{
  "abstract": "Single-cell multi-omics technologies simultaneously capture the transcriptome and TCR sequence of T cells, providing an opportunity to study the relationship between transcriptional states and clonal architectures. However, jointly modeling the relationships between transcriptional states and TCR sequences while preserving modality-specific information remains challenging. Here, we present ImmuneLens, an interpretable multimodal representation learning framework designed for paired single-cell transcriptome and TCR sequence data. ImmuneLens supports the construction of a transferable multi-cohort immune reference atlas and enables unsupervised mapping of external query data. The complementarity between GEX and TCR information improves the stability of antigen-specificity prediction. In neoadjuvant immunotherapy cohorts, ImmuneLens resolves response-associated T cell heterogeneity and reveals links between clonal expansion and CD8 T cell functional states. Overall, ImmuneLens provides a",
  "abstract_shown": true,
  "affiliations": [
    "Beijing Institute of Genomics, Chinese Academy of Sciences/ China National Center for Bioinformation"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "Z. Duan"
    },
    {
      "name": "Y. Wang"
    },
    {
      "name": "C. Li"
    },
    {
      "name": "G. Li"
    },
    {
      "name": "Y. Cao"
    },
    {
      "name": "X. Bai"
    },
    {
      "name": "F. Yang"
    },
    {
      "affs": [
        0
      ],
      "name": "S. Song"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b0kxdqagrcf7t.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.08.749998",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.08.749998v1.full",
  "id": "b0kxdqagrcf7t",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.08.749998v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7212724268",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.08.749998v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/b0kxdqagrcf7t.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:13Z",
      "native_id": "10.64898/2026.09.08.749998",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.08.749998v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "ImmuneLens: linking transcriptional states and TCR clonotypes through disentangled multimodal learning",
  "title_text": "ImmuneLens: linking transcriptional states and TCR clonotypes through disentangled multimodal learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:13Z",
  "url": "https://research.draconitic.com/paper/b0kxdqagrcf7t",
  "venue": "bioRxiv",
  "versions": []
}