GEO series

Targeting Senescent Hepatocytes for Treatment of Metabolic Dysfunction-associated Steatotic Liver Disease and Multi-organ Dysfunction

GSE288534 Homo sapiens (human) Single-nucleus RNA-seq 24 samples Submitted 2025/02/21 Platform GPL24676Platform GPL24247

At a glance

Single-nucleus RNA-seq · human · 24 samples

What it is: Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Homo sapiens (human)
Samples: 24 samples: 6 conditions, mostly with about 5 replicates eachGroups 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: Targeting senescent hepatocytes for treatment of metabolic dysfunction-associated steatotic liver disease and multi-organ dysfunctionNature communications · 2025

Summary (written by the submitters)

Senescent hepatocytes accumulate in metabolic dysfunction-associated steatotic liver disease (MASLD) and are linked to worse clinical outcomes. However, their heterogeneity and lack of specific markers have made them difficult to target therapeutically. Here, we define a senescent hepatocyte gene signature (SHGS) using in vitro and in vivo models and show that it tracks with MASLD progression/regression across mouse models and large human cohorts.

Read the full summary ↓

Single-nucleus RNA-sequencing and functional studies reveal that SHGS+ hepatocytes originate from p21+ cells, lose key liver functions and release factors that drive disease progression. One such factor, GDF15, increases in circulation alongside SHGS+ burden and disease progression. Through chemical screening, we identify senolytics that selectively eliminate SHGS+ hepatocytes and improve MASLD in mice. Notably, SHGS enrichment also correlates with dysfunction in other organs. These findings establish SHGS+ hepatocytes as key drivers of MASLD and highlight a potential therapeutic strategy for targeting senescent cells in liver disease and beyond.

Published in

Targeting senescent hepatocytes for treatment of metabolic dysfunction-associated steatotic liver disease and multi-organ dysfunction

Du K, Umbaugh DS, Wang L et al. · Nature communications 2025 · PMID 40155379 · doi:10.1038/s41467-025-57616-w

This dataset

Open GSE288534 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 GSE288534_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 PRJNA1218242 and SRA study SRP561020. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40155379) ↗

Study design

6 conditions, mostly with about 5 replicates each

Veh ×5 DpC ×5 Huh7_Veh ×4 Huh7_Dp44mT ×4 Control ×3 Palbo ×3

Supports a between-group comparison across 24 samples.

How we read this: 6 replicated groups read from 24 sample titles; they account for 24 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

GSM8769547 Veh_1 Veh
GSM8769548 Veh_2 Veh
GSM8769549 Veh_3 Veh
GSM8769550 Veh_4 Veh
GSM8769551 Veh_5 Veh
GSM8769552 DpC_1 DpC
GSM8769553 DpC_2 DpC
GSM8769554 DpC_3 DpC
GSM8769555 DpC_4 DpC
GSM8769556 DpC_5 DpC
GSM8769557 Huh7_Veh_1 Huh7_Veh
GSM8769558 Huh7_Veh_2 Huh7_Veh
GSM8769559 Huh7_Veh_3 Huh7_Veh
GSM8769560 Huh7_Veh_4 Huh7_Veh
GSM8769561 Huh7_Dp44mT_1 Huh7_Dp44mT
GSM8769562 Huh7_Dp44mT_2 Huh7_Dp44mT
GSM8769563 Huh7_Dp44mT_3 Huh7_Dp44mT
GSM8769564 Huh7_Dp44mT_4 Huh7_Dp44mT
GSM8769565 Control_1 Control
GSM8769566 Control_2 Control
GSM8769567 Control_3 Control
GSM8769568 Palbo_1 Palbo
GSM8769569 Palbo_2 Palbo
GSM8769570 Palbo_3 Palbo

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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.