GEO series

Single-cell transcriptomic and TCR profiling of peripheral double-positive T cells in healthy human blood [scRNA-seq]

GSE304309 Homo sapiens (human) Single-cell RNA-seq 8 samples Submitted 2026/08/01 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 8 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Samples: 8 samples: 4 × scRNA profiling of CD4+ T cells from dono vs 4 × scRNA profiling of CD8+ T cells from donoGroups 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)

CD4+CD8+ double-positive T (DPT) cells are typically associated with thymic development but have also been observed in peripheral blood under physiological and pathological conditions. However, their identity, functional significance, and developmental origin in the periphery remain poorly defined. This project was conducted to comprehensively characterize peripheral DPT cells in healthy human donors using single-cell transcriptomic and T cell receptor (TCR) profiling.

Read the full summary ↓

We aimed to define the molecular features that distinguish peripheral DPT cells from conventional single-positive CD4+ and CD8+ T (SPT) cells. Through the integration of single-cell RNA sequencing and paired TCR sequencing, we explored their transcriptional profiles, clonal architecture, and potential developmental trajectories. This allowed us to uncover unique gene expression patterns and TCR usage biases that suggest peripheral DPT cells represent a distinct, stable T cell subset rather than a transient or intermediate population. In addition, we developed a machine learning-based classifier that can accurately distinguish DPT cells from conventional T cell subsets using transcriptomic features. This approach enabled the identification of key marker genes and molecular pathways underlying the functional specialization of DPT cells. This dataset provides a valuable resource for the immunology community to further investigate peripheral DPT cells in health and disease. Generated from healthy individuals, it serves as a high-quality reference map of normal peripheral T cell states. In our study, this dataset was further integrated with publicly available single-cell datasets derived from patients with immune-related diseases, enabling comparative analyses to explore how the transcriptional identity and abundance of DPT cells may be altered under pathological conditions. By making this healthy reference dataset publicly available, we aim to support future investigations into T cell heterogeneity, immune dysregulation, and disease-specific perturbations of the T cell compartment.

This dataset

Open GSE304309 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 GSE304309_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 PRJNA1299552 and SRA study SRP605650. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

4 × scRNA profiling of CD4+ T cells from dono vs 4 × scRNA profiling of CD8+ T cells from dono

Supports a between-group comparison across 8 samples.

How we read this: 2 replicated groups read from 8 sample titles; they account for 8 of them. 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

GSM9147234 scRNA profiling of CD4+ T cells from donor 1 scRNA profiling of CD4+…
GSM9147235 scRNA profiling of CD4+ T cells from donor 2 scRNA profiling of CD4+…
GSM9147236 scRNA profiling of CD4+ T cells from donor 3 scRNA profiling of CD4+…
GSM9147237 scRNA profiling of CD4+ T cells from donor 4 scRNA profiling of CD4+…
GSM9147238 scRNA profiling of CD8+ T cells from donor 1 scRNA profiling of CD8+…
GSM9147239 scRNA profiling of CD8+ T cells from donor 2 scRNA profiling of CD8+…
GSM9147240 scRNA profiling of CD8+ T cells from donor 3 scRNA profiling of CD8+…
GSM9147241 scRNA profiling of CD8+ T cells from donor 4 scRNA profiling of CD8+…

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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.