Label-free determination of embryo viability using artificial confocal microscopy (ACM)

Three-dimensional (3D) cellular systems have been increasingly adopted over 2D cell monolayers to study disease mechanisms and discover drug therapeutics, as they more accurately recapitulate the in vivo cellular communication and development of extracellular matrices. However, significant challenges exist for the existing optical microscopy techniques when applied to increasingly thick tissue structures. Recently, we have reported artificial confocal microscopy (ACM), a laser scanning QPI system combined with deep learning algorithms, which renders “synthetic” fluorescence confocal images from thick unlabeled specimens. Here, we aim to determine quantitative markers that can report on the viability of mammalian embryos. We start with the identification of nuclei in the embryonic cells, as the presence of anucleated cells indicate low viability of the embryos. Using phase imaging with computational specificity (PICS), we show that nuclear mask can be predicted from GLIM images alone.