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Germline-like TCR alpha chains shared between autoreactive T cells in blood and pancreas [dataset1]

GSE255400 Homo sapiens (human) RNA-seq (gene expression) 799 samples Submitted 2024/04/16 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 799 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
799 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
Germline-like TCR-α chains shared between autoreactive T cells in blood and pancreasNature communications · 2024
Summary (written by the submitters)
How human islet antigen reactive CD4+ memory T cells (IAR T cells) from peripheral blood affect Type 1 Diabetes (T1D) progression in the pancreas is poorly understood. We identified paired alpha/beta (TRA/TRB) T cell receptors (TCRs) in IAR T cells from the blood of healthy, at-risk, new onset, and established T1D donors, and measured sequence overlap with TCRs in pancreata from organ donors.
Read the full summary ↓
We detected extensive TRA junction sharing between IAR T cells and pancreatic infiltrating T cells (PIT), with perfect or single mismatched TRA junction amino acid sequences comprising ~34% of unique IAR TRA junctions. PIT-matched TRA junctions were largely public, and showed significant nucleotide sequence convergence, increased use of germline-encoded residues in epitope engagement, and a potential for cross-reactivity. The link with T cells in the pancreas implicates autoreactive IAR T cells with shared TRA junctions in the prediabetic and new onset phases of T1D progression.
Published in
Germline-like TCR-α chains shared between autoreactive T cells in blood and pancreas
Linsley PS, Nakayama M, Balmas E et al. · Nature communications 2024 · PMID 38871688 · doi:10.1038/s41467-024-48833-w
This dataset
Open GSE255400 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 GSE255400_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1074806 and SRA study SRP489013. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38871688) ↗
Study design
A cohort of 799 individual samples

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

How we read this: Sample titles run lib52657 … lib53991 and carry no group information.

Samples in this study

+ 759 more — browse all 799 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.