GEO series

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

GSE275067 Homo sapiens (human) Single-cell RNA-seq 252 samples Submitted 2025/09/12 Platform GPL24676Platform GPL34281

At a glance

Single-cell RNA-seq · human · 252 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: 252 samples: 252 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 GSE275067 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 GSE275067_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 PRJNA1146896 and SRA study SRP525680. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

252 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 252 sample titles: 252 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

GSM8464886 PB03173-001 flex scRNA-seq
GSM8464887 PB03180-001 flex scRNA-seq
GSM8464888 PB03195-001 flex scRNA-seq
GSM8464889 PB03199-003 flex scRNA-seq
GSM8464890 PB03203-003 flex scRNA-seq
GSM8464891 PB03146-001 flex scRNA-seq
GSM8464892 PB03147-001 flex scRNA-seq
GSM8464893 PB03152-001 flex scRNA-seq
GSM8464894 PB03156-001 flex scRNA-seq
GSM8464895 PB03160-001 flex scRNA-seq
GSM8464896 PB03163-001 flex scRNA-seq
GSM8464897 PB03165-001 flex scRNA-seq
GSM8464898 PB03166-001 flex scRNA-seq
GSM8464899 PB03171-001 flex scRNA-seq
GSM8464900 PB03184-001 flex scRNA-seq
GSM8464901 PB03187-001 flex scRNA-seq
GSM8464902 PB03267-001 flex scRNA-seq
GSM8464903 PB03275-001 flex scRNA-seq
GSM8464904 PB03280-001 flex scRNA-seq
GSM8464905 PB03282-001 flex scRNA-seq
GSM8464906 PB03285-001 flex scRNA-seq
GSM8464907 PB03286-001 flex scRNA-seq
GSM8464908 PB03287-001 flex scRNA-seq
GSM8464909 PB03288-001 flex scRNA-seq
GSM8464910 PB03292-001 flex scRNA-seq
GSM8464911 PB03297-001 flex scRNA-seq
GSM8464912 PB03303-001 flex scRNA-seq
GSM8464913 PB03308-001 flex scRNA-seq
GSM8464914 PB03309-001 flex scRNA-seq
GSM8464915 PB03314-001 flex scRNA-seq
GSM8464916 PB03327-001 flex scRNA-seq
GSM8464917 PB03328-001 flex scRNA-seq
GSM8464918 PB03334-001 flex scRNA-seq
GSM8464919 PB03335-001 flex scRNA-seq
GSM8464920 PB03339-001 flex scRNA-seq
GSM8464921 PB03344-001 flex scRNA-seq
GSM8464922 PB03384-001 flex scRNA-seq
GSM8464923 PB03390-001 flex scRNA-seq
GSM8464924 PB03395-001 flex scRNA-seq
GSM8464925 PB03398-001 flex scRNA-seq

+ 212 more — browse all 252 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.