{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T15:46:39Z","timestamp":1792251999000,"version":"4.4.0"},"reference-count":62,"publisher":"American Association for the Advancement of Science (AAAS)","issue":"14","content-domain":{"domain":["www.science.org"],"crossmark-restriction":true},"short-container-title":["Sci. Adv."],"published-print":{"date-parts":[[2025,4,4]]},"abstract":"<jats:p>\n                    Joint analysis of transcriptomic and T cell receptor (TCR) features at single-cell resolution provides a powerful approach for in-depth T cell immune function research. Here, we introduce a deep learning framework for single\u2013T cell transcriptome and receptor analysis, MIST (Multi-insight for T cell). MIST features three latent spaces: gene expression, TCR, and a joint latent space. Through analyses of antigen-specific T cells, and T cell datasets related to lung cancer immunotherapy and COVID19, we demonstrate MIST\u2019s interpretability and flexibility. MIST easily and accurately resolves cell function and antigen specificity by vectorizing and integrating transcriptome and TCR data of T cells. In addition, using MIST, we identified the heterogeneity of CXCL13\n                    <jats:sup>+<\/jats:sup>\n                    subsets in lung cancer infiltrating CD8\n                    <jats:sup>+<\/jats:sup>\n                    T cells and their association with immunotherapy, providing additional insights into the functional transition of CXCL13\n                    <jats:sup>+<\/jats:sup>\n                    T cells related to anti\u2013PD-1 therapy that were not reported in the original study.\n                  <\/jats:p>","DOI":"10.1126\/sciadv.adr7134","type":"journal-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T13:59:14Z","timestamp":1743775154000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark","source":"Crossref","is-referenced-by-count":7,"title":["MIST: An interpretable and flexible deep learning framework for single\u2013T cell transcriptome and receptor analysis"],"prefix":"10.1126","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7743-3950","authenticated-orcid":true,"given":"Wenpu","family":"Lai","sequence":"first","affiliation":[{"name":"The First Affiliated Hospital, Jinan University, Guangzhou 510632, China."},{"name":"Key Laboratory for Regenerative Medicine of Ministry of Education, Institute of Hematology, School of Medicine, Jinan University, Guangzhou 510632, China."},{"name":"Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou 510632, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0974-4036","authenticated-orcid":true,"given":"Yangqiu","family":"Li","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital, Jinan University, Guangzhou 510632, China."},{"name":"Key Laboratory for Regenerative Medicine of Ministry of Education, Institute of Hematology, School of Medicine, Jinan University, Guangzhou 510632, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1266-3069","authenticated-orcid":true,"given":"Oscar Junhong","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou 510632, China."},{"name":"Key Laboratory of Viral Pathogenesis and Infection Prevention and Control (Jinan University), Ministry of Education, Guangzhou 510632, China."},{"name":"Zhuhai Institute of Jinan University, Jinan University, Zhuhai 519070, China."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aay3224"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41590-022-01129-x"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41576-023-00586-w"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-021-01201-8"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-024-02243-4"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-018-0694-x"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-21043-4"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.adk4670"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41587-021-00989-2"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-01020-3"},{"key":"e_1_3_2_12_2","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1186\/s13059-023-03129-y","article-title":"scNAT: A deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles","volume":"24","author":"Zhu B.","year":"2023","unstructured":"B. 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