DESCRIPTION (provided by applicant): The proposed work is aimed at demonstrating a high throughput, cost effective, and label-free microarray- based method for detecting macromolecular interactions. In particular, we propose a novel technology that involves the use of microarrays and a new label-free optical detection method based on the Resonant Cavity Imaging Biosensor (RCIB). While the use of optical resonators for sensing applications has been studied for many years, the unique capabilities of RCIB described in this proposal have been enabled by recent innovations developed at Boston University as well as advancements in optical instrumentation. One of the primary enabling factors is the invention of high-quality multi-layer dielectric reflectors developed at Boston University for the study of optoelectronic devices on silicon. This is accompanied by new techniques to sense small changes in material on layered structures. Resonant Cavity Imaging Biosensor (RCIB) promises to open the way to high throughput array based methods for profiling protein expression and for discovering intergenic regulatory regions. This proposal will focus on demonstrating and adapting RCIB detection techniques for protein oligonucleotides arrays, and assessing the sensitivity and specificity of the technology. Promoter sequences and potential promoter sequence regions will be photolithographically synthesized on a DNA microarray. Unlabeled TFs will be introduced to the microarray surface and allowed to bind to their corresponding promoter sequences. The presence of TFs will be detected and quantified using the label-free RCIB method. This work will achieve the following specific aims: 1. Demonstrate RCIB sensing and imaging of 0.1 nm high artificial structures patterned on SiO2; 2. Detect monolayer of streptavidin bound to biotinylated DNA patterned in an array format with 64 features each 50 mu m x 50 mu m in size; 3. Demonstrate specificity and sensitivity of different transcription factors (TFs) binding to their corresponding promoter sequences on a 100-feature microarray.