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Multi-modal single-cell sequencing reveals network transition in circulating monocytes that aligns with faster recovery in trauma patients and favors a response to M-CSF

GSE295174 Homo sapiens (human) Single-cell RNA-seq 16 samples Submitted 2026/06/19 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 16 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
16 samples: 16 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.
Summary (written by the submitters)
Previous transcriptomic studies to characterize the changes in circulating immune cells after major trauma in humans have demonstrated a dramatic early upregulation of inflammatory features and simultaneous downregulation of MHCII and IFN signaling pathways in monocytes; changes that gradually return to baseline. The magnitude and duration of these changes is associated with post-injury complications.
Read the full summary ↓
To determine if unique immune cell subsets or features emerging in the subacute time frame associated with patient outcomes after severe injury, we applied DOGMA-seq (single-cell RNA + ATAC + ADT sequencing) on peripheral blood mononuclear cells isolated at day 3 after injury. Patients were segregated into slow or fast recovery from critical illness (8 patients/ group) and compared to age and sex matched healthy controls. An analysis that integrated all three single cell modes identified a subset of CD172a hi/MHCII hi, CD14+ monocytes overrepresented in patients that recovered faster and distinct from baseline monocytes. This monocyte subset appeared prior to recovery and increased over time and was associated with changes in several key gene co-expression networks (referred to as a red-to-green network transition). A green-shifted pattern (associated with chemotaxis and cell adhesion) was overrepresented in fast recovery patients, continuously deviated from baseline, and specifically associated with increased accessibility of AP1 family motifs. In contrast, the red-shifted pattern (mainly pro-inflammatory genes) was associated with slow recovery, increased accessibility of CEBPB, less accessibility of IRF motifs, and gradually returned to baseline. Subsequent in vitro cell culture demonstrated that, compared to red-shifted cells, green shifted cells favored higher response to M-CSF-stimulated macrophage differentiation that included the upregulation of the genes involved in cell motility, while not obviously influenced the response to GM-CSF stimulation. Our findings add a new information layer to the current human paradigm for the immune response to injury, that includes the trauma-inducible features augmented along recovery leading to post-traumatic monocytes continuously different from baseline. The changes in circulating monocytes may have a wide influence on peripheral tissues via differentiated macrophages.
This dataset
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Download · which file do I need?

Direct links to NCBI, 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.

All supplementary files ↗
3 · The original raw files

Whole study · RAW.tar

Everything the authors deposited as one archive (GSE295174_RAW.tar), including the raw array or instrument files. Can be large; the picker in step 2 takes just the samples you need.

Whole study · RAW.tar ↓

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 PRJNA1253660. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
16 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 16 sample titles: 16 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
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