ArticleProceedings of the National Academy of Sciences of the United States of America2020
Holographic virtual staining of individual biological cells.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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46 citing papers in PubMed, 101 citations in OpenAlex.
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- A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology.Nature biomedical engineering · 2025Article
- Virtual Staining-Enabled Colorectal Cancer Metastasis Detection in Liquid Cytology Based on Digital Holography.Sensors (Basel, Switzerland) · 2025Article
- On the utility of virtual staining for downstream applications as it relates to task network capacity.Biomedical optics express · 2025Article
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- Label-free metabolic fingerprinting of motile mammalian spermatozoa with subcellular resolution.BMC biology · 2025Article
- Label-free spatiotemporal decoding of single-cell fate via acoustic driven 3D tomography.Materials today. Bio · 2024Article
- Exceeding the limit for microscopic image translation with a deep learning-based unified framework.PNAS nexus · 2024Article
- Virtual histological staining of unlabeled autopsy tissue.Nature communications · 2024Article
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- AI-driven projection tomography with multicore fibre-optic cell rotation.Nature communications · 2024Article
- On the use of deep learning for phase recovery.Light, science & applications · 2024Review
- Label-free biomedical optical imaging.Nature photonics · 2023Article
- Virtual staining for pixel-wise and quantitative analysis of single cell images.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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