Exploring new frontiers in type 1 diabetes through advanced mass-spectrometry-based molecular measurements

• Published in: Trends in molecular medicine
• Main Authors: Sarkar, Soumyadeep, Zheng, Xueyun, Clair, Geremy C., Kwon, Yu Mi, You, Youngki, Swensen, Adam C., Webb-Robertson, Bobbie-Jo M., Nakayasu, Ernesto S., Qian, Wei-Jun, Metz, Thomas O.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 15.08.2024
• Subjects: biomarkers; cross-linking MS; ion mobility spectrometry; machine learning; mass spectrometry; metabolite dark matter; neoepitopes; single-cell MS; spacial omics; type 1 diabetes; spacial omics; single-cell MS; type 1 diabetes; cross-linking MS; neoepitopes; biomarkers; ion mobility spectrometry; mass spectrometry; metabolite dark matter; machine learning
• ISSN: 1471-4914; 1471-499X; 1471-499X
• DOI: 10.1016/j.molmed.2024.07.009

MS-based omics technologies are uncovering novel biomarkers and elucidating the complex pathophysiology of type 1 diabetes (T1D), setting the stage for transformative diagnostic and therapeutic strategies.The integration of machine learning with omics data is refining T1D predictive models, advancing the field towards more accurate and dynamic risk assessments for precision diabetes management.Cutting-edge MS methodologies, including ion mobility spectrometry, are enhancing the speed and depth of biological analyses, enabling rapid, high-throughput diagnostics suitable for large-scale studies.Innovations in single-cell and spatial MS, such as nanoPOTS, are revealing molecular heterogeneity within T1D, providing a granular view of disease onset and progression critical for targeted therapies.The discovery of T1D-associated neoepitopes and metabolites through advanced omics techniques is providing new insights into disease mechanisms, potentially leading to novel, personalized interventions. Type 1 diabetes (T1D) is a devastating autoimmune disease for which advanced mass spectrometry (MS) methods are increasingly used to identify new biomarkers and better understand underlying mechanisms. For example, integration of MS analysis and machine learning has identified multimolecular biomarker panels. In mechanistic studies, MS has contributed to the discovery of neoepitopes, and pathways involved in disease development and identifying therapeutic targets. However, challenges remain in understanding the role of tissue microenvironments, spatial heterogeneity, and environmental factors in disease pathogenesis. Recent advancements in MS, such as ultra-fast ion-mobility separations, and single-cell and spatial omics, can play a central role in addressing these challenges. Here, we review recent advancements in MS-based molecular measurements and their role in understanding T1D. Type 1 diabetes (T1D) is a devastating autoimmune disease for which advanced mass spectrometry (MS) methods are increasingly used to identify new biomarkers and better understand underlying mechanisms. For example, integration of MS analysis and machine learning has identified multimolecular biomarker panels. In mechanistic studies, MS has contributed to the discovery of neoepitopes, and pathways involved in disease development and identifying therapeutic targets. However, challenges remain in understanding the role of tissue microenvironments, spatial heterogeneity, and environmental factors in disease pathogenesis. Recent advancements in MS, such as ultra-fast ion-mobility separations, and single-cell and spatial omics, can play a central role in addressing these challenges. Here, we review recent advancements in MS-based molecular measurements and their role in understanding T1D.