Type 1 diabetes (T1D) is a chronic autoimmune condition where the immune system mistakenly attacks and destroys the insulin secreting β-cells in the pancreatc islets of Langerhans. T1D affects millions worldwide, with an onset often happening already in childhood. Existing methods to assess T1D are predominantly for later stages of the disease, assessing disease symptoms, or detecting specific autoantibodies. Detection of earlier markers is important for development of improved treatment strategies.

Mass spectrometry proteomics provides a compelling option for such biomarker discovery, thanks to its capabilities to provide functional information beyond genomic studies, providing a more direct measure of the phenotype. Importantly, longitudinal proteome measurements allow revealing dynamic changes in the proteome over time, enabling early detection of diseases by identifying protein changes that precede clinical symptoms and allowing to account for within-individual variability. To fully utilize such longitudinal proteome measurements, we have developed a robust reproducibility optimization approach, which builds on our previously developed reproducibility-optimized statistical framework. Using this approach, we have discovered candidate protein markers associated with progression of T1D, including those associated with the very early stages of T1D. In particular, we have identified several proteins that are indicative of future decline in β-cell functionality. To furher bring the analysis to single-cell level, we have utilized mass cytometry, which can measure dozens of proteins simultaneously at the single-cell level, allowing for detailed profiling of complex biological samples. We have developed a comprehensive toolkit for the analysis of mass cytometry data, revealing different endotypes in children developing T1D, with distinct characteristics of early immune responses in blood, depending on the type of autoantibody that appears first. These findings the pave way for more personalized threatment of T1D in the future and highlight the potential of proteomics in biomarker discovery.