SUMMARY Systemic Lupus Erythematosus (SLE) is a multi-organ, systemic autoimmune disorder, estimated to affect at least 1.5 million Americans. The hallmark of SLE is the production of serum autoantibodies, a unifying feature present in over 99% of untreated patients. Such autoantibodies are directly pathogenic, eventually causing the symptoms of SLE including debilitating joint pain and rashes, followed by organ damage and early mortality. Previous work has shown that autoantibodies begin to accrue months to years before the symptoms of SLE appear which may allow a window for detecting them and starting medications to prevent or at least delay the onset of SLE. These serum autoantibodies, however, consist of a complex mixture in the blood including pathogenic, non-pathogenic, and beneficial antibodies which may number in the millions. While current diagnostic platforms can screen for total autoantibodies during autoimmune disease, finding specific monoclonal autoantibodies linked to the development of SLE is currently impossible. Thus, the lack of capability to directly detect monoclonal, pathogenic, autoantibodies presents a significant barrier in understanding how autoantibodies arise, and there is a critical need to develop advanced analytical tools to characterize these antibodies. Our long-term goal is to understand SLE autoantibody development at the monoclonal level and to develop high diagnostic value autoantibody biomarkers. The overall objective of this proposal to establish a novel integrated proteomics platform that employs two complementary scientific approaches, a quantitative top-down MS approach for autoantibody biomarker discovery, and a top-down proteogenomics sequencing approach for autoantibody biomarker validation and functional characterization. Our proposed top-down autoantibody proteomics platform will be applied to identify intact autoantibody Fab signatures in longitudinal SLE serum samples. As a result, we will provide a first top-down proteomics platform for characterizing SLE autoantibodies at the monoclonal level. Applying it to the analysis of SLE autoantibodies will provide foundations for new strategies in SLE prognosis, intervention, and prevention, and may lead to novel high diagnostic value biomarkers. After development, our top-down autoantibody characterization platform can be easily adapted to other autoimmune diseases such as Sjogren’s Syndrome.