Abstract
We present a method for virtual staining for morphological analysis of individual biological cells based on stain-free digital holography, allowing clinicians and biologists to visualize and analyze the cells as if they have been chemically stained. Our approach provides numerous advantages, as it 1) circumvents the possible toxicity of staining materials, 2) saves time and resources, 3) optimizes inter- and intralab variability, 4) allows concurrent staining of different types of cells with multiple virtual stains, and 5) provides ideal conditions for real-time analysis, such as rapid stain-free imaging flow cytometry. The proposed method is shown to be accurate, repeatable, and nonsubjective. Hence, it bears great potential to become a common tool in clinical settings and biological research.
Many medical and biological protocols for analyzing individual biological cells involve morphological evaluation based on cell staining, designed to enhance imaging contrast and enable clinicians and biologists to differentiate between various cell organelles. However, cell staining is not always allowed in certain medical procedures. In other cases, staining may be time-consuming or expensive to implement. Staining protocols may be operator-sensitive, and hence may lead to varying analytical results, as well as cause artificial imaging artifacts or false heterogeneity. We present a deep-learning approach, called HoloStain, which converts images of isolated biological cells acquired without staining by holographic microscopy to their virtually stained images. We demonstrate this approach for human sperm cells, as there is a well-established protocol and global standardization for characterizing the morphology of stained human sperm cells for fertility evaluation, but, on the other hand, staining might be cytotoxic and thus is not allowed during human in vitro fertilization (IVF). After a training process, the deep neural network can take images of unseen sperm cells retrieved from holograms acquired without staining and convert them to their stainlike images. We obtained a fivefold recall improvement in the analysis results, demonstrating the advantage of using virtual staining for sperm cell analysis. With the introduction of simple holographic imaging methods in clinical settings, the proposed method has a great potential to become a common practice in human IVF procedures, as well as to significantly simplify and radically change other cell analyses and techniques such as imaging flow cytometry.
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