Monitoring CSF proteome alterations in amyotrophic lateral sclerosis: obstacles and perspectives in translating a novel marker panel to the clinic

Abstract

Background: Amyotrophic lateral sclerosis (ALS) is a fatal disorder of the motor neuron system with poor prognosis and marginal therapeutic options. Current clinical diagnostic criteria are based on electrophysiological examination and exclusion of other ALS-mimicking conditions. Neuroprotective treatments are, however, most promising in early disease stages. Identification of disease-specific CSF biomarkers and associated biochemical pathways is therefore most relevant to monitor disease progression, response to neuroprotective agents and to enable early inclusion of patients into clinical trials.

Methods and findings: CSF from 35 patients with ALS diagnosed according to the revised El Escorial criteria and 23 age-matched controls was processed using paramagnetic bead chromatography for protein isolation and subsequently analyzed by MALDI-TOF mass spectrometry. CSF protein profiles were integrated into a Random Forest model constructed from 153 mass peaks. After reducing this peak set to the top 25%, a classifier was built which enabled prediction of ALS with high accuracy, sensitivity and specificity. Further analysis of the identified peptides resulted in a panel of five highly sensitive ALS biomarkers. Upregulation of secreted phosphoprotein 1 in ALS-CSF samples was confirmed by univariate analysis of ELISA and mass spectrometry data. Further quantitative validation of the five biomarkers was achieved in an 80-plex Multiple Reaction Monitoring mass spectrometry assay.

Conclusions: ALS classification based on the CSF biomarker panel proposed in this study could become a valuable predictive tool for early clinical risk stratification. Of the numerous CSF proteins identified, many have putative roles in ALS-related metabolic processes, particularly in chromogranin-mediated secretion signaling pathways. While a stand-alone clinical application of this classifier will only be possible after further validation and a multicenter trial, it could be readily used to complement current ALS diagnostics and might also provide new insights into the pathomechanisms of this disease in the future.

Figure 1. Workflow of CSF profiling by MALDI-TOF MS spectra acquisition and data processing.

After analysis by MALDI-TOF mass spectrometry, profile spectra were pre-processed. Discriminatory features were selected using a Random Forest Classifier. Prediction of diagnostic accuracy was assessed by cross-validation. Mass identification of discriminatory mass peaks was enabled by nanoLC-ESI-MS/MS. Validation was performed by ELISA. Further validation was performed using Multiple Reaction Monitoring (MRM) for the quantification of protein expression.

Figure 2. Reproducibility of MALDI-TOF profile spectra.

Principal component analysis (PCA) for the entire spectra (1–10 kDa) of four different samples using the ClinProTools Software (Version 2.1, Bruker Daltonics). A: Replicate spectra from one sample are colored red, green, blue and yellow in the stack view. B: Each dot in the PCA plot represents one technical replicate from two repeated sample preparations over the course of one month. Despite inevitable variations in ambient and instrument conditions, the replicates form distinct clusters according to their biological origin.

Figure 3. ROC plot analysis with area under the curve of CSF classification of ALS/non-ALS spectra.

The Random Forest and decision tree predictor assign a score between 0 ( = non-ALS) and 1 ( = ALS) to all spectra. A CSF sample that shows a higher score than a predefined cut-off is classified as an ALS sample. The ROC plot curve shows the sensitivity against the specificity for different cut-offs. The curves are color-coded according to the level of the cut-off. The results shown are for the Random Forest with the optimized set of 38 peaks.

Figure 4. BisoGenet Pathway.

The pathway shown was generated using BisoGenet . It shows the biomolecular relationships involving A1AT (alpha-1 antitrypsin; FC: 0.96), CST3 (cystatin-C precursor; FC: 0.96), CHGA (chromogranin A; FC: 0.90), VGF (nerve growth factor precursor; FC: 0.79) and SPP1 (secreted phospho-protein 1 isoform b; FC: 0.94). The red lines mark the shortest paths between the identified biomarker candidates which are represented by red nodes. FC: Fold Change.

Figure 5. Boxplots.

The four boxplots visualize the expression of A: A1AT, B: VGF 3405 Da peptide, C: SPP1 in the main patient cohort and D: SPP1 in the validation cohort as quantified by ELISA.