CTRL: a label-free method for dynamic measurement of single-cell volume

Abstract

AbstractMeasuring the physical size of the cell is valuable in understanding cell growth control. Current single-cell volume measurement methods for mammalian cells are labor-intensive, inflexible, and can cause cell damage. We introduce CTRL: Cell Topography Reconstruction Learner, a label-free technique incorporating Deep Learning and Fluorescence Exclusion for reconstructing cell topography and estimating mammalian cell volume from DIC microscopy images alone. The method achieves quantitative accuracy, requires minimal sample preparation, and applies to extensive biological and experimental conditions. Using this method, we observe a noticeable reduction in cell size fluctuations during cell cycle, which is consistent with the presence of a cell size checkpoint. (https://GitHub.com/sxslabjhu/CTRL)