conferring low autofluorescence; the high photostability allows for repeated imaging. Moreover, the fluorescent precipitate is extremely bright and thus only short exposure times are needed (i.e., ~10 ms). However, the current availability of only a single ELF substrate limits this approach to imaging only a single RNA per cell sample.

References Chou LS, Meadows C, Wittwer CT, Lyon E (2005) Unlabeled oligonucleotide probes modified with locked nucleic acids for improved mismatch discrimination in genotyping by melting analysis. Biotechniques 39:644, 646, 648 passim Johnson MP, Haupt LM, Griffiths LR (2004) Locked nucleic acid (LNA) single nucleotide polymorphism (SNP) genotype analysis and validation using real-time PCR. Nucleic Acids Res 32:e55 Kloosterman WP, Wienholds E, de Bruijn E, Kauppinen S, Plasterk RH (2006) In situ detection of miRNAs in animal embryos using LNA-modified oligonucleotide probes. Nat Methods 3:27–29 Lu J, Tsourkas A (2009) Imaging individual microRNAs in single mammalian cells in situ. Nucleic Acids Res doi:10.1093/nar/gkp482 Nelson PT, Baldwin DA, Kloosterman WP, Kauppinen S, Plasterk RH, Mourelatos Z (2005) RAKE and LNA-ISH reveal microRNA expression and localization in archival human brain. RNA 12:187–191 Paragas VB, ZhangYZ, Haugland RP, Singer VL (1997) The ELF-97 alkaline phosphatase substrate provides a bright, photostable, fluorescent signal amplification method for FISH. J Histochem Cytochem 45:345–357 Politz JC, Zhang F, Pederson T (2006) MicroRNA-206 colocalizes with ribosome-rich regions in both the nucleolus and cytoplasm of rat myogenic cells. Proc Natl Acad Sci USA 103: 18957–18962

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Silahtaroglu AN, Nolting D, Dyrskjot L, Berezikov E, Moller M, Tommerup N, Kauppinen S (2007) Detection of microRNAs in frozen tissue sections by fluorescence in situ hybridization using locked nucleic acid probes and tyramide signal amplification. Nat Protoc 2:2520–2528 Valoczi A, Hornyik C, Varga N, Burgyan J, Kauppinen S, Havelda Z (2004) Sensitive and specific detection of microRNAs by northern blot analysis using LNA-modified oligonucleotide probes. Nucleic Acids Res 32:e175 Wienholds E, Kloosterman WP, Miska E, Alvarez-Saavedra E, Berezikov E, de Bruijn E, Horvitz HR, Kauppinen S, Plasterk RH (2005) MicroRNA expression in zebrafish embryonic development. Science 309:310–311 You Y, Moreira BG, Behlke MA, Owczarzy R (2006) Design of LNA probes that improve mismatch discrimination. Nucleic Acids Res 34:e60

Chapter 31

Single Cell Stem-Looped Real-Time PCR

Abstract Profiling of miRNAs in individual cells is prerequisite in some instances where there is inherent variability among the cells, or because only a few such cells are available for analysis, as in the case of early developing embryos. It is also the case that seemingly uniform cells, such as stem cells, may indeed differ from each other, which can only be judged by analysis of single cells. Surani and colleagues from Wellcome Trust/Cancer Research UK Gurdon Institute of Cancer and Developmental Biology, University of Cambridge (Cambridge, UK) (Nucleic Acids Res 34:e9 2006a, Nat Protoc 1:1154–1159, 2006b) developed a Single Cell StemLooped Real-Time PCR (SC-SL-RT-PCR) protocol for the detection of the miRNA expression profile in a single cell by stem-looped real-time PCR. A single cell is first lysed by heat treatment without further purification. Then, 220 known miRNAs are reverse transcribed into corresponding cDNAs by stem-looped primers. This is followed by an initial PCR step to amplify the cDNAs and generate enough material to permit separate multiplex detection. The diluted initial PCR product is used as a template to check individual miRNA expression by real-time PCR. This sensitive technique permits miRNA expression profiling from a single cell, and allows analysis of a few cells from early embryos as well as individual cells (such as stem cells). The method is extremely sensitive and can be used routinely for the analysis of single cells presumably with 0.015 ng total RNA, which is thousands of times more sensitive than other commonly used miRNA profiling methods; thus, it can be used when only nanogram amounts of rare samples are available. And this method covers a linear dynamic range of six logs.

31.1

Introduction

In many previous studies on miRNA expression detection, total RNA extracted from a large number of possibly heterogeneous cells has been used for analysis. Even cultured cells can show inherent variations. The approaches used previously have hitherto been necessary since nearly all known miRNA profiling methods need Z. Wang and B. Yang, MicroRNA Expression Detection Methods, DOI 10.1007/978-3-642-04928-6_31, # Springer-Verlag Berlin Heidelberg 2010

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31 Single Cell Stem‐Looped Real‐Time PCR

microgram amounts of total RNA (Nelson et al. 2004; Babak et al. 2004; Barad et al. 2004; Krichevsky et al. 2003; Liang et al. 2005; Miska et al. 2004; Sun et al. 2004; Liu et al. 2004), which is required for the fractionation of small RNAs before subsequent analysis (Nelson et al. 2004; Babak et al. 2004; Barad et al. 2004; Krichevsky et al. 2003; Liang et al. 2005; Miska et al. 2004). As already addressed in the previous chapter, profiling of miRNAs in individual cells is prerequisite in some instances where there is inherent variability among the cells, or because only a few such cells are available for analysis, as in the case of early developing embryos. It is also the case that seemingly uniform cells, such as stem cells, may indeed differ from each other, which can only be judged by analysis of single cells. It has been indeed realized that because of the complex stochastic nature of gene expression in mammalian cells and the impact of these fluctuations on phenotypic diversity, looking at the average miRNA expression of cell populations could result in the loss of important information connecting miRNA expression and cell function. For this reason, it is highly desirable to develop efficient methods that provide unambiguous miRNA profile of individual specific cell types. In this regard, the recently developed method by Chen et al. (2005) using a looped real-time PCR-based technique to detect expression of miRNAs is potentially helpful. With this approach they can cover at least seven log of expression range that is accurate and specific for mature miRNA, which can clearly discriminate between mature miRNA and corresponding primary miRNA and precursor miRNA. On top of this approach, Surani and colleagues developed a Single Cell StemLooped Real-Time PCR (SC-SL-RT-PCR) protocol for the detection of the miRNA expression profile in a single cell by stem-looped real-time PCR (Tang et al. 2006a, b). A single cell is first lysed by heat treatment without further purification. Then, 220 known miRNAs are reverse transcribed into corresponding cDNAs by stem-looped primers. This is followed by an initial PCR step to amplify the cDNAs and generate enough material to permit separate multiplex detection. The diluted initial PCR product is used as a template to check individual miRNA expression by real-time PCR (Fig. 31.1). In this study the authors validated the technique with the following experiments. (1) They tested the sensitivity of the looped real-time PCRbased miRNA expression profiling method in multiplex format, and found that this approach works accurately for 8 log range of expression for miR-16, which is effective with 1 mg–0.01 pg of total RNA. As the usual amount of total RNA in a cell is around 15 pg, this strongly suggests that the method is sensitive enough for single cell miRNA expression profiling. (2) They confirmed that the method is sensitive and accurate for the analysis of total RNA from single cells. (3) They then verified that the method can work on the whole cell lysate rather than on purified total RNA. (4) They further demonstrated that the approach can indeed work directly on individual handpicked cells. To determine the extent to which individual cells may differ from each other, they picked 15 single embryonic cells (ES) that were analyzed separately. We observed ~2-fold difference in miR-16 among these 15 single ES cell samples. And (5) finally, the workers examine the ability of the technique for use to obtain a comprehensive miRNA expression profile of single cells. In principle, their single cell miRNA profiling data correlate well with the

31.2 Protocol

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Lysis Reverse Transcription A single cell

Cell lysate containing miRNAs 1st Strand cDNAs of miRNAs

Real-time Quantification

Pre-PCR miRNA-specific forward primer

Universal reverse primer

PCR products PCR Amplification 40 cycles

Fig. 31.1 Schematic representation of real-time PCR-based multiplex miRNA expression profiling method

cloning and Northern blot data although the published cloning frequencies are very low. This further proved that the Single-cell stem-loop expression profiling method works reliably for single whole ES cells (Tang et al. 2006a, b).

