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Temporal profiling of human lymphoid tissues reveals coordinated defence against viral challenge

GSE287808 Homo sapiens (human) RNA-seq (gene expression) 102 samples Submitted 2025/01/31 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 102 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
102 samples: 102 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
Temporal profiling of human lymphoid tissues reveals coordinated defense against viral challengeNature immunology · 2025
Summary (written by the submitters)
Adaptive immunity is generated in lymphoid organs, but how these structures defend themselves during infection in humans is unknown. The nasal epithelium is a major site of viral entry, with adenoid nasal-associated lymphoid tissue (NALT generating early adaptive responses. Here, using a nasopharyngeal biopsy technique, we investigated longitudinal immune responses in NALT following viral challenge, using SARS-CoV-2 infection as a natural experimental model.
Read the full summary ↓
In acute infection, infiltrating monocytes formed a subepithelial and peri-follicular shield, recruiting NET-forming neutrophils, whilst tissue macrophages expressed pro-repair molecules during convalescence to promote the restoration of tissue integrity. Germinal centre B cells expressed anti-viral transcripts that inversely correlated with fate-defining transcription factors. Among T cells, tissue-resident memory CD8 T cells alone showed clonal expansion and maintained cytotoxic transcriptional programmes into convalescence. Together our study provides unique insights into how human nasal adaptive immune responses are generated and sustained in the face of viral challenge.
Published in
Temporal profiling of human lymphoid tissues reveals coordinated defense against viral challenge
Coates ML, Richoz N, Tuong ZK et al. · Nature immunology 2025 · PMID 39890933 · doi:10.1038/s41590-024-02064-9
This dataset
Open GSE287808 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 GSE287808_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 PRJNA1214738 and SRA study SRP560904. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39890933) ↗
Study design
102 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 102 sample titles: 102 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

+ 62 more — browse all 102 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.