WoundPy: an improved automatized method for in vitro cell motility investigation based on wound alignment and absolute velocity profiling
TL;DR
WoundPy, a Python-based executable software featuring a user-friendly interface for semi-automatic, researcher-supervised Region of Interest detection, streamlines image analysis and management of replicates, providing rapid graphical outputs.
Abstract
The Wound Healing Assay is a standard technique for studying cell motility, yet it faces challenges in reproducibility and data interpretation. Here we present WoundPy, a Python-based executable software featuring a user-friendly interface for semi-automatic, researcher-supervised Region of Interest detection. WoundPy streamlines image analysis and management of replicates, providing rapid graphical outputs. A key pre-processing feature is the automated vertical wound alignment, which eliminates operator-dependent errors typical of optical microscopy. Velocity results are calculated as absolute values from multi-time-point imaging. The software was tested on three datasets from biological experiments, and a comparison with ImageJ Wound Healing Size tool revealed a significant underestimation of the wound area by the latter compared to WoundPy. In conclusion, this new software offers an extremely streamlined approach that easily enables to perform an analysis that is both accurate and fast, drastically reducing the time required for both numerical data acquisition and its subsequent analysis.
