Clinical diagnostics increasingly require multiplexed screening of numerous molecular markers per sample. While genetic approaches have rapidly progressed and allow multiplexed analyses, measurement of protein biomarkers or entire cells/ cellular components has been lacking behind. The objective in this particular application is to develop a new analytical detection platform that advances the highly promising micro- nuclear magnetic resonance (?NMR) technology to the level of a sophisticated, high sensitivity point-of-care device capable of high-throughput molecular analyses. New NMR electronics and complementary methods for data acquisition and reconstruction will be implemented with an emphasis on maximizing the throughput, accuracy, and sensitivity of biomarker analysis. The developed system will then be rigorously evaluated for its ability to rapidly analyses samples for key pathway markers. Guided by strong preliminary data, two specific aims will be addressed: 1) Design the next generation precision digitalNMR (d-?NMR) system with advanced electronics and optimized RF pulse sequences for high throughput, robust and sensitive detection and 2) Evaluation of the newly developed d-NMR platform and initial application to human samples. With the implementation of these two aims we will develop the d-NMR system would be ideally suited for this application, as it can offer high sensitivity and specificity as well as high-throughput detection with minimal processing steps. Applying the parallel detection mechanism, all 16 measurements will be complete rapidly (~1 min). Furthermore, we will evaluate its ability to detect and differentiate as little as 10 abnormal cancer cells in 1 ?L sample volume, for the first time.
The overall goal of this project is to develop the next generation high precision ?NMR system, a highly sensitive, point-of-care platform that is both accurate and low cost. This new technology will be capable of detecting unprecedentedly low concentrations of abnormal cells and thus capable of early detection of cancer. This new information will allow clinicians to intervene when treatments will be most effective and better inform their treatment decisions, which would ultimately lead to better patient outcomes in oncology as well as in other diseases.
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