Multiparametric Cell Cycle Analysis Using the Operetta High-Content Imager and Harmony Software with PhenoLOGIC

Area of Science:

• Cell Biology
• Pharmacology
• Drug Discovery

Background:

• High-content imaging (HCI) enables single-cell phenotype characterization.
• Understanding cell cycle distribution is crucial for oncology drug discovery and toxicology assessment.
• Existing methods for cell cycle analysis can be time-consuming and less high-throughput.

Purpose of the Study:

• To describe a novel, high-throughput phenotypic assay for determining cell cycle distribution using HCI.
• To validate the assay's ability to analyze cell cycle phases and enable multiplexing with other markers.
• To provide a cost-effective and rapid method for small molecule mechanism of action and toxicity studies.

Main Methods:

• Utilized PerkinElmer Operetta HCI and Harmony software with PhenoLOGIC machine learning for cell segmentation.
• Employed DNA content, EdU incorporation, and pHH3 (S10) expression to define cell cycle phases.
• Demonstrated multiplexing capability with pharmacodynamic markers like γH2AX, Ki67, and BrdU.

Main Results:

• Successfully segmented single cells from clumps using PhenoLOGIC.
• Accurately classified cells into G1, S, G2, and M phases based on multiple biomarkers.
• Showcased multiplexing to assess cell cycle effects in specific subpopulations (e.g., DNA-damaged cells).

Conclusions:

• The developed HCI assay offers a robust, rapid, and cost-effective method for cell cycle distribution analysis.
• This phenotypic assay is valuable for small molecule mechanism of action studies and drug toxicity profiling.
• The assay's multiplexing potential enhances its utility in detailed cellular response investigations.