An improved reference library and method for accurate cell-type deconvolution of bulk-tissue miRNA data

作者：Shaoying Zhu, Hui Yang, Jun Liu, Qingsheng Fu, Wei Huang, Qi Chen, Andrew E. Teschendorff, Yungang He, Zhen Yang · 发表于：Nature Communications · 年份：2025 · DOI：10.1038/s41467-025-60521-x · 被引用次数：8 · 研究领域：MicroRNA in disease regulation、Single-cell and spatial transcriptomics、RNA Research and Splicing

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. Currently, most miRNA profiling study of complex tissues are based on bulk sample sequencing, among which the cellular heterogeneity may become an important potential confounding factor. Here, the auth...