KI_2022_Klocke_et_al_SO_all_urine_cells.rds
收藏DataCite Commons2023-04-11 更新2024-08-18 收录
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https://figshare.com/articles/dataset/KI_2022_Klocke_et_al_SO_all_urine_cells_rds/22567201
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Acute kidney injury (AKI) is a major health issue, the outcome of which depends primarily on damage and reparative processes of tubular epithelial cells. Mechanisms underlying AKI remain incompletely understood, specific therapies are lacking and monitoring the course of AKI in clinical routine is confined to measuring urine output and plasma levels of filtration markers. Here we demonstrate feasibility and potential of a novel approach to assess the cellular and molecular dynamics of AKI by establishing a robust urine-to-single cell RNA sequencing (scRNAseq) pipeline for excreted kidney cells via flow cytometry sorting. We analyzed 42,608 single cell transcriptomes of 40 urine samples from 32 patients with AKI and compared our data with reference material from human AKI post-mortem biopsies and published mouse data. We demonstrate that tubular epithelial cells transcriptomes mirror kidney pathology and reflect distinct injury and repair processes, including oxidative stress, inflammation, and tissue rearrangement. We also describe an AKI-specific abundant urinary excretion of adaptive progenitor-like cells. Thus, single cell transcriptomics of kidney cells excreted in urine provides noninvasive, unprecedented insight into cellular processes underlying AKI, thereby opening novel opportunities for target identification, AKI sub-categorization, and monitoring of natural disease course and interventions.
急性肾损伤(Acute kidney injury,AKI)是一类重大健康问题,其转归主要取决于肾小管上皮细胞的损伤与修复过程。目前AKI的潜在发病机制尚未完全阐明,缺乏特异性治疗手段,临床常规中对AKI病程的监测仅局限于尿量与血浆滤过标志物水平的检测。本研究通过流式细胞分选技术,构建一套稳健可靠的尿液来源肾脏排泄细胞的单细胞RNA测序(single cell RNA sequencing,scRNAseq)流程,证实了以此解析AKI细胞与分子动态变化的全新方法的可行性与应用潜力。本研究对32例AKI患者的40份尿液样本中的42608个单细胞转录组进行了分析,并将所得数据与人类AKI尸检活检标本的参考数据集以及已发表的小鼠相关数据进行了比对。研究结果显示,肾小管上皮细胞的转录组特征可反映肾脏病理状态,并能体现不同的损伤与修复过程,包括氧化应激、炎症反应及组织重构。本研究同时发现,AKI患者尿液中会特异性大量排泄适应性祖细胞样细胞。综上,对尿液中排泄的肾脏细胞开展单细胞转录组学分析,可为AKI潜在的细胞过程提供无创且前所未有的深入认知,从而为靶点发现、AKI亚分类以及自然病程与干预措施的监测开辟全新的研究机遇。
提供机构:
figshare
创建时间:
2023-04-11



