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.