GO-CRISPR

GO-CRISPR implements guide-only control workflows and pooled sgRNA library quality assessments to improve detection of gene essentiality in loss-of-function genome-wide CRISPR screens, enabling analysis in contexts such as non-proliferative 3D spheroid models and dormant high-grade serous ovarian cancer cells while pairing with the TRACS software package for ranked analysis of screen data.

Key Features:

• Guide-only controls: Experimental guide-only controls permit discrimination of decreased sgRNA sequences in loss-of-function screens.
• Pooled sgRNA library quality assessment: Assessment of pooled sgRNA library representation and quality to support accurate interpretation of guide depletion.
• Enhanced detection of depleted sgRNAs: Improved sensitivity for detecting decreased sgRNA sequences and expanded dynamic range in loss-of-function screens.
• Compatibility with non-proliferative models: Enables gene essentiality analysis in non-proliferative 3D spheroid models and dormant cell states.
• TRACS (Toolset for the Ranked Analysis of GO-CRISPR Screens): Provides ranking and analysis of GO-CRISPR screen data to identify essential genes and pathways.
• Pathway discovery: Facilitates identification of molecular pathways involved in tumor dormancy.
• Genome-wide loss-of-function support: Applicable to genome-wide CRISPR loss-of-function screening formats and pooled sgRNA libraries.
• Demonstrated performance: Demonstrated improved performance relative to standard screening methods in dormant high-grade serous ovarian cancer cells.

Scientific Applications:

• Gene essentiality screening: Identify genes essential for cell fitness using changes in sgRNA abundance from loss-of-function CRISPR screens.
• 3D spheroid models: Analyze gene dependencies in non-proliferative 3D spheroid model systems.
• Tumor dormancy studies: Detect essential genes and pathways in dormant high-grade serous ovarian cancer cells.
• Pathway identification: Discover novel molecular pathways underlying tumor dormancy and related cellular processes.
• Sensitive sgRNA depletion detection: Improve detection of depleted sgRNAs in contexts with limited dynamic range compared to conventional screens.

Methodology:

TRACS ranks and analyzes GO-CRISPR screen data, and GO-CRISPR incorporates guide-only controls and pooled sgRNA library quality assessments to inform identification of essential genes.