Multi-Modal Data-Driven Platform for Multiplexed Cellular Antigen Classification using Nano-electronic Barcoded Particles for Whole Blood Applications

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This project is to develop a platform that can classify human leukocytes, which play a critical role in the body’s defense against a plethora of diseases like sepsis, cancer, and other chronic and acute diseases. State-of-the-art healthcare facilities rely on bulky and costly instruments which require manual sample processing and highly trained technical staff to perform blood analysis. In this work, an artificial intelligence enabled data-driven biosensor platform will be developed for hematology analysis. It will be based on multi-modal sensing, integrated microfluidics, and the ability to perform automated sample processing from whole blood samples. Furthermore, the proposed sensing platform will be equipped with real-time measurement capability and machine learning models to train the sensors data and provide reconfigurability and resource optimization as required. The proposed dynamic reconfigurable data driven biosensor will advance biomedical research and will have great potential to benefit human health and welfare. This cross disciplinary project will train undergraduate and graduate students in areas of sensors, systems, and bionanotechnology. The project will enable the integration of research into educational efforts directed towards engineering students. The PIs outreach activities will include engaging K-12 students, the local health-care industry, and the general public through educational lectures and making them available online for broad dissemination of knowledge. The proposal will enable the development of a next generation in-vitro diagnostic platform equipped with multi-model sensing and nano-barcoded particles to perform reconfigurable biomarker selection in whole blood samples. Human blood cells play a critical role in immune system activation in response to infections. The concentration of these immune cells in whole blood and their membrane receptor densities may change in different diseases and their respective pathogenesis. The heterogeneity of the cellular classification needs to be quantified to provide a personalized diagnostics and monitoring system for patients in a hospital setting. The biosensing platform will be integrated with multi-modal sensing including electrical and optical detectors which will allow to correct for inherent device-to-device variation to improve sensor performance. Immune cells conjugated with functionalized nano-barcoded particles will be quantified simultaneously with an impedance detector and smartphone image sensor. Further, the proposed biosensor will be equipped with real-time data analysis using machine learning to enable a reconfigurable system for resource optimization and biomarker selection. An integrated biochip will be used to perform reconfigurable multiplexing and quantify multiple inflammatory biomarkers from patient blood samples. The proposed sensor will enable multiplexed cellular antigen classification from a drop of whole blood with time to result (TOR) for less than 30 minutes. Sensors will be benchmarked with patient clinical samples. Furthermore, it is envisioned that the biosensor platform to be generic and reconfigurable with pre-functionalized cartridges that can be swapped out for different infectious diseases.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.