2025/11/03 by Benjamin Grädel, Lea Brönnimann, Paolo Armando Gagliardi +3 · 1 voice
Biochemistry, Genetics and Molecular Biology · Computer Science · #Cell Image Analysis Techniques #Cellular Mechanics and Interactions #Cluster analysis #Medical Image Segmentation Techniques #Merge (version control) #Microscopy #Pixel #Profiling (computer programming) #Segmentation #Software #Tracking (education)
paper · doi:10.1242/jcs.264022
openalex created_date 2025/11/03 · openalex publication_date 2025/11/03 · openalex updated_date 2026/07/28
Living cells contain dynamic structures that constantly change shape, merge together, split apart and travel in coordinated patterns, much like flocks of birds or schools of fish. Quantifying these complex, collective behaviours can be challenging, as most available tools are designed to follow discrete objects rather than distributed, shape-shifting pixel-level activity patterns. We developed ARCOS.px, a freely available software tool with a user-friendly graphical interface, to automatically identify and track these coordinated dynamic cellular events in time-lapse microscopy movies. The software works by taking semantically segmented binary images, in which pixels are classified as 'active' or 'inactive'. It then uses spatial clustering to group pixels into distinct coordinated events, and tracks how these events evolve over time. We tested our method by tracking cellular structures involved in cell movement and signalling in REF52 cells. Our analyses revealed how different drugs affect the behaviour of these structures and uncovered the timing relationships between different cellular components during wave-like spreading events. ARCOS.px fills a gap in current image analysis tools by enabling researchers to quantify coordinated intracellular phenomena, which was previously difficult to achieve.