GEO series

Multiplex generation and single cell analysis of structural variants in mammalian genomes [scRNA-seq]

GSE282634 Homo sapiens (human) Single-cell RNA-seq 8 samples Submitted 2024/12/01 Platform GPL30173Platform GPL30172

At a glance

Single-cell RNA-seq · human · 8 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: 8 samples: 8 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: Multiplex generation and single-cell analysis of structural variants in mammalian genomesScience (New York, N.Y.) · 2025

Summary (written by the submitters)

Studying the functional consequences of structural variants (SVs) in mammalian genomes is challenging because: 1) SVs arise much less commonly than single nucleotide variants or small indels; and 2) methods to generate, map and characterize SVs in model systems are underdeveloped. To address these challenges, we developed Genome-Shuffle-seq, a method that enables the multiplex generation and mapping of thousands of SVs (deletions, inversions, translocations, extrachromosomal circles) throughout mammalian genomes.

Read the full summary ↓

We also demonstrate the co-capture of SV identity with single-cell transcriptomes, facilitating the measurement of SVs’ impact on gene expression. We anticipate Genome-Shuffle-seq will be broadly useful for the systematic exploration of the functional consequences of SVs on gene expression, chromatin landscape, and 3D nuclear architecture, while also initiating a path towards a minimal mammalian genome.

Published in

Multiplex generation and single-cell analysis of structural variants in mammalian genomes

Pinglay S, Lalanne JB, Daza RM et al. · Science (New York, N.Y.) 2025 · PMID 39883753 · doi:10.1126/science.ado5978

This dataset

Open GSE282634 in finder → Find more 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 GSE282634_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 PRJNA1189510 and SRA study SRP547016. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39883753) ↗

Study design

8 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 8 sample titles: 8 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

GSM8647865 loxPsym_mESC_Parental_GEX_run2
GSM8647866 loxPsym_mESC_Cre_GEX_run2
GSM8647867 loxPsym_mESC_Parental_CS
GSM8647868 loxPsym_mESC_Cre_CS
GSM8647869 attB/P_mESC_K562_Lane1_FB_CS
GSM8647870 attB/P_K562_Lane2_FB_CS
GSM8647871 attB/P_mESC_K562_Lane1_GEX
GSM8647872 attB/P_K562_Lane2_GEX

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Search all RNA-seq datasets in GEO →

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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.