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

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 can obscure the detection of biologically distinct transcripts expressed up to several orders of magnitude lower levels. To address this challenge, here we introduce single-cell CRISPRclean (scCLEAN), a molecular method that globally recomposes scRNAseq libraries, providing a benefit that cannot be recapitulated with deeper sequencing. 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 experimentally apply scCLEAN to both heterogeneous immune cells and homogenous vascular smooth muscle cells to demonstrate its ability to uncover 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 possible 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.

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Publication Details

Journal
Nature Communications
Published
2025-05-19
DOI
https://doi.org/10.1038/s41467-025-59880-2
Citations
6
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
4.84

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article

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

Lars Michael Steinmetz, Amitabh C. Pandey, Dante DeAscanis, Sridhar Ranganathan et al.
6 citations
Nature Communications
Single-cell and spatial transcriptomics
4.84
article

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

Lars Michael Steinmetz, Amitabh C. Pandey, Dante DeAscanis, Sridhar Ranganathan, Austin Crinklaw, Jeffrey Deason, Tony Mandala, Azeem Siddique, Keith Brown, Jon R. Armstrong, Ethan Kirsch, Steven R. Head, Jasmin Hamidi, Farin Ahmed, Jon Bezney, Kumari Sonal Choudhary, Phillip Ordoukhanian, Eric J. Topol
article en
6 citations

Abstract

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 can obscure the detection of biologically distinct transcripts expressed up to several orders of magnitude lower levels. To address this challenge, here we introduce single-cell CRISPRclean (scCLEAN), a molecular method that globally recomposes scRNAseq libraries, providing a benefit that cannot be recapitulated with deeper sequencing. 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 experimentally apply scCLEAN to both heterogeneous immune cells and homogenous vascular smooth muscle cells to demonstrate its ability to uncover 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 possible 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.

Nature CommunicationsVol. 16(1)
Tulane University (US), Scripps Research Institute (US), Southeast Louisiana Veterans Health Care System (US), European Molecular Biology Laboratory (DE), Pathway Genomics (United States) (US), Stanford University (US)
American Heart Association, National Institutes of Health, National Center for Advancing Translational Sciences
Openalex Percentile: Top 5%
Single-cell and spatial transcriptomics
4.84
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