{
  "abstract": "The human kidney contains highly specialized cell populations. Despite numerous single-cell and single-nucleus transcriptomics studies, differences in cohorts, technologies, analytical pipelines, and annotation frameworks have limited the ability to define consensus kidney cell states, identify disease-associated populations and interpret kidney disease genetic susceptibility. Here, we assembled 18 human kidney single-cell and single-nucleus RNA-sequencing datasets spanning 232 donors and five major disease contexts into a uniformly processed and computationally integrated Human Kidney Cell Atlas (HKCA), comprising over one million high-quality cells (816,895) and nuclei (215,308). The HKCA resolves 63 cell types and 120 harmonized cell states, including rare epithelial and stromal populations associated with kidney diseases . Integration with spatial transcriptomics, intercellular communication networks, and human genetic association data further defined the anatomical context and disease relevance of these populations. The HKCA also provides a framework for automated annotation of independent kidney human and mouse datasets. Together, the HKCA establishes a comprehensive reference for human kidney biology, enabling disease interpretation and genetic risk localization at cellular resolution.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Pathology, ImmunoX, University of California, San Francisco, CA, USA"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 24,
  "authors": [
    {
      "name": "K. Stasinos"
    },
    {
      "name": "H. Wang"
    },
    {
      "name": "A. Predeus"
    },
    {
      "name": "N. Richoz"
    },
    {
      "name": "R. Menon"
    },
    {
      "name": "M. Thokadiwala"
    },
    {
      "name": "Y. Zhu"
    },
    {
      "name": "R. Tian"
    },
    {
      "name": "W. Zhou"
    },
    {
      "name": "A. Chatzigeorgiou"
    },
    {
      "name": "G. Yordanova"
    },
    {
      "name": "I. Zucchi"
    },
    {
      "name": "Z. Laszik"
    },
    {
      "name": "M. Müller"
    },
    {
      "name": "The Human Cell Atlas Kidney Bionetwork"
    },
    {
      "name": "A. Subramanian"
    },
    {
      "name": "A. Greka"
    },
    {
      "name": "A. Regev"
    },
    {
      "name": "M. Kretzler"
    },
    {
      "name": "J. Marioni"
    },
    {
      "name": "M. D. Luecken"
    },
    {
      "name": "M. Clatworthy"
    },
    {
      "name": "S. A. Teichmann"
    },
    {
      "affs": [
        0
      ],
      "name": "P. He"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cj5pjy29ar48h.bib",
  "cited_by": 1,
  "doi": "10.64898/2026.08.12.744548",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.08.12.744548v3.full",
  "id": "cj5pjy29ar48h",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.08.12.744548v3",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "genomics"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7203529436",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.08.12.744548v3.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-12",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cj5pjy29ar48h.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:40Z",
      "native_id": "10.64898/2026.08.12.744548",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.08.12.744548v3"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:genomics",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "An Integrated Atlas of the Human Kidney Spanning Health and Diseases",
  "title_text": "An Integrated Atlas of the Human Kidney Spanning Health and Diseases",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:40Z",
  "url": "https://research.draconitic.com/paper/cj5pjy29ar48h",
  "venue": "bioRxiv",
  "versions": []
}