GEO series

CRISPR activation screens identify the SWI-SNF ATPases as suppressors of ferroptosis [NGS3752_RNA]

GSE245239 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2024/09/22 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: 12 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.
Paper: CRISPR activation screens identify the SWI/SNF ATPases as suppressors of ferroptosisCell reports · 2024

Summary (written by the submitters)

Ferroptosis is an iron-dependent cell death mechanism characterized by an accumulation of toxic lipid peroxides and membrane rupture. The glutathione dependent enzyme, GPX4 (glutathione peroxidase 4), prevents ferroptosis by reducing these lipid peroxides into non-toxic lipid alcohols. Ferroptosis induction by GPX4 inhibition has emerged as a vulnerability of cancer cells, thus highlighting the need to identify ferroptosis regulators that may be exploited therapeutically.

Read the full summary ↓

Through genome-wide screens and a series of genetic, genomic, and quantitative imaging approaches, we identify the SWI-SNF ATPases BRM and BRG1 as ferroptosis suppressors. Mechanistically, they directly bind to and catalytically increase chromatin accessibility at NRF2 target loci, thus boosting NRF2 transcriptional output. This primes cells to counter lipid peroxidation and confers resistance to GPX4 inhibition and ferroptosis. Importantly, we demonstrate that the BRM/BRG1-ferroptosis connection can be leveraged to enhance the paralog dependency of BRG1-mutant lung cancer cells on BRM, especially in lines that are less sensitive to BRM inhibition or degradation. Our data reveal ferroptosis induction as a potential avenue for broadening the efficacy of BRM degraders/inhibitors and define a specific genetic context for exploiting GPX4 dependency.

Published in

CRISPR activation screens identify the SWI/SNF ATPases as suppressors of ferroptosis

Bhat KP, Vijay J, Vilas CK et al. · Cell reports 2024 · PMID 38870012 · doi:10.1016/j.celrep.2024.114345

This dataset

Open GSE245239 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 GSE245239_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 PRJNA1027456 and SRA study SRP466047. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38870012) ↗

Study design

12 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 12 sample titles: 12 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

GSM7839352 SAM24401733_WT_Rep1
GSM7839353 SAM24401734_WT_Rep2
GSM7839354 SAM24401735_WT_Rep3
GSM7839355 SAM24401736_TM_Rep1
GSM7839356 SAM24401737_TM_Rep2
GSM7839357 SAM24401738_TM_Rep3
GSM7839358 SAM24401739_KR_Rep1
GSM7839359 SAM24401740_KR_Rep2
GSM7839360 SAM24401741_KR_Rep3
GSM7839361 SAM24401742_LACZ_Rep1
GSM7839362 SAM24401743_LACZ_Rep2
GSM7839363 SAM24401744_LACZ_Rep3

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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.