Evidence map›Paper›PMID 40389438›Full record

ArticleNature communications2025

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

Amitabh C Pandey, Jon Bezney, Dante DeAscanis, Ethan B Kirsch, Farin Ahmed, Austin Crinklaw, Kumari Sonal Choudhary, Tony Mandala, Jeffrey Deason, Jasmin S Hamidi and 8 more

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Amitabh C Pandey *Section of Cardiology, Tulane Heart and Vascular Institute, Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA. apandey@tulane.edu.ORCID http://orcid.org/0000-0002-1541-7023
Jon Bezney *Genomics Core Facility, The Scripps Research Institute, La Jolla, CA, USA.
Dante DeAscanis *Jumpcode Genomics, San Diego, CA, USA.
Ethan B KirschDepartment of Molecular Medicine, Scripps Research Translational Institute, The Scripps Research Institute, La Jolla, CA, USA.
Farin AhmedGenomics Core Facility, The Scripps Research Institute, La Jolla, CA, USA.
Austin CrinklawJumpcode Genomics, San Diego, CA, USA.ORCID http://orcid.org/0000-0002-3586-4533
Kumari Sonal ChoudharyJumpcode Genomics, San Diego, CA, USA.
Tony MandalaGenomics Core Facility, The Scripps Research Institute, La Jolla, CA, USA.
Jeffrey DeasonJumpcode Genomics, San Diego, CA, USA.
Jasmin S HamidiDepartment of Molecular Medicine, Scripps Research Translational Institute, The Scripps Research Institute, La Jolla, CA, USA.
Azeem SiddiqueJumpcode Genomics, San Diego, CA, USA.
Sridhar RanganathanJumpcode Genomics, San Diego, CA, USA.
Keith BrownJumpcode Genomics, San Diego, CA, USA.ORCID http://orcid.org/0000-0003-3487-3005
Jon ArmstrongJumpcode Genomics, San Diego, CA, USA.
Steven HeadGenomics Core Facility, The Scripps Research Institute, La Jolla, CA, USA.
Phillip OrdoukhanianGenomics Core Facility, The Scripps Research Institute, La Jolla, CA, USA.
Lars M SteinmetzDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-3962-2865
Eric J TopolDepartment of Molecular Medicine, Scripps Research Translational Institute, The Scripps Research Institute, La Jolla, CA, USA.ORCID http://orcid.org/0000-0002-1478-4729

Funding

Scripps Translational Science InstituteUL1TR002550 · NCATS · SCRIPPS RESEARCH INSTITUTE, THE · PI TOPOL, ERIC JEFFREY · 2018 to 2022
$28.9M
Institutional Career Development CoreKL2TR002552 · NCATS · SCRIPPS RESEARCH INSTITUTE, THE · PI NICHOLSON, LAURA · 2018 to 2022
$5.2M
American Heart Association (American Heart Association, Inc.) 20CDA35310187NCATS NIH HHS KL2 TR002552NCATS NIH HHS UL1 TR002550U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (NCATS) KL2TR002552U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (NCATS) UL1TR002550
6 · The paper itself

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.

Indexed as

CRISPR-Cas SystemsGene Expression ProfilingSingle-Cell AnalysisTranscriptomeAnimalsHigh-Throughput Nucleotide SequencingHumansMiceMuscle, Smooth, VascularMyocytes, Smooth MuscleRNA-SeqSequence Analysis, RNA

Identifiers

PMID40389438
PMCPMC12089397

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.