Customizable and Interactive Visualizations for Investigating Spatio-Temporal Single-Cell Information

Modern live-cell imaging and machine-learning based image processing methods shed light on the secrets of the behavior and characteristics of individual cells including their growth and development into colonies. However, the amount and complexity of spatio-temporal live-cell data makes gaining insights notoriously difficult. Therefore, we present novel visualizations that combine spatial and temporal information in interconnected and highly customizable view components. We demonstrate the flexibility of these components by designing experiment-specific visualizations at the example of three live-cell imaging data sets and gain insights into cell division, population formation and dynamic growth rates. The visualizations are implemented in Python notebooks and publicly available at https://github.com/JuBiotech/Supplement-to-Seiffarth-et-al.-ISBI-2024.