A multi-biomarker micronucleus assay using imaging flow cytometry

Area of Science:

• Toxicology
• Molecular Biology
• Cell Biology

Background:

• In vitro genetic toxicity tests often yield misleading results, leading to unnecessary animal testing and the premature rejection of promising drug candidates.
• Understanding the chemical Mode of Action (MoA) is crucial for accurately assessing genotoxic potential and clinical risk.
• Current methods can struggle with high false positive rates, necessitating improved screening assays.

Purpose of the Study:

• To develop a robust, high-throughput protocol for genetic toxicity testing using imaging flow cytometry.
• To enable simultaneous assessment of DNA damage biomarkers and micronucleus (MN) formation in un-lysed cells.
• To accurately differentiate clastogenic and aneugenic chemical MoAs for improved drug development screening.

Main Methods:

• Developed a multiplex staining protocol for fixed human lymphoblast TK6 cells using antibodies against ɣH2AX, p53, pH3S28, and DRAQ5™ DNA stain.
• Utilized the Cytek® Amnis® ImageStream®X Mk II imaging flow cytometer for high-throughput data acquisition.
• Implemented an automated masking and gating strategy within IDEAS® 6.2 software for quantifying cell-cycle, MN, and biomarker populations.

Main Results:

• Successfully demonstrated a multiplex system for DNA damage assessment and MN identification in un-lysed cells.
• The developed gating strategy enables automated batch processing and simultaneous quantification of multiple cellular events.
• Proof-of-concept experiments with carbendazim and methyl methanesulphonate (MMS) accurately identified their respective clastogenic and aneugenic MoAs.

Conclusions:

• The presented imaging flow cytometry protocol offers a robust and efficient method for genetic toxicity testing.
• This multiplex approach enhances the accuracy of genotoxicity assessment, aiding in the identification of safer drug candidates.
• The ability to determine chemical MoA directly contributes to reducing false positives and optimizing drug development pipelines.