The methodological challenge in high-throughput profiling and quantifying microRNAs

Abstract

Background: MicroRNAs (miRNAs) play an essential role in various biological processes and signaling pathways through the regulation of gene expression and genome stability. Recent data indicated that the next-generation sequencing (NGS)-based high-throughput quantification of miRNAs from biofluids provided exciting possibilities for discovering biomarkers of various diseases and might help promote the development of the early diagnosis of cancer. However, the complex process of library construction for sequencing always introduces bias, which may twist the actual expression levels of miRNAs and reach misleading conclusions.

Results: We discussed the deviation issue in each step during constructing miRNA sequencing libraries and suggested many strategies to generate high-quality data by avoiding or minimizing bias. For example, improvement of adapter design (a blocking element away from the ligation end, a randomized fragment adjacent to the ligation junction and UMI) and optimization of ligation conditions (a high concentration of PEG 8000, reasonable incubation temperature and time, and the selection of ligase) in adapter ligation, high-quality input RNA samples, removal of adapter dimer (solid phase reverse immobilization (SPRI) magnetic bead, locked nucleic acid (LNA) oligonucleotide, and Phi29 DNA polymerase), PCR (linear amplification, touch-down PCR), and product purification are essential factors for achieving high-quality sequencing data. Moreover, we described several protocols that exhibit significant advantages using combinatorial optimization and commercially available low-input miRNA library preparation kits.

Conclusions: Overall, our work provides the basis for unbiased high-throughput quantification of miRNAs. These data will help achieve optimal design involving miRNA profiling and provide reliable guidance for clinical diagnosis and treatment by significantly increasing the credibility of potential biomarkers.

Graphical abstract

Keywords

microRNA / next-generation sequencing / library preparation / bias

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