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A CRISPR/Cas9-based enhancement of high-throughput single-cell transcriptomics

GSE283554 Homo sapiens (human) Single-cell RNA-seq 22 samples Submitted 2024/12/19 Platform GPL30173Platform GPL28352
At a glance

Single-cell RNA-seq · human · 22 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
22 samples: 5 conditions, mostly with about 4 replicates eachGroups 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
A CRISPR/Cas9-based enhancement of high-throughput single-cell transcriptomicsNature communications · 2025
Summary (written by the submitters)
Single-cell RNA-seq (scRNAseq) struggles to capture the cellular heterogeneity of transcripts within individual cells due to the prevalence of highly abundant and ubiquitous transcripts, which obscure the detection of biologically distinct transcripts expressed at up to several orders of magnitude lower levels. To address this challenge, we introduce single-cell CRISPRclean (scCLEAN), a molecular method that globally recomposes scRNAseq libraries, providing a benefit that cannot be recapitulated with deeper sequencing.
Read the full summary ↓
scCLEAN utilizes the programmability of CRISPR/Cas9 to target and remove less than 1% of the transcriptome while redistributing approximately half of reads, shifting the focus toward less abundant transcripts. We then experimentally apply scCLEAN to both heterogeneous immune cells and homogenous vascular smooth muscle cells to demonstrate its ability to uncover novel biological signatures in different biological contexts. We further emphasize scCLEAN’s versatility by applying it to a third-generation sequencing method, single-cell MAS-Seq, to increase transcript-level detection and discovery. Here we show, the projected utility of scCLEAN across a wide array of human tissues and cell types, indicating which contexts this technology proves beneficial and those in which its application is not advisable.
Published in
A CRISPR/Cas9-based enhancement of high-throughput single-cell transcriptomics
Pandey AC, Bezney J, DeAscanis D et al. · Nature communications 2025 · PMID 40389438 · doi:10.1038/s41467-025-59880-2
This dataset
Open GSE283554 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 GSE283554_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 PRJNA1194517 and SRA study SRP582612. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40389438) ↗
Study design
5 conditions, mostly with about 4 replicates each
VSMC, Pulmonary, 10xv3, Patient ×4 VSMC, Pulmonary, scCLEAN, Patient ×4 PBMC, scCLEAN, sample ×3 VSMC, Coronary, scCLEAN, Patient ×3 VSMC, Coronary, 10xv3, Patient ×2 PBMC, 10xv3, control, sample ×1 VSMC, Coronary, scCLEAN, Patient ×1 VSMC, Coronary 10xv3, Patient ×1 +3 more

Supports a case/control comparison: 15 samples read as cases, 1 as controls.

How we read this: 5 replicated groups read from 22 sample titles; they account for 16 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
Datasets on a similar topic

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