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

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.

Automated summary

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.