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scRNA‑seq of human intra‑hepatic CD4⁺ T cells reveals unique Treg transcriptional identities and two steady‑state tissue‑adaptation programs across healthy and HCC liver samples

GSE320155 Homo sapiens (human) Single-cell RNA-seq 60 samples Submitted 2026/02/25 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 60 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)
Topic
Hepatocellular carcinomaThis series is part of our curated briefing on it.
Samples
60 samples: 60 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)
We employed a single‑cell sequencing approach using the 10x Genomics platform, including scRNA‑seq, CITE‑seq, and paired TCR libraries, to investigate the molecular programs of human tissue‑resident regulatory T cells (Tregs). By analyzing CD4⁺ T cells from healthy liver and matched peripheral blood mononuclear cells (PBMCs), as well as hepatocellular carcinoma (HCC) tissue with paired non‑tumoral liver samples, we characterized the transcriptional adaptations of intra‑hepatic Tregs under steady‑state conditions and their reprogramming within the HCC tumor microenvironment.
This dataset
Open GSE320155 in finder → Find more human datasets → Hepatocellular carcinoma landscape →
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 GSE320155_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 PRJNA1426484 and SRA study SRP678528. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
60 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 60 sample titles: 60 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

+ 20 more — browse all 60 samples with per-sample file links →

More Hepatocellular carcinoma datasets

Search all human datasets in GEO →

Share this dataset

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.