A simple array platform for microRNA analysis and its application in mouse tissues
- 3 August 2007
- journal article
- Published by Cold Spring Harbor Laboratory in RNA
- Vol. 13 (10), 1803-1822
- https://doi.org/10.1261/rna.498607
Abstract
MicroRNAs (miRNAs) are a novel class of small noncoding RNAs that regulate gene expression at the post-transcriptional level and play a critical role in many important biological processes. Most miRNAs are conserved between humans and mice, which makes it possible to analyze their expressions with a set of selected array probes. Here, we report a simple array platform that can detect 553 nonredundant miRNAs encompassing the entire set of miRNAs for humans and mice. The platform features carefully selected and designed probes with optimized hybridization parameters. Potential cross-reaction between mature miRNAs and their precursors was investigated. The array platform was used to analyze miRNAs in the mouse central nervous system (CNS, spinal cord and brain), and two other non-CNS organs (liver and heart). Two types of miRNAs, differentially expressed organ/tissue-associated miRNAs and ubiquitously expressed miRNAs, were detected in the array analysis. In addition to the previously reported neuron-related miR-124a, liver-related miR-122a, and muscle-related miR-133a, we also detected new tissue-associated miRNAs (e.g., liver-associated miR-194). Interestingly, while the majority of pre-miRNAs were undetectable, miR690, miR709, and miR720 were clearly detected at both mature and precursor levels by the array analysis, indicating a limited cross-reaction between pre-miRNAs and their mature miRNAs. The reliability of this array technology was validated by comparing the results with independent Northern blot analyses and published data. A new approach of data normalization based on Northern blot analysis of one ubiquitously expressed miRNA is introduced and compared with traditional approaches. We expect this miRNA array platform to be useful for a wide variety of biological studies.Keywords
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