Cell type signatures in cell-free DNA fragmentation profiles reveal disease biology

Circulating cell-free DNA (cfDNA) fragments have characteristics that are specific to the cell types that release them. Current methods for cfDNA deconvolution typically use disease tailored marker selection in a limited number of bulk tissues or cell lines. Here, we utilize single cell transcriptom...

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Bibliographic Details
Published in:	Nature communications Vol. 15; no. 1; p. 2220
Main Authors:	Stanley, Kate E., Jatsenko, Tatjana, Tuveri, Stefania, Sudhakaran, Dhanya, Lannoo, Lore, Van Calsteren, Kristel, de Borre, Marie, Van Parijs, Ilse, Van Coillie, Leen, Van Den Bogaert, Kris, De Almeida Toledo, Rodrigo, Lenaerts, Liesbeth, Tejpar, Sabine, Punie, Kevin, Rengifo, Laura Y., Vandenberghe, Peter, Thienpont, Bernard, Vermeesch, Joris Robert
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 12-03-2024 Nature Publishing Group Nature Portfolio
Subjects:	38/22 38/23 38/39 45 631/114/2415 631/67/2322 692/53 Biology Biomarkers, Tumor - genetics Biopsy Cancer Cell-Free Nucleic Acids Colorectal carcinoma Deconvolution Deoxyribonucleic acid Disease DNA DNA Fragmentation DNA methylation Fragmentation Gene expression Genetics Genomes Hospitals Humanities and Social Sciences Humans Medicin och hälsovetenskap multidisciplinary Multiple myeloma Oncology Placenta Plasma Pre-eclampsia Prediction models Science Science (multidisciplinary) Signatures Transcriptome Transcriptomes
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Summary:	Circulating cell-free DNA (cfDNA) fragments have characteristics that are specific to the cell types that release them. Current methods for cfDNA deconvolution typically use disease tailored marker selection in a limited number of bulk tissues or cell lines. Here, we utilize single cell transcriptome data as a comprehensive cellular reference set for disease-agnostic cfDNA cell-of-origin analysis. We correlate cfDNA-inferred nucleosome spacing with gene expression to rank the relative contribution of over 490 cell types to plasma cfDNA. In 744 healthy individuals and patients, we uncover cell type signatures in support of emerging disease paradigms in oncology and prenatal care. We train predictive models that can differentiate patients with colorectal cancer (84.7%), early-stage breast cancer (90.1%), multiple myeloma (AUC 95.0%), and preeclampsia (88.3%) from matched controls. Importantly, our approach performs well in ultra-low coverage cfDNA datasets and can be readily transferred to diverse clinical settings for the expansion of liquid biopsy. Deconvolution of cfDNA fragmentation benefits from cell type-specific reference data. Here, the authors create a disease agnostic cfDNA cell type of origin analysis and show it can successfully predict cell types of origin from plasma samples.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2041-1723 2041-1723
DOI:	10.1038/s41467-024-46435-0