(Invited) Hypersectral Sensing of Biological Markers at the Point of Need

Abstract

There is an urgent need for truly reagent free and agnostic sensing at the point of need for biological and chemical targets. Indeed, the COVID-19 pandemic has re-emphasized the significant need for truly agnostic diagnostics at the point of need. Whereas highly targeted and tailored diagnostics can help us address a known and anticipated threat, they do not prepare us against the next emerging outbreak. To address the significant challenge of developing truly agnostic diagnostics that can improve situational awareness and guide decision making, our team has looked to spectroscopy as an inspiration for the sensing modality; and innate immunity as an inspiration for the biological process and signatures. All biological molecules emanate spectral signatures along the electromagnetic spectrum, integral information from which can provide insights into processes or signatures more effectively. Therefore, we propose the use of hyperspectral sensing as a modality for accurate diagnosis and detection of signatures. With regards to the biology - innate immunity is a broad, agnostic pathogen sensing strategy that allows for the early recognition of all pathogens – known and unknown – with an associated response. Our team has developed strategies to mimic this response in the laboratory, culminating with the (ongoing) development of a data-science and machine learning enabled effort to unravel the complexity of the immune recognition (host cytokine and chemokine response), allowing for their ability to inform on categories of pathogens/disease. It is important to integrate this knowledge with a point of contact diagnostic approach in order to be able to translate the data into usable diagnostic information. To this end, we are working on developing a hyperspectral approach that measures these signatures without reagents from saliva samples in a few seconds. Charged by a back-end machine learning/artificial intelligence algorithm, our approach uses Pattern’s ProSpectral Sensor that measures signatures across various realms of the electromagnetic spectrum. Data on developing this sensing modality as a diagnostic in real time – challenges and advantages – will be presented.