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Single-cell transcriptomics and TCR repertoire sequencing of T cells of NOD mice

GSE200695 Mus musculus (mouse) Single-cell RNA-seq 32 samples Submitted 2024/10/31 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 32 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
32 samples: 32 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)
T cells primarily drive the autoimmune destruction of pancreatic beta cells in Type 1 diabetes (T1D). However, the profound yet uncharacterized diversity of the T cell populations in vivo has hindered obtaining a clear picture of the T cell changes that occur longitudinally as T1D onset happens. Here we profiled the transcriptome and TCR repertoire of T cells at single-cell resolution from longitudinally collected peripheral blood and islets of Non-Obese Diabetic (NOD) mice using single-cell RNA sequencing technology.
Read the full summary ↓
We detected clonal expansion and characterized the transcriptional landscape of "islets-matching" T cell clones in the blood and "blood-matching" T cell clones in islets of diabetic NOD mice using the TCR as a molecular barcode. The clonally matching cells show enriched interferon-gamma response pathways than non-matching cells in blood and islets. In addition, we identified a set of transcriptional markers associated with the matching status of the T cell clones. This study provides a single-cell level transcriptome and TCR repertoire atlas of T cells in NOD mice. The results show that TCR and transcriptional signature can be used combinedly to develop biomarker panels for tracking and investigating islets infiltrating T cells.
This dataset
Open GSE200695 in finder → Find more mouse 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 GSE200695_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 PRJNA826100 and SRA study SRP369949. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
32 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 32 sample titles: 32 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
Datasets on a similar topic

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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.