Quantitative Mass Spectrometric Analysis of Autoantibodies as a Paradigm Shift in Autoimmune Serology

Affecting ~7–9% of individuals worldwide (1), autoimmunity is a relatively common condition that can cause substantial morbidity and mortality (2). However, there are considerable challenges in finding robust and accurate biomarkers for this heterogeneous group of diseases. Serum autoantibodies have served as archetypal diagnostic biomarkers for autoimmune diseases over decades (3). As pathologic species, they can be used to monitor disease activity and treatment responses (4).
Most diagnostic laboratory tests for autoantibodies utilize conventional assays such as the solid-phase enzyme immunoassay (EIA), enzyme-linked immunosorbent assay (ELISA), or radioimmunoassay (RIA). All of these assays quantitate amounts of autoantibodies in the bodily fluid but fail to delineate their molecular composition. Multiplex assays have emerged for autoantibody high-throughput screening that enable rapid identification of subsets of patients to facilitate diagnostic and predictive medicine (5). This is particularly important since multiple autoantibodies are often responsible for autoimmune disease (6). However, such conventional assays cannot unravel clonal evolution and dynamic autoimmune responses. Frustratingly, prediction of disease onset and flares with these biomarkers remains suboptimal.
Mass spectrometry (MS) is an analytical technique which can identify proteins by determining the amino acid sequence of peptides derived from each protein. MS can also measure changes in relative abundance of specific proteins as a consequence of treatment, and with appropriate standards, quantify absolute abundance. MS has been used previously to analyze specific antibodies or the repertoire of antibodies in order to better understand the dynamics of humoral immune responses in vaccinated animals (7).
This technology has been used to characterize autoantibodies in diseases such as systemic lupus erythematosus (SLE) and Sjögren's syndrome (SS) by identifying their immunoglobulin variable region (IgV) subfamily usage and mutational profiles at a molecular level (8). Despite conventional immunoassays determining stability in autoantibody profiles, MS-based quantitative proteomics has been used to uncover the dynamic changes in molecular signatures and levels of autoantibodies as the disease progresses (9). Subtle nuances in the molecular profile of patient autoantibodies can be identified, paving the way for new diagnostic biomarkers that can anticipate the onset or severity of disease before conventional biomarkers or immunoassays (10). This exciting technology hence offers a unique opportunity to identify pathogenic “rogue” and/or protective clonotypes that characterize autoimmune diseases. By deconstructing these clonotypes by quantitative proteomics and establishing a database of clonotypes with their corresponding pathogenicity, this would possibly facilitate identification of at-risk patients for deterioration, or predict response to targeted therapy.