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

Abstract

Measuring 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 the Deep Learning algorithm and the Fluorescence Exclusion method 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 a wide range of biological and experimental conditions. The method can be used to track single-cell volume dynamics over arbitrarily long time periods. Using this method, we observe that bigger newborn cells grow larger (sizer) for HT1080 fibrosarcoma cells and there is a noticeable reduction in cell size fluctuations at 25% completion of the cell cycle in HT1080 fibrosarcoma cells.