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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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| 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. |
| format | Article |
| id | doaj-art-e3afce246b804f9cad479dca1e812a5d |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| 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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