生物
电池类型
肾脏疾病
转录组
细胞
急性肾损伤
计算生物学
表观遗传学
神经科学
医学
遗传学
内科学
基因
内分泌学
基因表达
DNA甲基化
作者
Blue B. Lake,Rajasree Menon,Seth Winfree,Qiwen Hu,Ricardo Melo Ferreira,Kian Kalhor,Daria Barwinska,Edgar A. Otto,Michael J. Ferkowicz,Dinh Diep,Nongluk Plongthongkum,Amanda Knoten,Sarah Urata,Abhijit S. Naik,Sean Eddy,Bo Zhang,Yan Wu,Diane Salamon,James C. Williams,Xin Wang
标识
DOI:10.1101/2021.07.28.454201
摘要
Abstract Understanding kidney disease relies upon defining the complexity of cell types and states, their associated molecular profiles, and interactions within tissue neighborhoods. We have applied multiple single-cell or -nucleus assays (>400,000 nuclei/cells) and spatial imaging technologies to a broad spectrum of healthy reference (n = 42) and disease (n = 42) kidneys. This has provided a high resolution cellular atlas of 100 cell types that include rare and novel cell populations. The multi-omic approach provides detailed transcriptomic profiles, epigenomic regulatory factors, and spatial localizations for major cell types spanning the entire kidney. We further identify and define cellular states altered in kidney injury, encompassing cycling, adaptive or maladaptive repair, transitioning and degenerative states affecting several segments. Molecular signatures of these states permitted their localization within injury neighborhoods using spatial transcriptomics, and large-scale 3D imaging analysis of ∼1.2 million neighborhoods provided linkages to active immune responses. These analyses further defined biological pathways relevant to injury niches, including signatures underlying the transition from reference to predicted maladaptive states that were associated with a decline in kidney function during chronic kidney disease. This human kidney cell atlas, including injury cell states and neighborhoods, will be a valuable resource for future studies.
科研通智能强力驱动
Strongly Powered by AbleSci AI