Metabolic reprogramming and dysfunction resulting from amyloid β42-induced oxidative stress is emerging as an important mechanism at the onset of the pathophysiological changes that ultimately lead to Alzheimer’s disease (AD). Cellular metabolic function and interactions are highly dynamic and heterogeneous and thus require tools that are non-destructive and yield measurements with high spatial resolution. Label-free, two photon excited fluorescence (TPEF) imaging has been used by a number of groups to identify metabolic functional changes associated with AD and senile plaques. We propose to build on our established and validated multi- parametric optical metabolic readouts of mitochondrial dynamics and redox state, based on extraction and analysis of NAD(P)H and FAD TPEF images, to characterize the spatiotemporal dynamics of metabolic reprogramming that takes place at the very early stages of plaque formation in AD. To achieve this, we will rely on a novel 3D brain-like tissue model of HSV-1-induced AD. The tissues consist of a hybrid silk-collagen hydrogel embedded with human induced neural stem cells (hiNSCs) that are infected with HSV-1. Over the span of a week, the tissues develop several physiologically relevant AD traits, including neuronal loss, gliosis, neuroinflammation, synaptic dysfunction, and multicellular Aβ fibrillar plaque like formations (PLFs). These tissues are easily accessible for repeated label-free TPEF imaging over reasonable time scales to enable dynamic monitoring of the metabolic changes that occur in the immediate milieu of PLFs as they are forming. To ensure accurate monitoring of metabolic function, free of interference from other endogenous fluorophores, we aim to perform spectral imaging studies to characterize the excitation/emission profiles of key fluorescent contributors (Aim 1). This information will be essential for establishing and validating an efficient image acquisition protocol that relies on a limited number of images, which can be analyzed without compromising the potential to extract quantitative assessments of NAD(P)H, FAD (and corresponding redox ratio and mitochondrial clustering metrics), lipofuscin and plaque associated fluorescence. In addition, we propose to enhance the efficiency of image acquisition and analysis using deep learning approaches that combine denoising and object classification steps (Aim 2). We will demonstrate the potential of this label-free, multiparametric, TPEF imaging approach to yield unique insights on the dynamics of metabolic reprogramming events that are associated with AD development through repeated imaging of the HSV-1 treated hiNSCs in the absence or presence of anti-viral or metformin treatment (Aim 3). We expect our findings to provide a foundation for pursuing the use of this combined tissue model/label-free, metabolic imaging platform to address important mechanistic questions regarding AD etiology and the potential success of novel related treatments. The im...