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Divergent spatial microdomains drive inflammation and repair in Ulcerative and Immune Checkpoint Therapy Colitis - whole tissue scRNA-Seq

GSE190564 Homo sapiens (human) Single-cell RNA-seq 62 samples Submitted 2024/04/12 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 62 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
62 samples: 62 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.
Paper
Tracking in situ checkpoint inhibitor-bound target T cells in patients with checkpoint-induced colitisCancer cell · 2024
Summary (written by the submitters)
Adult inflammatory bowel disease is incompletely understood. We combine unbiased single-cell RNA sequencing (gene expression profiling, CITE-seq derived cell surface protein data, TCR and BCR sequence data) with unbiased spatial transcriptomics to interrogate changes across immune and non-immune populations in colitis and health, across tissue and blood.
Read the full summary ↓
We compare idiopathic ulcerative colitis with hitherto under-studied immune checkpoint therapy induced colitis, utilizing non-inflamed disease states as additional controls. We identify patterns of inflammation and response unique and common to both diseases, and infer changes in cell trafficking with potential therapeutic implications. We go on to localize disease-specific changes in tissue using spatial transcriptomics. We leverage this data to interrogate cellular interactions in an unbiased manner, allowing us to describe novel microdomains of inflammation and repair.
Published in
Tracking in situ checkpoint inhibitor-bound target T cells in patients with checkpoint-induced colitis
Gupta T, Antanaviciute A, Hyun-Jung Lee C et al. · Cancer cell 2024 · PMID 38744246 · doi:10.1016/j.ccell.2024.04.010
This dataset
Open GSE190564 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 GSE190564_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 PRJNA787561 and SRA study SRP350018. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38744246) ↗
Study design
62 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 62 sample titles: 62 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

+ 22 more — browse all 62 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.