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Immature neutrophil subsets and emergency granulopoiesis drive sepsis immune suppression and a specific extreme response to infection [WB_ECG]

GSE216009 Homo sapiens (human) Single-cell RNA-seq 96 samples Submitted 2024/10/16 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 96 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
96 samples: 96 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
Neutrophils and emergency granulopoiesis drive immune suppression and an extreme response endotype during sepsisNature immunology · 2023
Summary (written by the submitters)
Sepsis, the dysregulated host response to infection leading to organ dysfunction, arises from diverse mechanisms that are poorly resolved by sepsis as a syndromic classification, confounding immunotherapy trials. Here we delineate neutrophil and granulopoietic disturbances underlying sepsis pathophysiology. We present a whole blood single-cell multiomic sepsis response atlas (272,993 cells, n=39 individuals), revealing multiple immature neutrophil populations that were collectively immunosuppressive, inhibiting CD4+ T cell proliferation and activation in co-culture.
Read the full summary ↓
We traced this to altered granulopoiesis using single-cell multiomic mapping of circulating hematopoietic stem cells (HSCs) (29,366 cells, n=27). We show how these features are enriched in a subset of patients, resolving a specific poor outcome endotype (sepsis response signature SRS1). SRS1 patients have increased IL1R2+ immature neutrophils, epigenetic signatures of emergency granulopoiesis transcription factors in HSCs, and STAT3 features across infectious disease settings. Our findings delineate an immunohematopoietic axis, therapeutic targets and stratified medicine approach to sepsis.
Published in
Neutrophils and emergency granulopoiesis drive immune suppression and an extreme response endotype during sepsis
Kwok AJ, Allcock A, Ferreira RC et al. · Nature immunology 2023 · PMID 37095375 · doi:10.1038/s41590-023-01490-5
This dataset
Open GSE216009 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 GSE216009_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 PRJNA891701 and SRA study SRP576945. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 37095375) ↗
Study design
96 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 96 sample titles: 96 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

+ 56 more — browse all 96 samples with per-sample file links →

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.