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Kidney single cell sequencing results from 10-day unilateral ureteral obstruction mice

GSE264184 Mus musculus (mouse) Single-cell RNA-seq 2 samples Submitted 2024/04/19 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 2 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Mus musculus (mouse)
Samples
2 samples
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
GSDMD-dependent neutrophil extracellular traps promote macrophage-to-myofibroblast transition and renal fibrosis in obstructive nephropathyCell death & disease · 2022
Summary (written by the submitters)
Renal fibrosis is a common consequence of various progressive nephropathies, including obstructive nephropathy, and ultimately leads to kidney failure. Infiltration of inflammatory cells is a prominent feature of renal injury after draining blockages from the kidney, and correlates closely with the development of renal fibrosis. However, the underlying molecular mechanism behind the promotion of renal fibrosis by inflammatory cells remains unclear.
Read the full summary ↓
Herein, we showed that unilateral ureteral obstruction (UUO) induced Gasdermin D (GSDMD) activation in neutrophils, abundant neutrophil extracellular traps (NETs) formation and macrophage-to-myofibroblast transition (MMT) characterized by α-smooth muscle actin (α-SMA) expression in macrophages. Gsdmd deletion significantly reduced infiltration of inflammatory cells in the kidneys and inhibited NETs formation, MMT and thereby renal fibrosis. Chimera studies confirmed that Gsdmd deletion in bone marrow-derived cells, instead of renal parenchymal cells, provided protection against renal fibrosis. Further, specific deletion of Gsdmd in neutrophils instead of macrophages protected the kidney from undergoing fibrosis after UUO. Single-cell RNA sequencing identified robust crosstalk between neutrophils and macrophages. In vitro, GSDMD-dependent NETs triggered p65 translocation to the nucleus, which boosted the production of inflammatory cytokines and α-SMA expression in macrophages by activating TGF-β1/Smad pathway. In addition, we demonstrated that caspase-11, that could cleave GSDMD, was required for NETs formation and renal fibrosis after UUO. Collectively, our findings demonstrate that caspase-11/GSDMD-dependent NETs promote renal fibrosis by facilitating inflammation and MMT, therefore highlighting the role and mechanisms of NETs in renal fibrosis.
Published in
GSDMD-dependent neutrophil extracellular traps promote macrophage-to-myofibroblast transition and renal fibrosis in obstructive nephropathy
Wang Y, Li Y, Chen Z et al. · Cell death & disease 2022 · PMID 35941120 · doi:10.1038/s41419-022-05138-4
This dataset
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE264184_RAW.tar (it can run to tens of gigabytes).

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1101203 and SRA study SRP502194. Searching any of these in the dataset finder brings you back here.

Samples in this study

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