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CRISPR-StAR enables high-resolution genetic screening in complex in vivo models
Esther C H Uijttewaal1,2, Joonsun Lee1,2, Annika Charlotte Sell1
1Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Dr. Bohr-Gasse 3, Vienna BioCenter (VBC), Vienna, Austria.
Nature Biotechnology
|December 16, 2024
Summary
CRISPR-StAR improves genome-wide genetic screening by using internal controls within cell progeny. This novel method enhances data quality and overcomes limitations in complex biological models for more robust gene-phenotype mapping.
Area of Science:
- Genomics
- Molecular Biology
- Biotechnology
Background:
- Pooled CRISPR-Cas9 screening enables genome-wide gene-phenotype mapping.
- Assessing genetic perturbations requires large cell numbers to mitigate stochastic drift.
- Complex models like organoids and mouse xenografts present scaling challenges due to heterogeneity and bottlenecks.
Purpose of the Study:
- To introduce CRISPR-StAR, a novel screening method overcoming limitations of conventional pooled genetic screens.
- To generate clonal, single-cell-derived intrinsic controls for enhanced data quality.
- To identify in vivo-specific genetic dependencies in complex biological systems.
Main Methods:
- CRISPR-StAR activates single-guide RNAs (sgRNAs) in half of each cell's progeny post-re-expansion.
- This generates intrinsic controls within clonal cell populations.
- The method was applied to a genome-wide screen in mouse melanoma models.
Main Results:
- CRISPR-StAR effectively overcomes intrinsic and extrinsic cellular heterogeneity.
- The method mitigates genetic drift encountered in bottleneck events.
- Benchmarking demonstrated superior data quality compared to conventional screening approaches.
Conclusions:
- CRISPR-StAR provides a robust solution for high-resolution genetic screening in complex and heterogeneous models.
- The technology enables more accurate identification of gene functions and dependencies.
- This advancement facilitates the study of in vivo-specific genetic dependencies, particularly in cancer research.

