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

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.......

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 .......

Materials

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