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Publication Information

PubMed ID
Public Release Type
Journal
Publication Year
2023
Affiliation
Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.; Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, USA.; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.; Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.; Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.; Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.; Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, MI, USA.; Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.; Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.; Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.; Department of Medicine, Washington University School of Medicine, St Louis, MO, USA.; Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.; Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, MI, USA.; Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, MI, USA.; Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, MI, USA.; Department of Medicine, Washington University School of Medicine, St Louis, MO, USA.; Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.; Department of Medicine, Washington University School of Medicine, St Louis, MO, USA.; Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.; Broad Institute of Harvard and MIT, Cambridge, MA, USA.; Broad Institute of Harvard and MIT, Cambridge, MA, USA.; Broad Institute of Harvard and MIT, Cambridge, MA, USA.; Broad Institute of Harvard and MIT, Cambridge, MA, USA.; Broad Institute of Harvard and MIT, Cambridge, MA, USA.; Department of Medicine, Washington University School of Medicine, St Louis, MO, USA.; New York Genome Center, New York, NY, USA.; Broad Institute of Harvard and MIT, Cambridge, MA, USA.; Section of Nephrology, Boston University School of Medicine and Boston Medical Center, Boston, MA, USA.; Kidney and Hypertension Unit, Joslin Diabetes Center, Boston, MA, USA.; Department of Medicine, Yale University School of Medicine, New Haven, CT, USA.; Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.; Department of Medicine, Columbia University, New York, NY, USA.; Lerner Research and Glickman Urology and Kidney Institutes, Cleveland Clinic, Cleveland, OH, USA.; Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, USA.; Division of Nephrology, Johns Hopkins School of Medicine, Baltimore, MD, USA.; Department of Surgery, Washington University School of Medicine, St Louis, MO, USA.; New York Genome Center, New York, NY, USA.; Broad Institute of Harvard and MIT, Cambridge, MA, USA.; Broad Institute of Harvard and MIT, Cambridge, MA, USA.; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.; Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA.; Department of Pathology, University of Michigan, Ann Arbor, MI, USA.; Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA. meadon@iupui.edu.; Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA. pdaghe2@iu.edu.; Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA. telachka@iu.edu.; Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA. kzhang@bioeng.ucsd.edu.; Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, MI, USA. kretzler@med.umich.edu.; Department of Medicine, Washington University School of Medicine, St Louis, MO, USA. sanjayjain@wustl.edu.
Authors
Lake Blue B, Menon Rajasree, Winfree Seth, Hu Qiwen, Ferreira Ricardo Melo, Kalhor Kian, Barwinska Daria, Otto Edgar A, Ferkowicz Michael, Diep Dinh, Plongthongkum Nongluk, Knoten Amanda, Urata Sarah, Mariani Laura H, Naik Abhijit S, Eddy Sean, Zhang Bo, Wu Yan, Salamon Diane, Williams James C, Wang Xin, Balderrama Karol S, Hoover Paul J, Murray Evan, Marshall Jamie L, Noel Teia, Vijayan Anitha, Hartman Austin, Chen Fei, Waikar Sushrut S, Rosas Sylvia E, Wilson Francis P, Palevsky Paul M, Kiryluk Krzysztof, Sedor John R, Toto Robert D, Parikh Chirag R, Kim Eric H, Satija Rahul, Greka Anna, Macosko Evan Z, Kharchenko Peter V, Gaut Joseph P, Hodgin Jeffrey B, Eadon Michael T, Dagher Pierre C, El-Achkar Tarek M, Zhang Kun, Kretzler Matthias, Jain Sanjay
Studies

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.