{
  "abstract": "Comprehensively characterizing cellular diversity is critical for understanding tissue development, identifying disease-associated populations and investigating gene programmes linked to specialized functions. Advances in single-cell profiling reveal additional cell-specific transcripts and molecular associations, yet many foundation models represent only a restricted portion of this increasingly rich molecular information. Here we present CellNet, which enables panoramic modelling of single-cell transcriptomes by integrating DNA-derived gene identities and measured RNA expression in a shared image representation. Pretrained on 231 million cell profiles spanning 40 species, CellNet improves rare-population and condition-associated state recognition in evaluated cohorts while retaining competitive general cell representations. It predicts withheld coding-gene expression and selected non-coding targets in total-RNA data, and recovers lineage-specific expression patterns in spatial measurements. Prediction also extends across species, including to targets without expression-pretraining exposure. Its gene representations retrieve known non-coding modules and developmental or neural programmes, including from sparsely detected RNA queries. Together, these results establish CellNet as a scalable framework for interpreting cellular diversity through increasingly comprehensive transcriptomic measurements.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.21943",
  "author_total": 8,
  "authors": [
    {
      "name": "Xiaoqing Lian"
    },
    {
      "name": "Pei Liu"
    },
    {
      "name": "Jiahui Tan"
    },
    {
      "name": "Luoda Tan"
    },
    {
      "name": "Chaoyi Li"
    },
    {
      "name": "Quan Zou"
    },
    {
      "name": "Chen Lin"
    },
    {
      "name": "Xiangxiang Zeng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/az9wv07qeyqph.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.21943",
  "fulltext_url": null,
  "id": "az9wv07qeyqph",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.21943",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "q-bio.GN"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7222487580",
  "pdf_url": "https://arxiv.org/pdf/2610.21943",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/az9wv07qeyqph.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:55Z",
      "native_id": "2610.21943",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.21943"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.GN",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "Panoramic modelling of single-cell transcriptomes at scale with CellNet",
  "title_text": "Panoramic modelling of single-cell transcriptomes at scale with CellNet",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:29Z",
  "url": "https://research.draconitic.com/paper/az9wv07qeyqph",
  "venue": "arXiv",
  "versions": []
}