GEO series

Establishment of epithelial inflammatory injury model using adult kidney organoids [RNA-seq]

GSE259376 Homo sapiens (human) Single-cell RNA-seq 16 samples Submitted 2024/03/03 Platform GPL16791

At a glance

Single-cell RNA-seq · human · 16 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Homo sapiens (human)
Samples: 16 samples: 16 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
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.

Summary (written by the submitters)

Kidney organoids are emerging as an increasingly applied model system to hold great promise for transplantation to individuals with end-stage renal disease and as a useful tool for kidney research. Multiple iPSC-derived renal organoids have been established to understand kidney development and disease. However, few disease models originated from adult renal tissue with fully mature cell fates have been set up to recapitulate kidney inflammation or fibrosis.

Read the full summary ↓

Here we created a novel organoid model that faithfully representing the cellular state of inflammatory response during the progression of renal disease upon TNFα exposure. scRNA-seq showed signatured inflammatory chemokines, cytokines and inflammation-associated signaling pathways were activated in TNFα-treatment organoids together with injury-associated markers such as LCN2 and CLU. Donor age is associated with TNFα-induced inflammatory reaction in organoids. Kidney organoids from young donors showed more transcriptional changes by down-regulation of TGFβ and extracellular matrix genes. In summary, we established an in vitro TNFα-treated kidney organoid model that representing the cellular state of the inflammatory response during renal disease progression and provided a novel tool for studying inflammation-related kidney disease and drug discovery.

This dataset

Open GSE259376 in finder → Find more human RNA-seq datasets →

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.

Series matrix ↓

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 GSE259376_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

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.

Raw reads on ENA ↗

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 PRJNA1081385 and SRA study SRP492067. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

16 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 16 sample titles: 16 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

GSM8115015 F34_CON
GSM8115016 F34_TGFB
GSM8115017 F34_TNFA
GSM8115018 F64_CON
GSM8115019 F64_TNFA
GSM8115020 M35_CON
GSM8115021 M35_TNFA
GSM8115022 M41_CON
GSM8115023 M41_TGFB
GSM8115024 M41_TNFA
GSM8115025 M65_CON
GSM8115026 M65_TGFB
GSM8115027 M65_TNFA
GSM8115028 M71_CON
GSM8115029 M71_TGFB
GSM8115030 M71_TNFA

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.