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An atlas of healthy and injured cell states and niches in the human kidney

Author

Listed:
  • Blue B. Lake

    (University of California, San Diego
    San Diego Institute of Science, Altos Labs)

  • Rajasree Menon

    (University of Michigan)

  • Seth Winfree

    (University of Nebraska Medical Center)

  • Qiwen Hu

    (Harvard Medical School)

  • Ricardo Melo Ferreira

    (Indiana University School of Medicine)

  • Kian Kalhor

    (University of California, San Diego)

  • Daria Barwinska

    (Indiana University School of Medicine)

  • Edgar A. Otto

    (University of Michigan)

  • Michael Ferkowicz

    (Indiana University School of Medicine)

  • Dinh Diep

    (University of California, San Diego
    San Diego Institute of Science, Altos Labs)

  • Nongluk Plongthongkum

    (University of California, San Diego)

  • Amanda Knoten

    (Washington University School of Medicine)

  • Sarah Urata

    (University of California, San Diego)

  • Laura H. Mariani

    (University of Michigan)

  • Abhijit S. Naik

    (University of Michigan)

  • Sean Eddy

    (University of Michigan)

  • Bo Zhang

    (Washington University School of Medicine)

  • Yan Wu

    (University of California, San Diego
    San Diego Institute of Science, Altos Labs)

  • Diane Salamon

    (Washington University School of Medicine)

  • James C. Williams

    (Indiana University School of Medicine)

  • Xin Wang

    (Harvard Medical School)

  • Karol S. Balderrama

    (Broad Institute of Harvard and MIT)

  • Paul J. Hoover

    (Broad Institute of Harvard and MIT)

  • Evan Murray

    (Broad Institute of Harvard and MIT)

  • Jamie L. Marshall

    (Broad Institute of Harvard and MIT)

  • Teia Noel

    (Broad Institute of Harvard and MIT)

  • Anitha Vijayan

    (Washington University School of Medicine)

  • Austin Hartman

    (New York Genome Center)

  • Fei Chen

    (Broad Institute of Harvard and MIT)

  • Sushrut S. Waikar

    (Boston University School of Medicine and Boston Medical Center)

  • Sylvia E. Rosas

    (Joslin Diabetes Center
    Harvard Medical School)

  • Francis P. Wilson

    (Yale University School of Medicine)

  • Paul M. Palevsky

    (University of Pittsburgh School of Medicine)

  • Krzysztof Kiryluk

    (Columbia University)

  • John R. Sedor

    (Lerner Research and Glickman Urology and Kidney Institutes, Cleveland Clinic)

  • Robert D. Toto

    (UT Southwestern Medical Center)

  • Chirag R. Parikh

    (Johns Hopkins School of Medicine)

  • Eric H. Kim

    (Washington University School of Medicine)

  • Rahul Satija

    (New York Genome Center)

  • Anna Greka

    (Broad Institute of Harvard and MIT)

  • Evan Z. Macosko

    (Broad Institute of Harvard and MIT)

  • Peter V. Kharchenko

    (Harvard Medical School
    San Diego Institute of Science, Altos Labs)

  • Joseph P. Gaut

    (Washington University School of Medicine)

  • Jeffrey B. Hodgin

    (University of Michigan)

  • Michael T. Eadon

    (Indiana University School of Medicine)

  • Pierre C. Dagher

    (Indiana University School of Medicine)

  • Tarek M. El-Achkar

    (Indiana University School of Medicine)

  • Kun Zhang

    (University of California, San Diego
    San Diego Institute of Science, Altos Labs)

  • Matthias Kretzler

    (University of Michigan)

  • Sanjay Jain

    (Washington University School of Medicine
    Washington University School of Medicine)

Abstract

Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.

Suggested Citation

  • Blue B. Lake & Rajasree Menon & Seth Winfree & Qiwen Hu & Ricardo Melo Ferreira & Kian Kalhor & Daria Barwinska & Edgar A. Otto & Michael Ferkowicz & Dinh Diep & Nongluk Plongthongkum & Amanda Knoten , 2023. "An atlas of healthy and injured cell states and niches in the human kidney," Nature, Nature, vol. 619(7970), pages 585-594, July.
  • Handle: RePEc:nat:nature:v:619:y:2023:i:7970:d:10.1038_s41586-023-05769-3
    DOI: 10.1038/s41586-023-05769-3
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    Citations

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    Cited by:

    1. Yashvardhan Jain & Leah L. Godwin & Sripad Joshi & Shriya Mandarapu & Trang Le & Cecilia Lindskog & Emma Lundberg & Katy Börner, 2023. "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Kian Kalhor & Chien-Ju Chen & Ho Suk Lee & Matthew Cai & Mahsa Nafisi & Richard Que & Carter R. Palmer & Yixu Yuan & Yida Zhang & Xuwen Li & Jinghui Song & Amanda Knoten & Blue B. Lake & Joseph P. Gau, 2024. "Mapping human tissues with highly multiplexed RNA in situ hybridization," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    3. Andreas Fønss Møller & Jesper Grud Skat Madsen, 2023. "JOINTLY: interpretable joint clustering of single-cell transcriptomes," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    4. Wei E. Gordon & Seungbyn Baek & Hai P. Nguyen & Yien-Ming Kuo & Rachael Bradley & Sarah L. Fong & Nayeon Kim & Alex Galazyuk & Insuk Lee & Melissa R. Ingala & Nancy B. Simmons & Tony Schountz & Lisa N, 2024. "Integrative single-cell characterization of a frugivorous and an insectivorous bat kidney and pancreas," Nature Communications, Nature, vol. 15(1), pages 1-23, December.
    5. Lingzhi Li & Ting Xiang & Jingjing Guo & Fan Guo & Yiting Wu & Han Feng & Jing Liu & Sibei Tao & Ping Fu & Liang Ma, 2024. "Inhibition of ACSS2-mediated histone crotonylation alleviates kidney fibrosis via IL-1β-dependent macrophage activation and tubular cell senescence," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    6. Wenxu Zhang & Yajuan Li & Anthony A. Fung & Zhi Li & Hongje Jang & Honghao Zha & Xiaoping Chen & Fangyuan Gao & Jane Y. Wu & Huaxin Sheng & Junjie Yao & Dorota Skowronska-Krawczyk & Sanjay Jain & Ling, 2024. "Multi-molecular hyperspectral PRM-SRS microscopy," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    7. Nicolas Ledru & Parker C. Wilson & Yoshiharu Muto & Yasuhiro Yoshimura & Haojia Wu & Dian Li & Amish Asthana & Stefan G. Tullius & Sushrut S. Waikar & Giuseppe Orlando & Benjamin D. Humphreys, 2024. "Predicting proximal tubule failed repair drivers through regularized regression analysis of single cell multiomic sequencing," Nature Communications, Nature, vol. 15(1), pages 1-19, December.

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