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RNA sequestration in P-bodies sustains myeloid leukaemia

GSE224643 Homo sapiens (human) RNA-seq (gene expression) 14 samples Submitted 2024/08/08 Platform GPL21103Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 14 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)
Topic
Acute myeloid leukemiaThis series is part of our curated briefing on it.
Samples
14 samples: 3 × HL60_shDDX6_Input vs 3 × HL60_shDDX6_Polysomes vs 3 × HL60_shCTRL_Input vs 3 × HL60_shCTRL_PolysomesGroups 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
RNA sequestration in P-bodies sustains myeloid leukaemiaNature cell biology · 2024
Summary (written by the submitters)
Post-transcriptional mechanisms are fundamental safeguards of progenitor cell identity and are often dysregulated in cancer. Here, we identified regulators of P-bodies as crucial vulnerabilities in acute myeloid leukaemia (AML) through genome-wide CRISPR screens in normal and malignant haematopoietic progenitors. We found that leukaemia cells harbour aberrantly elevated numbers of P-bodies and show that P-body assembly is crucial for initiation and maintenance of AML.
Read the full summary ↓
Notably, P-body loss had little effect upon homoeostatic haematopoiesis but impacted regenerative haematopoiesis. Molecular characterization of P-bodies purified from human AML cells unveiled their critical role in sequestering messenger RNAs encoding potent tumour suppressors from the translational machinery. P-body dissolution promoted translation of these mRNAs, which in turn rewired gene expression and chromatin architecture in leukaemia cells. Collectively, our findings highlight the contrasting and unique roles of RNA sequestration in P-bodies during tissue homoeostasis and oncogenesis. These insights open potential avenues for understanding myeloid leukaemia and potential therapeutic interventions.
Published in
RNA sequestration in P-bodies sustains myeloid leukaemia
Kodali S, Proietti L, Valcarcel G et al. · Nature cell biology 2024 · PMID 39169219 · doi:10.1038/s41556-024-01489-6
This dataset
Open GSE224643 in finder → Find more RNA-seq datasets → Acute myeloid leukemia landscape →
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 GSE224643_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 PRJNA931996 and SRA study SRP421186. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39169219) ↗
Study design
3 × HL60_shDDX6_Input vs 3 × HL60_shDDX6_Polysomes vs 3 × HL60_shCTRL_Input vs 3 × HL60_shCTRL_Polysomes

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 14 sample titles; they account for 12 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
More Acute myeloid leukemia datasets

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