Experimental Design: Genome-Wide CRISPR Screen for Drug Resistance Mechanisms

1. Experimental Purpose

• Scientific Question: Which genes, when knocked out, confer resistance to drug X in cancer cell line Y?
• Design Rationale: Genome-wide CRISPR-Cas9 knockout screen allows unbiased identification of genes whose loss enables cell survival under drug selection pressure
• Follow-up Studies: Validate top hits with individual knockouts, investigate mechanism of resistance for key genes, test combinations of drug X with inhibitors targeting resistance pathways

2. Model System

• Primary System: Human cancer cell line relevant to drug X's therapeutic application (e.g., A375 melanoma cells for BRAF inhibitor screen)
• Rationale: Cancer cell lines provide stable Cas9 expression, consistent growth, and clinically relevant drug responses while enabling high-throughput screening
• Alternatives:
• Patient-derived xenografts (pros: better clinical relevance; cons: more variable, complex, expensive)
• Primary patient cells (pros: direct clinical relevance; cons: limited expansion, variable Cas9 efficiency)
• Immortalized non-cancer cells (pros: define cancer-specific vs. general mechanisms; cons: may lack disease context)
• Ethical Considerations: Cell line authentication, appropriate biosafety practices, responsible use of patient-derived materials if applicable

3. Measurement Approach

• Techniques:
• Lentiviral delivery of genome-wide gRNA library
• Next-generation sequencing of gRNA abundance
• Drug dose-response assays for validation
• Western blotting and RT-qPCR for mechanism studies
• Technical Replicates: Duplicate NGS library preparations
• Potential Biases:
• Variable Cas9 editing efficiency (use cells with validated high Cas9 activity)
• Lentiviral MOI variations (maintain >500x library coverage throughout)
• gRNA design efficiency differences (use validated libraries with multiple guides per gene)
• PCR amplification bias (minimize PCR cycles, use UMIs if possible)

4. Group Setting

• Experimental Groups:
• Treatment: Cells with gRNA library exposed to drug X at IC70-IC90 concentration
• Control 1: Cells with gRNA library without drug treatment (T0 reference)
• Control 2: Cells with gRNA library grown in parallel without drug (time-matched control)
• Control 3: Cells with non-targeting gRNA library with drug treatment
• Controlled Variables: Cell passage number, Cas9 expression level, library coverage, drug concentration, treatment duration
• Biological Replicates: 3-4 independent infections and selections
• Modified Design: Include multiple drug concentrations to identify dose-dependent resistance mechanisms, or combine with CRISPRa screen to identify both loss- and gain-of-function resistance mechanisms

5. Data Analysis & Presentation

• Data Processing:
• gRNA counting from raw NGS reads
• Normalization for sequencing depth
• Guide-level fold-change calculation (treatment vs. control)
• Gene-level enrichment scores using algorithms like MAGeCK or BAGEL
• Statistical Analysis:
• False discovery rate correction for multiple hypothesis testing
• Robust rank aggregation for combining multiple guides per gene
• Gene set enrichment analysis for pathway-level insights
• Principal component analysis to assess replicate consistency
• Data Presentation:
• Volcano plots showing gene enrichment/depletion significance
• Ranked bar charts of top hits with statistical significance
• Pathway enrichment bubble plots
• Validation data for selected hits showing individual knockout phenotypes
• Network visualization of functionally related hits
• Validation Methods:
• Individual CRISPR knockout of top hits
• Rescue experiments with cDNA expression
• Dose-response curves with and without gene knockout
• Combinatorial drug testing targeting resistance pathways
• Protein-protein interaction studies to elucidate mechanisms