Project description:Robust, reliable quantification of large sample cohorts is often essential for meaningful clinical or pharmaceutical proteomics investigations, but it is technically challenging. When analyzing very large numbers of samples, isotope labeling approaches may suffer from substantial batch effects; and even with label-free methods, it becomes evident that low-abundance proteins are not reliably measured due to missing data peaks. The MS1-based quantitative proteomics pipeline, IonStar, was designed to address these challenges. To demonstrate the capability of IonStar to achieve highly reproducible and robust proteomics quantification in large sample cohorts, we applied IonStar to proteomics investigation in serum samples collected from 60 human subjects with moderate acute respiratory distress syndrome (ARDS).
Project description:Epigenetic aberrations have been recognized as important contributors to cancer onset and development, and increasing evidence suggests that linker histone H1 variants may serve as biomarkers useful for patient stratification, as well as play an important role as drivers in cancer. Although traditionally histone H1 levels have been studied using antibody-based methods and RNA expression, these approaches suffer from limitations. Mass-spectrometry (MS)-based proteomics represents the ideal tool to accurately quantify relative changes in protein abundance within complex samples. In this study, we used a label-free quantification approach to simultaneously analyze all somatic histone H1 variants in clinical samples, and verified its applicability to laser microdissected tissue areas containing as low as 1000 cells.
Project description:Despite early clinical success, the mechanisms of action of low-dose interleukin-2 (LD-IL-2) immunotherapy remain only partly understood. This dataset was generated using samples from the DILfrequency clinical trail, to examine the effects of interval administration of low-dose recombinant IL-2 (iLD-IL-2) using high-resolution, single-cell multi-omics.
Project description:The consistent and accurate quantification of proteins is a challenging task for mass spectrometry (MS)-based proteomics. SWATH-MS uses data-independent acquisition (DIA) for label-free quantification. Here we evaluated five software tools for processing SWATH-MS data: OpenSWATH, SWATH2.0, Skyline, Spectronaut, DIA-Umpire, in collaboration with the respective developers to ensure an optimal use of each tool. We analyzed data from hybrid proteome samples of defined quantitative composition acquired on two different MS instruments applying different SWATH isolation windows setups. Using the resulting high-complexity datasets we benchmarked precision and accuracy of quantification and evaluated identification performance, robustness and specificity of each software tool. To consistently evaluate the high complexity datasets, we developed the LFQbench R-package. LFQbench results enabled developers to improve their software tools, thereby underlining the value of the reference datasets for software development and benchmarking. All tools provided highly convergent identification and reliable quantification performance, underscoring their robustness for label-free quantitative proteomics.