GEO series

Deep longitudinal immune profiling reveals reprogramming of memory T cells that links to B cell dysregulation with age

GSE271896 Homo sapiens (human) Single-cell RNA-seq 868 samples Submitted 2025/09/12 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 868 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Homo sapiens (human)
Samples: 868 samples: 868 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)

Immune aging is a dynamic process shaped by time and external perturbation. Here, we sought to untangle the complexity of the healthy human immunity across age using deep molecular profiling. Applying our new Healthy Immune Cell Atlas, we profiled the transcriptional dynamics of peripheral immune cells in a longitudinal cohort of ~100 healthy young and older adults followed over 2 years, both under homeostasis and perturbation induced by vaccination, building a scRNA-seq dataset of more than 13.5 million PBMCs.

Read the full summary ↓

From these data, we revealed that T cells exhibit substantially more age-related changes in transcription at homeostasis than other immune cell subsets, which persist both over time and across age and are distinct from those associated with biological sex or CMV infection status. B cells, which demonstrated few age-related differences at homeostasis, had numerous and persistent changes in vaccine-induced responses with age linked to an age- and GATA3-related transcriptional skewing in the central memory CD4 T cell compartment of older adults. Our study collectively highlights that gradual, age-related alterations in the homeostatic transcriptional networks in immune cells leads to shifts in the landscape of immune responsiveness as we age. This rich resource is further provided with data exploration tools at https://explore.allenimmunology.org/.

This dataset

Open GSE271896 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 GSE271896_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1133820. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

868 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 868 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

GSM8387811 PB00001-01 Flu Year 1 Day 0
GSM8387812 PB00008-01 Flu Year 1 Day 7
GSM8387813 PB00037-01 Flu Year 1 Day 90
GSM8387814 PB00002-01 Flu Year 1 Day 0
GSM8387815 PB00009-01 Flu Year 1 Day 7
GSM8387816 PB00034-01 Flu Year 1 Day 90
GSM8387817 PB00274-03 Flu Year 2 Stand-Alone
GSM8387818 PB00378-01 Flu Year 2 Day 0
GSM8387819 PB00400-01 Flu Year 2 Day 7
GSM8387820 PB00644-01 Flu Year 2 Day 90
GSM8387821 PB01544-01 Immune Variation Day 0
GSM8387822 PB01560-01 Immune Variation Day 7
GSM8387823 PB02013-01 Immune Variation Day 90
GSM8387824 PB00003-01 Flu Year 1 Day 0
GSM8387825 PB00007-01 Flu Year 1 Day 7
GSM8387826 PB00035-01 Flu Year 1 Day 90
GSM8387827 PB00276-03 Flu Year 2 Stand-Alone
GSM8387828 PB00355-01 Flu Year 2 Day 0
GSM8387829 PB00372-01 Flu Year 2 Day 7
GSM8387830 PB00643-01 Flu Year 2 Day 90
GSM8387831 PB01559-01 Immune Variation Day 0
GSM8387832 PB01574-01 Immune Variation Day 7
GSM8387833 PB02242-02 Immune Variation Day 90
GSM8387834 PB00004-01 Flu Year 1 Day 0
GSM8387835 PB00011-01 Flu Year 1 Day 7
GSM8387836 PB00265-01 Flu Year 1 Day 90
GSM8387837 PB00335-05 Flu Year 2 Day 0
GSM8387838 PB00343-01 Flu Year 2 Day 7
GSM8387839 PB00609-02 Flu Year 2 Day 90
GSM8387840 PB01526-01 Immune Variation Day 0
GSM8387841 PB01538-01 Immune Variation Day 7
GSM8387842 PB02245-02 Immune Variation Day 90
GSM8387843 PB02330-001 Flu Year 3 Stand-Alone
GSM8387844 PB00006-01 Flu Year 1 Day 0
GSM8387845 PB00013-01 Flu Year 1 Day 7
GSM8387846 PB00040-01 Flu Year 1 Day 90
GSM8387847 PB00576-01 Flu Year 2 Day 0
GSM8387848 PB00587-01 Flu Year 2 Day 7
GSM8387849 PB01457-01 Flu Year 2 Day 90
GSM8387850 PB02257-01 Immune Variation Day 0

+ 828 more — browse all 868 samples with per-sample file links →

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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.