Machine learning-based cytokine microarray digital immunoassay analysis.

Journal: Biosensors & bioelectronics
PMID: 33647790

Abstract

Serial measurement of a large panel of protein biomarkers near the bedside could provide a promising pathway to transform the critical care of acutely ill patients. However, attaining the combination of high sensitivity and multiplexity with a short assay turnaround poses a formidable technological challenge. Here, the authors develop a rapid, accurate, and highly multiplexed microfluidic digital immunoassay by incorporating machine learning-based autonomous image analysis. The assay has achieved 12-plexed biomarker detection in sample volume <15 μL at concentrations < 5 pg/mL while only requiring a 5-min assay incubation, allowing for all processes from sampling to result to be completed within 40 min. The assay procedure applies both a spatial-spectral microfluidic encoding scheme and an image data analysis algorithm based on machine learning with a convolutional neural network (CNN) for pre-equilibrated single-molecule protein digital counting. This unique approach remarkably reduces errors facing the high-capacity multiplexing of digital immunoassay at low protein concentrations. Longitudinal data obtained for a panel of 12 serum cytokines in human patients receiving chimeric antigen receptor-T (CAR-T) cell therapy reveals the powerful biomarker profiling capability. The assay could also be deployed for near-real-time immune status monitoring of critically ill COVID-19 patients developing cytokine storm syndrome.

Authors

  • Yujing Song
    Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Jingyang Zhao
    Department of Energy Engineering, Zhejiang University, Hangzhou, Zhejiang, 310027, China.
  • Tao Cai
    Department of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China.
  • Andrew Stephens
    Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
  • Shiuan-Haur Su
    Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
  • Erin Sandford
    Department of Internal Medicine, Division of Hematology/Oncology, University of Michigan, Ann Arbor, MI, 48109, USA.
  • Christopher Flora
    Department of Internal Medicine, Division of Hematology/Oncology, University of Michigan, Ann Arbor, MI, 48109, USA.
  • Benjamin H Singer
    Department of Internal Medicine, Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI, 48109, USA; Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI, 48109, USA.
  • Monalisa Ghosh
    Department of Internal Medicine, Division of Hematology/Oncology, University of Michigan, Ann Arbor, MI, 48109, USA.
  • Sung Won Choi
    Division of Hematology/Oncology, Department of Pediatrics, University of Michigan, Ann Arbor, MI.
  • Muneesh Tewari
    Division of Hematology/Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI.
  • Katsuo Kurabayashi
    Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA; Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI, 48109, USA; Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA. Electronic address: katsuo@umich.edu.

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