Efficient exchange of metabolic signals with other tissues turns the blood into an opportunity to monitor and diagnose physiological and pathological conditions. Among the constituents of blood, white blood cells represent a particularly rich source of information due to their active involvement in the immune response of the body. As such, technologies that can rapidly characterize blood samples and extract reliable information are in ever-increasing demand for both clinical and basic research applications. The proposed work aims to develop smart microchips that can reliably analyze white blood cells from small blood samples without any sample preparation. These microchips will be low-cost, disposable, and will include built-in electrodes that can convert the chemical information from white blood cells into electrical signals to be interpreted by a smartphone and transmitted to the healthcare provider. The proposed research therefore has the potential to revolutionize healthcare delivery by enabling people to self-administer blood tests at home or in mobile settings. Besides his research, the PI is fully committed to the educational aspects of his profession and aspires to be a role model for next-generation engineers. The PI's educational goal is to create application-focused multidisciplinary courses, research opportunities and learning experiences for students. To this end, the PI proposes (1) to organize innovation tournaments to develop micro/nanotechnologies for solving biomedical challenges, (2) to implement a laboratory module in the graduate- and undergraduate-level courses, (3) to involve and mentor undergraduate and graduate students in conducting the research activities of this proposal, (4) to mentor high school teachers to attract K-12 and High School Students and underrepresented groups to science, technology, engineering and mathematics (STEM) education.
Despite being highly effective in manipulating cells, microfluidic devices lack native sensing schemes and hence often act as upstream sample preparation elements before quantitative measurements typically performed with a laboratory instrument. The disconnect between microfluidic manipulation and quantitative measurements is an important limitation that hampers the widespread adoption of these tools outside of academic research laboratories, for example in resource-limited or in point-of-care settings, where they can be truly transformative in healthcare delivery. The PI's career goal is to develop polymer-based lab-on-a-chip (LoC) platforms with built-in sensor networks, whose purposely simple hardware will be augmented by complex computational algorithms, to function as content-aware, autonomous microfluidic devices for quantitative cell analysis. To achieve this goal, the PI will adopt a highly multidisciplinary approach combining traditionally-distant disciplines such as microsystem engineering, information theory, data science, and biomedicine. This proposal will (1) design and fabricate plastic microfluidic chips wired with networks of interconnected electrical micro-sensors, each individually designed to produce a signature response that can be recognized among others through computation, (2) develop computational algorithms to process compressed data from the sensor network by utilizing both model-based signal processing and machine learning approaches, (3) develop open- and closed-loop controlled microfluidic systems, where a host of actuators are combined with spatiotemporal data generated by networked sensors to extract biological information from the sample under test, (4) combine all of the developed concepts to create an autonomous and adaptive microfluidic system that can analyze whole blood samples by label-free immunophenotyping of white blood cells.
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.
