(HALPERN, JI) Metabolomic data can be used for disease diagnosis by evaluating metabolite profile changes with high performance liquid chromatography mass spectroscopy (HPLC-MS) or nuclear magnetic resonance (NMR). Current analytical techniques for metabolite identification and profiling (i.e. HPLC-MS & NMR) are cumbersome and low throughput, making them inappropriate for point-of-care diagnosis. Cross-reactive sensing, which is a signature-based profiling technique, simultaneously monitors multiple biomarkers. This signature-based profiling technique is a paradigm shift from specific sensors, which quantify a single biomarker. Cross-reactive sensing is a powerful strategy for disease diagnosis by identifying a diagnostic fingerprint, and the sensor array is trained for each metabolite profile associated with a disease for high content diagnostic screening. The long-term goal of our research program is to develop a cross-reactive electrochemical sensor array that serves as a low-cost and high-throughput point-of-care diagnostic tool. Our overall objective is to develop a point-of-care electrochemical sensor that can quickly identify amino acid profiles towards diagnosis. The working hypothesis is that an ex vivo point-of-care cross-reactive sensor can be developed to recognize metabolite profile differences in a mouse model. The rationale for this research is to develop and optimize a novel amino acid point-of-care sensor that can diagnose and monitor psychiatric diseases in a clinical setting. A point-of-care, high content diagnostic sensor capable of diagnosing psychiatric disorders would revolutionize patient care. We plan to achieve this objective by pursuing two specific aims.
Aim 1 will focus on the development of a novel electrochemical microelectrode sensor array. We will also qualify the detection of multiple analytes at the sensor, and start to identify metabolite profiles. We expect to develop a novel point-of-care cross-reactive electrochemical sensor array that can be trained to evaluate metabolite imbalances for any disease Aim 2 will focus on testing the array on transgenic C57Bl/6j mouse models, specifically AC3 knockout mouse model versus a wildtype littermate control. Results from our sensor array will be confirmed with a ultra-high performance liquid chromatography mass spectroscopy control. This study will lead to clinical collaborations to diagnose psychiatric diseases, evaluate the degree of the disease, and monitor treatment in humans. In addition, future basic science and animal experiments are expected with the rest of the COBRE team, specifically with Dr. Xuanmao Chen on the development and testing of new MDD animal models and Dr. Sergios Charnikov on the evaluation of nicotine addiction rat models.
