Salivary Biomarker Discovery by Raman Spectroscopy in ALS (SalivALS)

Salivary Raman Fingerprint as Biomarker for the Diagnosis of Amyotrophic Lateral Sclerosis

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