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Acute Immunological Phenotypes in Individuals with Traumatic Spinal Cord Injury.

GSE293559 Homo sapiens (human) RNA-seq (gene expression) 26 samples Submitted 2025/09/25 Platform GPL18573Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 26 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
26 samples: 26 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)
Acutely after traumatic spinal cord injury (SCI), the immune system responds with an inflammatory cascade that promotes secondary damage to the spinal cord and systemic inflammation, which promotes persistent medical consequences. Here, we combined clinical and research data to evaluate cellular and molecular changes in the systemic immune system of individuals with SCI (SCI, N = 36) within 0–4 days after injury compared to uninjured individuals (CTL, N = 36).
Read the full summary ↓
Analyzing blood samples by bulk-RNA Seq, 4752 differentially expressed (DE) gene transcripts were identified in SCI compared with CTLs, including increased expression of genes associated with inflammation and innate immunity (e.g., Neutrophil degranulation, Toll-Like Receptor signaling). Most participants with SCI had complete blood count data available, of whom 36% had elevated white blood cell and neutrophil counts, 24% had elevated monocytes, and 36% had lymphopenia. Significantly reduced expression of canonical natural killer (NK) cell, T cell and dendritic cell (DC) genes were identified, consistent with reduced frequencies of these cell types, determined by flow cytometry. Some molecular changes appeared to be influenced by motor completeness of injury. C-reactive protein, a validated clinical biomarker of inflammation, was significantly elevated after SCI, with levels higher in motor complete compared to motor incomplete injuries. This was also apparent for several other proinflammatory cytokines (e.g., High Mobility Group Box 1 protein, IL-6, IL-8). These data confirm and extend prior observations of cellular and molecular immunological changes, that may serve as potential biomarkers of injury severity, or as future therapeutic targets to improve health.
This dataset
Open GSE293559 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 GSE293559_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 PRJNA1245171 and SRA study SRP575905. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
26 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 26 sample titles: 26 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.