Advances in microscopy, microfluidics and optogenetics enable single-cell monitoring and environmental regulation and offer the means to control cellular phenotypes. The development of such systems is challenging and often results in bespoke setups that hinder reproducibility. To address this, we introduce Cheetah – a flexible computational toolkit that simplifies the integration of real-time microscopy analysis with algorithms for cellular control. Central to the platform is an image segmentation system based on the versatile U-Net convolutional neural network. This is supplemented with functionality to robustly count, characterise and control cells over time. We demonstrate Cheetah’s core capabilities by analysing long-term bacterial and mammalian cell growth and by dynamically controlling protein expression in mammalian cells. In all cases, Cheetah’s segmentation accuracy exceeds that of a commonly used thresholding-based method, allowing for more accurate control signals to be generated. Availability of this easy-to-use platform will make control engineering techniques more accessible and offer new ways to probe and manipulate living cells.
T.E.G. gratefully acknowledges the support of NVIDIA Corporation for the donation of a Titan Xp GPU used in this research. This work was supported by BrisSynBio, a BBSRC/EPSRC Synthetic Biology Research Centre grant BB/L01386X/1 (T.E.G., L.M.), a Royal Society University Research Fellowship grant UF160357 (T.E.G.), the EU Horizon 2020 research project COSY-BIO grant 766840 (L.M.), EPSRC grants EP/R041695/1 and EP/S01876X/1 (L.M.), and an MRC grant MR/N021444/1 (L.M.)
- Bristol BioDesign Institute
- image analysis
- deep learning
- synthetic biology