31.2

Protocol

31.2.1 Materials 1. 2. 3. 4. 5.

Thin-walled PCR Eppendorf tube Micropipette Mouth tubes Heat-polished Pasteur pipette 96-well plate

31.2.2 Instruments 1. Prism 7000 SDS (ABI) or any other real-time PCR instruments compatible with TaqMan probe-directed real-time PCR assays 2. Dissection microscope

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31.2.3 Reagents 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.

BSA (Sigma) PBS (Gibco; pH 7.2) Molecular biology grade H2O (Eppendorf) BSA–PBS solution: [0.1% (wt/vol) or 1 mg/mL BSA in PBS] EDTA–PBS solution: [0.038% (wt/vol) or 0.38 mg/mL EDTA in PBS] 1 trypsin–EDTA solution: [Gibco; 0.25% (wt/vol) or 2.5 mg/mL trypsin, 0.038% (wt/vol) or 0.38 mg/mL EDTA] 220-plex forward and reverse primers (synthesized by Integrated DNA Technologies Inc) Universal RP (URP; 100 mM; the sequence is: 50 -CTCAAGTGTCGTGGAGTCGGCAA-30 ; Integrated DNA Technologies Inc) 100 mM dNTP (ABI) 100 mM MgCl2 (ABI) 2 TaqMans Universal PCR Master Mix (2 UMM) without AmpErases UNG (ABI) RNase inhibitor (20 U/mL; ABI) Moloney murine leukemia virus (MMLV) reverse transcriptase (50 U/mL ABI: high capacity cDNA achieve kit) AmpliTaqGold DNA polymerase (5 U/ml; ABI) 1-plex FP (5 mM) plus TaqMan probe (1 mM) mix (synthesized by Integrated DNA Technologies Inc)

31.2.4 Procedures The protocols described in this section are essentially the same as reported in the studies by Surani and colleagues (Tang et al. 2006a, b).

31.2.4.1

Preparation of Single Cells

1. In order to pick up and transfer individual cells, ideally use a micropipette attached to, and controlled by, a mouth tube, which is commonly used for manipulating early mouse embryos under a dissection microscope. To commence, pick up an ES cell colony with a micropipette. 2. Transfer it to a drop of EDTA–PBS and incubate at room temperature (20–30 C) for 10 min. 3. Transfer the ES cell cluster to a drop of trypsin–EDTA solution for 10 min at 37 C. 4. Transfer the cell cluster to a drop of BSA–PBS, and then to another drop of BSA–PBS. 5. Gently pipette 20–30 times with a heat-polished Pasteur pipette or microcapillary until the cell cluster dissociates into single cells. Transfer a proportion

31.2 Protocol

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(i.e., numbers that are sufficient for later analysis) of the single cells to another drop of BSA–PBS. 31.2.4.2

Lysis of Single Cells and Reverse Transcription

1. Prepare an RT master mix containing the following and mix gently but thoroughly:

H2O 10 cDNA archiving kit buffer 220-plex RPs (200 nM) RNase inhibitor (20 U/mL) Total volume

X1 3.61 mL 0.5 mL 0.125 mL 0.065 mL 4.3 mL

X20X1.1 79.42 mL 11 mL 2.75 mL 1.43 mL 94.6 mL

2. Prepare an RT reaction medium by adding 4.3 mL RT master mix to each thinwalled PCR Eppendorf tube on ice 3. Pick and transfer a single cell into each tube. It is critical to ensure that the amount of BSA–PBS carry over with each cell is minimal 4. Centrifuge at 9,000g for 10 s, followed by treatment of the samples at 95 C for 5 min. Place on ice 5. Prepare an enzyme mix containing the following: RNase inhibitor (20 U/mL) MMLV RT (50 U/mL) dNTP (100 mM; with dTTP) Total volume

X1 0.065 mL 0.335 mL 0.25 mL 0.65 mL

X20X1.1 1.43 mL 7.37 mL 5.5 mL 14.3 mL

6. Add 0.65 mL enzyme mix into each tube, mix evenly by vortexing the samples briefly, and then spin down the samples by centrifuging at 9,000g for 10 s 7. Perform the RT reaction by performing the following cycles: first, 16 C for 30 min; then, 20 C for 30 s, 42 C for 30 s, and 50 C for 1 s for 60 cycles; then, 85 C for 5 min (to inactivate RT); and, finally, 4 C prior to the next step 31.2.4.3

Pre-PCR

1. Prepare a pre-PCR master mix by combining the following: 2 UMM 220-plex FPs (450 nM) H2O MgCl2 (100 mM) dNTP (100 mM) URP (100 mM) AmpliTaqGold polymerase (5 U/mL) Total volume

X1 12.5 mL 2.78 mL 0.72 mL 0.5 mL 1 mL 1.25 mL 1.25 mL 20 mL

X20X1.1 275 mL 61.16 mL 15.84 mL 11 mL 22 mL 27.5 mL 27.5 mL 440 mL

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2. Add 20 mL pre-PCR master mix into each RT reaction tube. Mix evenly by vortexing and then spin down the samples by centrifuging at 9,000g for 10 s 3. Perform the pre-PCR reaction by the following cycles: first, 95 C for 10 min (to activate the AmpliTaqGold polymerase); then, 55 C for 2 min; then, 95 C for 1 s and 65 C for 1 min for 18 cycles; and, finally, save at 4 C 4. Dilute the pre-PCR product to give a 1:4 dilution (i.e., 25 mL pre-PCR product plus 75 mL H2O)

31.2.4.4

Real-Time PCR for Quantification

1. Prepare a TaqMan master mix containing the following: 2 UMM (no UNG) URP (100 mM) 1:4 diluted pre-PCR product H2O Total volume

X1 5 mL 0.1 mL 0.1 mL 2.8 mL 8 mL

X220X1.1X1.1 1,331 mL 26.62 mL 26.62 mL 745.36 mL 2,129.6 mL

2. For the PCR reaction, combine 17.6 mL TaqMan master mix and 4.4 mL 1-plex FP (5 mM) plus TaqMan probe (1 mM), to give a total volume of 22 mL. 3. For the standard, prepare the following PCR master mix: 2 UMM (no UNG) H2O URP (100 mM) 1-plex FP (5 mM)/TaqMan probe (1 mM) Total volume

X1 5 mL 1.9 mL 0.1 mL 2 mL 9 mL

X40X1.1 220 mL 83.6 mL 4.4 mL 88 mL 396 mL

4. For the PCR reaction of standards, combine 19.8 mL PCR master mix and 2.2 mL standard cDNA sample to give a total volume of 22 mL. 5. Mix thoroughly and add 10 mL to each well of a 96-well plate. In the plate, samples S1–S4 represent the standard samples with serial tenfold dilution. NTC represents the no template control reaction. Samples A1–A43 represent the samples for assaying the miRNA expression. Each assay is duplicated. Ideally, assays for the same miRNA in different samples should be run on the same plate to reduce potential variations between different plates. 6. Load the plate into the ABI Prism 7000 SDS, or any other real-time PCR instruments compatible with the TaqMan probe-directed real-time PCR assay. 7. Run the following real-time PCR program: 95 C for 10 min; 95 C for 15 s and 60 C for 1 min for 40 cycles.

