Materials Research and Education Center, Materials Engineering, Department of Mechanical Engineering, Auburn University, Auburn, Alabama 36849, United States.
Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States.
ACS Nano. 2021 Nov 23;15(11):18023-18036. doi: 10.1021/acsnano.1c06623. Epub 2021 Oct 29.
Cytokine storm, known as an exaggerated hyperactive immune response characterized by elevated release of cytokines, has been described as a feature associated with life-threatening complications in COVID-19 patients. A critical evaluation of a cytokine storm and its mechanistic linkage to COVID-19 requires innovative immunoassay technology capable of rapid, sensitive, selective detection of multiple cytokines across a wide dynamic range at high-throughput. In this study, we report a machine-learning-assisted microfluidic nanoplasmonic digital immunoassay to meet the rising demand for cytokine storm monitoring in COVID-19 patients. Specifically, the assay was carried out using a facile one-step sandwich immunoassay format with three notable features: a microfluidic microarray patterning technique for high-throughput, multiantibody-arrayed biosensing chip fabrication; an ultrasensitive nanoplasmonic digital imaging technology utilizing 100 nm silver nanocubes (AgNCs) for signal transduction; a rapid and accurate machine-learning-based image processing method for digital signal analysis. The developed immunoassay allows simultaneous detection of six cytokines in a single run with wide working ranges of 1-10,000 pg mL and ultralow detection limits down to 0.46-1.36 pg mL using a minimum of 3 μL serum samples. The whole chip can afford a 6-plex assay of 8 different samples with 6 repeats in each sample for a total of 288 sensing spots in less than 100 min. The image processing method enhanced by convolutional neural network (CNN) dramatically shortens the processing time ∼6,000 fold with a much simpler procedure while maintaining high statistical accuracy compared to the conventional manual counting approach. The immunoassay was validated by the gold-standard enzyme-linked immunosorbent assay (ELISA) and utilized for serum cytokine profiling of COVID-19 positive patients. Our results demonstrate the nanoplasmonic digital immunoassay as a promising practical tool for comprehensive characterization of cytokine storm in patients that holds great promise as an intelligent immunoassay for next generation immune monitoring.
细胞因子风暴，也称为过度活跃的免疫反应，其特征是细胞因子的过度释放，已被描述为与 COVID-19 患者发生危及生命的并发症相关的特征。对细胞因子风暴及其与 COVID-19 的机制联系进行批判性评估需要创新的免疫分析技术，该技术能够在高通量下快速、灵敏、选择性地检测宽动态范围内的多种细胞因子。在这项研究中，我们报告了一种机器学习辅助的微流控纳米等离子体数字免疫分析方法，以满足 COVID-19 患者细胞因子风暴监测的需求。具体来说，该测定是使用简便的一步夹心免疫测定格式进行的，具有三个显著特征：用于高通量、多抗体阵列生物传感芯片制造的微流控微阵列图案化技术；利用 100nm 银纳米立方体 (AgNCs) 进行信号转导的超灵敏纳米等离子体数字成像技术；用于数字信号分析的快速准确的基于机器学习的图像处理方法。该开发的免疫分析方法允许在单个运行中同时检测六种细胞因子，工作范围为 1-10,000pg/mL，检测限低至 0.46-1.36pg/mL，使用少至 3μL 血清样本。整个芯片可以在不到 100 分钟的时间内对 8 个不同样本的 6 个重复进行 6 个样本的 6 重检测，总共 288 个检测点。通过卷积神经网络 (CNN) 增强的图像处理方法将处理时间缩短了约 6000 倍，处理过程大大简化，而与传统的手动计数方法相比，统计准确性更高。该免疫分析方法已通过金标准酶联免疫吸附测定 (ELISA) 进行了验证，并用于 COVID-19 阳性患者的血清细胞因子分析。我们的结果表明，纳米等离子体数字免疫分析是一种很有前途的实用工具，可用于全面描述患者的细胞因子风暴，并有望成为下一代免疫监测的智能免疫分析。
