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Graft-versus-host disease prophylaxis shapes T cell biology and immune reconstitution after hematopoietic cell transplant

GSE285821 Homo sapiens (human) RNA-seq (gene expression) 51 samples Submitted 2025/03/20 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 51 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
51 samples: 51 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
Graft-versus-host disease prophylaxis shapes T cell biology and immune reconstitution after hematopoietic cell transplantmedRxiv : the preprint server for health sciences · 2025
Summary (written by the submitters)
Successful hematopoietic cell transplant requires immunosuppression to prevent graft-versus-host disease (GVHD), a lethal, T-cell-mediated post-transplant complication. The phase 3 BMT CTN 1703 trial demonstrated superior GVHD-free/relapse-free survival for post-transplant cyclophosphamide (PT-Cy)-based GVHD prophylaxis versus tacrolimus/methotrexate (Tac/MTX), but did not improve overall survival.
Read the full summary ↓
To compare T-cell biology between GVHD prophylaxis regimens, 324 patients were co-enrolled onto BMT CTN 1801 (NCT03959241). We quantified T-cell immune reconstitution using multi-modal analysis, including T-cell receptor (TCR) sequencing of 2,359 longitudinal samples (180,432,350 T-cells). Compared to Tac/MTX, PT-Cy was associated with an early, substantial reduction in TCR diversity that was sustained for 2 years. PT-Cy led to a T-cell reconstitution bottleneck, including reduced thymic output and virus-associated TCRs. Decreased D+14 TCR diversity predicted prevention of chronic GVHD, but also correlated with increased moderate-to-severe infections. This study reveals how distinct immunosuppression strategies have significant effects on the global immune repertoire, underpinning post-transplant clinical outcomes.
Published in
Graft-versus-host disease prophylaxis shapes T cell biology and immune reconstitution after hematopoietic cell transplant
Siegel SJ, DeWolf S, Schmalz J et al. · medRxiv : the preprint server for health sciences 2025 · PMID 40061351 · doi:10.1101/2025.02.25.25322901
This dataset
Open GSE285821 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 GSE285821_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1206149. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40061351) ↗
Study design
51 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 51 sample titles: 51 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

+ 11 more — browse all 51 samples with per-sample file links →

Datasets on a similar topic

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.