Machine Learning-Based Protein Microarray Digital Assay Analysis

Song, Y.; Zhao, J.; Cai, T.; Su, S.-H.; Sandford, E.; Flora, C.; Singer, B. H.; Ghosh, M.; Choi, S. W.; Tewari, M.; Kurabayashi, K.

doi:10.1101/2020.08.04.236448

bioRxiv · 10.1101/2020.08.04.236448

Machine Learning-Based Protein Microarray Digital Assay Analysis

Song, Y.·Zhao, J.·Cai, T.·Su, S.-H.·Sandford, E.·Flora, C.·Singer, B. H.·Ghosh, M.·Choi, S. W.·Tewari, M.·Kurabayashi, K.

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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, we developed a rapid, accurate, and highly multiplexed microfluidic digital immunoassay by incorporating machine learning-based autonomous image analysis. The assay achieved 14-plexed biomarker detection at concentrations < 10pg/mL with a sample volume < 10 L, including all processes from sampling to analyzed data delivery within 30 min, while only requiring a 5-min assay incubation. The assay procedure applied 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 reduced errors facing the high-capacity multiplexing of digital immunoassay at low protein concentrations. Longitudinal data obtained for a panel of 14 serum cytokines in human patients receiving chimeric antigen receptor-T (CAR-T) cell therapy manifested the powerful biomarker profiling capability and great potential of the assay for its translation to near-real-time bedside immune status monitoring.

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Song, Y., Zhao, J., Cai, T., Su, S.-H., Sandford, E., Flora, C., Singer, B. H., Ghosh, M., Choi, S. W., Tewari, M., Kurabayashi, K.. 2020-08-04. Machine Learning-Based Protein Microarray Digital Assay Analysis. https://doi.org/10.1101/2020.08.04.236448

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