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Transplantation-induced reprogramming determines successful hepatocyte engraftment

GSE275278 Mus musculus (mouse) RNA-seq (gene expression) 37 samples Submitted 2025/08/18 Platform GPL24676Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 37 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
Mus musculus (mouse)
Samples
37 samples: 12 conditions, mostly in triplicateGroups 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)
While demonstrated safety and efficacy in treating liver diseases, hepatocyte-based therapy faces the challenge of limited engraftment of transplanted hepatocytes (Tx-Heps) in clinical settings, akin to issues encountered in cell therapy for other solid organs. Here, we identified a conserved reprogramming state of Tx-Heps during engraftment, marked by the activation of liver progenitor genes and bipotential differentiation capacity.
Read the full summary ↓
Transplantation reprogramming of Tx-Heps is crucial for successful engraftment, as proven by transplantation failure upon hepatocyte-specific ablation of Arid1a, which is required for Tx-Hep reprogramming. To validate translational potential, we artificially induced Tx-Hep reprogramming by in vivo treatment of either IL6 or IC7, a chimeric IL6 family protein, both of which significantly enhance hepatocyte engraftment. Our findings unveil early-stage cell fate reprogramming during transplantation and also propose a promising strategy to augment hepatocyte engraftment, which is an interesting framework to be studied in cell transplantation for other solid organs.
This dataset
Open GSE275278 in finder → Find more 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 GSE275278_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 PRJNA1150161 and SRA study SRP527563. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
12 conditions, mostly in triplicate
Tx-HepHIR-D1 ×5 Tx-PHH-D1 ×5 Tx-Hep-D1 ×4 Hep ×3 Chol ×3 LPLC ×3 Tx-HepArid1aWT-D1 ×3 Tx-HepArid1aKO-D1 ×3 +4 more

Supports a between-group comparison across 37 samples.

How we read this: 12 replicated groups read from 37 sample titles; they account for 37 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
Datasets on a similar topic

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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.