CancerCellTracker

CancerCellTracker analyzes time-lapse brightfield microscopy images to detect, track, and quantify behavior and drug responses of multiple myeloma (MM) cells for patient-specific drug response profiling.

Key Features:

• High-Throughput Capability: Processes large brightfield time-lapse datasets of patient-derived multiple myeloma cells cultured ex vivo over extended periods.
• Tumor Microenvironment Simulation: Analyzes co-cultures of cancer cells with bone marrow stromal cells to provide a more physiologically relevant context for drug testing.
• Drug Response Quantification: Evaluates cellular responses to 31 different drugs over up to six days, reporting measures of viability and behavioral changes under treatment.
• Cell Death Detection and Live Cell Estimation: Detects cell death events and estimates live cell percentages by identifying changes in cell attributes across time-lapse images.
• Image Processing and Tracking: Implements a digital image processing pipeline that detects, tracks, and analyzes individual cells across sequential frames.
• Validation and Benchmarking: Validates cell death timing estimates against fluorescent imaging with ethidium homodimer and benchmarks performance against a state-of-the-art algorithm implemented in ImageJ.

Scientific Applications:

• Precision Oncology: Quantifies cellular responses to therapies to support identification of effective treatment options for individual patients.
• Clinical Decision Support: Provides temporal measures of cell death and live cell percentages to inform clinical assessment of drug efficacy.

Methodology:

Employs a digital image processing pipeline that detects, tracks, and analyzes cells across time-lapse brightfield images and identifies changes in cell attributes indicative of cell death; validation includes comparison of cell death timing with fluorescent ethidium homodimer imaging and benchmarking against an ImageJ algorithm.