GEO series

Multiplexed single-cell characterization of alternative polyadenylation regulators (scRNA-Seq)

GSE269596 Homo sapiens (human) Single-cell RNA-seq 12 samples Submitted 2024/06/15 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 12 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
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: Multiplexed single-cell characterization of alternative polyadenylation regulatorsCell · 2024

Summary (written by the submitters)

Most mammalian genes have multiple polyA sites, representing a substantial source of transcript diversity regulated by the cleavage and polyadenylation (CPA) machinery. To better understand how these proteins govern polyA site choice we introduce CPA-Perturb-seq, a multiplexed perturbation screen dataset of 42 CPA regulators with a 3’ scRNA-seq readout that enables transcriptome-wide inference of polyA site usage.

Read the full summary ↓

We develop a framework to detect perturbation-dependent changes in polyadenylation and characterize modules of co-regulated polyA sites. We find groups of intronic polyA sites regulated by distinct components of the nuclear RNA life cycle including elongation, splicing, termination, and surveillance. We train and validate a multi-task deep neural network (APARENT-Perturb) for tandem polyA site usage, delineating a cis-regulatory code that predicts perturbation response and reveals interactions between regulatory complexes. Our work highlights the potential for multiplexed single-cell perturbation screens to further our understanding of post-transcriptional regulation.

Published in

Multiplexed single-cell characterization of alternative polyadenylation regulators

Kowalski MH, Wessels HH, Linder J et al. · Cell 2024 · PMID 38925112 · doi:10.1016/j.cell.2024.06.005

This dataset

Open GSE269596 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 GSE269596_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1122596 and SRA study SRP513269. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38925112) ↗

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

GSM8322741 HEK293FT, replicate 1 GEX
GSM8322742 HEK293FT, replicate 1 gRNA
GSM8322743 HEK293FT, replicate 1 HTO
GSM8322744 HEK293FT, replicate 2 GEX
GSM8322745 HEK293FT, replicate 2 gRNA
GSM8322746 HEK293FT, replicate 2 HTO
GSM8322747 K562, replicate 1 GEX
GSM8322748 K562, replicate 1 gRNA
GSM8322749 K562, replicate 1 HTO
GSM8322750 K562, replicate 2 GEX
GSM8322751 K562, replicate 2 gRNA
GSM8322752 K562, replicate 2 HTO

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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.