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Updated: Aug 29, 2025

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

Published on: August 21, 2019

miRSCAPE - inferring miRNA expression from scRNA-seq data.

Gulden Olgun¹, Vishaka Gopalan¹, Sridhar Hannenhalli¹

¹Cancer Data Science Lab, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.

|September 5, 2022

Summary

This summary is machine-generated.

A new tool, miRSCAPE, infers microRNA (miRNA) expression from RNA sequencing data. This method accurately reveals cell type-specific miRNA activities, advancing our understanding of miRNA biology at cellular resolution.

Keywords:

Biocomputational method Cancer systems biology Transcriptomics

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Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Standard single-cell RNA sequencing (scRNA-seq) cannot capture microRNAs (miRNAs).
This limitation hinders the study of miRNA activity at cellular resolution.
Understanding miRNA roles in cellular processes requires methods to infer their expression.

Purpose of the Study:

To introduce miRSCAPE, a novel tool for inferring miRNA expression from RNA-seq profiles.
To validate the accuracy and superiority of miRSCAPE compared to existing methods.
To apply miRSCAPE for analyzing cell type-specific miRNA activities in various cancer types and the human cell landscape.

Main Methods:

Development of miRSCAPE, a computational tool to infer miRNA expression from bulk RNA-seq data.
Validation of miRSCAPE accuracy across 10 tumor and normal cohorts.
Assessment of miRSCAPE's performance in inferring cell type-specific miRNA activities using two independent scRNA-seq datasets.
Application of miRSCAPE to analyze miRNA activities in pancreatic and lung adenocarcinomas, and the Human Cell Landscape (HCL).

Main Results:

miRSCAPE demonstrates superior accuracy in inferring miRNA expression compared to alternatives.
The tool accurately predicts cell type-specific miRNA activities with a high correlation (∼0.81) to observed fold-differences in scRNA-seq data.
miRSCAPE successfully recapitulates known miRNA associations with stemness and epithelial-mesenchymal transition (EMT) in cancer cell states.

Conclusions:

miRSCAPE effectively infers miRNA activities at cellular resolution from scRNA-seq data.
The tool enhances the understanding of miRNA biology in complex biological systems, including cancer.
miRSCAPE is freely available and applicable for broad use in single-cell transcriptomics research.