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Single cell RNA profiling of blood CD4+ T cells identifies distinct helper and dysfunctional regulatory clusters in children with SLE (5' PBMC validation dataset)

GSE298578 Homo sapiens (human) Single-cell RNA-seq 14 samples Submitted 2025/09/05 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 14 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
14 samples: 9 × PBMC from pediatric SLE dono vs 5 × PBMC from Healthy donoGroups 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)
Expansion of different CD4+ CXCR5+ and CXCR5- memory T helper cells has been reported in human systemic lupus erythematosus (SLE) blood. To understand their complexity, we profiled blood CD4+ T cells from heathy donors (HD) and pediatric SLE patients at the single cell level and identified naïve, memory, regulatory (Treg) and proliferative clusters, as well as a cluster defined by high expression of interferon-stimulated genes (ISGs).
Read the full summary ↓
Within the memory compartment, we mapped follicular (Tfh) and peripheral (Tph and Th10) helper cells and confirmed their expansion in patients with lupus nephritis (LN) and/or high disease activity. Cytotoxic programs mapped to effector memory T cells re-expressing CD45RA (TEMRA), as well as to two memory subclusters, one of which overlapped with Th10 cells. Importantly, we identified an expansion of dysfunctional Tregs in patients with LN, and an upregulation of TLR5 and FCRL3 in naïve SLE Tregs, potentially linking their dysfunction with mucosal microbial dysbiosis. Our studies shed light into CD4+ T cell subsets that may contribute to dysfunctional B cell help and T cell suppression in SLE.
This dataset
Open GSE298578 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 GSE298578_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 PRJNA1269967 and SRA study SRP588862. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
9 × PBMC from pediatric SLE dono vs 5 × PBMC from Healthy dono

Supports a between-group comparison across 14 samples.

How we read this: 2 replicated groups read from 14 sample titles; they account for 14 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.