ArticleScientific reports2021
miRNA activity inferred from single cell mRNA expression.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Differentially Expressed miRNAs in Ulcerative Colitis and Crohn's Disease.Frontiers in immunology · 2022Pooled it
- miRSiC: a regulatory-aware machine learning framework for microRNA expression inference across bulk and single-cell transcriptomes.Briefings in bioinformatics · 2026Article
- Target-site dynamics explain a large share of apparent microRNA differential expression.RNA (New York, N.Y.) · 2026Article
- Construction of a Computationally Inferred Single-Cell miRNA Activity Atlas and Analysis of Regulatory Networks in Spinal Cord Injury.Journal of molecular neuroscience : MN · 2026Article
- [Advances in the role of miR-378a in skeletal muscle development and diseases].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2026Review
- bayesReact: expression-coupled regulatory motif analysis detects microRNA activity across cancers, tissues, and at the single-cell level.Nucleic acids research · 2026Article
- The miR-16-1-3p passenger strand exhibits functional activity and suppresses malignant phenotypes in osteosarcoma.Frontiers in genetics · 2026Article
- Single-nuclei multiomics analysis identifies abnormal cardiomyocytes in a murine model of cardiac development.Nature communications · 2025Article
- Evaluating Genetic Regulators of MicroRNAs Using Machine Learning Models.International journal of molecular sciences · 2025Article
- Inferring single-cell and spatial microRNA activity from transcriptomics data.Communications biology · 2025Article
- Comprehensive genomic characterization of early-stage bladder cancer.Nature genetics · 2025Article
- The miR-941/FOXN4/TGF-β feedback loop induces N2 polarization of neutrophils and enhances tumor progression of lung adenocarcinoma.Frontiers in immunology · 2025Article
- Using deep neural networks and LASSO regression to predict miRNA expression changes based on mRNA data.Frontiers in bioinformatics · 2025Article
- Advances in applications of artificial intelligence algorithms for cancer-related miRNA research.Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2024Review
- Noncoding RNA circuitry in melanoma onset, plasticity, and therapeutic response.Pharmacology & therapeutics · 2023Review
- MicroRNA-27a-3p targets FoxO signalling to induce tumour-like phenotypes in bile duct cells.Journal of hepatology · 2023Article
- Immunomodulation-a general review of the current state-of-the-art and new therapeutic strategies for targeting the immune system.Frontiers in immunology · 2023Review
- High-throughput transcriptomics.Scientific reports · 2022Article
- miRSCAPE - inferring miRNA expression from scRNA-seq data.iScience · 2022Article
- Article
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2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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