  Due to its specificity, fluorescence microscopy (FM) has become a
quintessential imaging tool in cell biology. However, photobleaching,
phototoxicity, and related artifacts continue to limit FM's utility. Recently,
it has been shown that artificial intelligence (AI) can transform one form of
contrast into another. We present PICS, a combination of quantitative phase
imaging and AI, which provides information about unlabeled live cells with high
specificity. Our imaging system allows for automatic training, while inference
is built into the acquisition software and runs in real-time. Applying the
computed fluorescence maps back to the QPI data, we measured the growth of both
nuclei and cytoplasm independently, over many days, without loss of viability.
Using a QPI method that suppresses multiple scattering, we measured the dry
mass content of individual cell nuclei within spheroids. In its current
implementation, PICS offers a versatile quantitative technique for continuous
simultaneous monitoring of individual cellular components in biological
applications where long-term label-free imaging is desirable.
