Details

Autor(en) / Beteiligte

• 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

Titel

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

Ist Teil von

• Nature communications, 2024-03, Vol.15 (1), p.2220-2220, Article 2220

Ort / Verlag

London: Nature Publishing Group UK

Erscheinungsjahr

2024

Quelle

Electronic Journals Library

Beschreibungen/Notizen

• 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.