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Abstract
Holographic cytometry is introduced as an ultra-high throughput implementation of quantitative phase image based on off-axis interferometry capable of extracting information from millions of cells flowing through parallel microfluidic channels. The approach allows high quality phase imaging of a large number of cells greatly extending our ability to study cellular phenotypes using individual cell images. The large volume of individual cell imaging data provides suitable input for training sets to develop machine learning and deep learning algorithms. Here we present our findings on application of this technique to examining red blood cells and to distinguishing carcinogen-exposed cells from normal cells and cancer cells. A study of storage lesion, the degradation of red blood cells due to aging, is presented. By using logistic regression to analyze morphological cell features, high accuracy for discriminating cells by storage time is obtained. Further study of red blood cells shows the ability to detect sickle cell disease by implementing deep learning algorithms with careful selection of classifier training features, suggesting potential avenues for diagnosis and monitoring of this disease. Finally, studies of carcinogen exposed cells compared to cancer cell lines show distinct traits between cell populations. Use of deep learning algorithms enables high accuracy in detecting cell phenotype. This has potential application for environmental monitoring and cancer detection by analysis of cytology samples acquired via brushing or fine needle aspiration. The results of these studies demonstrate the potential of holographic cytometry as a diagnostic tool based on high throughput single cell imaging.
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Adam Wax "Holographic cytology", Proc. SPIE PC12622, Optical Methods for Inspection, Characterization, and Imaging of Biomaterials VI, PC126220G (12 August 2023); https://doi.org/10.1117/12.2679141
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KEYWORDS
Holography
Cell biology
Cancer detection
Deep learning
Red blood cells
Cell phenotyping
Diseases and disorders
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