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In This Article

  • Summary
  • Abstract
  • Introduction
  • Protocol
  • Representative Results
  • Discussion
  • Acknowledgements
  • Materials
  • References
  • Reprints and Permissions

Summary

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.

Abstract

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.

Introduction

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....

Protocol

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 CO2 inhalation, insert a 26-G needle into the mouse heart and collect 0.3 mL of blood. Directly put blood into a tube wit.......

Representative Results

Figure 1 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.......

Discussion

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 .......

Acknowledgements

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.

....

Materials

NameCompanyCatalog NumberComments
MouseDaehan BiolinkC57BL/6J mice gender and age-matched, 6 – 8 weeks
Falcon conical centrifuge tubeThermoFisher Scientific14-959-53A15 mL
Phosphate-buffered saline Sigma-Aldrich806544-500ML
Ammonium-chloride-potassium lysing buffer ThermoFisher ScientificA1049201
RPMI-1640 mediumSigma-AldrichR8758
Fetal bovine serumThermoFisher Scientific10438018
AntibodyBD Biosciences553140 (RRID:AB_394655)CD16/32 (clone 2.4G2)
AntibodyBD Biosciences555275 (RRID:AB_395699)CD3ε (clone 17A2)
AntibodyBiolegnd100734 (RRID:AB_2075238)CD8α (clone 53-6.7)
AntibodyBD Biosciences557655 (RRID:AB_396770)CD19 (clone 1D3)
AntibodyBD Biosciences557683 (RRID:AB_396793)CD45R/B220 (clone RA3-6B2)
AntibodyBD Biosciences552878 (RRID:AB_394507)NK1.1 (clone PK136)
AntibodyeBioscience11-0041-85 (RRID:AB_464893)CD4 (clone GK1.5)
DAPI Roche102362760014,6-diamidino-2-phenylindole
Flow cytometry BD BiosciencesAria II or III 
Imaging chamberTomocube, Inc.TomoDish
HolotomographyTomocube, Inc.HT-1H
Holotomography imaging softwareTomocube, Inc.TomoStudio
Image professing softwareMathWorksMatlab R2017b

References

  1. Alizadeh, A. A., et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 403 (6769), 503 (2000).
  2. Von Boehmer, H., Melchers, F. Checkpoints in lymphocyte dev....

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