Machine-Learning-Assisted Microfluidic Nanoplasmonic Digital Immunoassay for Cytokine Storm Profiling in COVID-19 Patients.

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.

Nanoplasmonic Sandwich Immunoassay for Tumor-Derived Exosome Detection and Exosomal PD-L1 Profiling.

Tumor-derived exosomes play a vital role in the process of cancer development. Quantitative analysis of exosomes and exosome-shuttled proteins would be of immense value in understanding cancer progression and generating reliable predictive biomarkers for cancer diagnosis and treatment. Recent studies have indicated the critical role of exosomal programmed death ligand 1 (PD-L1) in immune checkpoint therapy and its application as a patient stratification biomarker in cancer immunotherapy. Here, we present a nanoplasmonic exosome immunoassay utilizing gold-silver (Au@Ag) core-shell nanobipyramids and gold nanorods, which form sandwich immune complexes with target exosomes. The immunoassay generates a distinct plasmonic signal pattern unique to exosomes with specific exosomal PD-L1 expression, allowing rapid, highly sensitive exosome detection and accurate identification of PD-L1 exosome subtypes in a single assay. The developed nanoplasmonic sandwich immunoassay provides a novel and viable approach for tumor cell-derived exosome detection and analysis with quantitative molecular details of key exosomal proteins, manifesting its great potential as a transformative diagnostic tool for early cancer detection, prognosis, and post-treatment monitoring.

3D Phage-based biomolecular filter for effective high throughput capture of Salmonella Typhimurium in liquid streams.

Foodborne illnesses caused by pathogens on fresh produce remain one of the most critical food safety problems the world faces. The recalls of pasta salad in 2018 and pre-cut melons in 2019 imply current methods in identifying the source of pathogens and outbreak prevention are inappropriate and time consuming. In this article, a new technology, called the 3D phage-based biomolecular filter, was developed to simultaneously capture and concentrate foodborne pathogens from large volumes of liquid streams (food liquid or wash water streams). The 3D phage-based filter consisted of phage-immobilized magnetoelastic (ME) filter elements, a filter pipe system, and a uniform magnetic field to fix and align the ME filter elements in the 3D filter column. The closely packed ME filter elements display a 3D layered structure which allows for enhanced surface interaction of the immobilized bacteriophage with specific pathogens in the passing liquid streams. As a result, a pathogen capture rate of more than 90% was achieved at a high flow rate of 3 mm/s with 20,000 ME filter elements. The capability of the 3D phage-based filter to capture pathogens in liquid streams at different filter element packing densities was further validated by experiments, finite element analysis and theoretical calculations. The capture rate increases significantly with larger numbers of ME filter elements placed in the testing pipe, and the turbulence flow induced by the 3D stacking of ME filter elements can further improve the capture efficiency. This technology enables rapid capture and analysis of large volume of water in processing fresh fruit and vegetables for the presence of small quantities of pathogens, which will ultimately benefit producers, the food industry, and society with improved food safety and production efficiency.