Fig. 5

a Models trained with SyMBac were used to segment single-cell data throughout all growth curve regimes (colour-coded and used throughout the figure). b Example kympgraphs of 100× data showing cells in a variety of states (exponential growth, stationary phase, filamenting) with accompanying masks, highlighting the robustness of the model trained on mixed data to segment cells of multiple cell sizes and morphologies. c An example output showing the coordinate system applied to a cell mask, generated by Colicoords [16], allowing for highly accurate length and width prediction. d Example time series of the size of a single cell going through an entire growth curve. The inset shows cell length changes during the stationary phase. e–i During the exponential phase, cells exponentially increase their size with a mean growth rate of 2.6 volume doublings per hour, which is equivalent to a population doubling time of 23 min, consistent with the bulk growth measurements of cells in this richly defined medium [3]. The distribution of growth rates shifts to the left as cells enter the stationary phase (orange and green phase) and eventually stops 6 h into the stationary phase (pink). For all growth rates, corresponding standard deviations are also reported. j Cells show a wide distribution of lengths during the exponential phase which narrows greatly during entry to the stationary phase, as cells are “locked in” to their width. Interestingly, while the mean width decreases in the stationary phase, the variability in cell widths increases. k Example of a cell exiting stationary phase, showing the increase in length and width. l Comparison of initial length and the added length before the first division after exit from stationary phase shows that cells are noisy as sizers towards length regulation. m Comparison of the initial width and the added width before the division shows that E. coli is an almost perfect width-sizer, dividing only when individual cells reach a critical width