A Comprehensive Description of the Roadmap to Identify and Validate a Myelin Biomarker

Abstract

Background: Demyelination and remyelination are major issues for scientists dealing with myelin disorders in both clinical and research fields. Despite that, rapid, reliable and convenient tools to monitor myelin changes still lack both in central and peripheral nervous system. Given that myelin is enriched in specific lipids and proteins, it is reasonable they could represent eligible candidates as structural damage biomarkers for this characteristic membrane. Among them, we focused on sphingomyelin (SM) due to the enrichment in myelin and because it is easily measurable in different biological matrices.

Objective: Depicting the roadmap to identify and validate SM dosage as a myelin biomarker useful for pre-clinical and clinical practice.

Design: This study adheres to STROBE guidelines for observational cross-sectional studies on human patients and to ARRIVE guidelines for animal models.

Method: Following the recommendations of the Society for CSF Analysis and Clinical Neurochemistry, we describe the stepwise process to validate SM as a myelin biomarker, starting from the optimization of the fluorescence-based assay and analytical validation in experimental models until clinical and pathological validation in biological fluids of neurological patients.

Results: SM dosage monitors myelination, demyelination, remyelination and even small myelin changes associated to myelin pathology and pharmacological treatments in experimental models. SM is detectable in human biological fluids and informative of myelin damage in the CSF of neurological patients. SM dosage identifies myelin breakdown in the CSF of patients affected by Guillain-Barrè Syndrome (GBS) and Chronic Inflammatory Demyelinating Polyradiculoneuropathy (CIDP), identifying disease activity, axonal from demyelinating variants, and avoiding misdiagnosis.

Conclusion: SM dosage displayed extremely promising real-word performances being able to identify, monitor and stage myelin pathology. Given that it is simple, inexpensive and easily adaptable to routine use in any hospital setting, it might rapidly progress to the implementation and impact on clinical outcomes.

Keywords: CIDP; GBS; biomarker; myelin; sphingomyelin.

Figure 1.

SM monitors myelin deficit in CMT1A: (A) representative images of blue toluidine cross section of sciatic nerves of 30-day-old WT and CMT1A rats, (B) we found a remarkable decrease of SM content in homogenate of sciatic nerves from 30-day-old CMT1A rats (n = 8) compared to controls (n = 8; WT vs CMT1A: 3.342 ± 0.093 vs 1.160 ± 0.1316 nmol/mg), (C) quantitative neuropathology performed on the contralateral sciatic nerves of the same animals displayed a significant reduction in the percentage of myelinated area (WT vs CMT1A: 28.40 ± 1.206 vs 13.24 ± 1.066%), (D) SM levels in rat sciatic nerves progressively increased during the critical timing of peripheral myelination and detects a very early shortage of this myelin lipid in CMT1A (WT vs CMT1A, n = 3: 3-day-old, 0.038 ± 0.0036 vs 0.0397 ± 0.0036 nmol/mg; 5-day-old, 0.44 ± 0.0503 vs 0.2744 ± 0.1197 nmol/mg; 10-day-old, 1.578 ± 0.1995 vs 0.6768 ± 0.1028 nmol/mg; 20-day-old, 2.116 ± 0.2566 vs 0.8222 ± 0.1013 nmol/mg), and (E) SM levels in brain of WT and CMT1A rats did not change (WT vs CMT1A, n = 3: 100.0 ± 2.358% vs 110.0 ± 9.525%). Statistical difference between 2 groups in B, C and E was determined using the two-tailed Student’s t-test. Multiple group comparison in D was performed by 2-way ANOVA. *P < .05, **P < .01, ****P < .0001.

Figure 2.

SM is detectable in central nervous system and identifies central demyelination: (A) SM is detectable both in brain and spinal cord of 210-day-old mice by our assay and at a lesser extent than in sciatic nerve (brain vs spinal cord vs sciatic nerve, n = 2: 1.579 ± 0.2085 vs 2.846 ± 0.2572 vs 4.641 ± 0.2590 nmol/mg), (B) In lumbar spinal cord of EAE mice (n = 9) we found a reduction of SM levels compared to CTRL (n = 4; CTRL vs EAE: 1.710 ± 0.090 vs 2.393 ± 0.2043 nmol/mg), and (C) Notably, in sciatic nerves of both EAE and control mice we did not find any difference in SM levels (CTRL vs EAE, n = 3: 100.0 ± 5.580% vs 95.84 ± 6.315%). Statistical difference between 2 groups in B and C was determined using the two-tailed Student’s t-test. **P < .01.

