High throughput measurement of single HeLa cell volume growth and cell cycle progression using FXm

The way proliferating animal cells coordinate the growth of their mass, volume, and other relevant size parameters is a long-standing question in biology. Studies focusing on cell mass have identified patterns of mass growth as a function of time and cell cycle phase, but little is known about volume growth. To address this question, we improved our fluorescence exclusion method of volume measurement (FXm) and obtained 1696 single cell volume growth trajectories of asynchronous HeLa cells expressing hgeminin-GFP. The data were obtained over four independent replicates. For each cell, depending on the phase of the cell cycle it was observed, the following information is indicated: time of birth (onset of cytokinesis), time of G1/S transition (onset of the expression of hgeminin-GFP), time of entry into mitosis (onset of mitotic cell rounding). All the FXm movies and single cell volume trajectories were visually assessed to check and correct any mistake in the automated segmentation and lineage tracking that would give false volume fluctuations measurements. We thus present a dataset of well curated, high-throughput measurement of single cell volume available for the investigation of volume growth in animal cells. We find that, during most of the cell cycle, volume growth is close to exponential and proceeds at a higher rate in S-G2 than in G1. Comparing the data with a mathematical model, we establish that the cell-to-cell variability in volume growth arises from constant-amplitude fluctuations in volume steps, rather than fluctuations of the underlying specific growth rate. We hypothesize that such 'additive noise' could emerge from the processes that regulate volume adaptation to biophysical cues, such as tension or osmotic pressure. Future work combining our single cell volume measurements with the analysis of single cell mass growth curves will likely provide interesting perspectives.The protocol for the experiments and data analysis is described in details in the related article (Cadart et al., eLife, doi.org/10.7554/eLife.70816