Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex chronic multi-systemic disease characterized by extreme fatigue that is not improved by rest, and worsens after exertion, whether physical or mental. Previous studies have shown ME/CFS-associated alterations in the immune system and mitochondria. We used transmission electron microscopy (TEM) to investigate the morphology and ultrastructure of unstimulated and stimulated ME/CFS immune cells and their intracellular organelles, including mitochondria. PBMCs from four participants were studied: a pair of identical twins discordant for moderate ME/CFS, as well as two age- and gender- matched unrelated subjects—one with an extremely severe form of ME/CFS and the other healthy. TEM analysis of CD3/CD28-stimulated T cells suggested a significant increase in the levels of apoptotic and necrotic cell death in T cells from ME/CFS patients (over 2-fold). Stimulated Tcells of ME/CFS patients also had higher numbers of swollen mitochondria. We also found a large increase in intracellular giant lipid droplet-like organelles in the stimulated PBMCs from the extremely severe ME/CFS patient potentially indicative of a lipid storage disorder. Lastly, we observed a slight increase in platelet aggregation in stimulated cells, suggestive of a possible role of platelet activity in ME/CFS pathophysiology and disease severity. These results indicate extensive morphological alterations in the cellular and mitochondrial phenotypes of ME/CFS patients’ immune cells and suggest new insights into ME/CFS biology.

Mass spectrometry-based profiling of the phosphoproteome is a powerful method of identifying phosphorylation events at a systems level. Most phosphoproteomics studies have used data-dependent acquisition (DDA) mass spectrometry as their method of choice. In this Perspective, we review some recent studies benchmarking DDA and DIA methods for phosphoproteomics and discuss data analysis options for DIA phosphoproteomics. In order to evaluate the impact of data-dependent and data-independent acquisition (DIA) on identification and quantification, we analyze a previously published phosphopeptide-enriched data set consisting of 10 replicates acquired by DDA and DIA each. We find that though more unique identifications are made in DDA data, phosphopeptides are identified more consistently across replicates in DIA. We further discuss the challenges of identifying chromatographically coeluting phosphopeptide isomers and investigate the impact on reproducibility of identifying high-confidence site-localized phosphopeptides in replicates.

Multi-run alignment is widely used in proteomics to establish analyte correspondence across runs. Generally alignment algorithms return a cumulative score, which may not be easily interpretable for each peptide. Here a novel and interactive tool for cross-run chromatogram alignment visualization (DrawAlignR) of data-independent acquisition (DIA) data is presented. Furthermore, a novel C++ based implementation of raw chromatogram alignment which is 35 times faster than the previously published algorithm is developed. This not only enables users to plot alignment interactively by DrawAlignR, but also allows other software platforms to use the algorithm.

SWATH-MS has become a mainstream method for quantitative proteomics, however consistent quantification across multiple LC-MS/MS instruments remains a bottleneck in parallelizing the data-acquisition. To produce a highly consistent and quantitatively accurate data matrix, we have developed DIAlignR which uses raw fragment-ion chromatograms for cross-run alignment. Its performance on a gold standard annotated dataset, demonstrates a threefold reduction in the identification error-rate when compared to standard non-aligned SWATH-MS results. A similar performance is achieved for a dataset of 229 runs acquired using 11 different LC-MS/MS setups. Finally, the analysis of 949 plasma runs with DIAlignR increased the number of statistically significant proteins by 43% and 62% for insulin resistant (IR) and respiratory viral infection (RVI), respectively compared to prior analysis without it. Hence, DIAlignR fills a gap in analyzing SWATH-MS runs acquired in-parallel using different LC-MS/MS instrumentation.

Multi-run alignment is widely used in proteomics to establish analyte correspondence across runs. Generally alignment algorithms return a cumulative score, which may not be easily interpretable for each peptide. Here we present a novel tool, DrawAlignR, to visualize each chromatographic alignment for DIA/SWATH data. Furthermore, we have developed a novel C++ based implementation of raw chromatogram alignment which is 35 times faster than the previously published algorithm. This not only enables users to plot alignment interactively by DrawAlignR, but also allows other software platforms to use the algorithm. DrawAlignR is an open-source web application using R Shiny that can be hosted using the source-code available at https://github.com/Roestlab/DrawAlignR .