Enzyme Amplified Array Sensing of Proteins for Cancer Detection Array-based sensing (the "chemical nose/tongue" approach) provides a versatile alternative to standard immunosensing strategies. In this approach a sensor array is generated to provide differential interaction with analytes via selective receptors, generating a stimulus response pattern that can be statistically analyzed and used for the identification of individual target analytes and complex mixtures. This methodology has been applied to biosensing, including proteins and cell surfaces. Application of these sensors, however, has been limited due to their low sensitivity. To provide enhanced sensitivity, we will use enzymes to create array- based sensors that provide amplification of the recognition event. In this Enzyme Amplified Array Sensing (EAAS) approach, the sensitivity of the array will be amplified through an enzymatic reaction, coupling the signal amplification of ELISA with the versatility of the "chemical nose" approach. Our research will develop and optimize EAAS sensors and apply them to two important targets in biosensing:
Aim 1 : We will create enzyme-nanoparticle conjugates to detect and identify changes in protein levels, and determine the ability of these systems to differentiate cell types and states (e.g. healthy, cancerous, metastatic) based on protein patterns in lysate.
Aim 2 : We will use murine models to determine the effectiveness of our sensor system for detection and phenotypic identification of cancer using tissue samples. The goal of this two-year R21 grant is to create highly sensitive and discriminating sensors for proteins and cell surfaces that will serve as platforms for cancer diagnostics and pathogen detection. Results from these studies will be used as the foundation for animal and clinical studies in future funding periods.
Enzyme amplified array-based sensing provide a versatile and efficient alternative to current immunosensing strategies. In our proposed research we will develop sensors for proteins with direct applicability to diagnostic systems for cancer.