scMAGeCK links genotypes with multiple phenotypes in single-cell CRISPR screens.

Area of Science:

• Genomics
• Computational Biology
• Molecular Biology

Background:

• CRISPR/Cas9 screening is powerful for functional genomics.
• Single-cell RNA sequencing (scRNA-seq) provides high-resolution expression data.
• Linking genotype to phenotype at a single-cell level remains challenging.

Purpose of the Study:

• To develop a computational framework, scMAGeCK, for analyzing CRISPR/Cas9 screening data with scRNA-seq readout.
• To identify genomic elements associated with multiple expression-based phenotypes.
• To construct genotype-phenotype networks from single-cell screening data.

Main Methods:

• scMAGeCK integrates CRISPR/Cas9 screening data with scRNA-seq.
• It analyzes associations between genomic perturbations and gene expression profiles.
• The framework was applied to mouse embryonic stem cell data.

Main Results:

• scMAGeCK outperforms existing methods in identifying genotype-phenotype associations.
• It identified known and novel genes and enhancers affecting cell proliferation.
• Key genes linked to pluripotency states were discovered in mouse ESCs.

Conclusions:

• scMAGeCK is an effective tool for genotype-phenotype relationship studies at the single-cell level.
• It enhances the power and scope of single-cell CRISPR screening.
• The framework facilitates unbiased network construction and discovery of functional genomic elements.