Quantitative analysis of cell structures is essential for pharmaceutical drug screening and medical diagnostics. This work introduces a deep-learning-powered approach to extract quantitative biological information from brightfield microscopy images. Specifically, we train a conditional generative adversarial neural network (cGAN) to virtually stain lipid droplets, cytoplasm, and nuclei from brightfield images of human stem-cell-derived fat cells (adipocytes). Subsequently, we demonstrate that these virtually-stained images can be successfully employed to extract quantitative biologically relevant measures in a downstream cell-profiling analysis. To make this method readily available for future applications, we provide a Python software package that is available online for free access.
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