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PTPN23-dependent ESCRT machinery functions as a cell death checkpoint in restraining multiple cell death pathways

GSE272123 Homo sapiens (human) RNA-seq (gene expression) 76 samples Submitted 2024/10/16 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 76 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
76 samples: 27 conditions, mostly in triplicateGroups 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
PTPN23-dependent ESCRT machinery functions as a cell death checkpointNature communications · 2024
Summary (written by the submitters)
Cell death plasticity is crucial for modulating tissue homeostasis and immune responses, but our understanding of the molecular components that regulate cell death pathways to determine cell fate remains limited. Here, a CRISPR screen of acute myeloid leukemia cells identifies protein tyrosine phosphatase non-receptor type 23 (PTPN23) as essential for survival.
Read the full summary ↓
Loss of PTPN23 activates nuclear factor-kappa B, apoptotic, necroptotic, and pyroptotic pathways by causing the accumulation of death receptors and toll-like receptors (TLRs) in endosomes. These effects are recapitulated by depletion of PTPN23 co-dependent genes in the endosomal sorting complex required for transport (ESCRT) pathway. Through proximity-dependent biotin labeling, we show that NAK-associated protein 1 interacts with PTPN23 to facilitate endosomal sorting of tumor necrosis factor receptor 1 (TNFR1), sensitizing cells to TNF-alpha-induced cytotoxicity. Our findings reveal PTPN23-dependent ESCRT machinery as a cell death checkpoint that regulates the spatiotemporal distribution of death receptors and TLRs to restrain multiple cell death pathways.
Published in
PTPN23-dependent ESCRT machinery functions as a cell death checkpoint
Song D, Cen Y, Qian Z et al. · Nature communications 2024 · PMID 39609437 · doi:10.1038/s41467-024-54749-2
This dataset
Open GSE272123 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 GSE272123_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 PRJNA1135166 and SRA study SRP519534. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39609437) ↗
Study design
27 conditions, mostly in triplicate
NOMO-1, sgNEG control_for_CLTC, D6, ×3 NOMO-1, sgCLTC-1, D6, ×3 NOMO-1, sgCLTC-2, D6, ×3 NOMO-1, sgNEG control_for_HGS, D6, ×3 NOMO-1, sgHGS-1, D6, ×3 NOMO-1, sgHGS-2, D6, ×3 NOMO-1, sgNEG control_for_PTPN23, D6, ×3 NOMO-1, sgNEG control_for_TSG101, D6, ×3 +26 more

Supports a between-group comparison across 69 samples.

How we read this: 27 replicated groups read from 76 sample titles; they account for 69 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

+ 36 more — browse all 76 samples with per-sample file links →

Datasets on a similar topic

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