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1. Virtual staining of label-free tissue using deep learning

   https://doi.org/10.1117/12.2670987  

   Ozcan, Aydogan ( August 2023 , SPIE Optical Metrology)

   Ferraro, Pietro ; Grilli, Simonetta ; Psaltis, Demetri (Ed.)

   Deep learning techniques create new opportunities to revolutionize tissue staining methods by digitally generating histological stains using trained neural networks, providing rapid, cost-effective, accurate and environmentally friendly alternatives to standard chemical staining methods. These deep learning-based virtual staining techniques can successfully generate different types of histological stains, including immunohistochemical stains, from label-free microscopic images of unstained samples by using, e.g., autofluorescence microscopy, quantitative phase imaging (QPI) and reflectance confocal microscopy. Similar approaches were also demonstrated for transforming images of an already stained tissue sample into another type of stain, performing virtual stain-to-stain transformations. In this presentation, I will provide an overview of our recent work on the use of deep neural networks for label-free tissue staining, also covering their biomedical applications. 

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2. Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments

   https://doi.org/10.1038/s41467-020-20062-x  

   Kandel, Mikhail E. ; He, Yuchen R. ; Lee, Young Jae ; Chen, Taylor Hsuan-Yu ; Sullivan, Kathryn Michele ; Aydin, Onur ; Saif, M. Taher A. ; Kong, Hyunjoon ; Sobh, Nahil ; Popescu, Gabriel ( December 2020 , Nature Communications)

   Due to its specificity, fluorescence microscopy has become a quintessential imaging tool in cell biology. However, photobleaching, phototoxicity, and related artifacts continue to limit fluorescence microscopy’s utility. Recently, it has been shown that artificial intelligence (AI) can transform one form of contrast into another. We present phase imaging with computational specificity (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 quantitative phase imaging (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.

    

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3. PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning

   https://doi.org/10.1038/s41377-019-0129-y  

   Rivenson, Yair ; Liu, Tairan ; Wei, Zhensong ; Zhang, Yibo ; de Haan, Kevin ; Ozcan, Aydogan ( February 2019 , Light: Science & Applications)

   Using a deep neural network, we demonstrate a digital staining technique, which we term PhaseStain, to transform the quantitative phase images (QPI) of label-free tissue sections into images that are equivalent to the brightfield microscopy images of the same samples that are histologically stained. Through pairs of image data (QPI and the corresponding brightfield images, acquired after staining), we train a generative adversarial network and demonstrate the effectiveness of this virtual-staining approach using sections of human skin, kidney, and liver tissue, matching the brightfield microscopy images of the same samples stained with Hematoxylin and Eosin, Jones’ stain, and Masson’s trichrome stain, respectively. This digital-staining framework may further strengthen various uses of label-free QPI techniques in pathology applications and biomedical research in general, by eliminating the need for histological staining, reducing sample preparation related costs and saving time. Our results provide a powerful example of some of the unique opportunities created by data-driven image transformations enabled by deep learning.

    

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4. Molecular and biophysical analysis of apoptosis using a combined quantitative phase imaging and fluorescence resonance energy transfer microscope

   https://doi.org/10.1002/jbio.201800126  

   Eldridge, Will J. ; Hoballah, Jawad ; Wax, Adam ( August 2018 , Journal of Biophotonics)

   Apoptotic mechanisms are often dysregulated in cancerous phenotypes. Additionally, many anticancer treatments induce apoptosis and necrosis, and the monitoring of this apoptotic activity can allow researchers to identify therapeutic efficiency. Here, we introduce a microscope which combines quantitative phase imaging (QPI) with the ability to detect molecular events via fluorescence (or Förster) resonance energy transfer (FRET). The system was applied to study cells undergoing apoptosis to correlate the onset of apoptotic enzyme activity as observed using a FRET‐based apoptosis sensor with whole cell morphological changes analyzed via QPI. The QPI data showed changes in cell disorder strength during the initiation of apoptotic enzymatic activity.

    

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5. Phase-sensitive, angle-resolved light-scattering microscopy of single cells

   https://doi.org/10.1364/OL.409345  

   Draham, Robert L. ; Dunn, Kaitlin J. ; Berger, Andrew J. ( December 2020 , Optics Letters)

   We report what is to our knowledge the first use of Fourier phase microscopy (FPM) to estimate diameters of individual single-micrometer beads and to classify cells based upon changes in scatterer size distribution. FPM, a quantitative phase imaging (QPI) method, combines the planar illumination typically used in off-axis QPI (ideal for Mie theory analysis) with the common-path geometry typically used in on-axis QPI (ideal for optimizing angular scattering range). Low-spatial-frequency imaging artifacts inherent to FPM have negligible impact upon these angular-domain applications. The system is simple to align and stable, and requires no external reference beam. Angular scattering patterns obtained from single 1 µm polystyrene beads in glycerol ($\mathrm{\Delta <\#comment/>}n=0.11$) display unprecedented fidelity to Mie theory, produce diameter estimates consistent with the manufacturer’s specifications, and offer precision on the scale of tens of nanometers. Measurements of macrophages at different stages of antibody-dependent cellular phagocytosis demonstrate the ability to detect changes in a cell’s scattering caused by the presence of phagocytosed material within.

    

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