  Quantitative analysis of cell structures is essential for biomedical and
pharmaceutical research. The standard imaging approach relies on fluorescence
microscopy, where cell structures of interest are labeled by chemical staining
techniques. However, these techniques are often invasive and sometimes even
toxic to the cells, in addition to being time-consuming, labor-intensive, and
expensive. Here, we introduce an alternative deep-learning-powered approach
based on the analysis of brightfield images by a conditional generative
adversarial neural network (cGAN). We show that this approach can extract
information from the brightfield images to generate virtually-stained images,
which can be used in subsequent downstream quantitative analyses of cell
structures. Specifically, we train a cGAN to virtually stain lipid droplets,
cytoplasm, and nuclei using brightfield images of human stem-cell-derived fat
cells (adipocytes), which are of particular interest for nanomedicine and
vaccine development. Subsequently, we use these virtually-stained images to
extract quantitative measures about these cell structures. Generating
virtually-stained fluorescence images is less invasive, less expensive, and
more reproducible than standard chemical staining; furthermore, it frees up the
fluorescence microscopy channels for other analytical probes, thus increasing
the amount of information that can be extracted from each cell.