References

31.3

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Due to their extremely small size, most current miRNA profiling methods are not highly sensitive, and they usually require microgram quantities of total RNA for analysis, which correspond to hundreds of thousands of cells. For a typical hybridization-based miRNA profiling method, it is usually necessary to have 5–20 mg total RNA (Babak et al. 2004; Barad et al. 2004; Krichevsky et al. 2003; Liang et al. 2005; Miska et al. 2004; Sun et al. 2004; Liu et al. 2004). Moreover, most methods also have a relatively narrow linear dynamic range, usually < 3 logs. By comparison, the SC-SL-RT-PCR is sufficiently sensitive to generate a miRNA expression profile of single cells. In contrast, our method is extremely sensitive and can be used routinely for the analysis of single cells (0.015 ng total RNA), which is thousands of times more sensitive than other commonly used miRNA profiling methods (Chen et al. 2005; Lao et al. 2006; Tang et al. 2006a). And it is easy to cover a linear dynamic range of six logs by this method. The SC-SL-RT-PCR method should prove useful in many cases. For example, specification of cells, such as primordial germ cells in very early embryos, occurs in just 40 cells (Saitou et al. 2002). To understand the role that miRNA may play in this process requires analysis at the single cell level. Other relatively rare cells such as some stem cells in adults also require analysis of single cells. Tumors also often consist of a heterogeneous group of cells, so it is desirable to analyze them individually to determine what role miRNA plays in cancers (Wang and Dick 2005). We propose that the method we have described here will help to advance an understanding of the functions of miRNAs generally.

References Babak T, Zhang W, Morris Q, Blencowe BJ, Hughes TR (2004) Probing microRNAs with microarrays: tissue specificity and functional inference. RNA 10:1813–1819 Barad O, Meiri E, Avniel A, Aharonov R, Barzilai A, Bentwich I, Einav U, Gilad S, Hurban P, Karov Y, Lobenhofer EK, Sharon E, Shiboleth YM, Shtutman M, Bentwich Z, Einat P (2004) MicroRNA expression detected by oligonucleotide microarrays: system establishment and expression profiling in human tissues. Genome Res 14:2486–2494 Chen C, Ridzon DA, Broomer AJ, Zhou Z, Lee DH, Nguyen JT, Barbisin M, Xu NL, Mahuvakar VR, Andersen MR, Lao KQ, Livak KJ, Guegler KJ (2005) Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res 33:e179 Krichevsky AM, King KS, Donahue CP, Khrapko K, Kosik KS (2003) A microRNA array reveals extensive regulation of microRNAs during brain development. RNA 9:1274–1281 Lao K, Xu NL, Yeung V, Chen C, Livak KJ, Straus NA (2006) Multiplexing RT-PCR for the detection of multiple miRNA species in small samples. Biochem Biophys Res Commun 343:85–89 Liang RQ, Li W, Li Y, Tan CY, Li JX, Jin YX, Ruan KC (2005) An oligonucleotide microarray for microRNA expression analysis based on labeling RNA with quantum dot and nanogold probe. Nucleic Acids Res 33:e17

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Liu C-G, Calin GA, Meloon B, Gamliel N, Sevignani C, Ferracin M, Dumitru CD, Shimizu M, Zupo S, Dono M, Alder H, Bullrich F, Negrini M, Croce CM (2004) An oligonucleotide microchip for genome-wide microRNA profiling in human and mouse tissues. Proc Natl Acad Sci USA 101:9740–9744 Miska EA, Alvarez-Saavedra E, Townsend M, Yoshii A, Sestan N, Rakic P, Constantine-Paton M, Horvitz HR (2004) Microarray analysis of microRNA expression in the developing mammalian brain. Genome Biol 5:R68 Nelson PT, Baldwin DA, Scearce LM, Oberholtzer JC, Tobias JW, Mourelatos Z (2004) Microarray-based, high-throughput gene expression profiling of microRNAs. Nat Methods 1:155–161 Saitou M, Barton SC, Surani MA (2002) A molecular programme for the specification of germ cell fate in mice. Nature 418:293–300 Sun Y, Koo S, White N, Peralta E, Esau C, Dean NM, Perera RJ (2004) Development of a microarray to detect human and mouse microRNAs and characterization of expression in human organs. Nucleic Acids Res 32:e188 Tang F, Hajkova P, Barton SC, Lao K, Surani MA (2006a) MicroRNA expression profiling of single whole embryonic stem cells. Nucleic Acids Res 34:e9 Tang F, Hajkova P, Barton SC, O’Carroll D, Lee C, Lao K, Surani MA (2006b) 220-plex microRNA expression profile of a single cell. Nat Protoc 1:1154–1159 Wang JC, Dick JE (2005) Cancer stem cells: lessons from leukemia. Trends Cell Biol 15:494–501

Chapter 32

miRNA Function-Reporter Expression Assay

Abstract The detection of microRNAs (miRNAs) at single-cell resolution is crucial for acquiring knowledge about the role of these post-transcriptional regulators. Huttner and colleagues from the Max Planck Institute of Molecular Cell Biology and Genetics (Dresden, Germany) developed a miRNA Function-Reporter Expression assay for this purpose. It is a relatively simple and reliable system that allows the detection of miRNAs with cellular resolution in vivo without the need to generate transgenic animals (Biotechniques 41:727–732, 2006). The system is based on the acute administration of a dual-fluorescence GFP-reporter/mRFPsensor (DFRS) plasmid for a specific miRNA into the organism of interest. In their DFRS plasmids, both GFP and mRFP are under the control of identical constitutive promoters. The GFP-reporter is used to identify the cells actually expressing the plasmid, given that the sensor-based strategy relies on the silencing of a transcript. The mRFP-sensor contained a 30 untranslated region (30 UTR) with a tandem cassette complementary to the miRNA of interest. To establish a system allowing the monitoring of miRNA dynamics in defined cell lineages during mammalian embryonic development, the group explored the use of DFRS plasmids in conjunction with a combination of methods previously used to achieve acute expression of transgenes and RNA interference in developing mouse embryos. This combination consists of the topical release of nucleic acids in the proximity of a specific tissue of a mouse embryo developing either in culture or in utero and their delivery into this tissue by directed electroporation. This strategy provides a simple approach to study a specific miRNA in the tissue and cell lineage of interest.

