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Aberrant STAT signaling drives T cell dysregulation in a targetable pediatric sepsis endotype

GSE303333 Homo sapiens (human) Single-cell RNA-seq 24 samples Submitted 2026/05/18 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 24 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
24 samples: 24 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.
Summary (written by the submitters)
Sepsis is a leading cause of morbidity and mortality in critically ill children, yet heterogeneity in immune responses complicates the development of targeted therapies. Although immune dysregulation is associated with poor outcomes in sepsis, it remains unclear which host immune factors contribute causally to sepsis morbidity and mortality.
Read the full summary ↓
To address this gap, we integrated deep immune phenotyping, plasma proteomics, single-cell transcriptomics, and phosphoflow cytometry in a prospective cohort of 88 critically ill children to elucidate the immunologic mechanisms which underly disease heterogeneity. Unsupervised clustering of plasma cytokines identified three immunologic subgroups, including a high-severity group (“Group C”) characterized by marked hypercytokinemia, driven primarily by IL-6 and IFN-γ. Group C exhibited distinct alterations in immune cell frequency and activation status, along with a strong association between hyperinflammatory signaling and lymphocyte dysfunction. Single-cell RNA sequencing revealed transcriptional signatures of T cell activation and metabolic stress, and identified widespread suppression of a lymphoid protective gene program across CD8⁺ T cell subsets. In the setting of increased expression of activation markers, T cell receptor repertoire analysis revealed no dominant clonotypes, consistent with a bystander mechanism of T cell activation. Using phosphoflow cytometry, we demonstrated baseline hyperactivation of STAT1 and STAT3 in CD8⁺ T cells from patients in Group C, and these cells failed to respond to aCD3/aCD28 stimulation. Together, these findings define IL-6/IFN-γ–driven T-cell dysfunction as a distinct endotype of immune dysregulation in pediatric sepsis, highlighting the JAK/STAT axis as a potential future target for immunomodulatory therapy.
This dataset
Open GSE303333 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 GSE303333_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 PRJNA1294847 and SRA study SRP602818. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
24 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 24 sample titles: 24 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
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.