{
  "abstract": "Natural killer (NK) cells adapt their cellular states to diverse tissue and inflammatory environments, yet how this plasticity manifests across pathological contexts and relates to patient-level disease phenotypes remains poorly defined. Here we established the Human NK-cell Atlas (HNKA) from over 1 million NK cells, including samples from 1,632 donors spanning 50 diseases. Within a shared NK-cell state architecture, analyses of state abundance and matched-reference exceedance distinguished disease-biased redistribution from transcriptional displacement. Fine-grained analyses localized disease-associated remodeling to specific NK-cell states, including CD56dim NK ISG15 in infection and autoimmunity and CD56bright NK ITM2C in tumors. These patterns were accompanied by recurrent interferon responses and state-specific regulatory activity. Cytokine perturbations recapitulated key transcriptional features of disease-associated NK-cell states. We developed NKceiver, a model based on state-specific NK-cell expression profiles for patient-level prediction. It distinguished 18 diseases and predicted COVID-19 severity and response to immune checkpoint inhibitors, with predictive performance retained in unseen patients and studies. Collectively, our results reveal that disease-associated remodeling within the diverse NK-cell state repertoire resolved by HNKA is biologically interpretable and clinically informative, supporting disease discrimination and prediction of clinical phenotypes.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China; State Key Laboratory of Vascu"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "F. Meng"
    },
    {
      "name": "P. Lin"
    },
    {
      "name": "S. Zhang"
    },
    {
      "name": "T. Li"
    },
    {
      "name": "Z. Liu"
    },
    {
      "name": "P. Ye"
    },
    {
      "name": "C. Zhong"
    },
    {
      "affs": [
        0
      ],
      "name": "Y. Zhou"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7cr1vbqkjbvsq.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.22.760478",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.22.760478v1.full",
  "id": "7cr1vbqkjbvsq",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.22.760478v1",
  "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",
      "immunology"
    ],
    [
      "openalex",
      "2403|Immune Cell Function and Interaction"
    ]
  ],
  "openalex": "W7223967411",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.22.760478v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7cr1vbqkjbvsq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:53Z",
      "native_id": "10.64898/2026.10.22.760478",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.22.760478v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:immunology",
      "subfield": "immunology"
    }
  ],
  "title": "Disease-associated NK-cell state remodeling reveals transcriptional profiles predictive of clinical phenotypes",
  "title_text": "Disease-associated NK-cell state remodeling reveals transcriptional profiles predictive of clinical phenotypes",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:53Z",
  "url": "https://research.draconitic.com/paper/7cr1vbqkjbvsq",
  "venue": "bioRxiv",
  "versions": []
}