Comparison of SimoaTM and EllaTM to assess serum neurofilament-light chain in multiple sclerosis.

We compared SimoaTM and EllaTM immunoassays to assess serum neurofilament-light chain levels in 203 multiple sclerosis patients from the OFSEP HD study. There was a strong correlation (ρ = 0.86, p < 0.0001) between both platforms. The EllaTM instrument overestimated values by 17%, but as the data were linear (p = 0.57), it was possible to apply a correction factor to EllaTM results. As for SimoaTM , serum neurofilament-light chain levels measured by EllaTM were correlated with age and EDSS and were significantly higher in active multiple sclerosis, suggesting that these assays are equivalent and can be used in routine clinical practice.

Introduction

Neurofilaments (Nf) are major components of the neuronal cytoskeleton, consisting predominantly of three subunits: Nf‐light (NfL), Nf‐medium and Nf‐heavy chains. Upon neuro‐axonal damage of the central nervous system (CNS), NfL is released into the extracellular space and is detectable in the cerebrospinal fluid and blood. Thus, NfL levels are increased proportionally to the degree of damage, making serum NfL levels a useful biomarker for diagnosing and predicting disease progression of a variety of CNS disorders, including multiple sclerosis (MS). In MS, serum NfL is correlated with several factors including age, Expanded Disability Status Scale (EDSS), disease activity and disease‐modifying treatments.

Several ultrasensitive immunoassay technologies are available for quantification of serum NfL. The current reference method is the Single Molecular Array (Simoa™, Quanterix) using an antibody developed by Uman Diagnostics. Recently, several companies have acquired this antibody, allowing NfL quantification using the Simple PlexTM Ella (EllaTM) microfluidic platform (ProteinSimple). The EllaTM instrument allows rapid and ultra‐sensitive measurement of biomarkers. This platform allows quantitation of an analyte from 72 samples in a single disposable microfluidic cartridge, within 90 minutes (ProteinSimple, 2020). However, the comparability of the two technologies in measuring serum NfL levels in patients with MS remains to be determined.

The objective of this study was to compare the NfL values obtained using the SimoaTM platform with Ella™ instrument in MS patients and healthy controls (HCs). Correlations of the serum NfL measures were performed to evaluate whether EllaTM had good clinical performance in reflecting age, EDSS and disease activity, and could be routinely used to monitor MS patients in clinical practice.

Materials and methods

Serum samples

Anonymized serum samples were taken from 203 of the 1800 anticipated patients ≥15 years old with MS according to the revised McDonald diagnosis criteria included in the OFSEP "High Definition" cohort (NCT03981003), and from 30 HCs. Ethics approvals were obtained, and all patients and controls participated voluntarily in the study and provided written informed consent (Details in Supplementary materials and methods).

SimoaTM and EllaTM NfL assay

Serum NfL concentrations were prospectively determined in parallel with the SimoaTM Human Neurology 4‐Plex “A” kit (Quanterix Corp, Boston, MA) on SimoaTM HD‐1 analyzer and Simple PlexTM NfL Assay (ProteinSimple, CA, USA) on EllaTM instrument, according to the manufacturers’ instructions. Ella™ was calibrated using the in‐cartridge factory standard curve and Simoa™ using the provided standards. All samples were measured in simplicate, on the same day, after a single thaw, with a 1:2 dilution for EllaTM and 1:4 for SimoaTM. In each run, the HC, one control patient with active relapsing remitting MS (RRMS), and one high and one low concentration control sample provided with the kits were assayed. The lower limit of quantification is 0.241 pg/ml for SimoaTM and 2.70 pg/ml for EllaTM.

Statistical analysis

The intra‐assay coefficients of variation (CV) of manufacturer‐provided controls were automatically calculated in duplicate (SimoaTM) or internal triplicate (EllaTM). Repeatability tests were performed with samples at high (RRMS patient) and low (HC) concentrations by repeated measures for SimoaTM (30 times each) and for EllaTM (28 times and 25 times, respectively). Intra‐assay CV was calculated from the standard deviation of the average concentrations divided by the overall mean of the average concentrations.

