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

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 sing...

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Main Authors: Shaoying Zhu, Hui Yang, Jun Liu, Qingsheng Fu, Wei Huang, Qi Chen, Andrew E. Teschendorff, Yungang He, Zhen Yang
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-025-60521-x
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author Shaoying Zhu
Hui Yang
Jun Liu
Qingsheng Fu
Wei Huang
Qi Chen
Andrew E. Teschendorff
Yungang He
Zhen Yang
author_facet Shaoying Zhu
Hui Yang
Jun Liu
Qingsheng Fu
Wei Huang
Qi Chen
Andrew E. Teschendorff
Yungang He
Zhen Yang
author_sort Shaoying Zhu
collection DOAJ
description 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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issn 2041-1723
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publishDate 2025-07-01
publisher Nature Portfolio
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series Nature Communications
spelling doaj-art-e3afce246b804f9cad479dca1e812a5d2025-08-20T03:45:34ZengNature PortfolioNature Communications2041-17232025-07-0116111610.1038/s41467-025-60521-xAn improved reference library and method for accurate cell-type deconvolution of bulk-tissue miRNA dataShaoying Zhu0Hui Yang1Jun Liu2Qingsheng Fu3Wei Huang4Qi Chen5Andrew E. Teschendorff6Yungang He7Zhen Yang8Center for Medical Research and Innovation of Pudong Hospital, Fudan University Pudong Medical Center, and Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism (Ministry of Science and Technology), Institutes of Biomedical Sciences, Fudan UniversityAnhui Province Key Laboratory of Non-coding RNA Basic and Clinical Transformation, Tissue Bank, Central Laboratory, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College)Department of Gastrointestinal Surgery, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College)Department of Gastrointestinal Surgery, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College)Anhui Province Key Laboratory of Non-coding RNA Basic and Clinical Transformation, Tissue Bank, Central Laboratory, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College)Anhui Province Key Laboratory of Non-coding RNA Basic and Clinical Transformation, Tissue Bank, Central Laboratory, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College)CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of SciencesShanghai Fifth People’s Hospital, Fudan UniversityCenter for Medical Research and Innovation of Pudong Hospital, Fudan University Pudong Medical Center, and Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism (Ministry of Science and Technology), Institutes of Biomedical Sciences, Fudan UniversityAbstract 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.https://doi.org/10.1038/s41467-025-60521-x
spellingShingle Shaoying Zhu
Hui Yang
Jun Liu
Qingsheng Fu
Wei Huang
Qi Chen
Andrew E. Teschendorff
Yungang He
Zhen Yang
An improved reference library and method for accurate cell-type deconvolution of bulk-tissue miRNA data
Nature Communications
title An improved reference library and method for accurate cell-type deconvolution of bulk-tissue miRNA data
title_full An improved reference library and method for accurate cell-type deconvolution of bulk-tissue miRNA data
title_fullStr An improved reference library and method for accurate cell-type deconvolution of bulk-tissue miRNA data
title_full_unstemmed An improved reference library and method for accurate cell-type deconvolution of bulk-tissue miRNA data
title_short An improved reference library and method for accurate cell-type deconvolution of bulk-tissue miRNA data
title_sort improved reference library and method for accurate cell type deconvolution of bulk tissue mirna data
url https://doi.org/10.1038/s41467-025-60521-x
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