← BioTransfer GEO Dataset Finder
GEO series

Human regulatory memory B cells defined by expression of TIM-1 and TIGIT are dysfunctional in Multiple Sclerosis.

GSE261258 Homo sapiens (human) Single-cell RNA-seq 89 samples Submitted 2024/05/31 Platform GPL18573
At a glance

Single-cell RNA-seq · human · 89 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
89 samples: 89 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
Human regulatory memory B cells defined by expression of TIM-1 and TIGIT are dysfunctional in multiple sclerosisFrontiers in immunology · 2024
Summary (written by the submitters)
Regulatory B cells (Bregs) play a pivotal role in suppressing immune responses, yet there is still a lack of cell surface markers that can rigorously identify them. In mouse models for multiple sclerosis (MS), TIM-1 or TIGIT expression on B cells is required for maintaining self-tolerance and regulating autoimmunity to the central nervous system.
Read the full summary ↓
Here we investigated the activities of human memory B cells that differentially express TIM-1 and TIGIT to determine their potential regulatory function in healthy donors and patients with relapsing-remitting (RR) MS. FACS-sorted TIM-1+/-TIGIT+/- memory B (memB) cells co-cultured with allogenic CD4+ T cells were analyzed for proliferation and induction of inflammatory markers using flow cytometry and cytokine quantification, to determine Th1/Th17 cell differentiation. Transcriptional differences were assessed by SMARTSeq2 RNA sequencing analysis. TIM-1-TIGIT- double negative (DN) memB cells strongly induce T cell proliferation and pro-inflammatory cytokine expression. The TIM-1+ memB cells enabled low levels of CD4+ T cell activation and gave rise to T cells that co-express IL-10 with IFNg and IL-17A or FoxP3. T cells cultured with the TIM-1+TIGIT+ double positive (DP) memB cells exhibited reduced proliferation and IFNg, IL-17A, TNFa, and GM-CSF expression, and exhibited strong regulation in Breg suppression assays. The functional activity suggests the DP memB cells are a bonafide Breg population. However, MS DP memB cells were less inhibitory than HC DP memB cells. A retrospective longitudinal study of anti-CD20 treated patients found that post-treatment DP memB cell frequency and absolute number were associated with response to therapy. Transcriptomic analyses indicated that the dysfunctional MS-derived DP memB/Breg population exhibited increased expression of genes associated with T cell activation and survival (CD80, ZNF10, PIK3CA), and had distinct gene expression compared to the TIGIT+ or TIM-1+ memB cells. These findings demonstrate that TIM-1/TIGIT expressing memory B cell subsets have distinct functionalities. Co-expression of TIM-1 and TIGIT defines a regulatory memory B cell subset that is functionally impaired in MS.
Published in
Human regulatory memory B cells defined by expression of TIM-1 and TIGIT are dysfunctional in multiple sclerosis
Varghese JF, Kaskow BJ, von Glehn F et al. · Frontiers in immunology 2024 · PMID 38745667 · doi:10.3389/fimmu.2024.1360219
This dataset
Open GSE261258 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 GSE261258_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 PRJNA1086179 and SRA study SRP494364. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38745667) ↗
Study design
89 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 89 sample titles: 89 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

+ 49 more — browse all 89 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.