Related Experiment Video
Updated: Aug 8, 2026

Pooled CRISPR-Based Genetic Screens in Mammalian Cells
Published on: September 4, 2019
Dissecting cellular ecosystem with single-cell CRISPR screens
Zhirui Liu1,2,3, Zilin Lan1,2,3, Xiaoli Kang2
1State 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.
None:
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).

