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CD38 expression by neonatal human naïve CD4+ T cells shapes their unique metabolic state and high regulatory T cell potential

GSE282496 Homo sapiens (human) RNA-seq (gene expression) 41 samples Submitted 2026/02/12 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 41 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
41 samples: 41 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)
Neonatal life is marked by rapid antigen exposure, necessitating establishment of peripheral immune tolerance via conversion of naïve CD4+ T cells into regulatory T cells (Tregs). Here, we demonstrate that increased Treg differentiation by naive CD4+ T cells from human cord blood versus adult blood is integrally linked to their unique metabolic profile and their elevated expression of the NADase, CD38.
Read the full summary ↓
Early life naïve CD4+ T cells demonstrate a metabolic preference for glycolysis, which directly facilitates their Treg generation. We reveal an age-dependent gradient in CD38 levels on naïve CD4+ T cells and show that high CD38 expression contributes to both the glycolytic state and high Treg potential of neonatal CD4+ T cells, effects that are mediated at least in part via the NAD-dependent deacetylase SIRT1. Thus, the early life window for peripheral tolerance in humans is critically enabled by the immunometabolic state of the naïve CD4+ compartment.
This dataset
Open GSE282496 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 GSE282496_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 PRJNA1189025 and SRA study SRP546711. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
41 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 41 sample titles: 41 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

+ 1 more — browse all 41 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.