32.1

Introduction

The detection of microRNAs (miRNAs) at single-cell resolution is crucial for acquiring knowledge about the role of these post-transcriptional regulators. To detect miRNAs in tissues microscopically, two strategies have been used: in situ hybridization using locked nucleic acid (LNA)-modified DNA oligonucleotide probes, which detect the presence of miRNAs irrespective of their potential activity Z. Wang and B. Yang, MicroRNA Expression Detection Methods, DOI 10.1007/978-3-642-04928-6_32, # Springer-Verlag Berlin Heidelberg 2010

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32 miRNA Function-Reporter Expression Assay

(Wienholds et al. 2005), and the expression or administration of target mRNAs (sensors), which detect miRNAs via their degradation-triggering activity toward the sensor (Giraldez et al. 2005; Brennecke et al. 2003; Mansfield et al. 2004). Although both approaches are powerful, certain limitations remain. In situ hybridization using LNA probes requires tissue fixation, which prevents the monitoring of miRNA appearance/disappearance in a given cell lineage during cell fate change. While this limitation could potentially be overcome by in vivo expression of a sensor mRNA encoding a fluorescent protein, the latter approach has typically involved the generation of transgenic animals (Brennecke et al. 2003; Mansfield et al. 2004). Moreover, in the sensor approach, a lack of signal is interpreted as being indicative of the presence of a miRNA, which calls for some means of verification that the sensor mRNA is actually being transcribed in the cell lacking sensor protein. To overcome these limitations, Huttner and colleagues (De Pietri Tonelli et al. 2006) developed a miRNA Function-Reporter Expression assay, which is a relatively simple and reliable system that allows the detection of miRNAs with cellular resolution in vivo without the need to generate transgenic animals. The system is based on the acute administration of a dual-fluorescence GFP-reporter/mRFPsensor (DFRS) plasmid for a specific miRNA into the organism of interest. In their DFRS plasmids, both GFP and mRFP are under the control of identical constitutive promoters. The GFP-reporter is used to identify the cells actually expressing the plasmid, given that the sensor-based strategy relies on the silencing of a transcript. The mRFP-sensor contained a 30 untranslated region (30 UTR) with a tandem cassette (Brennecke et al. 2003; Mansfield et al. 2004) complementary to the miRNA of interest. To establish a system allowing the monitoring of miRNA dynamics in defined cell lineages during mammalian embryonic development, De Pietri Tonelli et al explored the use of DFRS plasmids in conjunction with a combination of methods previously used to achieve acute expression of transgenes and RNA interference in developing mouse embryos (De Pietri Tonelli et al. 2006). This combination consists of the topical release of nucleic acids in the proximity of a specific tissue of a mouse embryo developing either in culture or in utero and their delivery into this tissue by directed electroporation. This combination of methods has been successfully applied, in particular, to the developing mouse brain (Calegari et al. 2002; Takahashi et al. 2002). This strategy provides a simple approach to study a specific miRNA in the tissue and cell lineage of interest.

32.2

Protocol

32.2.1 Materials 1. Primers (F-mRFP-Nhe and R-mRFP-Eco, Sigma-Genosys Ltd., Pampisford, Cambrigdeshire, UK) 2. pCMS-EGFP vector (Clontech Laboratories, Mountain View, CA, USA)

32.2 Protocol

3. 4. 5. 6.

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pGEMT (Promega GmbH, Mannheim, Germany) Platinum electrodes (2 mm diameter) Tissue-Tek1 LNA-modified DNA oligonucleotides (Exiqon A/S, Vedbaek, Denmark)

32.2.2 Instruments 1. BTX1-ECM1830 electroporator (Harvard Apparatus, Holliston, MA, USA) 2. Standard upright microscope (Olympus1 Optical, Europe GmbH, Hamburg, Germany) 3. Model SZX12 dissecting microscope equipped with epifluorescence (Olympus, Hamburg, Germany) 4. IPlab software version 3.5.1 (Scanalytics, Rockville, MD, USA) or the Zeiss LSM Image Examiner software version 3.2.0.70 (Carl Zeiss GmbH)

32.2.3 Reagents 1. 4% paraformaldehyde 2. 120 mM phosphate buffer (pH7.4) 3. DIG oligonucleotide 30 end labeling kit (Roche Diagnostic GmbH, Mannheim, Germany) 4. Alkaline phosphatase-conjugated anti-DIG antibody 5. 5-bromo-4-chloro-3-indoxylphosphate/nitro blue tetrazolium (BCIP/NBT; Sigma-Aldrich Chemie GmbH, Taufkirchen, Germany)

32.2.4 Procedures The procedures described here primarily follow the study reported by De Pietri Tonelli et al. (2006).

32.2.4.1

Construction of Dual-Fluorescence GFP-reporter/mRFP-Sensor (DFRS) Plasmids

1. Use a plasmid containing the cDNA for monomeric red fluorescent protein (mRFP) (HHMI-UCSD La Jolla, CA, USA) (Campbell et al. 2002) as a template to obtain an mRFP cDNA by PCR, using the primers (F-mRFP-Nhe and R-mRFP-Eco). 2. Digest the PCR fragment with NheI and NotI, and ligate into the NheI and NotI sites of pCMS-EGFP vector to yield pCMS-EGFP-mRFP. 3. Amplify the simian virus 40 (SV40) promoter by PCR from pCMS-EGFP, using the oligonucleotides Bgl-SV40-F1 and Nhe-SV40-R1.

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4. Ligate the PCR products into BglII-NheI-digested pCMS-EGFP-mRFP to obtain the plasmid pDSV-GmR. 5. After removal of the polylinker 50 to the mRFP coding sequence by NheI-SacII digestion of pDSV-G-mR, fill in the overhangs by Klenow DNA polymerase-I, and relegate the plasmid to yield the plasmid pDSV2-G-mR. 6. Obtain two fragments of the Unc54 30 untranslated region (UTR) by reverse transcription PCR (RT-PCR) of Caenorhabditis elegans total RNA (mixed stage of development), using the sense/antisense oligonucleotides Eco-UncF/R-Unc-PacXho and F-Unc-XhoFse/R-Unc-Not for the 50 and the 30 fragment, respectively. 7. Ligate the 50 fragment of the unc54 30 UTR into pGEMT plasmid, yielding pGEMT-Unc-50 F. 8. Digest the 30 fragment of the Unc54 30 UTR with XhoI and EagI and ligate into XhoI-EagI-digested pGEMT-Unc-50 F to obtain pGEMTUnc-30 UTR. 9. Prepare the tandem cassettes complementary to the target miRNAs for detection, and the tandem cassettes containing the control sequence or the mutated miRNA complementary sequences by annealing synthetic oligonucleotides containing an overhang to generate a PacI-restricted 50 end, followed by two sequences complementary to the miRNAs of interest (separated by an AscI restriction site) and an overhang to generate a FseI-restricted 30 end. 10. Ligate the annealed product into PacI-FseI-digested pGEMT-Unc-30 UTR, yielding pGEMT-UncSh-SmiR-X (X represents the target miRNA or control or mutated sequence). 11. Digest the latter plasmids with EcoRI and NotI, and ligate the tandem cassette, containing modified Unc54 30 UTR into EcoRI-NotI-digested pDSV2-G-mR, yielding the dual-fluorescent green fluorescent protein (GFP)-reporter/mRFPsensor (DFRS) plasmids for the target miRNAs, the DFRS control plasmid, and the mutated DFRS plasmids (De Pietri Tonelli et al. 2006).

32.2.4.2

Mouse Embryo Electroporation

1. Determine the topology of the embryos using illumination and a dissecting microscope rather than ultrasound microscopy. Then perform in utero electroporation of mouse embryos (Takahashi et al. 2002), as below. 2. Anesthetize pregnant mice 13 days postcoitum with isofluorane vapor and expose their uteri. 3. Using a glass capillary, inject 1–3 mL PBS containing 3–5 mg/mL DFRS plasmid through the uterine wall into the lumen of the telencephalic vesicles or release in proximity of the ectoderm of the embryo. 4. Immediately after injection, deliver 6 square electrical pulses of 30 V, 50 ms each at 1-s intervals through platinum electrodes (2 mm diameter) using an electroporator. 5. Use the orientation of the electric field to direct the uptake of the plasmid to specific regions of the developing brain or ectoderm.


