Rapid and Label-Free Classification of Blood Leukocytes for Immune State Monitoring

Hyungkook Jeon; Maoyu Wei; Xiwei Huang; Jiangfan Yao; Wentao Han; Renjie Wang; Xuefeng Xu; Jin Chen; Lingling Sun; Jongyoon Han

doi:10.1021/acs.analchem.2c00906

Rapid and Label-Free Classification of Blood Leukocytes for Immune State Monitoring

Hyungkook Jeon, Maoyu Wei, Xiwei Huang, Jiangfan Yao, Wentao Han, Renjie Wang, Xuefeng Xu, Jin Chen, Lingling Sun, Jongyoon Han

Dept. of Manufacturing Systems and Design Engineering

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

A fully automated and label-free sample-to-answer white blood cell (WBC) cytometry platform for rapid immune state monitoring is demonstrated. The platform integrates (1) a WBC separation process using the multidimensional double spiral (MDDS) device and (2) an imaging process where images of the separated WBCs are captured and analyzed. Using the deep-learning-based image processing technique, we analyzed the captured bright-field images to classify the WBCs into their subtypes. Furthermore, in addition to cell classification, we can detect activation-induced morphological changes in WBCs for functional immune assessment, which could allow the early detection of various diseases. The integrated platform operates in a rapid (<30 min), fully automated, and label-free manner. The platform could provide a promising solution to future point-of-care WBC diagnostics applications.

Original language	English
Pages (from-to)	6394-6402
Number of pages	9
Journal	Analytical Chemistry
Volume	94
Issue number	16
DOIs	https://doi.org/10.1021/acs.analchem.2c00906
State	Published - 26 Apr 2022

Access to Document

10.1021/acs.analchem.2c00906

Cite this

@article{709c88e5ef724f739781fe9d7515803e,

title = "Rapid and Label-Free Classification of Blood Leukocytes for Immune State Monitoring",

abstract = "A fully automated and label-free sample-to-answer white blood cell (WBC) cytometry platform for rapid immune state monitoring is demonstrated. The platform integrates (1) a WBC separation process using the multidimensional double spiral (MDDS) device and (2) an imaging process where images of the separated WBCs are captured and analyzed. Using the deep-learning-based image processing technique, we analyzed the captured bright-field images to classify the WBCs into their subtypes. Furthermore, in addition to cell classification, we can detect activation-induced morphological changes in WBCs for functional immune assessment, which could allow the early detection of various diseases. The integrated platform operates in a rapid (<30 min), fully automated, and label-free manner. The platform could provide a promising solution to future point-of-care WBC diagnostics applications.",

author = "Hyungkook Jeon and Maoyu Wei and Xiwei Huang and Jiangfan Yao and Wentao Han and Renjie Wang and Xuefeng Xu and Jin Chen and Lingling Sun and Jongyoon Han",

note = "Publisher Copyright: {\textcopyright} 2022 American Chemical Society.",

year = "2022",

month = apr,

day = "26",

doi = "10.1021/acs.analchem.2c00906",

language = "English",

volume = "94",

pages = "6394--6402",

journal = "Analytical Chemistry",

issn = "0003-2700",

number = "16",

}

TY - JOUR

T1 - Rapid and Label-Free Classification of Blood Leukocytes for Immune State Monitoring

AU - Jeon, Hyungkook

AU - Wei, Maoyu

AU - Huang, Xiwei

AU - Yao, Jiangfan

AU - Han, Wentao

AU - Wang, Renjie

AU - Xu, Xuefeng

AU - Chen, Jin

AU - Sun, Lingling

AU - Han, Jongyoon

PY - 2022/4/26

Y1 - 2022/4/26

N2 - A fully automated and label-free sample-to-answer white blood cell (WBC) cytometry platform for rapid immune state monitoring is demonstrated. The platform integrates (1) a WBC separation process using the multidimensional double spiral (MDDS) device and (2) an imaging process where images of the separated WBCs are captured and analyzed. Using the deep-learning-based image processing technique, we analyzed the captured bright-field images to classify the WBCs into their subtypes. Furthermore, in addition to cell classification, we can detect activation-induced morphological changes in WBCs for functional immune assessment, which could allow the early detection of various diseases. The integrated platform operates in a rapid (<30 min), fully automated, and label-free manner. The platform could provide a promising solution to future point-of-care WBC diagnostics applications.

AB - A fully automated and label-free sample-to-answer white blood cell (WBC) cytometry platform for rapid immune state monitoring is demonstrated. The platform integrates (1) a WBC separation process using the multidimensional double spiral (MDDS) device and (2) an imaging process where images of the separated WBCs are captured and analyzed. Using the deep-learning-based image processing technique, we analyzed the captured bright-field images to classify the WBCs into their subtypes. Furthermore, in addition to cell classification, we can detect activation-induced morphological changes in WBCs for functional immune assessment, which could allow the early detection of various diseases. The integrated platform operates in a rapid (<30 min), fully automated, and label-free manner. The platform could provide a promising solution to future point-of-care WBC diagnostics applications.

UR - http://www.scopus.com/inward/record.url?scp=85128778961&partnerID=8YFLogxK

U2 - 10.1021/acs.analchem.2c00906

DO - 10.1021/acs.analchem.2c00906

M3 - Article

AN - SCOPUS:85128778961

SN - 0003-2700

VL - 94

SP - 6394

EP - 6402

JO - Analytical Chemistry

JF - Analytical Chemistry

IS - 16

ER -

Rapid and Label-Free Classification of Blood Leukocytes for Immune State Monitoring

Abstract

Access to Document

Other files and links

Fingerprint

Cite this