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Related Concept Videos

MicroRNAs01:22

MicroRNAs

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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
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MicroRNAs01:22

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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns—non-coding regions of a gene—or intergenic regions—stretches of DNA present between genes. Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After...
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Biotin-based Pulldown Assay to Validate mRNA Targets of Cellular miRNAs
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miRNA activity inferred from single cell mRNA expression.

Morten Muhlig Nielsen1,2, Jakob Skou Pedersen3,4,5

  • 1Department of Molecular Medicine (MOMA), Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200, Aarhus N, Denmark.

Scientific Reports
|April 29, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method to estimate microRNA (miRNA) activity from single-cell RNA sequencing (scRNAseq) data. The miReact software enables researchers to analyze miRNA dynamics at the single-cell level, advancing biological insights.

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Area of Science:

  • Computational Biology
  • Genomics
  • Molecular Biology

Background:

  • High-throughput single-cell RNA sequencing (scRNAseq) provides valuable mRNA expression data for thousands of cells.
  • Current scRNAseq technologies cannot directly measure microRNA (miRNA) expression at the same scale.
  • miRNAs regulate gene expression by binding to specific sequence motifs, typically leading to gene down-regulation.

Purpose of the Study:

  • To develop a method for estimating miRNA activity from scRNAseq data.
  • To enable the analysis of miRNA dynamics at the single-cell level.
  • To provide a tool for researchers applying scRNAseq across various biological fields.

Main Methods:

  • Utilized motif enrichment analysis, traditionally used for identifying regulatory factor binding sites, in a reversed manner.
  • Derived miRNA activity estimates by analyzing miRNA seed site enrichment in relation to target gene expression.
  • Validated the approach on bulk TCGA cancer samples and subsequently applied it to human and mouse scRNAseq datasets.
  • Implemented the methodology in the publicly available miReact software.

Main Results:

  • Established a robust correlation between measured miRNA expression and derived activity in bulk cancer samples.
  • Demonstrated the method's efficacy in handling sparse data comparable to scRNAseq experiments through downsampling.
  • Successfully generated miRNA activity measures for known tissue-specific miRNAs (e.g., miR-122, miR-1, miR-133a) in scRNAseq data, supported by existing literature.
  • Confirmed that miRNA activities can be reliably estimated at the single-cell level.

Conclusions:

  • The developed method successfully estimates miRNA activity from scRNAseq data, overcoming previous technological limitations.
  • miReact software provides a valuable tool for single-cell miRNA activity analysis.
  • This advancement opens new avenues for understanding miRNA-mediated gene regulation dynamics in diverse biological contexts studied by scRNAseq.