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SenCat: Redefining human cell senescence through multiomic profiling of multiple senescent cell types

GSE284666 Homo sapiens (human) Single-cell RNA-seq 190 samples Submitted 2026/06/15 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 190 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. 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
190 samples: 48 conditions, mostly with about 4 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.
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 GSE284666 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 GSE284666_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 PRJNA1200021 and SRA study SRP552543. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
48 conditions, mostly with about 4 replicates each
BJ_P ×4 BJ_ETIS ×4 BJ_IRIS ×4 BJ_OSIS ×4 BJ_EV ×4 BJ_OIS ×4 BMMSC_P ×4 BMMSC_ETIS ×4 +40 more

Supports a between-group comparison across 190 samples.

How we read this: 48 replicated groups read from 190 sample titles; they account for 190 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

+ 150 more — browse all 190 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.