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Secretory IgA binding to FCRL3 triggers shared inflammatory cytokine secretion by human regulatory T cells and effector T cells

GSE277028 Homo sapiens (human) RNA-seq (gene expression) 72 samples Submitted 2025/04/07 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 72 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
72 samples: 72 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
Secretory IgA binding to FCRL3 triggers shared inflammatory cytokine secretion by human regulatory T cells and effector T cellsJournal of leukocyte biology · 2025
Summary (written by the submitters)
Several human lymphocyte subsets express the novel secretory IgA receptor FCRL3. Secretory IgA binding to FCRL3 mAb diminishes the inhibitory capacity of regulatory T cells and promotes a Th17-like phenotype. Here we report that in CD4+ regulatory T cells and CD8+ terminal effector T cells secretory IgA induced a shared inflammatory gene signature that included PTGS2 encoding COX2, and the prototypic inflammatory cytokine genes IL1A, IL1B, and IL8.
Read the full summary ↓
Secretory IgA in regulatory T cells also elevated gene transcripts required for lineage identity and function. Secretory IgA promoted IL-1β, IL-6, IL-8, IL-10, IFN-γ, and TNF-α protein secretion by both T cell types. . We propose that secretory IgA provokes in regulatory and effector T cells a coordinated inflammatory response to facilitate mucosal pathogen clearance.
Published in
Secretory IgA binding to FCRL3 triggers shared inflammatory cytokine secretion by human regulatory T cells and effector T cells
Kraus Z, Birla S, Powell T et al. · Journal of leukocyte biology 2025 · PMID 40313182 · doi:10.1093/jleuko/qiaf054
This dataset
Open GSE277028 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 GSE277028_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: 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 PRJNA1160252 and SRA study SRP532317. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40313182) ↗
Study design
72 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 72 sample titles: 72 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

+ 32 more — browse all 72 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.