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A digital protein microarray for COVID-19 cytokine storm monitoring.

ABSTRACT: Despite widespread concern regarding cytokine storms leading to severe morbidity in COVID-19, rapid cytokine assays are not routinely available for monitoring critically ill patients. We report the clinical application of a digital protein microarray platform for rapid multiplex quantification of cytokines from critically ill COVID-19 patients admitted to the intensive care unit (ICU) at the University of Michigan Hospital. The platform comprises two low-cost modules: (i) a semi-automated fluidic dispensing/mixing module that can be operated inside a biosafety cabinet to minimize the exposure of the technician to the virus infection and (ii) a 12-12-15 inch compact fluorescence optical scanner for the potential near-bedside readout. The platform enabled daily cytokine analysis in clinical practice with high sensitivity (<0.4 pg mL^-1), inter-assay repeatability (∼10% CV), and rapid operation providing feedback on the progress of therapy within 4 hours. This test allowed us to perform serial monitoring of two critically ill patients with respiratory failure and to support immunomodulatory therapy using the selective cytopheretic device (SCD). We also observed clear interleukin-6 (IL-6) elevations after receiving tocilizumab (IL-6 inhibitor) while significant cytokine profile variability exists across all critically ill COVID-19 patients and to discover a weak correlation between IL-6 to clinical biomarkers, such as ferritin and C-reactive protein (CRP). Our data revealed large subject-to-subject variability in patients' response to COVID-19, reaffirming the need for a personalized strategy guided by rapid cytokine assays.

SUBMITTER: Song Y

PROVIDER: S-EPMC7855944 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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: (i) a microfluidic microarray patterning technique for high-throughput, multiantibody-arrayed biosensing chip fabrication; (ii) an ultrasensitive nanoplasmonic digital imaging technology utilizing 100 nm silver nanocubes (AgNCs) for signal transduction; (iii) 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-1 and ultralow detection limits down to 0.46-1.36 pg mL-1 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.

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A digital protein microarray for COVID-19 cytokine storm monitoring.

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