Reconfigurable sensing-systems are adaptable platforms that can detect and quantify any target on-demand; however, such systems have not been translated and applied to fields of biosensing and biotechnology. New biosensing capabilities are needed for a paradigm shift in sensor design, from tailoring the sensor to fit a narrow range of targets and conditions, towards a more adaptable platform, wherein the sensor architecture is unvaried, while its performance is tuned to match a particular concentration and complexity of the biological analyte. This project aims to investigate and engineer a new generation of reconfigurable biosensor platforms that can be used to measure multiple circulating biomarkers and inform the development and analysis of microphysiological models. Microphysiological models replicate human organ function, and they are promising technologies for fundamental biological research and discovery of translatable biomarkers, pharmaceuticals, and regenerative therapies; however, due to the anatomical and cellular complexity of microphysiological models, a major challenge exists in measuring and analyzing the function and performance of such complex systems. Reconfigurable, multiplexed sensors will provide a new technique for the parallelization of monitoring microphysiological models, i.e. many microphysiological models and biomarkers can be operated, monitored, and analyzed simultaneously. Such a technology is poised to better our understanding of the fundamental development of any engineered large tissue, organ, or model. This knowledge will accelerate biotechnology research by reducing variability and providing more statistically powerful trials, better informing animal or clinical testing, and identifying new targets for investigation. The interdisciplinary nature of this project, combining microelectronics, microfluidics, data science, and tissue engineering will require equally interdisciplinary education and global engagement plan, which will be implemented by collaborating with high school STEM teachers through authentic summer research experiences and participating in the international SensUs Biosensors Research Competition for undergraduate and graduate students.
The research objective of this proposal is to design, fabricate, and validate sensors for a reconfigurable, multiplexed microfluidic-microbalance system, which is comprised of an array of miniature quartz-crystal microbalances and integral microfluidics to characterize both biochemical and biophysical properties of microphysiological models. Specifically, the operational frequency, binding selectivity, and regeneration of the novel biosynthetic-recognition moieties will be investigated. Understanding these parameters will enable the microfluidic-microbalance platform to be reconfigured for different biomarkers; moreover, the multiplexed sensing can elucidate new correlations between sets of biomarkers and biological function. The proposed research will include (1) modelling of microfluidic delivery and operation of microfluidic-microbalance arrays in complex media, (2) microfabrication and experimental testing of sensor with novel biosynthetic-recognition elements, and (3) the development of the necessary hardware and computational algorithms to process the multiplexed data streams. To demonstrate these innovations, the sensors will be validated with a microphysiological model of human microvasculature to (1) extract, process, and biochemically analyze circulating media, (2) measure and correlate perfusion pressure and viscosity to microvascular development, and (3) harnesses these data streams to predict and optimize biological function of the model. This effort will be the foundation for new multiplexed sensing strategies to investigate microphysiological models and complex in vitro biological systems. Broadly, this research will illuminate a pathway for future research into innovative means of making sensors to monitor multiple biochemical analytes simultaneously, to be reconfigured for use in MPMs of different organs, and to generate data streams for the future development of machine learning methods to analyze and discover novel correlations between biomarkers.
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.
