Exploring microRNA expression and function has led to some important discoveries about the regulation of gene expression in normal biological processes and disease states, and has even recently given rise to unique circulating biomarker signatures. qPCR and microarrays are the traditional techniques used to assess microRNA expression, and they have their strong points – qPCR in particular is a sensitive technique with wide dynamic range that’s available to any lab. But they also have inherent limitations – limitations that, in recent years, are being overcome by using next-generation sequencing.
NGS for microRNA, or miRNA-seq, has a couple key advantages. Unlike qPCR and microarray, you don’t have to already know the sequence of a microRNA that you’re looking for, so novel discovery is possible. Moreover, while specificity is quite good with qPCR and microarray, it’s even better with miRNA-seq – that’s important if you’re looking at members of closely related families like let-7, which may be distinguished from one another by as little as 1–2 bases. For these reasons, miRNA-seq is growing in popularity as a tool to profile microRNA expression, and we here at QIAGEN have been working hard on developing solutions to the most common challenges facing researchers using miRNA-seq. Check out the flyer for our new QIAseq miRNA Library Prep Kit
New to miRNA-seq and would like to know more about how it works and why to use it? Read on!
miRNA-seq: methods and challenges
Like any sequencing experiment, miRNA-seq consists of 2 steps, library preparation and sequencing. To prepare the library, you start with RNA isolation from your sample and then perform adaptor ligation; this adds adaptors to the 3′ and 5′ ends of the miRNAs to help the primers bind for reverse transcription and PCR amplification. Then, you convert to cDNA and amplify in preparation for sequencing. Traditionally, the library is then run on a PAGE gel to select the miRNA fraction by size.
The main challenges of this workflow are threefold: eliminating adapter dimers, overcoming amplification bias that could generate misleading data, and the time-consuming size selection using gel excision. Our new QIAseq miRNA Library Kit addresses each of these challenges: in short, proprietary chemistry and bead-based cleanup gets rid of adapter dimers and eliminates the need for gel size selection, and Unique Molecular Index technology ensures that amplification bias doesn’t skew your results. Finally, once your library is ready, you run as usual on your sequencer and analyze your data.
What are we learning by using miRNA-seq?
Recent studies have used miRNA-seq to make exciting discoveries in fields from cardiovascular disease to neurobiology to cancer.
Cardiovascular disease/inflammation: Intermediate monocytes have been identified as more proinflammatory than other two human monocyte subsets, and may be useful as biomarkers for cardiovascular risk. Recently, Zawada et al. used small RNA sequencing to identify miRNA expression specific to this subset, finding 38 differentially expressed miRNAs linked to gene regulation, cell differentiation, Toll-like receptor signaling, and antigen presentation. The team proposed that these findings might be used to develop specific targeting strategies for intermediate monocytes. Link
Neurobiology: Zhao et al. used miRNA-seq to profile miRNA expression in neurons that had been developed from the iPSCs of controls and schizophrenia patients with a particular microdeletion, 22q11.2, which is a known genetic factor for this disorder. The sequencing data, after correction for genome-wide significance, revealed significant downregulation of 6 microRNAs, 4 mapping to the deleted region, and supported previous autopsy and peripheral cell data regarding the upregulation of others. Link
Cancer: Recently, Salem et al. used miRNA-seq data from The Cancer Genome Atlas (TCGA) dataset as a jumping-off point for exploring the functions of the 5’ isoform of miR-140-3p, 5’isomiR-140-3p. The canonical miRNA is a tumor suppressor that regulates breast cancer cell stemness, and the TCGA data showed that the isomiR is more highly expressed in cancer cell lines than its canonical counterpart. Salem et al.’s functional characterization revealed that 5’isomiR-140-3p lowered cell viability, arrested the cell cycle in the G0/G1 phase, and decreased cell migration. Link
As these and other studies make clear, a variety of fields are benefiting from miRNA-seq for regular microRNA expression profiling as well as revealing the expression of miRNA isoforms. As we mentioned above, however, current miRNA-seq methods have a number of previously unanswered challenges in the workflow. How can you overcome these to achieve more efficient, reliable miRNA-seq data?
Stay tuned to learn more! Upcoming posts in January will cover the nature of Unique Molecular Indices and how they differ from sample barcodes, as well as how to avoid off-target reads in miRNA-seq.