GEO series

Simultaneous single cell CRISPR, RNA, and ATAC-seq enables multiomic CRISPR screens to identify gene regulatory relationships

GSE288996 Homo sapiens (human) Single-cell RNA-seq 15 samples Submitted 2025/02/14 Platform GPL34281

At a glance

Single-cell RNA-seq · human · 15 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: 15 samples: 15 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.

Summary (written by the submitters)

Identifying gene functions and interactions in specific cellular contexts has been greatly enabled with functional genomics studies. CRISPR-based genetic screens have been proven invaluable in elucidating gene function in mammalian cells. Single cell functional genomics methods, such as Perturb-seq and Spear-ATAC, have made it possible to achieve high-throughput mapping of the functional effects of gene perturbations by profiling transcriptomes and DNA accessibility, respectively.

Read the full summary ↓

Combining single-cell chromatin accessibility and transcriptomic data via multiomics technologies facilitates the discovery of novel cis-regulatory and gene regulatory interactions, however, bulk readouts complicate this analysis due to a heterogeneous response of cells to the same genetic perturbation/s, which could be mitigated by using transcriptional profiles of single cells to subset the ATAC-seq data. Existing methods to capture CRISPR guide RNAs to simultaneously assess the impact of genetic perturbations on RNA and ATAC profiles either require cloning gRNA libraries in specialized vectors or use complex protocols with multiple rounds of barcoding. Here, we introduce CAT-ATAC, a technique that simply captures CRISPR gRNAs within the 10X Genomics Multiome assay, generating paired transcriptome, chromatin accessibility and perturbation identity data from the same cells. We demonstrate up to 77% guide capture efficiency for both arrayed and pooled lentiviral transductions of sgRNAs in induced pluripotent stem cells (iPSCs) and cancer cell lines. This capability allows us to construct gene regulatory networks (GRNs) in cells under drug and genetic perturbation. We identified a GRN associated for dasatinib resistance, indirectly activated by the HIC2 gene. Using loss of function experiments, we further validated that the gene, ZFPM2, a component of the predicted GRN, also contributes to dasatinib resistance. CAT-ATAC can thus be used to generate high-content multidimensional genotype-phenotype maps to reveal novel gene and cellular interactions and functions.

This dataset

Open GSE288996 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 GSE288996_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 PRJNA1220572 and SRA study SRP562191. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

15 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 15 sample titles: 15 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

GSM8780553 iPSC, scATACseq
GSM8780554 iPSC, GEX
GSM8780555 iPSC, guideRNA
GSM8780556 Multiome, K562, Dasatinib treated, Replicate 1, scATACseq
GSM8780557 Multiome, K562, Dasatinib treated, Replicate 2, scATACseq
GSM8780558 Multiome, K562, DMSO treated, Replicate 1, scATACseq
GSM8780559 Multiome, K562, DMSO treated, Replicate 2, scATACseq
GSM8780560 K562, Dasatinib treated, Replicate 1, guideRNA library
GSM8780561 K562, Dasatinib treated, Replicate 2, guideRNA library
GSM8780562 K562, DMSO treated, Replicate 1, guideRNA library
GSM8780563 K562, DMSO treated, Replicate 2, guideRNA library
GSM8780564 Multiome, K562, Dasatinib treated, Replicate 1, GEX
GSM8780565 Multiome, K562, Dasatinib treated, Replicate 2, GEX
GSM8780566 Multiome, K562, DMSO treated, Replicate 1, GEX
GSM8780567 Multiome, K562, DMSO treated, Replicate 2, GEX

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