GEO series

SenCat: Redefining human cell senescence through multiomic profiling of multiple senescent cell types [scRNA-seq]

GSE285108 Mus musculus (mouse) Single-cell RNA-seq 9 samples Submitted 2026/06/15 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 9 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Mus musculus (mouse)
Samples: 9 samples: 9 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)

There is an urgent need to comprehensively catalog senescence markers across a wide range of cell types in an organism. Here, we profiled the transcriptomes and proteomes in over 30 senescence paradigms employing 14 different primary human cell types. Our results indicate that senescent cells from all tissue types do not share a unique marker, but they do share in the activation of specific metabolic pathways and damage response pathways to elicit tissue repair.

Read the full summary ↓

Importantly, considering combinations of some of the most widely shared senescence markers validated the presence of senescent-like cells in mice through single-cell RNA-sequencing and immunostaining approaches. The enclosed catalog represents a much-needed resource to identify senescent cells across tissues in the body.

This dataset

Open GSE285108 in finder → Find more mouse 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 GSE285108_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 PRJNA1201187 and SRA study SRP553023. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

9 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 9 sample titles: 9 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

GSM8696856 AAC5_1_M1_DMSO
GSM8696857 AAC5_2_M2_DMSO
GSM8696858 AAC5_3_M3_DMSO
GSM8696859 AAC5_4_M4_doxo
GSM8696860 AAC5_5_M5_doxo
GSM8696861 AAC5_6_M6_doxo
GSM8696862 AAC5_10_M10_doxo_ABT
GSM8696863 AAC5_11_M11_doxo_ABT
GSM8696864 AAC5_12_M12_doxo_ABT

Datasets on a similar topic

GSE302792 SenCat: Cataloging human cell senescence through multiomic profiling of multiple senescent primary cell types 33 samples shares senescence, multiomic, senescent, multiple, sencat, types
GSE294362 Aged bone marrow myeloid and mesenchymal cells develop unique senescence phenotypes 80 samples shares senescence, types
GSE271670 Identifying specific functional roles for senescence across cell types 32 samples shares senescence, types
GSE268386 Senescent microglia limit remyelination through the senescence associated secretory phenotype 18 samples shares senescence, senescent
GSE318804 P21+TREM2+-Senescent Macrophages Fuel Inflammaging and Metabolic Dysfunction-Associated Steatotic Liver Disease. [macro_senescence] 12 samples shares senescence, senescent
GSE280554 NNMT promotes tubular senescence and fibrosis in chronic kidney disease 92 samples shares senescence
GSE239602 Single-cell and spatial transcriptomics uncovers the role of senescent vascular cells in pathological arterial remodeling during murine atherosclerosis 82 samples shares senescent
GSE234245 Multiomic Profile Integration Reveals Early Signatures of Amyotrophic Lateral Sclerosis and Suggests the MAPK Pathway as a Therapeutic Target [RNA-Seq] 79 samples shares multiomic

Search all mouse 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.