Evidence map›Paper›PMID 41356789›Full record

ArticleBlood science (Baltimore, Md.)2025

Dissecting cellular ecosystem with single-cell CRISPR screens.

Zhirui Liu, Zilin Lan, Xiaoli Kang, Yao Yao, Shuquan Rao

Abstract read
In one paragraph

Article in Blood science (Baltimore, Md.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Zhirui LiuState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China.
Zilin LanState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China.
Xiaoli KangHaihe Laboratory of Cell Ecosystem, Tianjin 300051, China.
Yao YaoState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China.
Shuquan RaoState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clustered regularly interspaced short palindromic repeats (CRISPR) screens represent a transformative force in biological discovery, enabling the unbiased interrogation of gene function in a wide range of applications. Traditional screening approaches predominantly hinge on cell fitness or established markers, which inherently constrain their abilities for unbiased biological discovery. By contrast, single-cell CRISPR screening technologies, which combine pooled CRISPR screens with an array of sophisticated single-cell omics platforms, permit comprehensive profiling of the transcriptome and epigenome following individual genetic manipulations within complex cellular ecosystems. Over the past decade, a panoply of single-cell CRISPR platforms has emerged, each tailored to address specific experimental challenges. Iterative refinements in protocols have bolstered precision, scalability, and reproducibility, thereby enormously advancing functional genomics and translational research. However, technical obstacles such as perturbation efficiency, scalability, and data integration persist, necessitating cross-disciplinary collaboration and innovation. As single-cell CRISPR platforms evolve to incorporate spatial resolution, multi-omics integration, and AI-guided design, they are poised to bridge the gap between genetic perturbation and system-level interpretation. Here, we summarize recent advances in single-cell CRISPR technologies, outline their applications, and provide a comparative framework to guide platform selection (Perturb-seq, CROP-seq, ECCITE-seq, Direct-seq, and Mosaic-seq).

Indexed as

Functional genomicsSingle-cell CRISPR screen

Identifiers

PMID41356789
PMCPMC12680425

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.