Robust global microRNA expression profiling using next-generation sequencing technologies

miRNAs are a class of regulatory molecules involved in a wide range of cellular functions, including growth, development and apoptosis. Given their widespread roles in biological processes, understanding their patterns of expression in normal and diseased states will provide insights into the consequences of aberrant expression. As such, global miRNA expression profiling of human malignancies is gaining popularity in both basic and clinically driven research. However, to date, the majority of such analyses have used microarrays and quantitative real-time PCR. With the introduction of digital count technologies, such as next-generation sequencing (NGS) and the NanoString nCounter System, we have at our disposal many more options. To make effective use of these different platforms, the strengths and pitfalls of several miRNA profiling technologies were assessed, including a microarray platform, NGS technologies and the NanoString nCounter System. Overall, NGS had the greatest detection sensitivity, largest dynamic range of detection and highest accuracy in differential expression analysis when compared with gold-standard quantitative real-time PCR. Its technical reproducibility was high, with intrasample correlations of at least 0.95 in all cases. Furthermore, miRNA analysis of formalin-fixed, paraffin-embedded (FFPE) tissue was also evaluated. Expression profiles between paired frozen and FFPE samples were similar, with Spearman’s ρ>0.93. These results show the superior sensitivity, accuracy and robustness of NGS for the comprehensive profiling of miRNAs in both frozen and FFPE tissues.

[1]  Quaid Morris,et al.  Probing microRNAs with microarrays: tissue specificity and functional inference. , 2004, RNA.

[2]  C. Croce,et al.  MicroRNA gene expression deregulation in human breast cancer. , 2005, Cancer research.

[3]  Marcel Martin Cutadapt removes adapter sequences from high-throughput sequencing reads , 2011 .

[4]  David E. Misek,et al.  Profiling of pathway-specific changes in gene expression following growth of human cancer cell lines transplanted into mice , 2003, Genome Biology.

[5]  Pan Du,et al.  lumi: a pipeline for processing Illumina microarray , 2008, Bioinform..

[6]  C. Croce,et al.  A microRNA expression signature of human solid tumors defines cancer gene targets , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Jeffrey Shelton,et al.  An optimized isolation and labeling platform for accurate microRNA expression profiling. , 2005, RNA.

[8]  Murat Gunel,et al.  Sequence Variants in SLITRK1 Are Associated with Tourette's Syndrome , 2005, Science.

[9]  Gozoh Tsujimoto,et al.  Intra-Platform Repeatability and Inter-Platform Comparability of MicroRNA Microarray Technology , 2009, PloS one.

[10]  Frédéric Lemoine,et al.  Concordance among digital gene expression, microarrays, and qPCR when measuring differential expression of microRNAs. , 2010, BioTechniques.

[11]  Edwin Cuppen,et al.  Diversity of microRNAs in human and chimpanzee brain , 2006, Nature Genetics.

[12]  V. Detours,et al.  Human cancer cell lines: Experimental models for cancer cells in situ? For cancer stem cells? , 2009, Biochimica et biophysica acta.

[13]  Gordon K Smyth,et al.  Statistical Applications in Genetics and Molecular Biology Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments , 2011 .

[14]  Richard Durbin,et al.  Sequence analysis Fast and accurate short read alignment with Burrows – Wheeler transform , 2009 .

[15]  Claus Lindbjerg Andersen,et al.  Normalization of Real-Time Quantitative Reverse Transcription-PCR Data: A Model-Based Variance Estimation Approach to Identify Genes Suited for Normalization, Applied to Bladder and Colon Cancer Data Sets , 2004, Cancer Research.

[16]  Jennifer L. Osborn,et al.  Direct multiplexed measurement of gene expression with color-coded probe pairs , 2008, Nature Biotechnology.

[17]  Andrew Williams,et al.  Cross-platform analysis of global microRNA expression technologies , 2010, BMC Genomics.

[18]  N. Rajewsky,et al.  A pancreatic islet-specific microRNA regulates insulin secretion , 2004, Nature.

[19]  Sam Griffiths-Jones,et al.  miRBase: the microRNA sequence database. , 2006, Methods in molecular biology.

[20]  Eugene Berezikov,et al.  Many novel mammalian microRNA candidates identified by extensive cloning and RAKE analysis. , 2006, Genome research.

[21]  C. Burge,et al.  Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that Thousands of Human Genes are MicroRNA Targets , 2005, Cell.

[22]  David E. Misek,et al.  Analysis of Tumor-Host Interactions by Gene Expression Profiling of Lung Adenocarcinoma Xenografts Identifies Genes Involved in Tumor Formation , 2005, Molecular Cancer Research.

[23]  Jeffrey G. Reid,et al.  Expression profiling of microRNAs by deep sequencing , 2009, Briefings Bioinform..

[24]  Gordon K. Smyth,et al.  limma: Linear Models for Microarray Data , 2005 .

[25]  V. Ambros,et al.  The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14 , 1993, Cell.

[26]  R. Corn,et al.  Multiplexed detection methods for profiling microRNA expression in biological samples. , 2008, Angewandte Chemie.

[27]  C. Croce,et al.  An oligonucleotide microchip for genome-wide microRNA profiling in human and mouse tissues. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Stijn van Dongen,et al.  miRBase: microRNA sequences, targets and gene nomenclature , 2005, Nucleic Acids Res..

[29]  R. Ach,et al.  Measuring microRNAs: Comparisons of microarray and quantitative PCR measurements, and of different total RNA prep methods , 2008, BMC biotechnology.

[30]  Vladimir Benes,et al.  A sensitive array for microRNA expression profiling (miChip) based on locked nucleic acids (LNA). , 2006, RNA.

[31]  F. Speleman,et al.  Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes , 2002, Genome Biology.

[32]  Muller Fabbri,et al.  A MicroRNA signature associated with prognosis and progression in chronic lymphocytic leukemia. , 2005, The New England journal of medicine.

[33]  Stijn van Dongen,et al.  miRBase: tools for microRNA genomics , 2007, Nucleic Acids Res..

[34]  B. Cullen,et al.  MicroRNAs and small interfering RNAs can inhibit mRNA expression by similar mechanisms , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[35]  Mark D. Robinson,et al.  Moderated statistical tests for assessing differences in tag abundance , 2007, Bioinform..

[36]  Ana Kozomara,et al.  miRBase: integrating microRNA annotation and deep-sequencing data , 2010, Nucleic Acids Res..

[37]  Jean YH Yang,et al.  Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.

[38]  Michael Z. Man,et al.  The influence of tumor size and environment on gene expression in commonly used human tumor lines , 2004, BMC Cancer.

[39]  C. Croce,et al.  MicroRNA signatures in human ovarian cancer. , 2007, Cancer research.

[40]  D. Bartel MicroRNAs Genomics, Biogenesis, Mechanism, and Function , 2004, Cell.

[41]  R. Stephens,et al.  Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. , 2006, Cancer cell.

[42]  Lucian Ilie,et al.  SHRiMP2: Sensitive yet Practical Short Read Mapping , 2011, Bioinform..

[43]  Sam Griffiths-Jones,et al.  The microRNA Registry , 2004, Nucleic Acids Res..

[44]  H. Horvitz,et al.  MicroRNA expression profiles classify human cancers , 2005, Nature.

[45]  Mark D. Robinson,et al.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..

[46]  J. Castle,et al.  Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs , 2005, Nature.