Figure 3.

SM assay monitors small myelin rearrangements in an in vitro model of demyelination and remyelination: (A) schematic representation of the in vitro model of demyelination and remyelination that we used (see methods section), (B) SM dosage proved sensitive enough to detect both dose-dependent demyelination and remyelination (Vehicle vs +Fsk 20 μM vs +Fsk 40 μM vs −Fsk 40 μM, n = 6: 100 ± 2.309 vs 66.96 ± 2.982 vs 29.68 ± 0.5772 vs 73.11 ± 2.694%). This trend closely parallels myelin changes quantified as the percentage of myelinated area (ie, MBP-positive myelinated fibers) in each condition (Vehicle vs +Fsk 20 μM vs +Fsk 40 μM vs −Fsk 40 μM: 100 ± 2.309 vs 66.96 ± 2.982 vs 29.68 ± 0.5772 vs 73.11 ± 2.694%), (C) moreover, we found a statistically strong correlation between percentage of MBP positive area and SM amount (nmol/µl; r² = .9152). Multiple group comparison was performed by 1-way analysis of variance (ANOVA) followed by the Holm-Sidak test in B. Correlation coefficient was estimated by the Spearman’s rank correlation test. **P < .01, ***P < .001.

Figure 4.

Optimization of SM dosage in human biological fluids: (A and B) we tested the sensitivity of the SM fluorescence-based assay, loading increasing volume of serum (A) and CSF (B) from a neurological patient. SM was detectable in both the biological fluids and increased proportionally to the volume of the sample, (C) to support the reliability of SM dosage, we performed an agreement analysis comparing CSF SM levels quantified by our fluorescence-based assay with those measured by LC-MS/MS in 31 neurological patients. Linear regression analysis displayed a linear relationship between the 2 methods, as outlined by a goodness of fit (r²) = 0.8448. SM values reported on x axis resulted from the sum of the major SM species detected by LC-MS/MS, namely SM 16:0 (51.22%), SM 18:0 (32.07%), SM 24:1 (10.61%) and SM 24:0 (6.1%), (D and E) SM levels quantified in the serum (D) and CSF (E) of 262 neurological patients were extremely variable to suggest a potential clinical relevance for patients affected by neurological disorders, (F) Correlation analysis between SM levels in the CSF and serum of 262 neurological patients did not show any correlation (r = .1487; P > .05), and (G) correlation analysis in the same cohort of patients between CSF SM levels and age did not show any correlation (r = .1995; P > .01). Linear relationship between the 2 methods in C was estimated by linear regression analysis. Correlation coefficient was estimated by the Spearman’s rank correlation test in F and G.

Figure 5.

SM as a biomarker of demyelination in immune-mediated demyelinating neuropathies: (A) we assigned patients to 3 major cohorts, namely CIDP/AIDP (n = 61) OND (n = 87), and non-immune-mediated axonal neuropathies (n = 26). Using the validated cut-off for SM (0.9819 pmol/µL), we unambiguously identify demyelination typical of active CIDP and AIDP (Active CIDP/AIDP vs OND vs axonal neuropathies: 1.706 ± 0.1277 vs 0.3874 ± 0.029 vs 0.5811 ± 0.0041 pmol/µL), (B) SM levels differentiate active (n = 43) from stable (n = 18) stage of the disease and from suspected CIDP non-confirmed according to published criteria (NO EAN/PNS CIDP, n = 31; Active CIDP vs Stable CIDP vs NO EAN/PNS CIDP: 1.793 ± 0.1552 vs 0.5858 ± 0.0074 vs 0.4092 ± 0.1997 pmol/µL). Multiple group comparison was performed by 1-way analysis of variance (ANOVA) followed by the Holm-Sidak test. ****P < .0001.