Tomocube, Inc. 1 article published in JoVE Immunology and Infection Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning Jonghee Yoon1, YoungJu Jo2,3,4,7, Young Seo Kim3,4,5, Yeongjin Yu2,3, Jiyeon Park6, Sumin Lee4, Wei Sun Park2,3, YongKeun Park2,3,4 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.