Invasive Aspergillosis (IA) is a devastating and frequently fatal infection in immunocompromised patients, in part because of poor early detection tools. The ability to promptly launch antifungal therapy for Aspergillosis and other invasive fungal diseases is critical for positive patient outcomes. However, early detection is complicated by low levels of antigenic markers that signal disease onset and by poor specificity of current diagnostic assays. The aim of this proposal is to develop an antibody-based diagnostics platform comprised of new monoclonal antibodies coupled with the ultrasensitive detection capabilities of surface enhanced Raman spectroscopy and gold nanoparticle sandwich assays. To address the challenges in the early detection of IA, we have assembled a collaborative research team of scientists from the University of Utah and North Dakota State University composed of immunologists with expertise in monoclonal antibody development and screening for Aspergillus spp;medical microbiologists with strengths in basic mycology, fungal assay development and validation;analytical chemists with a strong focus in the creation of novel, ultrasensitive and selective diagnostic tests;and infectious disease clinicians with proficiency in the design and implementation of patient-oriented research. We will address the specific aims of this project in two phases. The aims for the R21 phase of the project are: (1) Identify protein-based fungal targets through the development of monoclonal antibodies from unique, stage-specific biomarkers of Aspergillus spp. (2) Evaluate monoclonal antibody combinations with surface enhanced Raman spectroscopy-based sandwich immunoassay techniques. (3) Define assay performance metric comparisons using diverse human sample matrices. Upon meeting the milestones of the first stage of the project, the aims of the R33 phase are: (1) Characterize fungal target proteins based on growth stage specificity. (2) Optimize analysis protocols to maximize fungal detection. (3) Standardize components of a unique analysis kit for early clinical Aspergillosis diagnosis. The detection strategy created through the successful completion of this project will redefine the diagnosis of IA through augmented sensitivity and specificity, permitting treatment at the earliest stages of disease.