FreeClimber

FreeClimber quantifies Drosophila climbing performance as a measure of negative geotaxis and organismal health.

Key Features:

• Background subtraction algorithm: Performs static background subtraction from video frames to improve spot detection in heterogeneous backgrounds.
• Background-subtracting particle detection: Detects spots representing flies after background subtraction for downstream analysis.
• Local linear regression velocity estimation: Applies local linear regression techniques to calculate the vertical velocity of points moving in a common direction.
• Climbing velocity calculation: Determines climbing velocity by computing the slope of the most linear segment within a mean vertical position versus time plot for a cohort of flies.
• Output types: Produces calculated slopes representing climbing velocities, spot locations for subsequent tracking, and visualization and diagnostic plots.
• Implementation: Implemented as an open-source Python-based pipeline supporting high-throughput automated batch processing.

Scientific Applications:

• Negative geotaxis quantification: Measuring climbing performance as an indicator of health and vigor in Drosophila populations.
• Longitudinal endurance studies: Applied to longitudinal analyses of endurance exercise performance across six distinct mitochondrial haplotypes paired with a common w1118 nuclear background.
• Bias reduction and large-scale analysis: Eliminating systematic biases inherent in manual scoring and enabling large-scale behavioral and comparative genetic studies.

Methodology:

Performs static background subtraction on video frames, applies a background-subtracting particle detection algorithm, computes vertical velocities using local linear regression, and estimates climbing velocity by finding the slope of the most linear segment in the mean vertical position versus time plot for a cohort of flies.