An integrated microflow cytometry platform with artificial intelligence capabilities for point-of-care cellular phenotype analysis.

Journal: Biosensors & bioelectronics
PMID:

Abstract

The EZ DEVICE is an integrated fluorescence microflow cytometer designed for automated cell phenotyping and enumeration using artificial intelligence (AI). The platform consists of a laser diode, optical filter, objective lens, CMOS image sensor, and microfluidic chip, enabling automated sample pretreatment, labeling, and detection within a single compact unit. AI algorithms segment and identify objects in images captured by the CMOS sensor at 532 and 586 nm emission wavelengths. The device's counting performance, tested with rainbow and FITC-labeled beads, closely matched results from manual counting using an Olympus IX73 microscope. Antibody-staining efficiency was evaluated with antibody-particle beads and IgG k isotype-FITC, achieving a 99.06% staining efficiency. Practical feasibility was demonstrated through the phenotyping of immune cell lines (Jurkat T and THP-1) using specific fluorescent antibodies. The system successfully detected MHC I expression and distinguished CD3 expression patterns in Jurkat T-cells, demonstrating its capability to recognize distinct cell statuses. With its compact design, automated features, and efficient staining and detection, the EZ DEVICE shows promise for diverse applications, including clinical diagnostics, point-of-care testing, and research, enabling real-time immune monitoring and precise cell analysis. Future enhancements, such as optimized microfluidics, advanced imaging, and AI-driven algorithms, aim to improve single-cell throughput and expand its utility in personalized medicine and public health.

Authors

  • Ju-Nan Kuo
    Department of Automation Engineering, National Formosa University, No. 64, Wunhua Rd, Huwei Township, Yunlin County, 63201, Taiwan.
  • Ming-Shen Jian
    Department of Computer Science and Information Engineering, National Formosa University, No. 64, Wunhua Rd, Huwei Township, Yunlin County, 63201, Taiwan.
  • Chia-Huang Chiang
    Department of Biotechnology, National Formosa University, No. 64, Wunhua Rd, Huwei Township, Yunlin County, 63201, Taiwan.
  • Wen-Kai Kuo
    Department of Electro-Optical Engineering, National Formosa University, No. 64, Wunhua Rd, Huwei Township, Yunlin County, 63201, Taiwan.
  • I-En Lin
    Department of Power Mechanical Engineering, National Formosa University, No. 64, Wunhua Rd, Huwei Township, Yunlin County, 63201, Taiwan.
  • Yung-Ming Kuo
    Department of Electronic Engineering, National Formosa University, Yunlin, Taiwan.
  • Chung-Yu Chen
    Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, National Taiwan University Hospital Yunlin Branch, Douliu, Taiwan.
  • Yi-Ling Ye
    Department of Biotechnology, National Formosa University, No. 64, Wunhua Rd, Huwei Township, Yunlin County, 63201, Taiwan. Electronic address: yilingye@nfu.edu.tw.