Median NfL values obtained by each platform were compared using the Wilcoxon–Mann–Whitney test. Spearman correlation coefficients were calculated to assess the association between concentrations obtained by each platform, presented with 95% confidence interval (95%CI). The Bland–Altman method was used to measure mean difference and 95% limit of agreement between log‐transformed concentrations obtained by each platform. The regression relationship between the two platforms was evaluated using Passing–Bablok. Finally, correlations of serum NfL levels with clinical parameters were analyzed using linear regression (age, EDSS) or Wilcoxon–Mann–Whitney (e.g. RRMS vs. progressive MS).

Statistical analyses were performed on Prism 8.3.0.538 (GraphPad). A p‐value <0.05 was considered statistically significant.

Data availability statement

Anonymized data will be shared by request from any qualified investigator.

Results

Repeatability tests were performed by measuring 25‐30 times one sample at low concentration (HC) and one sample at high concentration (RRMS patient) and showed similar CVs with both platforms (Supplementary Figure A). The mean [min‐max] intra‐assay CVs on EllaTM technology was 2.12% [1.53‐2.70] vs 3.78% [2.93‐4.63] on SimoaTM platform. The mean [min‐max] inter‐assay CV of the three runs was 12.93% [7.59‐18.27] on EllaTM and 5.54% [5.08‐6.00] on SimoaTM. In MS patients, median serum NfL levels [interquartile range] measured by EllaTM were higher than by SimoaTM (13.90 pg/ml [10.73‐18.48] for EllaTM vs. 9.46 pg/ml [6.94‐12.9] for SimoaTM, p < 0.001) (Figure 1A). Serum NfL levels were strongly correlated between the two technologies in MS patients (Spearman r = 0.86, 95% CI [0.821‐0.895]) (Figure 1B) and in HCs (Spearman r = 0.76, 95%CI [0.533‐0.882], Supplementary Figure B).

Figure 1

Properties of serum NfL values measured by the SimoaTM and EllaTM platforms. A, Quantitation of NfL concentration (pg/ml) in serum with EllaTM and SimoaTM platforms shown in logarithmic scale. Red lines represent median NfL level. The statistical difference was evaluated by Wilcoxon–Mann–Whitney with 203 samples. ***p < 0.001. B, Spearman correlation (r) between NfL concentration values obtained by the EllaTM compared to the SimoaTM instruments (p < 0001). C, Bland–Altman plots comparing agreement between NfL concentrations determined using the SimoaTM and EllaTM platforms. The solid red line represents the bias between assays (17.6%), the dashed red lines represent 95% limits of agreement (−10.61% to 45.81%). D, Passing–Bablok regression analysis of NfL concentration calculated on 203 samples by the EllaTM compared to the SimoaTM platform. It shows the value of slope (1.161) and intercept (2.917). Solid gray line: Passing–Bablok regression line; solid red line: identity line (x = y).

The Bland–Altman method depicted a mean bias of 17.6% for the NfL concentrations between the assays performed with the two technologies. Thus, EllaTM showed a 17.6% “overestimation” compared with SimoaTM. Overall, 95% of observations were within the limit of agreement (Figure 1C). The slope of the Passing–Bablok regression line was 1.161 (95% CI [1.091‐1.240], p < 0.0001) and the intercept was 2.917 pg/ml (95% CI [2.132‐3.676], p < 0.0001). The 95% CI of intercept and slope values differ from zero and one, respectively, indicating a method agreement and allowing application of a correction coefficient. Moreover, the linearity test demonstrated no significant deviation from linearity between the two datasets (p = 0.57), suitable for concluding on method agreement (Figure 1D).

Both platforms exhibited significant correlations of serum NfL with age, EDSS and disease form (Figure 2). Especially, serum NfL levels were higher in RRMS patients than in age‐matched HCs, higher in active MS than in inactive MS, higher during relapses than in patients with a stable disease and higher in PMS than in RRMS patients with both platforms (Figure 2B). The last comparison was no longer significant in a multivariate model including age.

