Evaluation of a new high-dimensional miRNA profiling platform

Julie M. Cunningham, Ann L. Oberg, Pedro M. Borralho, Betsy T. Kren, Amy J. French, Liang Wang, Brian M. Bot, Bruce W. Morlan, Kevin A Silverstein, Rod Staggs, Yan Zeng, Anne Francoise Lamblin, Christopher A. Hilker, Jian Bing Fan, Clifford J Steer, Stephen N. Thibodeau

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Background. MicroRNAs (miRNAs) are a class of approximately 22 nucleotide long, widely expressed RNA molecules that play important regulatory roles in eukaryotes. To investigate miRNA function, it is essential that methods to quantify their expression levels be available. Methods. We evaluated a new miRNA profiling platform that utilizes Illumina's existing robust DASL chemistry as the basis for the assay. Using total RNA from five colon cancer patients and four cell lines, we evaluated the reproducibility of miRNA expression levels across replicates and with varying amounts of input RNA. The beta test version was comprised of 735 miRNA targets of Illumina's miRNA profiling application. Results. Reproducibility between sample replicates within a plate was good (Spearman's correlation 0.91 to 0.98) as was the plate-to-plate reproducibility replicates run on different days (Spearman's correlation 0.84 to 0.98). To determine whether quality data could be obtained from a broad range of input RNA, data obtained from amounts ranging from 25 ng to 800 ng were compared to those obtained at 200 ng. No effect across the range of RNA input was observed. Conclusion. These results indicate that very small amounts of starting material are sufficient to allow sensitive miRNA profiling using the Illumina miRNA high-dimensional platform. Nonlinear biases were observed between replicates, indicating the need for abundance-dependent normalization. Overall, the performance characteristics of the Illumina miRNA profiling system were excellent.

Original languageEnglish (US)
Article number57
JournalBMC Medical Genomics
Volume2
DOIs
StatePublished - 2009

Bibliographical note

Funding Information:
This work was supported by grants from the Minnesota Partnership for Biotechnology and Medical Genomics [2006-100412]; and by Mayo Clinic Comprehensive Cancer Center Shared Resource Grant [CA15083].

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