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Single cell RNA and TCR sequencing reveals substantive similarities between synovial fluid and synovial tissue T-cells in inflammatory arthritis.

GSE250242 Homo sapiens (human) Single-cell RNA-seq 21 samples Submitted 2024/08/22 Platform GPL30173
At a glance

Single-cell RNA-seq · human · 21 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
21 samples: 21 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
Substantive Similarities Between Synovial Fluid and Synovial Tissue T cells in Inflammatory Arthritis Via Single-Cell RNA and T Cell Receptor SequencingArthritis & rheumatology (Hoboken, N.J.) · 2024
Summary (written by the submitters)
Objective: Synovial fluid (SF) derived T-cells are frequently studied as a proxy for investigating the synovial tissue (ST) T-cell infiltrate in inflammatory arthritis. However, since ST is the primary site of inflammatory activity, there is debate as to whether SF provides a true reflection of the ST T-cell population.
Read the full summary ↓
Methods: In this study, we used single cell RNA sequencing paired with single cell TCR sequencing to directly compare memory T-cells from paired samples of SF and ST from 6 patients with inflammatory arthritis to investigate their similarity in terms of T-cell receptor (TCR) repertoire and T-cell subset composition. Results: The TCR repertoires of SF and ST T-cells were strikingly similar, particularly for CD8+ T-cells. A median of 49% of the total CD8+ TCR repertoire in SF was shared with ST, compared to 20% shared with blood and 47% of the ST CD8+ TCR repertoire was shared with SF compared to 25% with blood. Furthermore, once the effect of collagenase digestion on gene expression by ST T-cells had been accounted for, the frequencies of specific CD8+ and CD4+ T-cell subsets were, in general, very similar in SF and ST and were distinct from blood. Conclusion: Our results suggest that T-cells migrate and equilibrate between SF and ST and maintain similar phenotypes in both sites. We conclude that SF is an appropriate proxy for investigating the T-cell infiltrate in inflamed synovium using single cell RNA sequencing, particularly in terms of investigating the TCR repertoire.
Published in
Substantive Similarities Between Synovial Fluid and Synovial Tissue T cells in Inflammatory Arthritis Via Single-Cell RNA and T Cell Receptor Sequencing
Durham LE, Humby FC, Ng N et al. · Arthritis & rheumatology (Hoboken, N.J.) 2024 · PMID 38973560 · doi:10.1002/art.42949
This dataset
Open GSE250242 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 GSE250242_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 PRJNA1052724 and SRA study SRP478130. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38973560) ↗
Study design
21 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 21 sample titles: 21 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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