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Longitudinally stable T cell and innate immune activity distinguish healthy adult immunotypes [II]

GSE279552 Homo sapiens (human) RNA-seq (gene expression) 864 samples Submitted 2025/04/24 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 864 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
864 samples: 864 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
Longitudinally stable T cell function and innate immune activation distinguish healthy adult immunotypesScience translational medicine · 2025
Summary (written by the submitters)
Using cytometric, proteomic, and transcriptomic longitudinal data derived from whole blood, we identified four robust longitudinally stable immunotypes associated with innate immune activation and T-cell function in healthy adults.
Published in
Longitudinally stable T cell function and innate immune activation distinguish healthy adult immunotypes
Smithmyer ME, Hu A, Dufort MJ et al. · Science translational medicine 2025 · PMID 40465686 · doi:10.1126/scitranslmed.adt4605
This dataset
Open GSE279552 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 GSE279552_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 PRJNA1173254 and SRA study SRP538763. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40465686) ↗
Study design
864 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 the first 400 of 864 sample titles: 400 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

+ 824 more — browse all 864 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.