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The embryonic origins of site-specific arthritis; Bulk RNA sequencing of CD34+(Pi16+) fibroblasts stimulated with TNF and IL1b.

GSE328951 Homo sapiens (human) Single-cell RNA-seq 24 samples Submitted 2026/04/28 Platform GPL34284
At a glance

Single-cell RNA-seq · human · 24 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
24 samples: 6 conditions, mostly in duplicateGroups 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)
The cellular basis for site-specific inflammation remains unclear. In human fingers, proximal interphalangeal (PIP) joints are preferentially affected by inflammatory arthritis, whereas distal interphalangeal (DIP) joints are spared, providing a model to investigate the predilection of inflammation to distinct sites. We combine single-cell RNA sequencing, imaging and X-ray tomography to examine cellular composition, spatial organization and structure of finger joints during foetal development.
Read the full summary ↓
PIP joints were enriched for Pi16+ fibroblasts, which are located in perivascular regions and at tendon–ligament interfaces. To investigate whether Pi16+ fibroblasts have a specific response to inflammatory cytokines Pi16+ and Pi16- cells were sorted from foetal fingers using the co-expressed marker CD34. Thes cells were cultured in vitro with and without TNF and IL1b and analyzed using bulk RNA sequencing.
This dataset
Open GSE328951 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 GSE328951_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 PRJNA1457067 and SRA study SRP694291. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly in duplicate
CD34p_61 ×2 CD34p_T_61 ×2 CD34p_T_95 ×2 CD34p_T_96 ×2 Thy1p_61 ×2 Thy1p_T_61 ×2 CD34p_95 ×1 CD34p_95_2b ×1 +10 more

Supports a between-group comparison across 12 samples.

How we read this: 6 replicated groups read from 24 sample titles; they account for 12 of them. 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.