Urinary single cell profiling captures cellular diversity of the kidney
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA662313 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR12607400_1.fastq.gz | Fastqsanger.gz | |||
| SRR12607400_2.fastq.gz | Fastqsanger.gz | |||
| SRR12607401_1.fastq.gz | Fastqsanger.gz | |||
| SRR12607401_2.fastq.gz | Fastqsanger.gz | |||
| SRR12607402_1.fastq.gz | Fastqsanger.gz |
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Project description:We performed single cell transcriptomic analysis on 17 urine samples obtained from five subjects at two different occasions using both spot and 24-hour urine collection. In addition, we used a combined spot urine samples of five healthy individuals as a control sample. We sequenced a total of 71,667 cells. After quality control and downstream analysis, we found that epithelial cells were the most common cell types in the urine. We were also able to identify most kidney cell types in the urine, such as podocyte, proximal, and collecting duct (CD), in addition to macrophages, monocytes and lymphocytes.
2021-02-02 | GSE157640 | GEO
Project description:BackgroundMicroscopic analysis of urine sediment is probably the most commonly used diagnostic procedure in nephrology. The urinary cells, however, have not yet undergone careful unbiased characterization.MethodsSingle-cell transcriptomic analysis was performed on 17 urine samples obtained from five subjects at two different occasions, using both spot and 24-hour urine collection. A pooled urine sample from multiple healthy individuals served as a reference control. In total 23,082 cells were analyzed. Urinary cells were compared with human kidney and human bladder datasets to understand similarities and differences among the observed cell types.ResultsAlmost all kidney cell types can be identified in urine, such as podocyte, proximal tubule, loop of Henle, and collecting duct, in addition to macrophages, lymphocytes, and bladder cells. The urinary cell-type composition was subject specific and reasonably stable using different collection methods and over time. Urinary cells clustered with kidney and bladder cells, such as urinary podocytes with kidney podocytes, and principal cells of the kidney and urine, indicating their similarities in gene expression.ConclusionsA reference dataset for cells in human urine was generated. Single-cell transcriptomics enables detection and quantification of almost all types of cells in the kidney and urinary tract.
| S-EPMC7920183 | biostudies-literature
Project description:Urinary single-cell sequencing captures intrarenal injury and repair processes in human acute kidney injury
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Project description:Background: Acute kidney injury (AKI) is a common sequela of infection with SARS-CoV-2 and contributes to the severity and mortality from COVID-19. Here, we tested the hypothesis that kidney alterations induced by COVID-19-associated AKI could be detected in cells collected from urine. Methods: We performed single-cell RNA sequencing (scRNAseq) on cells recovered from the urine of eight hospitalized COVID-19 patients with (n=5) or without AKI (n=3) as well as four non-COVID-19 AKI patients (n=4) to assess differences in cellular composition and gene expression during AKI. Results: Analysis of 30,076 cells revealed a diverse array of cell types, most of which were kidney, urothelial, and immune cells. Pathway analysis of tubular cells from patients with AKI showed enrichment of transcripts associated with damage-related pathways compared to those without AKI. ACE2 and TMPRSS2 expression were highest in urothelial cells amongst cell types recovered. Notably, in one patient we detected SARS-CoV-2 viral RNA in urothelial cells. These same cells were enriched for transcripts associated with anti-viral and anti-inflammatory pathways. Conclusions: We successfully performed scRNAseq on urinary sediment from hospitalized patients with COVID-19 to noninvasively study cellular alterations associated with AKI and established a dataset that includes both injured and uninjured kidney cells. Additionally, we provide preliminary evidence of direct infection of urinary bladder cells by SARS-CoV-2. The urinary sediment contains a wealth of information and is a useful resource for studying the pathophysiology and cellular alterations that occur in kidney diseases.
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Project description:Clinical and animal studies have demonstrated the increasing evidence of oxidative stress in kidney stone disease. Recent findings have shown that the interactions between calcium oxalate (CaOx) crystals and renal tubular cells can promote many cellular events such as cell proliferation, cell death, cellular injury, mitochondrial dysfunction and inflammatory cascade. All of these cellular events are associated with oxidative stress and overproduction of free radicals and reactive oxygen species (ROS) such as superoxide and hydrogen peroxide in renal tubular cells. However, almost all of these references have shown that oxidative stress occurs after the causative crystals have been deposited in the kidney or exposed to renal tubular cells, whereas its primary role as the etiology remained unclear. In this study, we examined effects of oxidative modifications of urinary proteins on CaOx stone formation processes. Urinary proteins were modified by performic oxidation and the presence of oxidatively modified urinary proteins was verified, quantified and characterized by Oxyblot assay and tandem mass spectrometry (nanoLC-ESI-LTQ-Orbitrap-MS/MS). Subsequently, activities of oxidatively modified urinary proteins on CaOx stone formation processes were examined.
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Project description:To identify urinary RNA as non-invasive biomarkers for progression of chronic kidney disease
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Project description:Acute kidney injury (AKI) is a major health issue, the outcome of which depends primarily on damage and reparative processes of tubular epithelial cells (TEC). 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 AKI patients and compared our data with reference material from human AKI post-mortem biopsies and published mouse data. We demonstrate that TEC transcriptomes mirror intrarenal pathology and reflect distinct injury and repair processes, including oxidative stress, inflammation, and tissue rearrangement. In conclusion, single cell transcriptomics of kidney cells excreted in urine provides non-invasive, 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.
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