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Immunology and Infection

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published: November 19th, 2018

DOI:

10.3791/58305

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.

We describe here a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and machine learning. Identification of lymphocyte subtypes is important for the study of immunology as well as diagnosis and treatment of various diseases. Currently, standard methods for classifying lymphocyte types rely on labeling specific membrane proteins via antigen-antibody reactions. However, these labeling techniques carry the potential risks of altering cellular functions. The protocol described here overcomes these challenges by exploiting intrinsic optical contrasts measured by 3D quantitative phase imaging and a machine learning algorithm. Measurement of 3D refractive index (RI) tomograms of lymphocytes provides quantitative information about 3D morphology and phenotypes of individual cells. The biophysical parameters extracted from the measured 3D RI tomograms are then quantitatively analyzed with a machine learning algorithm, enabling label-free identification of lymphocyte types at a single-cell level. We measure the 3D RI tomograms of B, CD4+ T, and CD8+ T lymphocytes and identified their cell types with over 80% accuracy. In this protocol, we describe the detailed steps for lymphocyte isolation, 3D quantitative phase imaging, and machine learning for identifying lymphocyte types.

Lymphocytes can be classified into various subtypes including B, helper (CD4+) T, cytotoxic (CD8+) T, and regulatory T cells. Each lymphocyte type has a different role in the adaptive immune system; for example, B lymphocytes produce antibodies, whereas T lymphocytes detect specific antigens, eliminate abnormal cells, and regulate B lymphocytes. Lymphocyte function and regulation is tightly controlled by and related to various diseases including cancers1, autoimmune diseases2, and viral infections3. Thus, the identification of lymphocyte types is important to understand their pathophysiological ro....

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Animal care and experimental procedures were performed under the approval of the Institutional Animal Care and Use Committee of KAIST (KA2010-21, KA2014-01, and KA2015-03). All the experiments in this study were carried out in accordance with the approved guidelines.

1. Lymphocyte Isolation from Mouse Blood

  1. Once a C57BL/6J mouse is euthanized via COinhalation, insert a 26-G needle into the mouse heart and collect 0.3 mL of blood. Directly put blood into a tube wit.......

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Figure shows the schematic process of the entire protocol. Using the procedure presented here, we isolated B (n = 149), CD4+ T (n = 95), and CD8+ T (n = 112) lymphocytes. To obtain phase and amplitude information at various angles of illumination, multiple 2D holograms of each lymphocyte were measured by changing the angle of illumination (from -60° to 60°). Typically, 50 holograms can be used to reconstruct a 3D RI tomogram, but the numb.......

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We present a protocol that enables the label-free identification of lymphocyte types exploiting 3D quantitative phase imaging and machine learning. Critical steps of this protocol are quantitative phase imaging and feature selection. For the optimal holographic imaging, the density of cells should be controlled as described above. Mechanical stability of the cells is also important to obtain a precise 3D RI distribution because floating or vibrational cellular motions will disturb hologram measurements upon illumination .......

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This work was supported by the KAIST BK21+ Program, Tomocube, Inc., and the National Research Foundation of Korea (2015R1A3A2066550, 2017M3C1A3013923, 2018K000396). Y. Jo acknowledges support from the KAIST Presidential Fellowship and Asan Foundation Biomedical Science Scholarship.

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Name Company Catalog Number Comments
Mouse Daehan Biolink C57BL/6J mice  gender and age-matched, 6 – 8 weeks
Falcon conical centrifuge tube ThermoFisher Scientific 14-959-53A 15 mL
Phosphate-buffered saline  Sigma-Aldrich 806544-500ML
Ammonium-chloride-potassium lysing buffer  ThermoFisher Scientific A1049201
RPMI-1640 medium Sigma-Aldrich R8758
Fetal bovine serum ThermoFisher Scientific 10438018
Antibody BD Biosciences 553140 (RRID:AB_394655) CD16/32 (clone 2.4G2)
Antibody BD Biosciences 555275 (RRID:AB_395699) CD3ε (clone 17A2)
Antibody Biolegnd 100734 (RRID:AB_2075238) CD8α (clone 53-6.7)
Antibody BD Biosciences 557655 (RRID:AB_396770) CD19 (clone 1D3)
Antibody BD Biosciences 557683 (RRID:AB_396793) CD45R/B220 (clone RA3-6B2)
Antibody BD Biosciences 552878 (RRID:AB_394507) NK1.1 (clone PK136)
Antibody eBioscience 11-0041-85 (RRID:AB_464893) CD4 (clone GK1.5)
DAPI  Roche 10236276001 4,6-diamidino-2-phenylindole
Flow cytometry  BD Biosciences Aria II or III 
Imaging chamber Tomocube, Inc. TomoDish
Holotomography Tomocube, Inc. HT-1H
Holotomography imaging software Tomocube, Inc. TomoStudio
Image professing software MathWorks Matlab R2017b

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