Figure 2

Comparison of serum NfL values measured by the SimoaTM and EllaTM platforms. A, Association of age with NfL concentration (pg/ml, shown in logarithmic scale) in serum determined by EllaTM (light gray) and SimoaTM (dark gray) platforms were estimated using the linear regression with 203 samples (b = 0.18, p = 0.002, r2 = 0.045 in SimoaTM and b = 0.21, p < 001, r2 = 0.057 in EllaTM). B: Comparison of NfL levels (pg/ml, shown in logarithmic scale) in serum for HCs and MS patients, obtained by the SimoaTM (dark gray, left) and the EllaTM (light gray, right) instruments. Serum NfL levels were higher in RRMS patients than in HCs (p = 0.021 and p < 0001, respectively), higher in active MS than in inactive MS (p = 0.0080 and p = 0.0356, respectively), higher during relapses than in patients with a stable disease (p = 0.0153 and p = 0.0373, respectively), and lower in RRMS than in PMS patients (p = 0.0007 and p = 0.0021, respectively) (*p < 05, **p < 01, ***p < 001, ****p < 0001). C: Association of EDSS with NfL concentration (pg/ml, shown in logarithmic scale) in serum determined by SimoaTM (left, dark gray boxplots) and EllaTM (right, light gray boxplots) platforms were estimated using linear regression with 203 samples (b = 0.83, p = 0.026, r2 = 0.026 in SimoaTM and b = 0.96, p = 0.015, r2 = 0.031 in EllaTM).

Discussion

Blood NfL is a biomarker associated with several clinical parameters in MS. We showed that both EllaTM and SimoaTM platforms offer excellent sensitivity, detecting serum NfL concentrations in the picogram range, SimoaTM platform offering the lowest inter‐assay imprecision at low analyte levels. A limitation of our study was restricting the analysis to three runs, making the inter‐assay CV harder to accurately define. The two systems use different methods to determine intra‐assay CV, using technical duplicate or triplicate readings, preventing direct comparison. However, Simple PlexTM runs the samples in parallel at the same time, assuring the exact same conditions for replicate analysis, an advantage over the SimoaTM platform that processes serial measures. Moreover, calibrators are directly integrated in the Simple PlexTM cartridges, providing best calibration for each run.

The main finding of this study is the demonstration of a concordance between NfL levels measured using both platforms, even at low levels in the HC group. This is potentially the result of using the same anti‐NfL antibody and of heterophilic blockers limiting potential cross‐reaction between anti‐NfL antibody and antibodies in the serum for both platforms. However, we observed significant differences in absolute biomarker concentrations between these two instruments. Using different calibrators (naturally derived bovine NfL for EllaTM and a recombinant human NfL for SimoaTM) has been associated with differences in NfL measure and could explain the differences in absolute values obtained by both assays. The NfL raw concentrations measured by SimoaTM were globally lower vs EllaTM, as confirmed by the Bland–Altman plot. The “spike recovery” reported in the data sheet of the two assays is 68% for SimoaTM NfL kit and 108% for Simple PlexTM NfL, suggesting that SimoaTM could underestimate the values of NfL by 17% compared to EllaTM due to a greater effect of the serum matrix than in the Simple PlexTM method. Passing–Bablok allowed the bias to be evaluated over the entire measurement range and the linear test shows that the data are linear (p = 0.57). Thus, it is possible to apply a correction factor 2.917. Therefore, EllaTM technology, with the advantage of small footprint and a robust and cheaper platform, represents a reliable substitute for SimoaTM to measure serum NfL.

Moreover, we demonstrate that serum NfL levels determined by EllaTM show the same properties, concerning correlation of serum NfL with age, EDSS and disease activity. This is crucial, since future studies with EllaTM can directly resume previous results already published using SimoaTM. However, NfL cannot be used in combination with other brain biomarkers that remain unavailable on this platform, such as glial fibrillary acidic protein, available on the SimoaTM platform which currently has a larger range of biomarkers.

