R. Mahrat, R. Chaturvedi, D. Thomas, D. Gracias, I. Berman
RamanID,
United States
Keywords: POC, Raman spectroscopy, surface enhanced Raman substrate, multiplexing, SARCoV-2
Summary:
A critical step in controlling viral outbreaks is the timely and accurate characterization of emerging and re-emerging virus strains. The COVID-19 pandemic revealed significant gaps in our ability to perform effective virus surveillance and large-scale disease monitoring. Indeed, in the initial days of the pandemic, the diagnosis of SARS-CoV-2 infection was often confused with that of influenza and seasonal upper respiratory tract viral infections. Accurate population-wide testing remains crucial, yet challenging, to prevent infectious spread between persons and communities, including asymptomatic infected individuals. To address the technological gaps, RamanID has developed a rapid, scalable, and cost-effective biosensing platform that offers direct detection and differentiation of viruses without requiring any sample preparation. By leveraging advances in photonics, plasmonics, and AI, our platform allows highly sensitive and multiplexed quantification of viruses. Together, these salient features of our platform enable the development of an effective respiratory disease-monitoring system and broaden virus surveillance by enabling near-real-time virus identification in body fluids. To achieve these goals, we harnessed the molecular fingerprinting capabilities of Raman scattering to detect and quantify viruses. However, spontaneous Raman scattering does not provide the necessary sensitivity to detect a few copies of the virus in the specimen. Surface-enhanced Raman spectroscopy (SERS), which amplifies Raman scattering of molecules adsorbed on noble metal nanoparticles or nanostructures, combines high molecular specificity with near single-molecule sensitivity and permits spectroscopic quantification of multiple pathogen concentrations in small volumes. To enhance the weak Raman signal from virus-containing samples, we developed radically different nanomanufacturing paradigms for large-area rigid and flexible SERS substrates for ultrasensitive detection. Virus nucleic acids, unique membranes, and surface envelope proteins that manifest in distinct fingerprints are directly exploited for SERS-based detection without requiring specific molecular recognition elements. As a case study, for enhancement of the weak Raman signal from the SARS-CoV-2 virus, novel nanomanufacturing paradigms have been developed to create large-area metal-insulator-metal (MIM) substrates composed of multiple alternate stacks of silver and silica and patterned by nanoimprint lithography (NIL) combined with transfer printing. Further work has allowed the team to develop a viral sensor, in which the plasmonically active viral sensing area is composed of a hybrid structure of electrically conductive surface and metal nanofractals (MN), forming a SERS substrate with remarkably large and uniform distribution of "hot spots". In this context, deep learning (DL) classifiers provide an essential tool to uncover latent differences in the recorded spectral profiles of different virus strains. Multilabel DL classification models have been trained to ignore matrix effects. Comparison to competing for RT-PCR and lateral flow technologies demonstrates RamanID superiority on the following parameters: time to result read ( < $1), multiplexing without adaptation. The RamanID pathogen identification system is superior to currently available diagnostics due to its rapidity, accuracy, and low cost per test. The sensor is pathogen-agnostic, requires no reagents, and matches the Raman spectra of samples to a continuously updated, cloud-based descriptive library.