Dixon, J. C.; Frick, C. L.; Leveille, C. L.; Garrison, P.; Lee, P. A.; Mogre, S. S.; Morris, B.; Nivedita, N.; Vasan, R.; Chen, J.; Fraser, C. L.; Gamlin, C. R.; Harris, L. K.; Hendershott, M. C.; Johnson, G. T.; Klein, K. N.; Oluoch, S. A.; Thirstrup, D. J.; Sluzewski, M. F.; Wilhelm, L.; Yang, R.; Toloudis, D. M.; Viana, M. P.; Theriot, J. A.; Rafelski, S. M.

To investigate the fundamental question of how cellular variations arise across spatiotemporal scales in a population of identical healthy cells, we focused on nuclear growth 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 an interactive timelapse viewer for exploring quantitative features of nuclear size and shape. We performed a data-driven analysis of nuclear growth variations across timescales. We found that individual nuclear volume growth trajectories arise from short timescale variations attributable to their spatiotemporal context within the colony. We identified a strikingly 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.