Biomarker Discovery in Autoimmune Diseases: A Proteomics Approach
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Proteins constitute the functional machinery of cells and are prime candidates for disease marker discovery. Mass spectrometry-based proteomics biomarker discovery holds the ability to interrogate a constellation of proteins simultaneously in a high-throughput manner to uncover a panel of markers that are specific to the presence of a disease. However, the rate of introduction of novel biomarkers with clinical currency has declined in the past few years due to challenges faced by both the discovery and validation stages. Surface retentate chemistry-mass spectrometry is a powerful platform that allows on-chip simplification of complex biological samples to better match the current dynamic range of mass spectrometers. Initial reports on differential protein profiling using this approach produced profiles with high sensitivities and specificities for disease classification. As with any maturing technology, issues that were overlooked during its introduction are now the main barriers to its clinical utility. The improved workflow described here aims to address some of these pending issues. Specifically, the experimental design incorporated knowledge of the disease pathway into sample selection, elected sample sources that are rich in diagnostic markers, and adopted biological and technical replicates to minimize variance. To ensure reproducibility, complete automation of the process from sample preparation to data acquisition was incorporated along with the adoption of a high performance mass spectrometer with minimal mass drift. A robust data analysis approach was implemented to overcome the issue of overfitting and to effectively trim down the list of candidate biomarkers to the selected few with true discriminatory power to facilitate downstream validation. As a demonstration of the robustness and utility of the workflow, profiling studies were performed on two autoimmune diseases. Protein profiles with high mass peak fidelity were obtained with high discriminatory power. Selective differential peaks were further investigated and confirmed to display differential levels in clinical samples. Validation in a larger sample set should determine the diagnostic potential of these markers for clinical application. Finally, a high-throughput study is reported showing that peptoids are, in general, a relatively more cell permeable class of molecules than peptides, rendering them ideal for drug development to target disease biomarkers.