ABSTRACT
Low-cost, instrument-free colorimetric tests were developed to detect SARS-CoV-2 using plasmonic biosensors with Au nanoparticles functionalized with polyclonal antibodies (f-AuNPs). Intense color changes were noted with the naked eye owing to plasmon coupling when f-AuNPs form clusters on the virus, with high sensitivity and a detection limit of 0.28 PFU mL-1 (PFU stands for plaque-forming units) in human saliva. Plasmon coupling was corroborated with computer simulations using the finite-difference time-domain (FDTD) method. The strategies based on preparing plasmonic biosensors with f-AuNPs are robust to permit SARS-CoV-2 detection via dynamic light scattering and UV-vis spectroscopy without interference from other viruses, such as influenza and dengue viruses. The diagnosis was made with a smartphone app after processing the images collected from the smartphone camera, measuring the concentration of SARS-CoV-2. Both image processing and machine learning algorithms were found to provide COVID-19 diagnosis with 100% accuracy for saliva samples. In subsidiary experiments, we observed that the biosensor could be used to detect the virus in river waters without pretreatment. With fast responses and requiring small sample amounts (only 20 µL), these colorimetric tests can be deployed in any location within the point-of-care diagnosis paradigm for epidemiological control.
ABSTRACT
CRISPR (Clustered regularly interspaced short palindromic repeats)-based diagnostic technologies have emerged as a promising alternative to accelerate delivery of SARS-CoV-2 molecular detection at the point of need. However, efficient translation of CRISPR-diagnostic technologies to field application is still hampered by dependence on target amplification and by reliance on fluorescence-based results readout. Herein, an amplification-free CRISPR/Cas12a-based diagnostic technology for SARS-CoV-2 RNA detection is presented using a smartphone camera for results readout. This method, termed Cellphone-based amplification-free system with CRISPR/CAS-dependent enzymatic (CASCADE) assay, relies on mobile phone imaging of a catalase-generated gas bubble signal within a microfluidic channel and does not require any external hardware optical attachments. Upon specific detection of a SARS-CoV-2 reverse-transcribed DNA/RNA heteroduplex target (orf1ab) by the ribonucleoprotein complex, the transcleavage collateral activity of the Cas12a protein on a Catalase:ssDNA probe triggers the bubble signal on the system. High analytical sensitivity in signal detection without previous target amplification (down to 50 copies µL-1) is observed in spiked samples, in ≈71 min from sample input to results readout. With the aid of a smartphone vision tool, high accuracy (AUC = 1.0; CI: 0.715 - 1.00) is achieved when the CASCADE system is tested with nasopharyngeal swab samples of PCR-positive COVID-19 patients.
ABSTRACT
Deep-learning (DL)-based image processing has potential to revolutionize the use of smartphones in mobile health (mHealth) diagnostics of infectious diseases. However, the high variability in cellphone image data acquisition and the common need for large amounts of specialist-annotated images for traditional DL model training may preclude generalizability of smartphone-based diagnostics. Here, we employed adversarial neural networks with conditioning to develop an easily reconfigurable virus diagnostic platform that leverages a dataset of smartphone-taken microfluidic chip photos to rapidly generate image classifiers for different target pathogens on-demand. Adversarial learning was also used to augment this real image dataset by generating 16,000 realistic synthetic microchip images, through style generative adversarial networks (StyleGAN). We used this platform, termed smartphone-based pathogen detection resource multiplier using adversarial networks (SPyDERMAN), to accurately detect different intact viruses in clinical samples and to detect viral nucleic acids through integration with CRISPR diagnostics. We evaluated the performance of the system in detecting five different virus targets using 179 patient samples. The generalizability of the system was confirmed by rapid reconfiguration to detect SARS-CoV-2 antigens in nasal swab samples (n = 62) with 100% accuracy. Overall, the SPyDERMAN system may contribute to epidemic preparedness strategies by providing a platform for smartphone-based diagnostics that can be adapted to a given emerging viral agent within days of work.