{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T18:21:31Z","timestamp":1785522091538,"version":"3.56.0"},"posted":{"date-parts":[[2026,4,28]]},"group-title":"Immunology","reference-count":23,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T00:00:00Z","timestamp":1777334400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,7,28]]},"abstract":"<jats:title>\n                  <jats:bold>Abstract<\/jats:bold>\n                <\/jats:title>\n                <jats:p>\n                  Accurate cell annotation remains a central challenge in single-cell analysis, particularly when datasets contain rare populations, transitional states, and tissue-adapted phenotypes. We previously developed scODIN as an expert-guided framework for immune cell annotation in single-cell RNA-sequencing data. Here, we present pyODIN, a major extension of this framework for Python. pyODIN supports multimodal annotation using RNA, antibody-derived tag (ADT), or combined RNA-ADT information. It also substantially expands the annotation database from a CD4 T-cell-centred framework to a broader cell-type reference resource spanning major and minor peripheral blood cell populations as well as tissue-associated subsets. We benchmarked pyODIN against CellTypist, MMoCHi and HiCAT using independently curated immune reference datasets. pyODIN demonstrated the highest overall classification accuracy, and at the top-lineage level, pyODIN reduced cross-lineage errors relative to the benchmark methods. At finer resolution, pyODIN preserved substantially greater CD4 T-cell subtype structure than CellTypist and HiCAT, resolving regulatory, follicular, helper, memory, and cytotoxic states that were collapsed into broader categories by the comparator methods. In a controlled marker-ablation experiment, pyODIN outperformed MMoCHi when key lineage-defining RNA markers were removed from the expression feature set, and the addition of ADT information restored NK and CD8 T-cell annotation, demonstrating the value of multimodal annotation when transcript-level evidence is incomplete. Finally, application to liver fine-needle aspirate data showed that the expanded framework supports annotation beyond PBMC datasets. Together, pyODIN provides an expert-guided, adaptable cell annotation framework for Python, designed for multimodal cell phenotyping across blood and tissue single-cell datasets. pyODIN is freely available at\n                  <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/SondergaardLab\/pyODIN\">https:\/\/github.com\/SondergaardLab\/pyODIN<\/jats:ext-link>\n                  .\n                <\/jats:p>","DOI":"10.64898\/2026.04.24.717187","type":"posted-content","created":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T22:00:12Z","timestamp":1777413612000},"source":"Crossref","is-referenced-by-count":0,"title":["Accurate and memory-efficient cell type annotation from multimodal single-cell RNA and protein data"],"prefix":"10.64898","author":[{"given":"Siddharth S.","family":"Tomar","sequence":"first","affiliation":[{"name":"Univ. Lille, Inserm, CHU Lille, Institut Pasteur Lille, U1011- EGID, F-59000 Lille, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joel T.","family":"Haas","sequence":"additional","affiliation":[{"name":"Univ. Lille, Inserm, CHU Lille, Institut Pasteur Lille, U1011- EGID, F-59000 Lille, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bart","family":"Staels","sequence":"additional","affiliation":[{"name":"Univ. Lille, Inserm, CHU Lille, Institut Pasteur Lille, U1011- EGID, F-59000 Lille, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Dombrowicz","sequence":"additional","affiliation":[{"name":"Univ. Lille, Inserm, CHU Lille, Institut Pasteur Lille, U1011- EGID, F-59000 Lille, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4438-6756","authenticated-orcid":false,"given":"Jonas N\u00f8rskov","family":"S\u00f8ndergaard","sequence":"additional","affiliation":[{"name":"Univ. Lille, Inserm, CHU Lille, Institut Pasteur Lille, U1011- EGID, F-59000 Lille, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026073110100617000_2026.04.24.717187v2.1","doi-asserted-by":"publisher","DOI":"10.1038\/nri.2017.76"},{"key":"2026073110100617000_2026.04.24.717187v2.2","doi-asserted-by":"publisher","DOI":"10.1038\/nature20105"},{"issue":"9","key":"2026073110100617000_2026.04.24.717187v2.3","doi-asserted-by":"crossref","first-page":"1596","DOI":"10.1038\/s41590-025-02255-y","article-title":"Single-cell atlas of human liver and blood immune cells across fatty liver disease stages reveals distinct signatures linked to liver dysfunction and fibrogenesis","volume":"26","year":"2025","journal-title":"Nat Immunol"},{"key":"2026073110100617000_2026.04.24.717187v2.4","doi-asserted-by":"publisher","DOI":"10.1038\/s41576-018-0088-9"},{"key":"2026073110100617000_2026.04.24.717187v2.5","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.2967"},{"key":"2026073110100617000_2026.04.24.717187v2.6","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.4380"},{"key":"2026073110100617000_2026.04.24.717187v2.7","doi-asserted-by":"publisher","DOI":"10.1016\/J.CELL.2021.04.048"},{"key":"2026073110100617000_2026.04.24.717187v2.8","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-024-03183-0"},{"key":"2026073110100617000_2026.04.24.717187v2.9","doi-asserted-by":"publisher","DOI":"10.1016\/j.immuni.2018.02.010"},{"issue":"11","key":"2026073110100617000_2026.04.24.717187v2.10","doi-asserted-by":"crossref","first-page":"3131","DOI":"10.1093\/jimmun\/vkaf183","article-title":"Optimized detection and inference of immune cell type names in single-cell RNA sequencing data","volume":"214","year":"2025","journal-title":"J Immunol"},{"issue":"8094","key":"2026073110100617000_2026.04.24.717187v2.11","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1038\/s41586-025-09686-5","article-title":"Multi-omic profiling reveals age-related immune dynamics in healthy adults","volume":"648","year":"2025","journal-title":"Nature"},{"key":"2026073110100617000_2026.04.24.717187v2.12","doi-asserted-by":"crossref","unstructured":"Villani AC , Satija R , Reynolds G et al. 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