ArticleNature communications2025
An improved reference library and method for accurate cell-type deconvolution of bulk-tissue miRNA data.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- miRSiC: a regulatory-aware machine learning framework for microRNA expression inference across bulk and single-cell transcriptomes.Briefings in bioinformatics · 2026Article
- Multi-omics biomarkers in endometrial receptivity: from mechanisms to clinical translation.Journal of translational medicine · 2026Review
- Circulating Extracellular Vesicles in the Pathogenesis of Heart Failure in Patients With Chronic Kidney Disease.Circulation · 2026Article
- Review
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9 authors.
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Abstract
MicroRNAs (miRNAs) play key roles in development and disease, and have great biomarker potential. However, because miRNA expression is highly cell-type specific, identifying miRNA biomarkers from complex tissues is hampered by the underlying cell-type heterogeneity. Due to that current single-cell RNA-Seq protocols are lagging behind for quantification of miRNA expression, and most miRNA profiling samples do not have matched mRNA expression or DNA methylation data for cell-type deconvolution, it is an urgent need to develop computational methods for cell-type proportion estimation of bulk-tissue miRNA data. Here we present a novel miRNA expression reference library and deconvolution tool for cell-type composition estimation of complex tissues. We show that our tool is accurate and robust for deconvolution in whole blood as well as in different solid tissues. By applying this tool to a range of different biological contexts, we demonstrate its value for screening of age-associated miRNAs, for monitoring the immune landscape in infectious diseases like COVID-19, as well as for identifying cell-type-specific miRNA biomarkers for early diagnosis and prognosis of human cancers. Our work establishes a computational framework for accurate cell-type mixture deconvolution of miRNA data.
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