GEO series

Deciphering sepsis molecular subtypes using large-scale data to identify subtype-specific drug repurposing

GSE310929 Homo sapiens (human) RNA-seq (gene expression) 193 samples Submitted 2026/02/05 Platform GPL18573

At a glance

RNA-seq (gene expression) · human · 193 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: 193 samples: 65 × K vs 53 × RS vs 38 × S vs 37 × LPGroups 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: Deciphering sepsis molecular subtypes using large-scale data to identify subtype-specific drug repurposingbioRxiv : the preprint server for biology · 2026

Summary (written by the submitters)

Rationale: Sepsis is a life-threatening syndrome that can quickly cause organ failure and death if untreated. Patient variability complicates therapy development, and its molecular mechanisms remain poorly understood. Advancing knowledge of these mechanisms is essential for effective treatments and clearer phenotype definitions.

Read the full summary ↓

Objectives: To address this issue, we created a large-scale transcriptomic atlas of publicly available adult sepsis patient data with which we performed molecular phenotyping to evaluate patterns of gene expression and identified potential phenotype-specific drug therapies. Methods: We identified studies of bacterial sepsis in three genomic databases (SRA, GEO, and refine.bio) and reviewed metadata from each study to ensure samples met our predetermined inclusion criteria. We combined this data into one large comprehensive data atlas and harmonized gene expression and associated metadata from each sample. Molecular phenotypes of sepsis were identified via clustering analysis of this sepsis transcriptomic data atlas. We then examined clinical correlates of each phenotype and identified gene signatures associated with each. We performed gene set enrichment analysis on those signatures and identified phenotype-specific potential drug repurposing candidates. We also evaluated the associations between computed phenotypes and mortality. Measurements and Main Results: We harmonized data from 3,713 samples (all are included in this dataset) across 28 data sets, of which 2,251 were sepsis patients. Clustering analysis identified four phenotypes within the data. We identified statistically significant phenotype associations with survival, disease, and age. Pathway analysis revealed that MHC class II functions, DNA damage, homeostatic pathways and coagulation may characterize underlying response phenotypes, and may have the potential to guide drug development of sepsis therapeutics. Conclusions: We created the largest transcriptomic sepsis atlas to date, from which we identified four molecular sepsis phenotypes. We described underlying dysregulated molecular mechanisms of these phenotypes, associated clinical covariates, and several potential candidate therapies specific to each phenotype. Future studies should seek to validate such drug-phenotype links to advance sepsis precision medicine.

Published in

Deciphering sepsis molecular subtypes using large-scale data to identify subtype-specific drug repurposing

Smith LA, Augustin B, Jacob V et al. · bioRxiv : the preprint server for biology 2026 · PMID 41959519 · doi:10.64898/2026.03.28.714506

This dataset

Open GSE310929 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 GSE310929_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 PRJNA1367552 and SRA study SRP647534. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41959519) ↗

Study design

65 × K vs 53 × RS vs 38 × S vs 37 × LP

Supports a between-group comparison across 193 samples.

How we read this: 4 replicated groups read from 193 sample titles; they account for 193 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

GSM9312392 K001 K
GSM9312393 K002 K
GSM9312394 K003 K
GSM9312395 K004 K
GSM9312396 K005 K
GSM9312397 K006 K
GSM9312398 K008 K
GSM9312399 K009 K
GSM9312400 K010 K
GSM9312401 K011 K
GSM9312402 K012 K
GSM9312403 K013 K
GSM9312404 K014 K
GSM9312405 K015 K
GSM9312406 K017 K
GSM9312407 K018 K
GSM9312408 K019 K
GSM9312409 K020 K
GSM9312410 K021 K
GSM9312411 K022 K
GSM9312412 K023 K
GSM9312413 K024 K
GSM9312414 K025 K
GSM9312415 K026 K
GSM9312416 K027 K
GSM9312417 K028 K
GSM9312418 K029 K
GSM9312419 K030 K
GSM9312420 K032 K
GSM9312421 K033 K
GSM9312422 K034 K
GSM9312423 K035 K
GSM9312424 K037 K
GSM9312425 K038 K
GSM9312426 K039 K
GSM9312427 K040 K
GSM9312428 K041 K
GSM9312429 K042 K
GSM9312430 K045 K
GSM9312431 K046 K

+ 153 more — browse all 193 samples with per-sample file links →

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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.