Label-free and reference region-free X-ray cross-beta index for quantifying protein aggregates of neurodegenerative diseases

Abstract

Background and objectivesRecent advancements in therapies targeting various protein aggregates, ranging from oligomers to fibrils, in neurodegenerative diseases exhibit considerable promise. This underscores the imperative for robust quantitative methods capable of accurately detecting and quantifying these biomarker aggregates across different structural states, even when present in sparse quantities during the early stages of the disease continuum. In response to this exigency, we propose and assess an X-ray-based quantitative metric designed for the global and region-specific detection and quantification of oligomers and fibrils within tissues. This methodology proves applicable to a broad spectrum of neurodegenerative diseases, including Alzheimers and Parkinsons. Notably, unlike positron emission tomography (PET)-based biomarker quantification methods, our approach obviates the need for a contrast agent or a reference region. MethodsWe assessed the proposed metric, termed X-ray cross-{beta} aggregate index (X{beta}AI), in a sheep brain model and brain tissue phantoms, incorporating synthetic oligomers and fibrils characterized against amyloid {beta}-42 and -synuclein aggregates. Detection of these biomarkers utilized laboratory-based monochromatic, and polychromatic X-ray sources, specifically targeting the cross-{beta} substructure of protein aggregates. We employed a peak-location, knowledge-based material decomposition approach to extract target signals from the complex X-ray scattering spectrum originating from a mixture of tissue, water, and aggregate signals. ResultsClinically relevant quantities of oligomers and fibrils were detected in tissues from different brain regions using the laboratory-based X-ray scattering method, without the need for a contrast agent. The signals from protein aggregates were successfully recovered from composite X-ray scattering spectra through material decomposition, eliminating the need for a reference region. The area under the peak of the decomposed inter-{beta}-strand signal correlated well with aggregate burden in synthetically diseased brain tissues. The X-ray cross-{beta} aggregate index (X{beta}AI) accurately quantified aggregate burden in heterogeneous tissues across various brain regions and effectively tracked the deposition increments in specific tissue regions. ConclusionOur study introduces a novel metric, for both regional and global quantification of protein aggregates linked to various protein misfolding diseases, including synucleinopathies.