Surface plasmon resonance imaging (SPRi) in combination with machine learning for microarray analysis of multiple sclerosis biomarkers in whole serum

Abstract

Multiple sclerosis (MS) is the most common autoimmune disease observed in young adults and is known to be exceptionally difficult to diagnose accurately. Current diagnostic methods are considered unreliable and inefficient, and they typically lack the needed specificity that allows for routine monitoring of disease progression. In this work, we report a surface plasmon resonance imaging (SPRi) method in combination with carbohydrate microarrays for the detection of multiple sclerosis biomarkers in undiluted serum. A working range of 1–100 ng/mL was demonstrated with the limit of detection (LODs) below 7 ng/mL. The microarrays utilized in this work were coated with perfluorodecyltrichlorosilane (PFDTS) to interact strongly with the hydrophobic tails of the ganglioside antigens, allowing for desirable antigenic display in a manner mimicking a myelin sheath. Machine learning (ML) algorithms were applied to the carbohydrate array/SPRi data analysis to understand and characterize the cross reactivities observed between the antibodies. Both endpoint results and SPRi sensorgrams were analyzed with statistical models for the evaluation of binding events that include kinetic and steady state components. In addition, K-nearest neighbor (kNN) and neural net (nnet) were utilized to examine specific and cross-reactive binding, yielding higher accuracy than what traditional methods can achieve. The combination of ML models and microarray data provides a comprehensive understanding of complex interactions and could be used to differentiate and identify closely behaving biomarkers in a clinical setting.