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The genomic landscape of monocytes and CD4+ T cells after BCG vaccination in Malawian infants

GSE288201 Homo sapiens (human) RNA-seq (gene expression) 53 samples Submitted 2025/05/30 Platform GPL15520
At a glance

RNA-seq (gene expression) · human · 53 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
53 samples: 53 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
BCG Vaccination Potentially Modulates the Transcriptome of Infant CD4 T Cells in Addition to Age-Dependent Immune Ontogeny-Associated ChangesVaccines · 2025
Summary (written by the submitters)
The Bacille Calmette-Guérin (BCG) vaccine administration studies in human adults determined that BCG-mediated mechanisms of protection include epigenetic reprogramming of innate immune cells and heterologous T cell immunity. The current study aimed to confirm that BCG immunization similarly impacts the functionally distinct infant immune system.
Read the full summary ↓
Towards this goal, we applied RNA sequencing to assess molecular changes in circulating monocytes and CD4+ T cells of Malawian infants prior to and 2 to 13 weeks after BCG immunization. We identified 168 differentially expressed genes (DEGs), 14 upregulated and 152 downregulated, in monocytes after BCG vaccination. The upregulated monocyte DEGs were enriched in genes associated with metabolic pathways, in particular glycolysis and lipid metabolism. In CD4 T cells, 322 DEGs (95 upregulated and 226 downregulated) were identified after BCG vaccination, and these DEGs were representative of activation, proliferation, differentiation, and cell survival. These findings are consistent with data reported in BCG vaccinated adults and contribute to the understanding of molecular changes in monocytes and CD4 T cells that may explain the improved capacity of the infant immune system to respond to pathogens after BCG vaccination.
Published in
BCG Vaccination Potentially Modulates the Transcriptome of Infant CD4 T Cells in Addition to Age-Dependent Immune Ontogeny-Associated Changes
Kandiyil VVK, Kang E, Coates E et al. · Vaccines 2025 · PMID 40733683 · doi:10.3390/vaccines13070706
This dataset
Open GSE288201 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 GSE288201_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 PRJNA1216563 and SRA study SRP560375. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40733683) ↗
Study design
53 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 53 sample titles: 53 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

+ 13 more — browse all 53 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.