Single-molecule characterization of salivary protein aggregates from Parkinson's disease patients: a pilot study

Saliva is a convenient and accessible biofluid that has potential as a future diagnostic tool for Parkinson's disease. Candidate diagnostic tests for Parkinson's disease to date have predominantly focused on measurements of alpha-synuclein in CSF, but there is a need for accurate tests utilizing more easily accessible sample types. Prior studies utilizing saliva have used bulk measurements of salivary alpha-synuclein to provide diagnostic insight. Aggregate structure may influence the contribution of alpha-synuclein to disease pathology. Single-molecule approaches can characterize the structure of individual aggregates present in the biofluid and may, therefore, provide greater insight than bulk measurements. We have employed an antibody-based single-molecule pulldown assay to quantify salivary alpha-synuclein and amyloid-beta peptide aggregate numbers and subsequently super-resolved captured aggregates using direct Stochastic Optical Reconstruction Microscopy to describe their morphological features. We show that the salivary alpha-synuclein aggregate/amyloid-beta aggregate ratio is increased almost 2-fold in patients with Parkinson's disease (n = 20) compared with controls (n = 20, P < 0.05). Morphological information also provides insight, with saliva from patients with Parkinson's disease containing a greater proportion of larger and more fibrillar amyloid-beta aggregates than control saliva (P < 0.05). Furthermore, the combination of count and morphology data provides greater diagnostic value than either measure alone, distinguishing between patients with Parkinson's disease (n = 17) and controls (n = 18) with a high degree of accuracy (area under the curve = 0.87, P < 0.001) and a larger dynamic range. We, therefore, demonstrate for the first time the application of highly sensitive single-molecule imaging techniques to saliva. In addition, we show that aggregates present within saliva retain relevant structural information, further expanding the potential utility of saliva-based diagnostic methods.