{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T18:07:09Z","timestamp":1780596429693,"version":"3.54.1"},"reference-count":54,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T00:00:00Z","timestamp":1780531200000},"content-version":"vor","delay-in-days":34,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Major Project of Guangzhou National Laboratory","award":["GZNL2024A03006"],"award-info":[{"award-number":["GZNL2024A03006"]}]},{"name":"TianYuan funds for Mathematics of the National Science Foundation of China","award":["12326604"],"award-info":[{"award-number":["12326604"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Tracing the tissue and cell-type origins of extracellular vesicles (EVs) in blood is critical for liquid biopsy and precision medicine, yet existing deconvolution methods remain limited by the need for labor-intensive reference signatures and poor adaptability to distribution shifts between tissue\/cell-type datasets and EV transcriptomes. We introduce DADA-EV (Domain-Adaptive Diffusion Autoencoder for EVs), a hybrid deep learning framework that combines an autoencoder backbone with a generative simulation module and adversarial domain adaptation. DADA-EV features three key innovations: (1) a reference-free design that eliminates reliance on predefined signatures; (2) cross-domain generalization by aligning feature distributions between source (tissue\/cell-type) and target (EV) domain; and (3) reduced dependence on source data during target-domain training. Extensive evaluations on pseudo-EV data show that DADA-EV consistently outperforms existing approaches, yielding accurate fraction estimates across diverse tissues and gene sets. Validation using in vitro cell-line mixtures further confirms its reliability in resolving complex compositions, demonstrating high sensitivity in detecting low-abundance targets. Applied to real EV transcriptomes, it reveals tissue- and cell-type heterogeneity across patient groups. In summary, DADA-EV provides a robust, reference-free, and generalizable solution for EV origin tracing, with strong potential to advance diagnosis, prognosis, and treatment monitoring via liquid biopsy.<\/jats:p>","DOI":"10.1093\/bib\/bbag281","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T12:05:43Z","timestamp":1778760343000},"source":"Crossref","is-referenced-by-count":0,"title":["DADA-EV: domain-adaptive diffusion autoencoder for estimating tissue- and cell-type-specific origin in extracellular vesicle transcriptomes"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7067-2078","authenticated-orcid":false,"given":"Shuilin","family":"Liao","sequence":"first","affiliation":[{"name":"Faculty of Innovation Engineering, Macau University of Science and Technology , Avenida Wai Long, Taipa, Macau 999078 ,","place":["China"]},{"name":"Guangzhou National Laboratory , No. 9 XingDaoHuanBei Road , , Guangzhou, Guangdong 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,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiwen","family":"Li","sequence":"additional","affiliation":[{"name":"Guangzhou Exope Medical Biotechnology Co., Ltd ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Zhu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University , Guangzhou Medical University, Guangzhou, Guangdong 510120 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Le","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Innovation Engineering, Macau University of Science and Technology , Avenida Wai Long, Taipa, Macau 999078 ,","place":["China"]},{"name":"Basic Teaching Department, Zhuhai Campus of Zunyi Medical University , No. 368, Jinwan Road, Jinwan District, Zhuhai, Guangdong Province 519000 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