TBX21+CD21loCXCR3+B cell expansion with an EBV-associated phenotype in clinically isolated syndrome conversion to multiple sclerosis
Single-cell RNA-seq · human · 32 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
- 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.
- Paper
- Early multiple sclerosis activity associated with TBX21+CD21loCXCR3+ B cell expansion resembling EBV-induced phenotypesJCI insight · 2025
Read the full summary ↓
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 GSE267750_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: 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 PRJNA1112789 and SRA study SRP508353. 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 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.
- GSM8275069 PBMCs, LTNP_1_t1 (scRNA-seq)
- GSM8275070 PBMCs, LTNP_1_t2 (scRNA-seq)
- GSM8275071 PBMCs, MS_1_t1 (scRNA-seq)
- GSM8275072 PBMCs, MS_1_t2 (scRNA-seq)
- GSM8275073 PBMCs, MS_2_t1 (scRNA-seq)
- GSM8275074 PBMCs, MS_2_t2 (scRNA-seq)
- GSM8275075 PBMCs, MS_3_t1 (scRNA-seq)
- GSM8275076 PBMCs, MS_3_t2 (scRNA-seq)
- GSM8275077 PBMCs, MS_4_t1 (scRNA-seq)
- GSM8275078 PBMCs, MS_4_t2 (scRNA-seq)
- GSM8275079 PBMCs, MS_5_t1 (scRNA-seq)
- GSM8275080 PBMCs, MS_5_t2 (scRNA-seq)
- GSM8275081 PBMCs, MS_6_t1 (scRNA-seq)
- GSM8275082 PBMCs, MS_6_t2 (scRNA-seq)
- GSM8275083 PBMCs, LTNP_2_t1 (scRNA-seq)
- GSM8275084 PBMCs, LTNP_2_t2 (scRNA-seq)
- GSM8275085 PBMCs, MS_7_t1 (scRNA-seq)
- GSM8275086 PBMCs, MS_7_t2 (scRNA-seq)
- GSM8275087 PBMCs, MS_8_t1 (scRNA-seq)
- GSM8275088 PBMCs, MS_8_t2 (scRNA-seq)
- GSM8275089 PBMCs, MS_9_t1 (scRNA-seq)
- GSM8275090 PBMCs, MS_9_t2 (scRNA-seq)
- GSM8275091 PBMCs, MS_10_t1 (scRNA-seq)
- GSM8275092 PBMCs, MS_10_t2 (scRNA-seq)
- GSM8275093 PBMCs, MS_11_t1 (scRNA-seq)
- GSM8275094 PBMCs, MS_11_t2 (scRNA-seq)
- GSM8275095 PBMCs, MS_12_t1 (scRNA-seq)
- GSM8275096 PBMCs, MS_12_t2 (scRNA-seq)
- GSM8275097 PBMCs, LTNP_3_t1 (scRNA-seq)
- GSM8275098 PBMCs, LTNP_3_t2 (scRNA-seq)
- GSM8275099 PBMCs, MS_13_t1 (scRNA-seq)
- GSM8275100 PBMCs, MS_13_t2 (scRNA-seq)
- GSE288991 Emerin Dysregulation Drives the Very-Small-Nuclear Phenotype and Lineage Plasticity that Associates with a Clinically Aggressive Subtype of Prostate Cancer 18 samples shares clinically, phenotype
- GSE256256 Seven chain adaptive immune receptor repertoire analysis in rheumatoid arthritis reveals novel features associated with disease and clinically relevant phenotypes 112 samples shares clinically, phenotype
- GSE226100 TBX21 attenuates colorectal cancer progression via a ARHGAP29/RSK/GSK3β dependent manner 6 samples shares tbx21
- GSE310339 TBX21 facilitates the progression of prostate cancer by modulating the immunosuppression microenvironment via enhancing the proportion of Treg cells 6 samples shares tbx21
- GSE131050 Purity Independent Subtyping of Tumors (PurIST), a clinically robust single-sample classifier for tumor subtyping in pancreatic cancer (RNA-Seq) 191 samples shares clinically
- GSE329380 A contamination-adjusted tissue thrombopoietic framework identifies a clinically relevant CAF-myeloid niche in non-small cell lung cancer: a paired transcriptomic and survival study 137 samples shares clinically
- GSE262671 NanoCMSer: A Clinically Applicable Tool for Stratification of Colorectal Cancer Samples [March2024.MATCH] 113 samples shares clinically
- GSE319229 Lipotoxic fingerprints in clinically relevant postoperative pancreatic fistula 72 samples shares clinically
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.