Colony context and size-dependent compensation mechanisms give rise to variations in nuclear growth trajectories.

To investigate how cellular variations arise across spatiotemporal scales in a population of identical healthy cells, we performed a data-driven analysis of nuclear growth variations in hiPS cell colonies as a model system. We generated a 3D timelapse dataset of thousands of nuclei over multiple days and developed open-source tools for image and data analysis and feature-based timelapse data exploration. Together, these data, tools, and workflows comprise a framework for systematic quantitative analysis of dynamics at individual and population levels, and the analysis further highlights important aspects to consider when interpreting timelapse data. We found that individual nuclear volume growth trajectories arise from short-timescale variations attributable to their spatiotemporal context within the colony. We identified a time-invariant volume compensation relationship between nuclear growth duration and starting volume across the population. Notably, we discovered that inheritance plays a crucial role in determining these two key nuclear growth features while other growth features are determined by their spatiotemporal context and are not inherited.