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6. After electroporation, relocate the uterus into the peritoneal cavity, and suture the abdomen. 7. Kill the mice either 24 or 72 h after in utero electroporation, and collect the embryos for further analyses. 8. Perform ex utero electroporation of DFRS plasmids into telencephalic vesicles of E10 mouse embryos followed by 24 h of whole-embryo culture as described previously (Calegari et al. 2002).

32.2.4.3

In situ Hybridization on Cryosections Using LNA-Modified Oligonucleotide Probes

1. Fix whole-mount E11 and E14 mouse embryos overnight at 4 C in 4% paraformaldehyde in 120 mM phosphate buffer equilibrated in 30% sucrose in PBS 2. Embed in Tissue-Tek1 3. Prepare 10-mm cryosections 4. Perform in situ hybridization according to standard protocols with the following modifications. (1) Label LNA-modified DNA oligonucleotides with digoxygenin (DIG)-ddUTP using the DIG oligonucleotide 30 end labeling kit according to manufacturer’s instructions. (2) Prehybridize the cryosections by incubating overnight at 50–60 C in hybridization buffer containing 200 pmol/mL of DIG-labeled LNA-oligonucleotide. (3) Following incubation with alkaline phosphatase-conjugated anti-DIG antibody at 4 C overnight, stain with 5-bromo-4-chloro-3-indoxylphosphate/nitro blue tetrazolium (BCIP/NBT) at 37 C for 2 h and then either at 4 C for 1–2 days or at room temperature for 6–12 h 5. Acquire images with a standard upright microscope

32.2.4.4

Fluorescence Microscopy

1. Image whole-mount mouse embryos using a Model SZX12 dissecting microscope equipped with epifluorescence 2. Process images using the IPlab software version 3.5.1 or the Zeiss LSM Image Examiner software version 3.2.0.70

32.3


The a miRNA Function-Reporter Expression assay through acutely administering a DFRS plasmid for a specific miRNA offers a convenient method to detect these important post-transcriptional regulators with single-cell resolution and to monitor their dynamics in vivo. The approach, which presumably is applicable to a wide variety of species, circumvents the need to generate transgenic organisms, which is

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much more labor intensive (Mansfield et al. 2004; Brennecke et al. 2003). Moreover, the topical administration of DFRS plasmids, followed by their directed electroporation (Osumi and Inoue, 2001; Calegari et al. 2002), provides a simple approach to study a specific miRNA in the tissue and cell lineage of interest.

References Brennecke J, Hipfner DR, Stark A, Russell RB, Cohen SM (2003) bantam encodes a developmentally regulated microRNA that controls cell proliferation and regulates the proapoptotic gene hid in Drosophila. Cell 113:25–36 Calegari F, Haubensak W, Yang D, Huttner WB, Buchholz F (2002) Tissue-specific RNA interference in postimplantation mouse embryos with endoribonuclease-prepared short interfering RNA. Proc Natl Acad Sci USA 99:14236–14240 Campbell RE, Tour O, Palmer AE, Steinbach PA, Baird GS, Zacharias DA, Tsien RY (2002) A monomeric red fluorescent protein. Proc Natl Acad Sci USA 99:7877–7882 De Pietri Tonelli P, Calegari F, Fei JF, Nomura T, Osumi N, Heisenberg CP, Huttner WB (2006) Single-cell detection of microRNAs in developing vertebrate embryos after acute administration of a dual-fluorescence reporter/sensor plasmid. Biotechniques 41:727–732 Giraldez AJ, Cinalli RM, Glasner ME, Enright AJ, Thomson JM, Baskerville S, Hammond SM, Bartel DP, Schier AF (2005) MicroRNAs regulate brain morphogenesis in zebrafish. Science 308:833–838 Mansfield JH, Harfe BD, Nissen R, Obenauer J, Srineel J, Chaudhuri A, Farzan-Kashani R, Zuker M, Pasquinelli AE, Ruvkun G, Sharp PA, Tabin CJ, McManus MT (2004) MicroRNAresponsive ‘sensor’ transgenes uncover Hox-like and other developmentally regulated patterns of vertebrate microRNA expression. Nat Genet 36:1079–1083 Osumi N, Inoue T (2001)Gene transfer into cultured mammalian embryos by electroporation. Methods 24:35–42 Takahashi M, Sato K, Nomura T, Osumi N (2002) Manipulating gene expressions by electroporation in the developing brain of mammalian embryos. Differentiation 70:155–162 Wienholds E, Kloosterman WP, Miska E, Alvarez-Saavedra E, Berezikov E, de Bruijn E, Horvitz HR, Kauppinen S, Plasterk RH (2005) MicroRNA expression in zebrafish embryonic development. Science 309:310–311

Part XI

Whole Mount In Situ Analysis

Chapter 33

Whole Mount In Situ Hybridization (WM-ISH) for miRNA Expression Profiling During Vertebrate Development

Abstract Knowledge of tissue-specific and cell-specific expression patterns of miRNAs can directly inform functional studies. However, detailed analysis of spatial patterns of miRNA expression has been technically challenging. While the regular ISH technique has been extensively used for characterizing cellular localization and tissue distribution of miRNAs in tissue and cell preparations, it cannot be efficiently applied to monitor miRNA expression in whole animals. Utilization of locked nucleic acids (LNAs) in ISH, however, has allowed for the whole mount in situ hybridization techniques (WM-ISH) to monitor miRNA expression in whole animals or animal embryos. The WM-ISH techniques have been tested in Drosophila, zebrafish, chicken, and mouse embryos by several laboratories (Proc Natl Acad Sci USA 102:18017–18022, 2005; Science 309:310–311, 2005; Nat Methods 3:27– 29, 2006; Dev Dyn 235:3156–3165, 2006). These studies led us to tremendous insight into the spatial and temporal patterns of miRNA expression and control of embryonic development by miRNAs. The major features and advantages of the WM-ISH techniques are whole mount analysis and high-throughput profiling of miRNAs. The data from WM-ISH highlight cell-type, organ or structure-specific expression, localization within germ layers and their derivatives, and expression in multiple cell and tissue types and within subregions of structures and tissues, the information which is otherwise inaccessible with other miRNA expression detection methods. This chapter mainly introduces the methods provided in the study by Darnell et al. (Dev Dyn 235:3156–3165, 2006) from the Department of Cell Biology and Anatomy, University of Arizona (Tucson, Arizona, USA).

33.1

Introduction

Knowledge of tissue-specific and cell-specific expression patterns of miRNAs can directly inform functional studies (Aboobaker et al. 2005). For example, murine miR-181 was isolated on the basis of its predominant expression in the thymus and Z. Wang and B. Yang, MicroRNA Expression Detection Methods, DOI 10.1007/978-3-642-04928-6_33, # Springer-Verlag Berlin Heidelberg 2010

