Comprehensive single-cell transcriptional profiling defines shared and unique epithelial injury responses during kidney fibrosis.
Comprehensive single-cell transcriptional profiling defines shared and unique epithelial injury responses during kidney fibrosis.
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DOI:
10.1016/j.cmet.2022.09.026
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发表时间:
2022-12-06
期刊:
影响因子:
29
通讯作者:
Humphreys, Benjamin D.
中科院分区:
文献类型:
--
作者:
Li, Haikuo;Dixon, Eryn E.;Wu, Haojia;Humphreys, Benjamin D.
The underlying cellular events driving kidney fibrogenesis and metabolic dysfunction are incompletely understood. Here, we employed single-cell combinatorial indexing RNA-sequencing to analyze 24 mouse kidneys from two fibrosis models. We profiled 309,666 cells in one experiment, representing 50 cell types/states encompassing epithelial, endothelial, immune and stromal populations. Single-cell analysis identified diverse injury states of the proximal tubule, including two distinct early-phase populations with dysregulated lipid and amino acid metabolism, respectively. Lipid metabolism was defective in the chronic phase but was transiently activated in the very early stages of ischemia-induced injury, where we discovered increased lipid deposition and increased fatty acid β-oxidation. Perilipin 2 was identified as a surface marker of intracellular lipid droplets and its knockdown in vitro disrupted cell energy state maintenance during lipid accumulation. Surveying epithelial cells across nephron segments identified shared and unique injury responses. Stromal cells exhibited high heterogeneity and contributed to fibrogenesis by epithelial-stromal crosstalk. Li et al. profile the full-time courses of mouse kidney fibrogenesis using single-cell combinatorial indexing RNA-sequencing. They describe diverse injury states of proximal tubular cells, including one cell state with enhanced lipid metabolism at an early phase of ischemia-induced injury. This single-cell atlas defines kidney epithelial injury responses in fibrosis.
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影响因子:
64.8
作者:
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通讯作者:
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DOI:
10.1093/bioinformatics/btu638
发表时间:
2015-01-15
期刊:
Bioinformatics (Oxford, England)
影响因子:
--
作者:
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通讯作者:
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通讯作者:
Chang BH
DOI:
10.1073/pnas.2026684118
发表时间:
2021-07-06
影响因子:
11.1
作者:
Gerhardt LMS;Liu J;Koppitch K;Cippà PE;McMahon AP
通讯作者:
McMahon AP