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Potential pitfalls in microRNA profiling

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Pauline Chugh, Dirk P. Dittmer

Published Online: May 07 2012

DOI: 10.1002/wrna.1120

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MicroRNAs (miRNAs) are small, noncoding RNAs that post‐transcriptionally influence a wide range of cellular processes such as the host response to viral infection, innate immunity, cell cycle progression, migration, and apoptosis through the inhibition of target mRNA translation. Owing to the growing number of miRNAs and identification of their functional roles, miRNA profiling of many different sample types has become more expansive, especially with relevance to disease signatures. In this review, we address some of the advantages and potential pitfalls of the currently available methods for miRNA expression profiling. Some of the topics discussed include isomiRNAs, comparison of different profiling platforms, normalization strategies, and issues with regard to sample preparation and experimental analyses. WIREs RNA 2011 DOI: 10.1002/wrna.1120

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Figure 1.

Profiling of microRNAs (miRNAs) at various stages provides nonredundant information. miRNA profiling can be performed at several different steps during miRNA biogenesis. The pri‐miRNA DNA form can provide insight into deletions and SNP (single‐nucleotide polymorphism) amplifications while the pre‐miRNA yields information on the transcriptional regulation of miRNAs. Both these forms display less stability than the mature miRNA form, which is helpful in obtaining an overall profile of the miRNA repertoire of the cell and can also lead to findings regarding miRNA processing, stability, and export. Therefore, profiling of different miRNA forms can yield nonredundant information on the state of miRNA expression in a given cell.

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Figure 2.

Schematic of the microRNA (miRNA) processing steps that can influence the profile of pri‐, pre‐, and mature miRNAs. The rate of each processing step is denoted by a k value: k₁–k₆. Each k value has the potential to affect the turnover and size of the miRNA pool to be analyzed. The different miRNA forms that can be profiled are shown along with their target size, ranging from a large miRNA gene to only 22 nt for the mature miRNA. The mature miRNA is the smallest of these and is subject to the greatest amount of processing and therefore can be affected by many k values or processing rates. The opposite is true for the pri‐miRNA and miRNA gene (larger size and less influential k values). The pri‐miRNA can provide insight into miRNA regulation, whereas the mature form gives data more relevant to miRNA function.

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Figure 3.

Dominant isomiRNAs of KSHV K12‐2 are revealed by Next Generation sequencing. Illumina small RNA sequencing data was aligned to the KSHV miR‐K12‐2 seed sequence for homology. The square root of the frequency of these reads was calculated and is displayed for each isomir matching the KSHV miR‐K12‐2 seed sequence. Although many isomiRNAs seem to be expressed at relatively low frequencies, two dominant isomiRNAs of KSHV K12‐2 emerge and contribute to the majority of the signal detected. The sequences are shown and differ by a single nucleotide in length, characteristic of many isomiRNAs.

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Figure 4.

Comparison of qPCR‐based microRNA (miRNA) profiling platforms reveal differences in target specificity. Two qPCR‐based miRNA profiling platforms were tested—one that uses sequence‐specific primers for the reverse transcription (RT) reaction and another that uses a universal tailing primer for cDNA synthesis. RT reactions were performed according to each manufacturer's protocols and the cDNA was used as template for qPCR profiling of miRNAs. For profiling, the corresponding miRNA arrays from each company were run using the appropriate cycling parameters. The top 10 highest, middle 10, and lowest 10 abundant miRNAs were then run on a Caliper LabChipGX gel for further analysis and gel images for both platforms are shown. The top and bottom bands denote the upper and lower markers, respectively, of the LabChip HT DNA 1k kit and do not correspond to amplified PCR products.

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