Journal
ANALYTICAL CHEMISTRY
Volume 94, Issue 16, Pages 6394-6402Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.analchem.2c00906
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Funding
- Singapore-MIT Alliance for Research and Technology (SMART) Centre
- Critical Analytics for Manufacturing Personalized-Medicine (CAMP) Interdisciplinary Research Group (IRG)
- Anti-Microbial Resistance (AMR) IRG
- Zhejiang Provincial Natural Science Foundation of China [LZ22F010007]
- National Natural Science Foundation of China [61827806]
- Fundamental Research Funds for the Provincial Universities of Zhejiang [GK219909299001-410, GK209907299001-305]
- Talent Cultivation Project by Zhejiang Association for Science and Technology [CTZB-2020080127-19]
- Qian-jiang Talent Project Type-D of Zhejiang [QJD1802021]
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This study demonstrates a fully automated and label-free sample-to-answer platform for rapid immune state monitoring using white blood cells (WBCs). The platform integrates WBC separation and image processing to classify WBCs into their subtypes and detect activation-induced morphological changes for functional immune assessment, potentially enabling early detection of diseases.
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.
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