Salivary Biomarker Discovery by Raman Spectroscopy in ALS (SalivALS)
Salivary Biomarker Discovery by Raman Spectroscopy in ALS (SalivALS)
BACKGROUND/RATIONALE: The paucity of biomarkers for the diagnosis and monitoring of patients
 affected by Amyotrophic Lateral Sclerosis (ALS) is one of the greatest concerns in ALS
 clinics and research. Phenotypic signs, electrophysiological test and clinical scales are
 currently used for ALS diagnosis and follow up before and after treatments. Nowadays, the
 diagnosis and differential diagnosis used to discriminate ALS from other comparable
 neurodegenerative diseases, are time-consuming and complex processes that reduce the time for
 a prompt intervention. Thus, the scientific community is asked to strive for new, measurable,
 fast and objective biomarkers for the diagnosis and stratification of patients. Saliva is a
 complex biofluid composed of bioactive molecules that can be collected by means of a
 non-invasive procedure. The possibility to simultaneously monitor all the variations in the
 endocrine, electrolytic and metabolic messengers in saliva has recently suggested its use for
 the diagnosis of complex diseases, like neurodegenerative diseases, but only limited
 information are available on the potential of saliva as alternative carrier of ALS
 biomarkers.

 OBJECTIVES: The aim of the present project is to optimize an innovative, non-invasive and
 fast procedure for the ALS onset and for the stratification of ALS patients, taking advantage
 of the sensitivity of Raman Spectroscopy (RS) and of accessible saliva. Fondazione Don
 Gnocchi (FDG) preliminary results on a small cohort of subjects demonstrated the feasibility
 of the methodology and the ability of LABION protocol to obtain a reproducible Raman
 fingerprint of saliva that can be used for the discrimination of healthy subjects, ALS
 patients and subjects affected by other types of neurological diseases.

 METHODS: Starting from FDG preliminary results, the biochemical composition of saliva in
 patients with diagnosed ALS will be evaluated and statistically compared with the one
 obtained from age and sex-matched healthy subjects and from patients affected by other
 neurological diseases (Parkinson's and Alzheimer's diseases). Moreover, an intra-group ALS
 clustering will be analysed in order to verify a different Raman fingerprint obtained from
 ALS patients with a bulbar or spinal onset. The collected Raman data will be processed using
 a multivariate analysis approach through Principal Component Analysis - Linear Discriminant
 Analysis (PCA-LDA). The classification model will be created using cross-validation and
 subset validation. Thanks to RS, the overall composition of saliva will be established with
 minimal sample preparation, providing comprehensive biochemical fingerprint of the sample. In
 parallel, routine salivary parameters will be measured including viscosity, pH, total protein
 and carbohydrates concentration, amylase and pepsin, cortisol and Chromogranin A.

 EXPECTED RESULTS: By the end of this study, the investigators expected to verify the
 possibility to use the Raman salivary pattern as new promising biomarker for ALS diagnosis
 and progression to be related with clinical scales for the personalized and fine tuning of
 the therapeutic approach. The intent of this project is to create a classification model able
 to:

 1. Determine the ALS onset

 2. Discriminate the signal obtained from ALS patients from the one collected from other
 neurodegenerative diseases

 3. Stratify ALS patients into bulbar and spinal onset

 4. Correlate the Raman data with clinical and paraclinical scales used nowadays for ALS
 diagnosis and monitoring
SAMPLE COLLECTION: Saliva collection from all the selected subjects will be performed
 following the Salivette (SARSTEDT) manufacturer's instructions. To limit variability in
 salivary content not related to ALS, saliva will be obtained from all subjects at a fixed
 time, 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 removed, 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: Before the Raman acquisition, saliva samples will be filtered with
 different cut-off ranges (3, 10 and 30 kDa), collecting and analysing by RS the eluted sample
 and discarding the concentrated counterpart. 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).

 DATA COLLECTION: Raman and SERS spectra will be acquired using an Aramis Raman microscope
 (Horiba Jobin-Yvon, France) equipped with a laser light source operating at 785 nm with laser
 power ranging from 25-100% (Max power 512 mW). Acquisition time between 10-30 seconds will be
 used. The instrument will be calibrated before each analysis using the reference band of
 silicon at 520.7 cm-1. Raman spectra will be collected from 15 points following a line-map
 from the edge to the centre of the drop. Spectra will be acquired in the region between 400
 and 1800 cm-1 using a 50x objective (Olympus, Japan). Spectra resolution is about 1.2 cm-1.
 The software package LabSpec 6 (Horiba Jobin-Yvon, France) will be used for map design and
 the acquisition of spectra.

 DATA PROCESSING: All the acquired spectra will be fit with a fifth-degree polynomial baseline
 and normalized by unit vector using the dedicated software LabSpec 6. The contribution of the
 substrate will be removed from each spectra. 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).
Inclusion Criteria:

 -  Diagnosis of Amyotrophic Lateral Sclerosis (ALS) according to the El Escorial criteria
 and classification based on the main genetic mutations for the ALS group

 -  Diagnosis of Alzheimer's disease according to the ATN criteria for the Alzheimer's
 group

 -  Diagnosis of Parkinson's disease according to the Unified Parkinson's Disease Rating
 Scale and with Hoehn and Yahr scores for the Parkinson's group

 -  Male

 -  Age between 50 and 70 years

 Exclusion Criteria:

 -  Oncologic Diseases

 -  Immune and hematological diseases

 -  Bacterial or fungal infections in progress (e.g. oral candidiasis)

 -  Female

 -  Age less than 50 years or above 70 years