Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine LearningJonghee Yoon 1, YoungJu Jo 2,3,4,7, Young Seo Kim 3,4,5, Yeongjin Yu 2,3, Jiyeon Park 6, Sumin Lee 4, Wei Sun Park 2,3, YongKeun Park 2,3,4
1Department of Physics, University of Cambridge, 2Department of Physics, Korea Advanced Institute of Science and Technology, 3KAIST Institute for Health Science and Technology, Korea Advanced Institute of Science and Technology, 4Tomocube, Inc., 5Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, 6Department of Biological Sciences, Korea Advanced Institute of Science and Technology, 7Department of Applied Physics, Stanford University
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.
