Rapid and Label-Free Classification of Blood Leukocytes for Immune State Monitoring.
Hyungkook JeonMaoyu WeiXiwei HuangJiangfan YaoWentao HanRenjie WangXuefeng XuJin ChenLingling SunJongyoon HanPublished in: Analytical chemistry (2022)
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.
Keyphrases
- label free
- deep learning
- high throughput
- single cell
- convolutional neural network
- artificial intelligence
- machine learning
- cell therapy
- loop mediated isothermal amplification
- high resolution
- current status
- oxidative stress
- mass spectrometry
- peripheral blood
- drug induced
- photodynamic therapy
- quantum dots
- optical coherence tomography