GEO series

Senolytic-Resistant Senescent Cells Have a Distinct SASP Profile and Functional Impact: The Path to Developing Senosensitizers

GSE268701 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2025/05/30 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 12 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: 12 samples
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)

The senescent cell (SC) fate is linked to aging, multiple disorders and diseases, and physical dysfunction. Senolytics, agents that selectively eliminate 30-70% of SCs, act by transiently disabling the senescent cell anti-apoptotic pathways (SCAPs), which defend those SCs that are pro-apoptotic from their own senescence-associated secretory phenotype (SASP).

Read the full summary ↓

Consistent with this, a JAK/STAT inhibitor, Ruxolitinib, which attenuates the pro-apoptotic SASP of senescent human preadipocytes, caused them to become “senolytic-resistant”. Administering senolytics to obese mice selectively decreased abundance of the subset of SCs that is pro-inflammatory. In cell cultures, the 30-70% of human senescent preadipocytes or human umbilical vein endothelial cells (HUVECs) that are senolytic-resistant (to Dasatinib or Quercetin, respectively) had increased p16INK4a, p21CIP1, senescence-associated β-galactosidase (Saβgal), γH2AX, and proliferative arrest similar to the total SC population (comprising senolytic-sensitive plus -resistant SCs). However, the SASP of senolytic-resistant SCs entailed less pro-inflammatory/apoptotic factor production, induced less inflammation in non-senescent cells, and was equivalent or richer in growth/ fibrotic factors. Senolytic-resistant SCs released less mitochondrial DNA (mtDNA) and more highly expressed the anti-inflammatory, immune evasion signal, glycoprotein non-melanoma-B (GPNMB). Transplanting senolytic-resistant SCs intraperitoneally into younger mice caused less physical dysfunction than transplanting the total SC population. Since Ruxolitinib attenuates SC release of pro-apoptotic SASP factors while pathogen-associated molecular pattern factors (PAMPs) can amplify release of these factors rapidly (acting as “senosensitizers”), senolytic-resistant and senolytic-sensitive SCs appear to be interconvertible.

This dataset

Open GSE268701 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 GSE268701_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 PRJNA1118291 and SRA study SRP510869. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Samples in this study

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+ 12 more — browse all 12 samples with per-sample file links →

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GSE319937 Mitochondrial metabolism and epigenetic crosstalk drive the SASP (RNA-Seq_2) 18 samples shares sasp

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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.