Skip to content
DraconiticResearch
Search Sources API
203,203 studies31 sourcesUpdated 24 min ago
PreprintCC BY-NC-NDbioRxiv

An Integrated Atlas of the Human Kidney Spanning Health and Diseases

  • K. Stasinos
  • H. Wang
  • A. Predeus
  • N. Richoz
  • R. Menon
  • M. Thokadiwala
  • Y. Zhu
  • R. Tian
  • W. Zhou
  • A. Chatzigeorgiou
  • G. Yordanova
  • I. Zucchi
12 more authors
  • Z. Laszik
  • M. Müller
  • The Human Cell Atlas Kidney Bionetwork
  • A. Subramanian
  • A. Greka
  • A. Regev
  • M. Kretzler
  • J. Marioni
  • M. D. Luecken
  • M. Clatworthy
  • S. A. Teichmann
  • P. He1
  1. 1Department of Pathology, ImmunoX, University of California, San Francisco, CA, USA
Read full textPDFDOICite

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.

Related

  1. A Community-Driven Single-Cell PBMC Reference Integrating Landmark Datasets Spanning Health and Disease
    A.-M. Cujba, S. Aguilar-Fernandez, A. Antoinette +36
    bioRxivPreprintAll rights reservedFull textImmunologybioRxiv
  2. NeoOmni Atlas: an integrated database for pan-cancer neoantigen derived from proteogenomics analysis
    T. Zheng, Z. Ran, J. Zeng +10
    bioRxivPreprintAll rights reservedFull textBioinformatics & computational biologybioRxiv
  3. An integrated multi-omic atlas of the choroid plexus highlights conserved molecular programs of epithelial function
    E. Dennis, E. Tieu, S. B. Fass +16
    bioRxivPreprintAll rights reservedFull textNeurosciencebioRxiv
  4. Histologic atlas of the Egyptian rousette (Rousettus aegyptiacus) with an integrated review of macroscopic anatomy
    Jessica A. Elbert, Brian R. Amman, Tara K. Sealy +3
    Veterinary ResearchPeer-reviewedCC BY 4.0Full textGeneral biologyHAL
  5. An Integrated Single-Nucleus Atlas Resolves Cell-Type-Specific Programs and Molecular Subtypes in Alzheimer's Disease
    N. Rahimzadeh, S. Morabito, S. Khullar +3
    bioRxivPreprintCC BYFull textNeurosciencebioRxiv

Cite

BibTeXRIS
BibTeX
@misc{stasinos2026integrated,
  title = {{An Integrated Atlas of the Human Kidney Spanning Health and Diseases}},
  author = {K. Stasinos and H. Wang and A. Predeus and N. Richoz and R. Menon and M. Thokadiwala and Y. Zhu and R. Tian and W. Zhou and A. Chatzigeorgiou and G. Yordanova and I. Zucchi and Z. Laszik and M. Müller and The Human Cell Atlas Kidney Bionetwork and A. Subramanian and A. Greka and A. Regev and M. Kretzler and J. Marioni and M. D. Luecken and M. Clatworthy and S. A. Teichmann and P. He},
  howpublished = {bioRxiv},
  year = {2026},
  month = aug,
  doi = {10.64898/2026.08.12.744548},
  url = {https://www.biorxiv.org/content/10.64898/2026.08.12.744548v3},
  note = {CC BY-NC-ND},
}
RIS
TY  - UNPB
TI  - An Integrated Atlas of the Human Kidney Spanning Health and Diseases
AU  - K. Stasinos
AU  - H. Wang
AU  - A. Predeus
AU  - N. Richoz
AU  - R. Menon
AU  - M. Thokadiwala
AU  - Y. Zhu
AU  - R. Tian
AU  - W. Zhou
AU  - A. Chatzigeorgiou
AU  - G. Yordanova
AU  - I. Zucchi
AU  - Z. Laszik
AU  - M. Müller
AU  - The Human Cell Atlas Kidney Bionetwork
AU  - A. Subramanian
AU  - A. Greka
AU  - A. Regev
AU  - M. Kretzler
AU  - J. Marioni
AU  - M. D. Luecken
AU  - M. Clatworthy
AU  - S. A. Teichmann
AU  - P. He
PY  - 2026
DA  - 2026/08/12
PB  - bioRxiv
DO  - 10.64898/2026.08.12.744548
UR  - https://www.biorxiv.org/content/10.64898/2026.08.12.744548v3
AB  - 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.
ER  -