Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor symptoms such as bradykinesia, tremor, rigidity and postural instability. An accurate rehabilitation program designed considering the specific characteristics of the patient allows you to maximize the effect of drug therapy, improve the patient's quality of life, while also limiting secondary complications related to the progression of the disease. At the time of diagnosis, however, it is particularly important to be able to quickly identify individuals with idiopathic Parkinson's and distinguish them from those who have an atypical form of Parkinsonism, such as Progressive Supranuclear Palsy (PSP), Multiple System Atrophy (MSA), Corticobasal Degeneration (CBD) and Dementia with Lewy bodies (DLB). Atypical Parkinsonisms are in fact progressive diseases that share some signs and symptoms with PD, however these patients do not respond as effectively to drug therapy since the cellular and degenerative mechanisms that characterize them are very different. For these reasons, their early identification is particularly important to identify the best pharmacological and rehabilitative path for each patient.
To ensure a more accurate patient profiling that takes into account the individual complex clinical picture, the discovery of a biomarker that can be periodically measured in an easily accessible biofluid would allow for better patient stratification, monitoring of the course of the disease and careful study of the effects of the pharmacological and rehabilitation program as well as its personalization, with a view to precision medicine designed, defined and built on the patient.
The Laboratory of Nanomedicine and Clinical Biophotonics (LABION) of Fondazione Don Gnocchi has been working for years on using innovative methods such as Raman spectroscopy to identify biomarkers of neurodegenerative diseases in easily accessible biological fluids, such as blood and saliva. Raman Spectroscopy (RS) allows to obtain a specific and complete characterization of a specific fluid in a rapid, sensitive and non-destructive way, without particular procedures for the preparation of the sample to be analyzed. In RS the entire spectrum obtained from the sample is used as a highly specific "fingerprint" for the selected sample (eg saliva, blood, serum, cerebrospinal fluid) which represents the diagnostic biomarker. The Raman analysis of saliva has already demonstrated the possibility of profiling patients with progressive pathologies with good accuracy and, specifically, of distinguishing subjects suffering from amyotrophic lateral sclerosis compared to subjects with other types of neurodegenerative diseases.
At the same time, LABION has verified the possibility of characterizing by Raman spectroscopy, the extracellular vesicles (EV) circulating in the blood of patients with PD. Since 2017, LABION has been working on the biochemical study of circulating EVs in the serum of PD patients by analyzing the EVs in spectroscopy and the ability of RS to identify a specific biochemical profile of blood vesicles that correlates with clinical scales has been demonstrated. UPDRS III and Hoehn and Yahr. The analysis of the EVs present in the saliva of patients with PD could help to understand the origin of biochemical alterations in the saliva as well as identify even more specific markers.
Raman spectroscopy is therefore proposed as a useful method for the rapid and comprehensive biochemical characterization of saliva and the vesicular component present within it, without the use of staining and labeling procedures.
The objective of this project is the validation of a specific Raman molecular signature for the different experimental groups, which can lead to the determination of a biomarker useful for the differential diagnosis of people with PD compared to subjects with atypical Parkinsonism, through the analysis of a biological fluid that is not invasive, thus filling the current lack of a measurable biomarker for rapid differential diagnosis and for monitoring the evolution of the diseases.
The rapid identification and differential diagnosis of subjects with Parkinson's disease and atypical parkinsonisms will allow to promptly identify the optimal pharmacological and rehabilitative therapy for each subject, leading to a significant improvement in the quality of life for the patient and, in the future, an increased probability slowing the progression of the disease.
SAMPLE COLLECTION: Saliva collection from all the selected subjects will be performed following the Salivette (SARSTEDT) manufacturer's instructions. Saliva will be obtained from all subjects after an appropriate lag time from feeding and teeth brushing. Pre-analytical parameters (i.e. storage temperature and time between collection and processing), dietary and smoking habit will be properly recorded. Briefly, the swab will be placed in the mouth and chewed for 60 seconds to stimulate salivation. Then the swab will be centrifuged for 2 minutes at 1,000 g to remove cells fragments and food debris. Collected samples will be stored at -80° C.
SAMPLE PROCESSING: For the Raman analysis, a drop of each sample will be casted on an aluminium foil in order to achieve the Surface Enhanced Raman Scattering (SERS).
EV ISOLATION: different isolation methods will be tested for effective EV isolation. Purified EVs will be then concentrated and analysed by means of standard techniques and by Raman spectroscopy following a previously optimized protocol for blood EVs. The experimetal settings will be adapted to the salivary EVs, considering variations in substrate, acquisition time, etc.
DATA COLLECTION: Salivary and EV spectra will be acquired using an Aramis Raman microscope (Horiba Jobin-Yvon, France) equipped with a laser light source operating at 785 nm and 532 nm. The instrument will be calibrated before each analysis using the reference band of silicon at 520.7 cm-1. Raman spectra will be acquired in the region between 400 and 1600 cm-1 for saliva, 500-1800 nad 2600-3200 cm-1 for EVs using a 50x objective. The software package LabSpec 6 (Horiba Jobin-Yvon, France) will be used for the acquisition of spectra.
DATA PROCESSING: All the acquired spectra will be baseline corrected and normalized by unit vector using the dedicated software LabSpec 6. The contribution of the substrate will be removed from each spectra, if necessary. The statistical analysis to validate the method, will be performed using a multivariate analysis approach. Principal Component analysis (PCA) will be performed in order to reduce data dimensions and to evidence major trends. The first 20 resultant Principal Components (PCs) will be used in a classification model, Linear Discriminant Analysis (LDA), to discriminate the data maximizing the variance between the selected groups. The smallest number of PCs will be selected to prevent data overfitting. Leave-one-out cross-validation and confusion matrix test will be used to evaluate the method sensitivity, precision and accuracy of the LDA model. Mann-Whitney will be performed on PCs scores to verify the differences statistically relevant between the analysed groups. Correlation and partial correlation analysis will be performed using the Spearman's test, assuming as valid correlation only the coefficients with a p-value lower than 0.05. The statistical analysis will be performed using Origin2018 (OriginLab, USA).
ROC Curve will be calculated to assess thediagnostic potential of the method.