Although the EllaTM platform showed a greater inter‐assay variation compared to SimoaTM, it seems an attractive choice for routine quantification of serum NfL considering the reduced cost, high performance and small footprint while maintaining a high concordance with SimoaTM. Serum NfL biomarker can be quantified using automated EllaTM instrument to reliably and rapidly monitor disease activity and treatment in MS as well as in many other CNS pathological conditions, thus optimizing quality of care.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Audrey Gauthier: nothing to disclose; Sébastien Viel: nothing to disclose; Magali Perret: nothing to disclose; Sabine Laurent‐Chabalier: nothing to disclose; Marc Debouverie: nothing to disclose; Gilles Edan: consultancy and lecturing fees from Bayer‐Schering, Biogen, LFB, Merck, Novartis, Roche, Sanofi; research grants from Bayer, Biogen, Genzyme, Mercks, Novartis, Roche, Teva, and the ARSEP foundation. He has been principal investigator in phase 2 and 3 clinical studies conducted by Bayer, Biogen, Merck, Novartis, Sanofi‐Aventis Teva, and 4 academic programs (programmes hospitaliers de recherche clinique, PHRC) on MS sponsored by Rennes University Hospital; Sandra Vukusic: grants, personal fees and non‐financial support from Biogen, grants and personal fees from Geneuro, grants, personal fees and non‐financial support from Genzyme, grants and personal fees from Medday, grants, personal fees and non‐financial support from Merck‐Serono, grants, personal fees and non‐financial support from Novartis, grants, personal fees and non‐financial support from Roche, grants, personal fees and nonfinancial support from Sanofi, personal fees from Teva; Christine Lebrun‐Frénay: fees for consulting or lectures from Novartis, Genzyme, Roche; Jérôme De Sèze: consulting and lecturing fees, travel grants and unconditional research support from Biogen, Genzyme, Novartis, Roche, Sanofi Aventis and Teva Pharma; David Axel Laplaud: served on scientific advisory boards for Roche, Sanofi, Novartis, MedDay, Merck and Biogen, received conference travel support and/or speaker honoraria from Novartis, Biogen, Roche, Sanofi, Celgene and Merck and received research support from Fondation ARSEP and Agence Nationale de la Recherche; Olivier Gout: nothing to disclose; Aurélie Ruet: consultancy fees, speaker fees, research grants (non‐personal), or honoraria approved by the institutions from Novartis, Biogen Idec, Genzyme, Medday, Roche, Teva and Merck; Thibaud Moreau: fees as scientific adviser from Biogen, Medday, Novartis, Genzyme, Sanofi; Olivier Casez: funding for travel and honoraria from Biogen, Merck Serono, Novartis, Sanofi‐Genzyme and Roche; Pierre Clavelou: consulting and lecturing fees, travel grants and unconditional research support from Actelion, Biogen, Genzyme, Novartis, Medday, Merck Serono, Roche, and Teva Pharma; Eric Berger: honoraria and consulting fees from Novartis, Sanofi Aventis, Biogen, Genzyme, Roche and Teva Pharma; Hélène Zephir: consulting or lectures, and invitations for national and international congresses from Biogen, Merck, Teva, Sanofi‐Genzyme, Novartis and Bayer, as well as research support from Teva and Roche, and academic research grants from Académie de Médecine, LFSEP, FHU Imminent and ARSEP Foundation; Guillaume Brocard: nothing to disclose; Romain Casey: nothing to disclose; Christine Lombard: nothing to disclose; Sophie Trouillet‐Assant: nothing to disclose; Eric Thouvenot : consulting and lecturing fees, travel grants or unconditional research support from the following pharmaceutical companies: Actelion, Biogen, Celgene, Genzyme, Merck Serono, Novartis, Roche, Teva pharma.

Funding information

The study was funded by CHU de Nimes and has also been supported by a grant provided by the French State and handled by the "Agence Nationale de la Recherche," within the framework of the "Investments for the Future" programme, under the reference ANR‐10‐COHO‐002 Observatoire Français de la Sclérose en plaques (OFSEP).

