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Python code used to detect and track blebs frame-to-frame, and to analyze bleb size statistics before and after photoactivation.

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Driscoll-Worrying-Paper

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This bundle contains code used in Proteolysis-free amoeboid migration through crowded environment via bleb-driven worrying, Developmental Cell, 2024, written by Meghan K. Driscoll, Erik S. Welf, Andrew Weems, Etai Sapoznik, Felix Zhou, Vasanth S. Murali, Juan Manuel Garcia-Arcos, Minna Roh-Johnson, Matthieu Piel, Kevin M. Dean, Reto Fiolka, Gaudenz Danuser. Additional information can be found in the Methods section of this paper.

2D Bleb tracking and analysis – Python code used to detect and track blebs frame-to-frame, and to analyze bleb size statistics before and after photoactivation.

The code is organized into steps:

Step1_Detection_and_Tracking: two scripts: bleb_tracking.py module file containing the reusable scripts for tracking and ‘step1_detect-blebs_and_track.py’, the main script for extracting the bleb timeseries for analysis.

Step2_Before_After_bleb_stats: one script to compute the before and after bleb sizes and perform statistical test, another script to coplot the conditions.

Step3_Time_Correlation_Analysis: one script to compute the cross-correlation of the timeseries of bleb area and Arp2/3, another script to coplot.

The code is shared for transparent documentation of the analyses performed in the manuscript by Driscoll et al.

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Python code used to detect and track blebs frame-to-frame, and to analyze bleb size statistics before and after photoactivation.

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