{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T14:14:25Z","timestamp":1787062465915,"version":"build-2736575974"},"posted":{"date-parts":[[2026,6,4]]},"group-title":"Bioinformatics","reference-count":190,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T00:00:00Z","timestamp":1780531200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Spanish Association Against Cancer","award":["PI049999"],"award-info":[{"award-number":["PI049999"]}]},{"DOI":"10.13039\/501100002347","name":"Federal Ministry of Research, Technology and Space","doi-asserted-by":"crossref","award":["001001KT2322"],"award-info":[{"award-number":["001001KT2322"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Cancer Institute","award":["R35 CA263816"],"award-info":[{"award-number":["R35 CA263816"]}]},{"name":"National Cancer Institute","award":["U24 CA213274"],"award-info":[{"award-number":["U24 CA213274"]}]},{"name":"National Cancer Institute","award":["P30 CA008748"],"award-info":[{"award-number":["P30 CA008748"]}]},{"DOI":"10.13039\/501100004504","name":"Research Council of Lithuania","doi-asserted-by":"crossref","award":["P-MIP-24-93"],"award-info":[{"award-number":["P-MIP-24-93"]}],"id":[{"id":"10.13039\/501100004504","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,6,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Accurate immune cell classification is essential for interpreting single-cell RNA sequencing (scRNA-seq) data. However, progress in automating cell type annotation is constrained by the lack of independent, high-resolution benchmarks, as routine data integration introduces statistical dependencies that inflate model generalizability. Here, we present the single-cell universal classification omnibus (Suco), a resource of independent, uniform expert annotations, and Compocyte, a modular hierarchical classifier. Together, they establish a framework that substantially outperforms existing classifiers while facilitating expert review of ambiguous annotations. Applying Compocyte across 50 studies, including three newly generated datasets, we classified 15.6 million leukocytes from 3,965 patients. Within this cohort, we identified a new tumor-associated resorptive macrophage phenotype, a non-canonical monocyte subtype in subclinical cytokine release syndrome, and the programmatic erosion of T cell memory stemness across metastatic sites. Suco and Compocyte thus provide a generalizable framework to uncover the principles governing human immunity at population scale.<\/jats:p>\n                <jats:sec>\n                  <jats:title>Abstract Figure<\/jats:title>\n                  <jats:fig id=\"ufig1\" position=\"float\" orientation=\"portrait\" fig-type=\"figure\">\n                    <jats:graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"728980v2_ufig1\" position=\"float\" orientation=\"portrait\"\/>\n                  <\/jats:fig>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>In brief<\/jats:title>\n                  <jats:p>The single-cell universal classification omnibus and the modular hierarchical classifier Compocyte enable annotating single cell RNA sequencing data from 3,965 patients, revealing novel resorptive macrophage and vaccination-associated monocyte states, alongside the erosion of T cell memory stemness as a hallmark of solid tumor metastases.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Highlights<\/jats:title>\n                  <jats:list list-type=\"bullet\">\n                    <jats:list-item>\n                      <jats:p>Suco, a benchmark enabling novel single cell artificial intelligence models<\/jats:p>\n                    <\/jats:list-item>\n                    <jats:list-item>\n                      <jats:p>Compocyte, a hierarchical cell type classifier outperforming current architectures<\/jats:p>\n                    <\/jats:list-item>\n                    <jats:list-item>\n                      <jats:p>Macrophages adopt osteoclast-like gene expression states across cancer types<\/jats:p>\n                    <\/jats:list-item>\n                    <jats:list-item>\n                      <jats:p>Stem-like programs erode in metastasis-infiltrating T memory cells across tumors<\/jats:p>\n                    <\/jats:list-item>\n                  <\/jats:list>\n                <\/jats:sec>","DOI":"10.64898\/2026.05.30.728980","type":"posted-content","created":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T18:00:16Z","timestamp":1780596016000},"source":"Crossref","is-referenced-by-count":1,"title":["Hierarchical classification of immune cell transcriptomes at population-scale"],"prefix":"10.64898","author":[{"given":"Christopher","family":"Beltz","sequence":"first","affiliation":[{"name":"Department of Radiooncology and Radiotherapy, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Germany"},{"name":"German Cancer Research Center, Clinical Cooperation Unit Molecular Radiooncology, Heidelberg, Germany"},{"name":"Department of Medical Oncology, National Center for Tumor Diseases (NCT) & Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Germany"},{"name":"Computational Cancer Biomedicine, Institut d\u2019Investigacions Biom\u00e8diques August Pi i Sunyer (IDIBAPS), Hospital Cl\u00ednic, University of Barcelona, Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zekai","family":"Qiu","sequence":"additional","affiliation":[{"name":"Department of Radiooncology and Radiotherapy, Heidelberg University Hospital, Medical Faculty Heidelberg, Heidelberg University, Germany"},{"name":"German Cancer Research Center, Clinical Cooperation Unit Molecular Radiooncology, Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leon","family":"Sadowski","sequence":"additional","affiliation":[{"name":"German Cancer Research Center, Clinical Cooperation Unit Molecular Radiooncology, Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8567-6020","authenticated-orcid":false,"given":"Joscha A.","family":"Kraske","sequence":"additional","affiliation":[{"name":"German Cancer Research Center, Clinical Cooperation Unit Molecular 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