Smartphone-linked system for diagnosis and epidemiological reporting of pathogens at the point of care

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Abstract Infectious disease remains the world’s top contributor to death and disability, and, due to their ability to spread rapidly through insect vectors or contact with bodily fluids, there is an urgent need for simple, sensitive and easily translatable point-of-care tests, particularly for infections that are difficult to differentiate based upon clinical symptoms alone. This project develops a novel point-of-care platform to quantitatively diagnose viral infectious diseases (Zika, Dengue (types 1 and 3), and Chikungunya) from whole blood samples using a microfluidic platform that performs sample pre-processing in a microfluidic cartridge followed by real-time reverse-transcription loop-mediated isothermal amplification (RT-LAMP) in the same cartridge with pre-dried primers specific to pathogen targets. Our handheld, inexpensive (~$500), point-of-care platform communicates its measurements to a smartphone to process a sequence of fluorescence images of the amplification reaction. Image analysis of the dynamic amplification process is used to estimate the pathogen count from amplification initiation points using a novel approach called “spatial” LAMP (S-LAMP). Our preliminary data shows that the approach provides selectivity and detection limits that are equivalent to conventional Polymerase Chain Reaction (PCR) or LAMP reactions performed with conventional laboratory instruments, and the ability to detect the targets with high specificity from complex media. The S-LAMP approach demonstrates potential to detect 1-5 pathogen copies per reaction. The system will be evaluated using a rigorous tiered approach using plasmids containing the target nucleic acid sequence for initial characterization of detection limits, followed by nucleic acid pathogen extracts, culminating of detection of the pathogens in whole blood using chemical lysis to release target nucleic acids. Partnering with collaborators at the Institute for Infectious Diseases in Brazil, and Carle Clinic in Urbana, the system will be utilized on patient sample repositories and measurements compared to gold standard analysis. Importantly, as detection, image processing, and quantitation are performed with the smartphone microprocessor, detection is easily integrated with a cloud-based database reporting system that facilitates communication with remotely-located physicians and tracking of epidemiological data by health services. The resulting platform will be broadly applicable to a wide variety of multiplexed panels of pathogens that are of interest for human health, animal health, food safety, and environmental monitoring.