PNNL

Abstract

Type 1 diabetes (T1D) is a significant chronic disease in children, leading to a reduced life expectancy. It is characterized by an autoimmune attack on pancreatic ß cells, driven by genetic and environmental factors. The lack of early detection assays and preventive treatments highlight the need for biomarkers and better understanding of the disease mechanisms. Recent advancements in mass spectrometry (MS)-based omics methodologies have shown great promise for contributing to both these needs. MS analyses of human biofluids have identified lipids, proteins and metabolites with potential in clinical prognostics applications. Furthermore, the machine learning-based integration of omics data has improved the prediction of T1D progression by identifying multi-molecule biomarker panels, moving closer to precision diabetes. Ion mobility spectrometry (IMS) coupled with MS has improved the specificity and sensitivity of analyses, enabling high-throughput diagnostics and a deeper understanding of biological complexities. Additionally, advancements in spatial omics and single-cell MS have provided insights into the molecular heterogeneity of T1D-related cell populations, opening avenues for personalized medicine. In more mechanistic studies, MS has contributed to the discovery of neoepitopes and pathways involved in disease development, expanding our knowledge of T1D pathogenesis and potential therapeutic strategies. However, challenges remain in identifying small molecule signatures and understanding the role of metabolites and similar compounds in disease pathogenesis. The integration of computational predictions of molecular properties and multi-dimensional measurements may overcome these obstacles, facilitating the identification of uncharacterized molecules and resulting in new assays for use in clinical settings. Here, we highlight the latest advancements in MS-methods for comprehensive biomarker discovery and disease understanding that may revolutionize T1D diagnosis and treatment, paving the way for more effective interventions and improved patient outcomes.