Song Yujing, Zhao Jingyang, Cai Tao, Stephens Andrew, Su Shiuan-Haur, Sandford Erin, Flora Christopher, Singer Benjamin H, Ghosh Monalisa, Choi Sung Won, Tewari Muneesh, Kurabayashi Katsuo
Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
Department of Energy Engineering, Zhejiang University, Hangzhou, Zhejiang, 310027, China.
Biosens Bioelectron. 2021 May 15;180:113088. doi: 10.1016/j.bios.2021.113088. Epub 2021 Feb 20.
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.
在床边对大量蛋白质生物标志物进行连续测量，可能为改变急性病患者的重症监护提供一条有前景的途径。然而，要实现高灵敏度、多重检测以及短检测周转时间的结合，面临着巨大的技术挑战。在此，作者通过纳入基于机器学习的自主图像分析，开发了一种快速、准确且高度多重的微流控数字免疫测定法。该测定法在样品体积<15μL、浓度<5 pg/mL的情况下实现了12重生物标志物检测，同时仅需5分钟的测定孵育时间，使得从采样到得出结果的所有过程能在40分钟内完成。该测定程序应用了一种空间光谱微流控编码方案以及一种基于机器学习与卷积神经网络（CNN）的图像数据分析算法，用于预平衡的单分子蛋白质数字计数。这种独特方法显著减少了低蛋白浓度下数字免疫测定法进行高容量多重检测时面临的误差。在接受嵌合抗原受体T（CAR-T）细胞疗法的人类患者中，对一组12种血清细胞因子获得的纵向数据揭示了其强大的生物标志物分析能力。该测定法还可用于对发生细胞因子风暴综合征的重症COVID-19患者进行近实时免疫状态监测。
