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Single cell transcriptional landscape of liver transplant rejection reveals tissue persistence of clonally expanded, treatment-resistant T cells

GSE256141 Homo sapiens (human) Single-cell RNA-seq 90 samples Submitted 2025/08/20 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 90 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
90 samples: 90 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
Single-cell transcriptional landscape of liver transplant rejection reveals tissue persistence of clonally expanded, treatment-resistant T cellsAmerican journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2025
Summary (written by the submitters)
T cell mediated rejection (TCMR) affects ∼50% of pediatric liver transplant recipients within 5 years, and late TCMR is associated with graft failure due to ineffective treatment. Although CD8+ T cells promote late TCMR, their clonal expansion, intragraft persistence, localization, and gene expression remain largely undefined. Herein, single-cell RNA sequencing and immune repertoire profiling of 30 cryopreserved liver biopsies from rejecting and non-rejecting pediatric patients identified expanded intragraft CD8+ T cell clonotypes (CD8EXP) and their gene expression profiles.
Read the full summary ↓
Expanded CD8+ clonotypes (CD8EXP) bore markers of effector and CD56hiCD161- 'NK-like' T cells, retaining their clonotype identity and phenotype in serial biopsies despite histologic TCMR resolution. CD8EXP clonotypes localized to portal infiltrates during active TCMR and persisted in the lobule after histologic TCMR resolution. MultiNicheNet analysis revealed differential crosstalk between Kupffer cells (KC) and CD8EXP, with activation of TGFß and downregulation of CD47 immune checkpoint signaling. Therefore, persistenting intragraft CD8EXP clones remain active despite TCMR treatment and may contribute to long-term allograft fibrosis and failure of operational tolerance.
Published in
Single-cell transcriptional landscape of liver transplant rejection reveals tissue persistence of clonally expanded, treatment-resistant T cells
Peters AL, DePasquale EAK, Begum G et al. · American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons 2025 · PMID 40812614 · doi:10.1016/j.ajt.2025.08.004
This dataset
Open GSE256141 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 GSE256141_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 PRJNA1078320 and SRA study SRP490655. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40812614) ↗
Study design
90 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 90 sample titles: 90 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

+ 50 more — browse all 90 samples with per-sample file links →

Datasets on a similar topic

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