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LamelliQuant: An Automated Analysis Pipeline for Lamellipodia Protrusion Dynamics
Ian Eder1, Abrahim Kashkoush1, Partha Roy1,2
1Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Abstract:
We developed LamelliQuant, an automated image analysis pipeline for quantifying lamellipodial protrusion and retraction dynamics from time-lapse microscopy of migrating cells. The workflow begins with the generation of kymographs along user-defined lines across lamellipodia, capturing edge motion over time. An image analysis algorithm extracts the cell edge trajectory and applies LOESS smoothing combined with peak detection to identify cycles of protrusion and retraction. LamelliQuant integrates ImageJ for image processing (movie import, ROI selection, kymograph generation) and R for quantitative analysis (smoothing and event detection), with user-adjustable parameters throughout to enable customization. We validated the pipeline by comparing automated measurements to manual tracking, demonstrating strong concordance and the ability to reproduce published results. LamelliQuant offers a robust, high-throughput alternative to manual kymograph analysis for investigating lamellipodia and membrane protrusion dynamics.
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