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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning - a 2 minute Preview of the Experimental Protocol
Jonghee Yoon, YoungJu Jo, Young Seo Kim, Yeongjin Yu, Jiyeon Park, Sumin Lee, Wei Sun Park, YongKeun Park
University of Cambridge, Department of Physics; Korea Advanced Institute of Science and Technology, Department of Physics; Korea Advanced Institute of Science and Technology, KAIST Institute for Health Science and Technology; Tomocube, Inc.,; Korea Advanced Institute of Science and Technology, Department of Chemical and Biomolecular Engineering; Korea Advanced Institute of Science and Technology, Department of Biological Sciences; Stanford University, Department of Applied Physics;
We describe a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and a machine learning algorithm. Measurements of 3D refractive index tomograms of lymphocytes present 3D morphological and biochemical information for individual cells, which is then analyzed with a machine-learning algorithm for identification of cell types.
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