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Effect of autophagy inhibition (ATG7/13 KD) on the transcriptome of proliferating and senescent cells

GSE272675 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2024/10/02 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 16 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
16 samples: 8 conditions, mostly in duplicateGroups 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
Autophagy-dependent splicing control directs translation toward inflammation during senescenceDevelopmental cell · 2025
Summary (written by the submitters)
To identify differentially expressed genes by autophagy inhibition during cellular senescence, we performed transcriptional profiling by RNA sequencing (RNA-seq) with or without autophagy inhibition (using a shRNA targeting either ATG7 or ATG13), before and after senescence induction using a stress-induced senescence model (bleomycin-induced senescence)
Published in
Autophagy-dependent splicing control directs translation toward inflammation during senescence
Kim J, Lee Y, Jeon T et al. · Developmental cell 2025 · PMID 39510077 · doi:10.1016/j.devcel.2024.10.008
This dataset
Open GSE272675 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 GSE272675_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 PRJNA1137945 and SRA study SRP521104. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39510077) ↗
Study design
8 conditions, mostly in duplicate
Proliferating IMR90 shATG13 dox ×2 Proliferating IMR90 shATG13 dox+ ×2 Senescent IMR90 shATG13 dox ×2 Senescent IMR90 shATG13 dox+ ×2 Proliferating IMR90 shATG7 dox ×2 Proliferating IMR90 shATG7 dox+ ×2 Senescent IMR90 shATG7 dox ×2 Senescent IMR90 shATG7 dox+ ×2

Supports a between-group comparison across 16 samples.

How we read this: 8 replicated groups read from 16 sample titles; they account for 16 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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