Supplementary Figure S1. Comparison of SimoaTM and EllaTM platforms at low serum NfL levels. A: Repeatability tests of both platforms using samples from one HC and from one RRMS patient tested 30 times. For SimoaTM, average NfL concentrations were 6.55 pg/ml and 14.22 pg/ml and CVs were 11.3% and 8.1%, respectively. For EllaTM, average serum NfL concentrations were 8.60 pg/ml and 38.38 pg/ml and CVs were 12.8% and 8.9%, respectively, as indicated on the graph. B: Spearman correlation (r) between NfL concentration values obtained by the EllaTM compared to the SimoaTM instruments in a cohort of 29 HCs (r = 0.76, p < 0.0001).

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Supplementary Material and Methods. Origin of serum samples.

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Name	Location	Role	Contribution
Audrey Gauthier, MSc	École Pratique des Hautes Études, Paris	Author	major role in the acquisition of data analysis or interpretation of the data drafting or revising the manuscript for intellectual content
Sébastien Viel, PharmD, PhD	Hospices Civils de Lyon, Lyon	Author	design or conceptualization of the study analysis or interpretation of the data drafting or revising the manuscript for intellectual content
Magali Perret, MSc	Hospices Civils de Lyon, Lyon	Author	major role in the acquisition of data
Guillaume Brocard, MSc	Hospices Civils de Lyon, Lyon	Author	major role in the acquisition of data
Romain Casey, PhD	Hospices Civils de Lyon, Lyon	Author	design or conceptualization of the study analysis or interpretation of the data drafting or revising the manuscript for intellectual content
Christine Lombard, MSc	Hospices Civils de Lyon, Lyon	Author	major role in the acquisition of data
Sabine Laurent‐Chabalier, PhD	CHU de Nîmes, Nîmes	Author	analysis or interpretation of the data drafting or revising the manuscript for intellectual content
Marc Debouverie, MD, PhD	CHU de Nancy, Nancy	Author	major role in the acquisition of data
Gilles Edan, MD, PhD	CHU Pontchaillou, Rennes	Author	major role in the acquisition of data
Sandra Vukusic, MD, PhD	Hospices Civils de Lyon, Lyon	Author	major role in the acquisition of data
Christine Lebrun‐Frénay, MD, PhD	CHU Pasteur, Nice	Author	major role in the acquisition of data
Jérôme De Sèze, MD, PhD	CHU de Strasbourg, Strasbourg	Author	major role in the acquisition of data
David Axel Laplaud, MD, PhD	CHU de Nantes, Nantes	Author	major role in the acquisition of data
Giovanni Castelnovo, MD	CHU de Nîmes, Nîmes	Author	major role in the acquisition of data
Olivier Gout, MD	Fondation Rotschild, Paris	Author	major role in the acquisition of data
Aurélie Ruet, MD, PhD	CHU de Bordeaux, Bordeaux	Author	major role in the acquisition of data
Thibault Moreau, MD, PhD	CHU de Dijon, Dijon	Author	major role in the acquisition of data
Olivier Casez, MD	CHU de Grenoble, Grenoble	Author	major role in the acquisition of data
Pierre Clavelou, MD, PhD	CHU de Clermont‐Ferrand, Clermont‐Ferrand	Author	major role in the acquisition of data
Eric Berger, MD	CHU de Besançon, Besançon	Author	major role in the acquisition of data
Hélène Zephir, MD, PhD	CHU de Lille, Lille	Author	major role in the acquisition of data
Sophie Trouillet‐Assant, PhD	Hospices Civils de Lyon, Lyon	Author	design or conceptualization of the study analysis or interpretation of the data drafting or revising the manuscript for intellectual content
Eric Thouvenot, MD, PhD	CHU de Nîmes, Nîmes	Author	major role in the acquisition of data design or conceptualization of the study analysis or interpretation of the data drafting or revising the manuscript for intellectual content