377

378

33 Whole Mount In Situ Hybridization

proved to regulate cell fate choice in the hematopoietic lineage (Chen et al. 2004). miR-375 is specifically expressed in pancreatic islet cells, where it regulates genes involved in insulin secretion (Poy et al. 2004). miR-1 is found exclusively in muscles, where it regulates cardiomyocyte proliferation in vertebrates (Zhao et al. 2005) and muscle physiology in flies (Sokol and Ambros 2005) of ASE neurons, and they control the identity of these two neurons by inhibiting different transcription factors that regulate ASE left/right cell fate (Chang et al. 2004; Johnston and Hobert 2003). Although temporal expression of miRNAs can be assessed by Northern analysis, methods to analyze their spatial expression have been limited. The strategy most widely used has been Northern analysis using RNA from dissected vertebrate organs (Lagos-Quintana et al. 2002). However, this process provides coarse spatial and cell type resolution as most organs have many cell types, which may or may not all express a given miRNA. Detailed analysis of spatial patterns of miRNA expression has been technically challenging (Aboobaker et al. 2005). For these reasons, methods to directly visualize miRNAs in situ are desirable. The application of the in situ hybridization (ISH) technique to miRNA detection has been described in detail in Section IV. While the regular ISH technique has been extensively used for characterizing cellular localization and tissue distribution of miRNAs in tissue and cell preparations, it cannot be efficiently applied to monitor miRNA expression in whole animals. With the development of locked nucleic acids (LNAs), however, it is now possible to design RNA probes that can hybridize to target RNA sequences with extremely high specificity and stability (Wahlestedt et al. 2000; Elmen et al. 2005). And utilization of LNAs in ISH directly the whole mount in situ hybridization techniques (WM-ISH) has been successfully employed to monitor miRNA expression in whole animals or animal embryos (Aboobaker et al. 2005; Wienholds et al. 2005; Kloosterman et al. 2006). WM-ISH has proven effective for WM-ISH detection of miRNA expression (Wienholds et al. 2005; Kloosterman et al. 2006). Aboobaker et al reported the spatial patterns of miRNA transcription during Drosophila embryonic development using WM-ISH methods (Aboobaker et al. 2005). A WM-ISH overview of miRNA expression in zebrafish detected the majority of miRNAs and demonstrated dynamic spatial and temporal expression patterns (Wienholds et al. 2005). By contrast, a WM-ISH screen in mouse embryo established the first comprehensive set of miRNA expression patterns in animal development (Kloosterman et al. 2006). The study found these patterns to be remarkably specific and diverse, which suggests highly specific and diverse roles for miRNAs. Most miRNAs are expressed in a tissue-specific manner during segmentation and later stages but are not present during early development. Using their improved WM-ISH techniques, Darnell et al. (2006) presented a comprehensive view of miRNA expression during embryogenesis in chicken to map expression of 135 miRNA genes including five miRNAs that had not been previously reported in chicken. They detected 84 miRNAs before day 5 of embryogenesis, of which 75 miRNAs showed differential expression. Whereas few miRNAs were expressed during formation of the primary germ layers, the number of miRNAs detected increased rapidly during organogenesis.

33.2 Protocol

379

The chicken embryo provides an advantageous alternative to mouse for miRNA expression analyses. As an amniote, developmental processes in chicken closely mimic those in mammalian species, including mouse and humans. Whole mount in situ hybridization protocols have also been optimized for chick to give outstanding sensitivity and low background (Nieto et al. 1996; Bell et al. 2004), and embryos are easily and inexpensively obtained.

33.2

Protocol

33.2.1 Materials 1. 2. 3. 4.

Fertile chicken eggs (HyLine, Iowa; not a commercially available source) 6- or 12-well plates 24-well plates 15- or 24-mm Netwell Inserts with 74-mM polyester mesh bottoms (Corning, Inc) 5. Paraplast (Kendall)

33.2.2 Instruments 1. 2. 3. 4. 5.

Nutator Leica PlanApo stereomicroscope Digital acquisition system Cytoseal XYL (Richard-Allan Scientific) Leica DMRXE microscope

33.2.3 Reagents 1. 2. 3. 4.

5. 6. 7. 8.

Chick saline (123 mM NaCl in nanopure water) PBS DIG Oligonucleotide 3’ end labeling kit (Roche) Prehybridization solution (50% formamide, 5x SSC, 2% blocking powder, 0.1% Tween-20, 0.1% CHAPS, 50 mg/mL yeast RNA, 5 mM EDTA, 50 mg/ mL heparin, DEPC water) 2 SSC 0.1% Chaps 20% sheep serum KTBT (50 mM Tris, pH7.5, 150 mM NaCl, 10 mM KCl, 1% Tween-20)


380

9. NTMT (two solutions changes 10 min; 100 mM NaCl, 100 mM Tris of pH9.5, 50 mM MgCl2, 0.1% Tween-20) 10. 0.1% sodium azide 11. Xylene

33.2.4 Procedures The procedures described herein are based on the studies reported by Kloosterman et al. (2006) and by Darnell et al. (2006).

33.2.4.1

Embryo Collection and Preparation

1. Incubate fertile chicken eggs in a forced-draft, humidified incubator at 37.5 C for 0.5–5 days, depending on the stages desired. 2. Collect embryos into chilled chick saline, remove from the vitelline membrane, and clean of yolk. 3. Open extraembryonic membranes and large body cavities (brain vesicles, atria, allantois, eye) to minimize trapping of the in situ reagents. 4. Fix embryos in fresh, cold 4% paraformaldehyde in PBS overnight at 4 C. Maintain them at 4 C during collection, because significant loss of labeling is correlated with increased time at room temperature during collection. 5. Rinse the embryos in PBS, then in PBS plus 1% Tween-20 (PBT). 6. Dehydrate the embryos by steps (25, 50, 75, 100, 100%) into methanol before being cooled to 20 C overnight (or up to 10 days). 7. Rehydrate to reverse this series. 8. Rinse the embryos twice in PBS and treat older embryos with proteinase K: stages 8–13 and 14–18 at 10 mg/mL of proteinase K for 10 and 20 min, respectively; stages 19 and older at 20 mg/mL of proteinase K for 20 min. 9. Rinse the embryos repeatedly in PBT to stop the digestion. 10. Place them into prehybridization. 11. Store the embryos until use either at the methanol step or in prehybridization at 20 C for fewer than 10 days. (Embryos stored for more than 10 days will have a considerable decrease in hybridization signal and increase in background.)

33.2.4.2

Probe Preparation

1. Obtain sequences for the miRNAs of interest from the miRNA Registry (microrna.sanger.ac.uk). 2. Design Locked Nucleic Acid modified DNA oligonucleotides (LNAs) capture probes complementary to the mature miRNAs and synthesize by Exiqon A/S.

33.2 Protocol

381

Add digoxigenin-labeled UTP to the 3’ end of the LNAs using a DIG Oligonucleotide 3’ end labeling kit.

33.2.4.3

In Situ Hybridization

1. Transfer the prepared embryos into a standard prehybridization solution and incubate for 2 h in 24-well plates (1 ml/well) in a shaking hybridization oven at a temperature between 21 and 23 C below the reported melting temperature of the LNAs. 2. Add probe to 1 ml fresh prehyb buffer and perform hybridization overnight at the prehybridization temperature. 3. Transfer embryos after hybridization to 6- or 12-well plates containing 15- or 24-mm Netwell Inserts, respectively, with attached 74-mM polyester mesh bottoms in 2 SSC, 0.1% Chaps prewarmed to the hybridization temperature. Prewarming the wash solutions to the hybridization temperature before washing is crucial for maximum signal-to-background ratio and is not available in some robots. Embryos in the Netwell inserts could be moved quickly into plates filled with prewarmed wash buffer, minimizing cooling for high throughput processing. 4. Wash embryos 3 20 min in the high salt wash, then 3 20 min in 0.2 SSC, 0.1% Chaps. 5. Rinse the embryos twice in KTBT and transfer back into clean 24-well plates to minimize volume for the antibody step. 6. Pretreat the embryos in 20% sheep serum in KTBT at 4 C for 2–3 h or longer. 7. Perform anti-DIG antibody binding (1:2,000–1:4,000) in 24-well plates at 4 C on a nutator. Final washes were in KTBT in large Netwell inserts at room temperature for a minimum of 5 changes over 5 h, but often including overnight at 4 C. 8. Transfer the embryos back to 24-well plates into fresh NTMT. 9. Color reactions (NBT/BCIP in NTMT) for 1–6 h at room temperature on a nutator until signal or background becomes visible, followed by overnight washing in KTBT. 10. Perform a second or third round of color reaction until each probe yields a strong signal, or until the negative control begin to show background label. 11. Stop reactions with KTBT and wash the embryos in PBS. 12. Dehydrate by a methanol series described earlier to remove background and enhance signal, then rehydrate and store in PBS plus 0.1% sodium azide.

