AI Article Synopsis
- Type 1 diabetes (T1D) is an autoimmune disease, and mass spectrometry (MS) is being used to find new biomarkers and understand the disease's mechanisms better.
- The combination of MS and machine learning has led to the identification of multi-molecular biomarker panels and the discovery of relevant pathways and therapeutic targets.
- Despite progress, challenges like understanding tissue environments and the impact of external factors persist, but advancements in MS technologies, like ultra-fast separations and single-cell analysis, may help overcome these obstacles.
Article Abstract
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.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631641 | PMC |
| http://dx.doi.org/10.1016/j.molmed.2024.07.009 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!