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Clonal lineage tracing and parallel multiomics profiling reveal transcriptional diversification induced by ARID1A deficiency

GSE296529 Homo sapiens (human) Single-cell RNA-seq 1 samples Submitted 2025/05/23 Platform GPL18573
At a glance

Single-cell RNA-seq · human · 1 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Samples
1 samples
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.
Summary (written by the submitters)
Phenotypic heterogeneity among genetically identical cancer cells underpins tumor progression and therapy resistance. However, how epigenetic dysregulation drives such divergence remains unclear. Here, we introduce IMPACH, a scalable platform that integrates lineage tracing, genetic perturbation, and multimodal single-cell analysis. Applying IMPACH, we demonstrate that loss of epigenetic regulators—particularly ARID1A—enhances transcriptomic and epigenetic heterogeneity under controlled conditions.
Read the full summary ↓
ARID1A deficiency promotes stochastic chromatin opening and induces an atypical gene expression program associated with poor clinical outcomes. Despite increased epigenetic randomness, chromatin changes remain localized to biologically meaningful regulatory elements, partially linked to SMARCA4 binding sites. These findings reveal that chromatin remodeling defects promote clonal diversification through both stochastic and constrained mechanisms. Given its scalability and versatility, IMPACH provides a powerful framework for dissecting how epigenetic perturbations shape cellular heterogeneity in cancer.
This dataset
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.

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 GSE296529_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1259907 and SRA study SRP583554. Searching any of these in the dataset finder brings you back here.

Samples in this study

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