Compartmentalization and persistence of dominant (regulatory) T cell clones indicates antigen skewing in juvenile idiopathic arthritis
RNA-seq (gene expression) · human · 48 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
- 48 samples: 48 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.
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE196301_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA804209 and SRA study SRP358738. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 48 sample titles: 48 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.
- GSM5870182 Pat 1 Visit 1 PB Treg
- GSM5870183 Pat 1 Visit 1 PB non-Treg
- GSM5870184 Pat 1 Visit 1 SF right Treg
- GSM5870185 Pat 1 Visit 1 SF right non-Treg
- GSM5870186 Pat 1 Visit 2 PB Treg
- GSM5870187 Pat 1 Visit 2 PB non-Treg
- GSM5870188 Pat 1 Visit 2 SF right Treg
- GSM5870189 Pat 1 Visit 2 SF right non-Treg
- GSM5870190 Pat 1 Visit 3 SF right Treg
- GSM5870191 Pat 1 Visit 3 SF right non-Treg
- GSM5870192 Pat 1 Visit 4 SF right Treg
- GSM5870193 Pat 1 Visit 4 SF right non-Treg
- GSM5870194 Pat 2 Visit 1 PB Treg
- GSM5870195 Pat 2 Visit 1 PB non-Treg
- GSM5870196 Pat 2 Visit 1 SF left Treg
- GSM5870197 Pat 2 Visit 1 SF left non-Treg
- GSM5870198 Pat 2 Visit 2 SF left Treg
- GSM5870199 Pat 2 Visit 2 SF left non-Treg
- GSM5870200 Pat 3 Visit 1 SF right Treg
- GSM5870201 Pat 3 Visit 1 SF right non-Treg
- GSM5870202 Pat 3 Visit 2 SF right Treg
- GSM5870203 Pat 3 Visit 2 SF right non-Treg
- GSM5870204 Pat 3 Visit 3 SF right Treg
- GSM5870205 Pat 3 Visit 3 SF right non-Treg
- GSM5870206 Pat 4 Visit 1 SF left Treg
- GSM5870207 Pat 4 Visit 1 SF left non-Treg
- GSM5870208 Pat 4 Visit 2 SF left Treg
- GSM5870209 Pat 4 Visit 2 SF left non-Treg
- GSM5870210 Pat 5 Visit 1 SF right Treg
- GSM5870211 Pat 5 Visit 1 SF right non-Treg
- GSM5870212 Pat 5 Visit 1 SF left Treg
- GSM5870213 Pat 5 Visit 1 SF left non-Treg
- GSM5870214 Pat 5 Visit 2 PB Treg
- GSM5870215 Pat 5 Visit 2 PB non-Treg
- GSM5870216 Pat 5 Visit 2 SF left Treg
- GSM5870217 Pat 5 Visit 2 SF left non-Treg
- GSM5870218 Pat 6 Visit 1 PB Treg
- GSM5870219 Pat 6 Visit 1 PB non-Treg
- GSM5870220 Pat 6 Visit 1 SF left Treg
- GSM5870221 Pat 6 Visit 1 SF left non-Treg
+ 8 more — browse all 48 samples with per-sample file links →
- GSE253553 Two Broad Categories Overlapping With Rheumatoid Arthritis Observed in Synovial Biopsies from Patients With Juvenile Idiopathic Arthritis 24 samples shares idiopathic, arthritis, juvenile
- GSE215274 Identification of Whole Blood Gene Expression Signature to Predict the Therapeutic Effect of Nintedanib in Idiopathic Pulmonary Fibrosis 177 samples shares idiopathic
- GSE256068 Identification of gene regulatory networks affected across drug-resistant epilepsies 162 samples shares regulatory
- GSE303045 Single Cell Multiomics Across Nine Mammals Reveals Cell Type Specific Regulatory Conservation in the Brain 132 samples shares regulatory
- GSE256141 Single cell transcriptional landscape of liver transplant rejection reveals tissue persistence of clonally expanded, treatment-resistant T cells 90 samples shares persistence
- GSE261258 Human regulatory memory B cells defined by expression of TIM-1 and TIGIT are dysfunctional in Multiple Sclerosis. 89 samples shares regulatory
- GSE278330 Systemic IL-23 blockade downregulates IL-23 receptor-expressing Type 17 T-cell subsets and upregulates regulatory gene expression in myeloid cells in human psoriasis skin 63 samples shares regulatory
- GSE282496 CD38 expression by neonatal human naïve CD4+ T cells shapes their unique metabolic state and high regulatory T cell potential 41 samples shares regulatory
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.