Analyzing Aβ Aggregates with High Resolution Microscopy

Abstract

In a world where more people grow older aging-related neurodegeneration like Alzheimer's disease (AD) affects more and more people. Today, AD can be diagnosed with certainty only post mortem, detecting insoluble b-amyloid peptide (Ab) aggregates and neurofibrillary tangles in the patient's brain tissue. Aggregates consisting of Ab are a fundamental pathologic feature of AD. Today in many studies, concentrations of monomeric Ab in body fluids are investigated, especially for diagnostic purposes. Nevertheless, for the detection, quantitation and qualification of aggregated pathologic Ab forms, also in the course of aging, a highly sensitive detection assay system for aggregated Ab species is necessary. We developed an ultra-sensitive assay for the detection of aggregated protein species out of body fluids. This highly specific and sensitive assay uses confocal fluorescence spectroscopy methods and is sensitive enough to detect single aggregates. For the procedure, pathologic aggregates out of body fluids are immobilized on a glass chip, subsequently fluorescence labeled and detected via confocal spectroscopy. Actually, we are optimizing the assay in concerns of instrumentation (imaging) and microscopy high-resolution and even super-resolution methods. We are developing methods to analyze aggregates via super-resolution microscopy. Setups like PAINT (Point Accumulation for Imaging in Nanoscale Topography) or STORM (Stochastic Optical Reconstruction Microscopy) allow resolutions in nanometer-range. PAINT is based on replacing the point-spreadfunction (PSF) of a fluorophore by a point in the middle of a 2D gaussian fit. First measurements show resolutions of 30 nm. STORM is based on highaccuracy localization of photoswitchable fluorophores. During one imaging cycle, only a small part of the fluorophores is turned on. This allows a high accuracy in determining the fluorophore position by replacing the PSF. The fluorophore positions obtained from a series of imaging cycles can be used to reconstruct the whole image.