33.2.4.4

Imaging and Histology

1. Photograph the embryos on a Leica PlanApo stereomicroscope using a digital acquisition system. 2. Transmit lateral and/or direct illumination.


382

Select a miRNA of interest

Collect embryos from mouse or chicken

Synthesize LNA-FISH miRNA capture probes with LNA modification and a 3'digoxigenin

Fix the embryos with 4% formaldehyde

Dehydrate the embryos with methanol

Rehydrate the embryos

Hybridization of cells with LNA-FISH probe

Treatment with anti-DIG antibody

Perform 3 runs of color reactions (NBT/BCIP in NTMT)

Dehydrate the preparation with methanol

Photograph the embryos

Fig. 33.1 Flowchart of the LNA-ELF-FISH approach for miRNA expression detection. According to Darnell et al. (2006)

3. Dehydrate some embryos into methanol (25, 50, 75, 100, 100% in PBS), and transfer to xylene (2 changes for 10 min), and embed them in paraplast. 4. Cut sections at 14 mm, mount the sections using Cytoseal XYL. 5. Photograph the sections using DIC optics on a Leica DMRXE microscope.


383

Modify the images using Adobe Photoshop only to correct brightness, contrast, color balance, and to remove particulates (Fig. 33.1).

33.2.4.5

Northern Blot Analysis

1. Heart, limb, head, and trunk tissues were dissected from stage-24 embryos, suspended in Trizol (Invitrogen Corp), and total RNA was isolated by following the manufacturer’s protocol. Forty micrograms of total RNA and 5.8 pmol of 22mer DNA control oligos were fractionated by 15% PAGE and transferred to Nytran N_membrane. Nucleic acids were UV crosslinked to membranes and baked at 80 for 30 min. Blots were pehybridized in 5_SSC, 20 mM Na2HPO4, pH 7.2, 7% SDS, 40 _g/ml yeast tRNA, and 2_Denhardt’s solution at 50 C for 2 h, followed by hybridization overnight in 25 ml of fresh hybridization solution containing 25 pmol of DIG-labeled LNA probe at 50 C. Blots were rinsed 2_in 45 ml of buffer (3_SSC, 25 nM NaH2PO4, pH 7.5, 5% SDS, and 10_Denhardt’s solution) at 25 C, followed by 30 min at 50 C, 30 min at 50 C in 1_SSC and 1% SDS, and 2_for 30 min in 0.1_SSC and 0.1% SDS at 65 C. To visualize bound probe, blots were washed briefly with 150 ml of KTBT at room temperature, then in 200 ml blocking solution (5% Skim Milk Powder, 20% sheep serum in KTBT) overnight at 4 C, then incubated in blocking solution containing a 1:2,500 dilution of anti-Digoxigenin-AP Fab Fragment (Roche) for 2 h at room temperature. Blots were washed 3_at room temperature on a nutator in KTBT, followed by two washes in NTMT. Blots were stained with BOLD APB Chemiluminescent substrate (Molecular Probes) according to the manufacturer’s protocol. Chemiluminescent signal was detected by exposing on BioMax Light X-ray film 15 min to 3 h.

33.3


The WM-ISH techniques have been tested in Drosophila, zebrafish, chicken, and mouse embryos by several laboratories (Aboobaker et al. 2005; Wienholds et al. 2005; Kloosterman et al. 2006; Darnell et al. 2006). These studies led us to tremendous insight into the spatial and temporal patterns of miRNA expression and control of embryonic development by miRNAs. The major features and advantages of the WM-ISH techniques are whole mount analysis and high-throughput profiling of miRNAs. The data from WM-ISH highlight cell-type, organ or structure-specific expression, localization within germ layers and their derivatives, and expression in multiple cell and tissue types and within subregions of structures and tissues, the information which is otherwise inaccessible with other miRNA expression detection methods.

384


References Aboobaker AA, Tomancak P, Patel N, Rubin GM, Lai EC (2005) Drosophila microRNAs exhibit diverse spatial expression patterns during embryonic development. Proc Natl Acad Sci USA 102:18017–18022. Bell GW, Yatskievych TA, Antin PB (2004) GEISHA, A whole-mount in situ hybridization gene expression screen in chicken embryos. Dev Dyn 229:677–687. Chang S, Johnston RJ Jr, Frøkjaer-Jensen C, Lockery S, Hobert O (2004) MicroRNAs act sequentially and asymmetrically to control chemosensory laterality in the nematode. Nature 430:785–789. Chen C-Z, Li L, Lodish HF, Bartel DP (2004) MicroRNAs modulate hematopoietic lineage differentiation. Science 303:83–86. Darnell DK, Kaur S, Stanislaw S, Konieczka JH, Yatskievych TA, Antin PB (2006) MicroRNA expression during chick embryo development. Dev Dyn 235:3156–3165. Elmen J, Thonberg H, Ljungberg K, Frieden M, Westergaard M, Xu YH, Wahren B, Liang ZC, Urum H, Koch T, Wahlestedt C (2005) Locked nucleic acid (LNA) mediated improvements in siRNA stability and functionality. Nucleic Acids Res 33:439–447. Johnston RJ, Hobert O (2003) A microRNA controlling left/right neuronal asymmetry in Caenorhabditis elegans. Nature 426:845–849. Kloosterman WP, Wienholds E, de Bruijn E, Kauppinen S, Plasterk RHA (2006) In situ detection of miRNAs in animal embryos using LNA-modified oligonucleotide probes. Nat Methods 3:27–29. Lagos-Quintana M, Rauhut R, Yalcin A, Meyer J, Lendeckel W, Tuschl T (2002) Identification of tissue-specific microRNAs from mouse. Curr Biol 12:735–739. Nieto MA, Patel K, Wilkinson DG (1996) In situ hybridization analysis of chick embryos in whole mount and tissue sections. In: Methods in cell biology. New York: Academic Press, Inc. Poy MN, Eliasson L, Krutzfeldt J, Kuwajima S, Ma XS, MacDonald PE, Pfeffer B, Tuschl T, Rajewsky N, Rorsman P, Stoffel M (2004) A pancreatic islet-specific microRNA regulates insulin secretion. Nature 432:226–230. Sokol NS, Ambros V (2005) Mesodermally expressed Drosophila microRNA-1 is regulated by Twist and is required in muscles during larval growth. Genes Dev 19:2343–2354. Wahlestedt C, Salmi P, Good L, Kela J, Johnsson T, Hokfelt T, Broberger C, Porreca F, Lai J, Ren KK, Ossipov M, Koshkin A, Jakobsen N, Skouv J, Oerum H, Jacobsen MH, Wengel J (2000) Potent and nontoxic antisense oligonucleotides containing locked nucleic acids. Proc Natl Acad Sci USA 97:5633–5638. Wienholds E, Kloosterman WP, Miska E, Alvarez-Saavedra E, Berezikov E, de Bruijn E, Horvitz HR, Kauppinen S, Plasterk RHA (2005) MicroRNA expression in zebrafish embryonic development. Science 309:310–311. Zhao Y, Samal E, Srivastava D (2005) Serum response factor regulates a muscle-specific microRNA that targets Hand2 during cardiogenesis. Nature 436:214–220.

