TY - JOUR
KW - Cellular signalling networks
KW - translational research
AU - Blue B. Lake
AU - Rajasree Menon
AU - Seth Winfree
AU - Qiwen Hu
AU - Ricardo Melo Ferreira
AU - Kian Kalhor
AU - Daria Barwinska
AU - Edgar A. Otto
AU - Michael Ferkowicz
AU - Dinh Diep
AU - Nongluk Plongthongkum
AU - Amanda Knoten
AU - Sarah Urata
AU - Laura H. Mariani
AU - Abhijit S. Naik
AU - Sean Eddy
AU - Bo Zhang
AU - Yan Wu
AU - Diane Salamon
AU - James C. Williams
AU - Xin Wang
AU - Karol S. Balderrama
AU - Paul J. Hoover
AU - Evan Murray
AU - Jamie L. Marshall
AU - Teia Noel
AU - Anitha Vijayan
AU - Austin Hartman
AU - Fei Chen
AU - Sushrut S. Waikar
AU - Sylvia E. Rosas
AU - Francis P. Wilson
AU - Paul M. Palevsky
AU - Krzysztof Kiryluk
AU - John R. Sedor
AU - Robert D. Toto
AU - Chirag R. Parikh
AU - Eric H. Kim
AU - Rahul Satija
AU - Anna Greka
AU - Evan Z. Macosko
AU - Peter V. Kharchenko
AU - Joseph P. Gaut
AU - Jeffrey B. Hodgin
AU - Michael T. Eadon
AU - Pierre C. Dagher
AU - Tarek M. El-Achkar
AU - Kun Zhang
AU - Matthias Kretzler
AU - Sanjay Jain
AB - 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.
BT - Nature
DA - 2023-07
DO - 10.1038/s41586-023-05769-3
IS - 7970
LA - en
N2 - 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.
PY - 2023
SP - 585
EP - 594
T2 - Nature
TI - An atlas of healthy and injured cell states and niches in the human kidney
UR - https://www.nature.com/articles/s41586-023-05769-3
VL - 619
Y2 - 2023-09-15
SN - 1476-4687
ER -