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Many medical and biological protocols for analyzing individual biologicalcells involve morphological evaluation based on cell staining, designed toenhance imaging contrast and enable clinicians and biologists to differentiatebetween various cell organelles. However, cell staining is not always allowedin certain medical procedures. In other cases, staining may be time consumingor expensive to implement. Here, we present a new deep-learning approach,called HoloStain, which converts images of isolated biological cells acquiredwithout staining by holographic microscopy to their virtually stained images.We demonstrate this approach for human sperm cells, as there is awell-established protocol and global standardization for characterizing themorphology of stained human sperm cells for fertility evaluation, but, on theother hand, staining might be cytotoxic and thus is not allowed during human invitro fertilization (IVF). We use deep convolutional Generative AdversarialNetworks (DCGANs) with training that is based on both the quantitative phaseimages and two gradient phase images, all extracted from the digital hologramsof the stain-free cells, with the ground truth of bright-field images of thesame cells that subsequently underwent chemical staining. To validate thequality of our virtual staining approach, an experienced embryologist analyzedthe unstained cells, the virtually stained cells, and the chemically stainedsperm cells several times in a blinded and randomized manner. We obtained a5-fold recall (sensitivity) improvement in the analysis results. With theintroduction of simple holographic imaging methods in clinical settings, theproposed method has a great potential to become a common practice in human IVFprocedures, as well as to significantly simplify and facilitate other cellanalyses and techniques such as imaging flow cytometry.

HoloStain: Holographic virtual staining of individual biological cells