Index

A Array fabrication, 74, 212

B Bead, 11, 40, 174, 176, 177, 180, 183, 185, 289–294, 297, 340, 342 Bioluminescence, 40, 295–302 Biomarker, 21, 28, 37–38, 131, 134, 139, 331, 332, 337, 345 Biosensor, 52, 192, 208–210, 212–214, 328

F FISH. See Fluorescence in situ hybridization (FISH) Fluorescence correlation spectroscopy (FCS), 118, 257, 258 Fluorescence in situ hybridization (FISH), 70, 104, 110, 353–358, 382 FRET oligonucleotide, 250, 251, 253–254

G Gold, 39, 40, 52, 53, 85, 99, 132, 156, 163, 164, 167, 175, 178, 192, 199–201, 203–205, 212, 214, 217–224, 276, 364

C Cancer, 20–31, 37, 38, 126, 154, 214, 263, 264, 276, 293, 302, 331, 337, 339, 340, 367 Cardiac, 11, 16, 31–34, 334, 335, 337 Cardiovascular, 31–35, 38 Cell lysis, 222–223, 245, 358 Circulating miRNAs, 38, 331, 332, 337 Conducting polymer nanowires, 207–215

E Electrocatalytic, 40, 52, 191–197, 321–328 Electrocatalytic nanoparticle tags, 191–197 End-Point Stem-Loop Real-Time RT-PCR, 131–139 ENT enzyme-labeled fluorescence (ELF), 335–336 Enzymatic miRNA detection, 267, 274

H High-throughput, 27, 39, 51, 139, 153–156, 160, 169, 200, 205, 239, 255, 281, 282, 287, 290, 294, 308 Hybridization, 27, 39, 51, 52, 68–71, 73, 76–78, 84, 85, 88–89, 91, 92, 95–99, 133, 135, 137, 191–194, 196–200, 204, 205, 208–210, 213, 217, 218, 223, 224, 230, 238, 245, 247, 258, 260–263, 267–270, 272–273, 282–284, 286, 287, 289, 290, 292, 295, 300, 301, 303, 304, 306, 308, 309, 326, 354–356, 358, 367, 369, 373

I IDT. See Integrated DNA Technologies (IDT)

385

386

In situ hybridization (ISH), 9, 10, 18, 39, 40, 97, 103–127, 247, 355, 356, 373, 377–383 Integrated DNA Technologies (IDT), 73, 124, 137, 155, 195, 202, 203, 212, 253, 259, 261, 271, 292, 317, 364 Invader miRNA, 249–252, 254–255 In vitro transcription, 97, 99, 244, 317

L Laser induced fluorescence (LIF), 257, 259 Ligation, 35, 40, 52, 160, 163–165, 167, 168, 177, 178, 184, 186, 192, 197, 214, 229–239, 241, 242, 244, 245, 264, 272, 289, 322, 327 LNA. See Locked nucleic acid (LNA) Locked nucleic acid (LNA), 10, 51, 67, 69, 70, 73, 77, 91–96, 105, 106, 116–123, 126, 133, 192, 199, 200, 202–204, 258–262, 353–358, 369–371, 373, 378, 380–383 LongSage, 176, 178, 180, 183, 184, 187

M Microarray, 9–11, 15, 17, 19, 20, 22, 24, 27–30, 33, 35–37, 39, 40, 51, 52, 67–78, 132, 180, 192, 199, 200, 203–206, 214, 239, 263, 268, 274, 282, 284–287, 290, 332, 337 miRAGE, 39, 40, 162, 173–188 miRNA amplification profiling (mRAP), 159–169, 249 miRNA serial analysis of gene expression (miRAGE/SAGE), 173–188 miR-Q RT-PCR, 141–146 Molecular beacons, 303–310, 315, 318 mRAP. See miRNA amplification profiling (mRAP) Multiplexing RT-PCR, 153–156

N Nanoparticle, 39, 40, 52, 53, 191–197, 199–206, 213, 214, 217–224, 276 Nanoparticle-amplified surface plasmon resonance imaging (Nanoparticleamplified SPRI), 192, 199–206

Index

Neuronal, 35–36 Northern blot, 10, 25, 30, 39, 40, 51, 52, 83–99, 116, 132, 148, 151, 197, 238, 239, 243–246, 254, 263, 282–285, 287, 363, 383

O Oligonucleotide capture probe (OCP), 191, 195, 196, 289 Oligonucleotides, 15, 32, 34, 51–53, 68–70, 73, 85, 91–99, 105–109, 111–112, 114, 122, 124, 126, 141–144, 155, 166, 167, 176, 191, 194, 195, 202–204, 208, 209, 219–222, 230, 232, 235–238, 243, 247, 249–255, 258, 260–261, 268, 270–274, 282, 284, 286, 289, 291, 292, 297, 299–300, 303, 314, 315, 325, 332, 354, 355, 369, 371–373, 379, 380

P Padlock-probes and rolling-circle amplification, 241–247 PCR. See Polymerase chain reaction (PCR) Peptide nucleic acid (PNA), 52, 67, 69, 208–210, 212, 213 Plasma, 18, 24, 38, 301, 326, 331–337, 339–343, 345–348 Polymerase chain reaction (PCR), 10, 12, 17, 19, 21–25, 28–30, 36–40, 51–53, 70, 97, 99, 123, 125, 126, 131–139, 141–151, 153–156, 159, 161, 164–169, 173, 174, 176–178, 180, 182–184, 187, 214, 224, 230, 237, 239, 263, 269, 272, 282, 289–292, 299, 301, 307–310, 317, 322, 332, 333, 335–337, 340, 342, 346–348, 358, 361–367, 371, 372 Poly(A)-Tailed Universal Reverse Transcription, 147–151 Probe, 19, 68, 84, 104, 134, 154, 160, 175, 191, 199, 208, 217, 238, 241–247, 249, 258, 267, 281, 289, 295, 303, 318, 325, 335, 346, 354, 363, 369, 378

Q qRT-PCR, 19, 22, 25, 28, 29, 37, 51, 52, 133, 141–146, 180, 239, 335

Index

R Real-time RT-PCR, 10, 12, 17, 23, 40, 131–139, 310, 332, 342 Ribozyme, 40, 313–319 Rluc plasmid, 299 RNA-primed, array-based Klenow enzyme (RAKE), 40, 52, 123, 281–287 RT-PCR, 25, 29, 30, 36, 38–40, 51, 52, 147, 148, 151, 153–156, 224, 263, 289, 290, 310, 332, 342, 358, 362, 367, 372

S SAGE. See Serial analysis of gene expression (SAGE) Serial analysis of gene expression (SAGE), 40, 173–188 Serum, 34, 38, 39, 107–109, 115, 164, 167, 269, 297, 301, 331–337, 379, 381, 383

387

Silver, 39, 192, 214, 217, 218, 223, 224, 276, 278, 280 Single-cell, 51, 126, 131, 133, 139, 154, 353, 354, 361–367, 369, 373 Single molecule, 40, 257–264, 270, 275, 353, 358 Single molecule detection (SMD), 40, 257, 259–260 Splinted-ligation, 229–239 Stem-loop Real-time RT-PCR, 131–139 Surface-enhanced Raman scattering (SERS), 275, 276, 278–280

W Whole mount in situ hybridization (WM-ISH), 377–383

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