Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning

Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning

Abstract Identification of lymphocyte cell types are crucial for understanding their pathophysiological roles in human diseases. Current methods for discriminating lymphocyte cell types primarily rely on labelling techniques with magnetic beads or fluorescence agents, which take time and have costs...

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Autores principales: Jonghee Yoon, YoungJu Jo, Min-hyeok Kim, Kyoohyun Kim, SangYun Lee, Suk-Jo Kang, YongKeun Park
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2017
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Acceso en línea:https://doaj.org/article/ded0362970904ae4984e1ec9cf6eb7f9
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spelling oai:doaj.org-article:ded0362970904ae4984e1ec9cf6eb7f92021-12-02T15:06:13ZIdentification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning10.1038/s41598-017-06311-y2045-2322https://doaj.org/article/ded0362970904ae4984e1ec9cf6eb7f92017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-06311-yhttps://doaj.org/toc/2045-2322Abstract Identification of lymphocyte cell types are crucial for understanding their pathophysiological roles in human diseases. Current methods for discriminating lymphocyte cell types primarily rely on labelling techniques with magnetic beads or fluorescence agents, which take time and have costs for sample preparation and may also have a potential risk of altering cellular functions. Here, we present the identification of non-activated lymphocyte cell types at the single-cell level using refractive index (RI) tomography and machine learning. From the measurements of three-dimensional RI maps of individual lymphocytes, the morphological and biochemical properties of the cells are quantitatively retrieved. To construct cell type classification models, various statistical classification algorithms are compared, and the k-NN (k = 4) algorithm was selected. The algorithm combines multiple quantitative characteristics of the lymphocyte to construct the cell type classifiers. After optimizing the feature sets via cross-validation, the trained classifiers enable identification of three lymphocyte cell types (B, CD4+ T, and CD8+ T cells) with high sensitivity and specificity. The present method, which combines RI tomography and machine learning for the first time to our knowledge, could be a versatile tool for investigating the pathophysiological roles of lymphocytes in various diseases including cancers, autoimmune diseases, and virus infections.Jonghee YoonYoungJu JoMin-hyeok KimKyoohyun KimSangYun LeeSuk-Jo KangYongKeun ParkNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Jonghee Yoon
YoungJu Jo
Min-hyeok Kim
Kyoohyun Kim
SangYun Lee
Suk-Jo Kang
YongKeun Park
Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning
description Abstract Identification of lymphocyte cell types are crucial for understanding their pathophysiological roles in human diseases. Current methods for discriminating lymphocyte cell types primarily rely on labelling techniques with magnetic beads or fluorescence agents, which take time and have costs for sample preparation and may also have a potential risk of altering cellular functions. Here, we present the identification of non-activated lymphocyte cell types at the single-cell level using refractive index (RI) tomography and machine learning. From the measurements of three-dimensional RI maps of individual lymphocytes, the morphological and biochemical properties of the cells are quantitatively retrieved. To construct cell type classification models, various statistical classification algorithms are compared, and the k-NN (k = 4) algorithm was selected. The algorithm combines multiple quantitative characteristics of the lymphocyte to construct the cell type classifiers. After optimizing the feature sets via cross-validation, the trained classifiers enable identification of three lymphocyte cell types (B, CD4+ T, and CD8+ T cells) with high sensitivity and specificity. The present method, which combines RI tomography and machine learning for the first time to our knowledge, could be a versatile tool for investigating the pathophysiological roles of lymphocytes in various diseases including cancers, autoimmune diseases, and virus infections.
format article
author Jonghee Yoon
YoungJu Jo
Min-hyeok Kim
Kyoohyun Kim
SangYun Lee
Suk-Jo Kang
YongKeun Park
author_facet Jonghee Yoon
YoungJu Jo
Min-hyeok Kim
Kyoohyun Kim
SangYun Lee
Suk-Jo Kang
YongKeun Park
author_sort Jonghee Yoon
title Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning
title_short Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning
title_full Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning
title_fullStr Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning
title_full_unstemmed Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning
title_sort identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning
publisher Nature Portfolio
publishDate 2017
url https://doaj.org/article/ded0362970904ae4984e1ec9cf6eb7f9
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AT minhyeokkim identificationofnonactivatedlymphocytesusingthreedimensionalrefractiveindextomographyandmachinelearning
AT kyoohyunkim identificationofnonactivatedlymphocytesusingthreedimensionalrefractiveindextomographyandmachinelearning
AT sangyunlee identificationofnonactivatedlymphocytesusingthreedimensionalrefractiveindextomographyandmachinelearning
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