Multiplexed reverse-transcriptase quantitative polymerase chain reaction using plasmonic nanoparticles for point-of-care COVID-19 diagnosis.

Quantitative polymerase chain reaction (qPCR) offers the capabilities of real-time monitoring of amplified products, fast detection, and quantitation of infectious units, but poses technical hurdles for point-of-care miniaturization compared with end-point polymerase chain reaction. Here we demonstrate plasmonic thermocycling, in which rapid heating of the solution is achieved via infrared excitation of nanoparticles, successfully performing reverse-transcriptase qPCR (RT-qPCR) in a reaction vessel containing polymerase chain reaction chemistry, fluorescent probes and plasmonic nanoparticles. The method could rapidly detect SARS-CoV-2 RNA from human saliva and nasal specimens with 100% sensitivity and 100% specificity, as well as two distinct SARS-CoV-2 variants. The use of small optical components for both thermocycling and multiplexed fluorescence monitoring renders the instrument amenable to point-of-care use. Overall, this study demonstrates that plasmonic nanoparticles with compact optics can be used to achieve real-time and multiplexed RT-qPCR on clinical specimens, towards the goal of rapid and accurate molecular clinical diagnostics in decentralized settings.

Rapid video-based deep learning of cognate versus non-cognate T cell-dendritic cell interactions.

Identification of cognate interactions between antigen-specific T cells and dendritic cells (DCs) is essential to understanding immunity and tolerance, and for developing therapies for cancer and autoimmune diseases. Conventional techniques for selecting antigen-specific T cells are time-consuming and limited to pre-defined antigenic peptide sequences. Here, we demonstrate the ability to use deep learning to rapidly classify videos of antigen-specific CD8+ T cells. The trained model distinguishes distinct interaction dynamics (in motility and morphology) between cognate and non-cognate T cells and DCs over 20 to 80 min. The model classified high affinity antigen-specific CD8+ T cells from OT-I mice with an area under the curve (AUC) of 0.91, and generalized well to other types of high and low affinity CD8+ T cells. The classification accuracy achieved by the model was consistently higher than simple image analysis techniques, and conventional metrics used to differentiate between cognate and non-cognate T cells, such as speed. Also, we demonstrated that experimental addition of anti-CD40 antibodies improved model prediction. Overall, this method demonstrates the potential of video-based deep learning to rapidly classify cognate T cell-DC interactions, which may also be potentially integrated into high-throughput methods for selecting antigen-specific T cells in the future.