miRNA activity inferred from single cell mRNA expression

Abstract High throughput single-cell RNA sequencing (scRNAseq) can provide mRNA expression profiles for thousands of cells. However, miRNAs cannot currently be studied at the same scale. By exploiting that miRNAs bind well-defined sequence motifs and typically down-regulate target genes, we show tha...

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Autores principales:	Morten Muhlig Nielsen, Jakob Skou Pedersen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/72cae6fbba4641afbfee8eb283e5e781
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spelling	oai:doaj.org-article:72cae6fbba4641afbfee8eb283e5e7812021-12-02T16:55:45ZmiRNA activity inferred from single cell mRNA expression10.1038/s41598-021-88480-52045-2322https://doaj.org/article/72cae6fbba4641afbfee8eb283e5e7812021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88480-5https://doaj.org/toc/2045-2322Abstract High throughput single-cell RNA sequencing (scRNAseq) can provide mRNA expression profiles for thousands of cells. However, miRNAs cannot currently be studied at the same scale. By exploiting that miRNAs bind well-defined sequence motifs and typically down-regulate target genes, we show that motif enrichment analysis can be used to derive miRNA activity estimates from scRNAseq data. Motif enrichment analyses have traditionally been used to derive binding motifs for regulatory factors, such as miRNAs or transcription factors, that have an effect on gene expression. Here we reverse its use. By starting from the miRNA seed site, we derive a measure of activity for miRNAs in single cells. We first establish the approach on a comprehensive set of bulk TCGA cancer samples (n = 9679), with paired mRNA and miRNA expression profiles, where many miRNAs show a strong correlation with measured expression. By downsampling we show that the method can be used to estimate miRNA activity in sparse data comparable to scRNAseq experiments. We then analyze a human and a mouse scRNAseq data set, and show that for several miRNA candidates, including liver specific miR-122 and muscle specific miR-1 and miR-133a, we obtain activity measures supported by the literature. The methods are implemented and made available in the miReact software. Our results demonstrate that miRNA activities can be estimated at the single cell level. This allows insights into the dynamics of miRNA activity across a range of fields where scRNAseq is applied.Morten Muhlig NielsenJakob Skou PedersenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Morten Muhlig Nielsen Jakob Skou Pedersen miRNA activity inferred from single cell mRNA expression
description	Abstract High throughput single-cell RNA sequencing (scRNAseq) can provide mRNA expression profiles for thousands of cells. However, miRNAs cannot currently be studied at the same scale. By exploiting that miRNAs bind well-defined sequence motifs and typically down-regulate target genes, we show that motif enrichment analysis can be used to derive miRNA activity estimates from scRNAseq data. Motif enrichment analyses have traditionally been used to derive binding motifs for regulatory factors, such as miRNAs or transcription factors, that have an effect on gene expression. Here we reverse its use. By starting from the miRNA seed site, we derive a measure of activity for miRNAs in single cells. We first establish the approach on a comprehensive set of bulk TCGA cancer samples (n = 9679), with paired mRNA and miRNA expression profiles, where many miRNAs show a strong correlation with measured expression. By downsampling we show that the method can be used to estimate miRNA activity in sparse data comparable to scRNAseq experiments. We then analyze a human and a mouse scRNAseq data set, and show that for several miRNA candidates, including liver specific miR-122 and muscle specific miR-1 and miR-133a, we obtain activity measures supported by the literature. The methods are implemented and made available in the miReact software. Our results demonstrate that miRNA activities can be estimated at the single cell level. This allows insights into the dynamics of miRNA activity across a range of fields where scRNAseq is applied.
format	article
author	Morten Muhlig Nielsen Jakob Skou Pedersen
author_facet	Morten Muhlig Nielsen Jakob Skou Pedersen
author_sort	Morten Muhlig Nielsen
title	miRNA activity inferred from single cell mRNA expression
title_short	miRNA activity inferred from single cell mRNA expression
title_full	miRNA activity inferred from single cell mRNA expression
title_fullStr	miRNA activity inferred from single cell mRNA expression
title_full_unstemmed	miRNA activity inferred from single cell mRNA expression
title_sort	mirna activity inferred from single cell mrna expression
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/72cae6fbba4641afbfee8eb283e5e781
work_keys_str_mv	AT mortenmuhlignielsen mirnaactivityinferredfromsinglecellmrnaexpression AT jakobskoupedersen mirnaactivityinferredfromsinglecellmrnaexpression
_version_	1718382805789966336

miRNA activity inferred from single cell mRNA expression

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