Lysosomal storage diseases (LSDs) comprise ~50 monogenic disorders marked by the buildup of cellular material in lysosomes, yet systematic global molecular phenotyping of proteins and lipids is lacking. We present a nanoflow-based multiomic single-shot technology (nMOST) workflow that quantifies HeLa cell proteomes and lipidomes from over two dozen LSD mutants. Global cross-correlation analysis between lipids and proteins identified autophagy defects, notably the accumulation of ferritinophagy substrates and receptors, especially in NPC1-/- and NPC2-/- mutants, where lysosomes accumulate cholesterol. Autophagic and endocytic cargo delivery failures correlated with elevated lysophosphatidylcholine species and multilamellar structures visualized by cryo-electron tomography. Loss of mitochondrial cristae, MICOS complex components, and OXPHOS components rich in iron-sulfur cluster proteins in NPC2-/- cells was largely alleviated when iron was provided through the transferrin system. This study reveals how lysosomal dysfunction affects mitochondrial homeostasis and underscores nMOST as a valuable discovery tool for identifying molecular phenotypes across LSDs.

As lipidomics experiments increase in scale and complexity, data processing tools must support workflows for new liquid chromatography-mass spectrometry (LC-MS) methods while simultaneously supporting quality controls to maximize the confidence in lipid identifications. LipiDex 2 improves lipidomics data processing algorithms from LipiDex 1 and introduces new tools for spectral matching and peak annotation functions, with improvements in speed and user-friendliness. In silico spectral library generation now supports tandem mass spectral (MSn) tree-based fragmentation methods, and the LipiDex 2 workflow fully integrates the fragmentation logic into the data processing steps to enable lipid identification at the appropriate level of structural resolution. Finally, LipiDex 2 features new modules for automated quality control checks that also allow users to visualize data quality in a data dashboard user interface.

Therapeutic RNAs are routinely modified during their synthesis to ensure proper drug uptake, stability, and efficacy. Phosphorothioate (PS) RNA, molecules in which one or more backbone phosphates are modified with a sulfur atom in place of standard nonbridging oxygen, is one of the most common modifications because of ease of synthesis and pharmacokinetic benefits. Quality assessment of RNA synthesis, including modification incorporation, is essential for drug selectivity and performance, and the synthetic nature of the PS linkage incorporation often reveals impurities. Here, we present a comprehensive analysis of PS RNA via tandem mass spectrometry (MS). We show that activated ion-negative electron transfer dissociation MS/MS is especially useful in diagnosing PS incorporation, producing diagnostic a- and z-type ions at PS linkage sites, beyond the standard d- and w-type ions. Analysis using resonant and beam-type collision-based activation reveals that, overall, more intense sequence ions and base-loss ions result when a PS modification is present. Furthermore, we report increased detection of b- and x-type product ions at sites of PS incorporation, in addition to the standard c- and y-type ions. This work reveals that the gas-phase chemical stability afforded by sulfur alters RNA dissociation and necessitates inclusion of additional product ions for MS/MS of PS RNA.

Type 2 diabetes is a challenge in modern healthcare, and animal models are necessary to identify underlying mechanisms. The Nile rat ( Arvicanthis niloticus ) develops diet-induced diabetes rapidly on a conventional rodent chow diet without genetic or chemical manipulation. Unlike common laboratory models, the outbred Nile rat model is diurnal and has a wide range of overt diabetes onset and diabetes progression patterns in both sexes, better mimicking the heterogeneous diabetic phenotype in humans. While fasted blood glucose has historically been used to monitor diabetic progression, postprandial blood glucose is more sensitive to the initial stages of diabetes. However, there is a long-held assumption that ad libitum feeding in rodent models leads to increased variance, thus masking diabetes-related metabolic changes in the plasma. Here we compared repeatability within triplicates of non-fasted or fasted plasma samples and assessed metabolic changes relevant to glucose tolerance in fasted and non-fasted plasma of 8–10-week-old male Nile rats. We used liquid chromatography–mass spectrometry lipidomics and polar metabolomics to measure relative metabolite abundances in the plasma samples. We found that, compared to fasted metabolites, non-fasted plasma metabolites are not only more strongly associated with glucose tolerance on the basis of unsupervised clustering and elastic net regression model, but also have a lower replicate variance. Between the two sampling groups, we detected 66 non-fasted metabolites and 32 fasted metabolites that were associated with glucose tolerance using a combined approach with multivariable elastic net and individual metabolite linear models. Further, to test if metabolite replicate variance is affected by age and sex, we measured non-fasted replicate variance in a cohort of mature 30-week-old male and female Nile rats. Our results support using non-fasted plasma metabolomics to study glucose tolerance in Nile rats across the progression of diabetes.

In mass spectrometry-based lipidomics, complex lipid mixtures undergo chromatographic separation, are ionized, and are detected using tandem MS (MSn) to simultaneously quantify and structurally characterize eluting species. The reported structural granularity of these identified lipids is strongly reliant on the analytical techniques leveraged in a study. For example, lipid identifications from traditional collisionally activated data-dependent acquisition experiments are often reported at either species level or molecular species level. Structural resolution of reported lipid identifications is routinely enhanced by integrating both positive and negative mode analyses, requiring two separate runs or polarity switching during a single analysis. MS3+ can further elucidate lipid structure, but the lengthened MS duty cycle can negatively impact analysis depth. Recently, functionality has been introduced on several Orbitrap Tribrid mass spectrometry platforms to identify eluting molecular species on-the-fly. These real-time identifications can be leveraged to trigger downstream MSn to improve structural characterization with lessened impacts on analysis depth. Here, we describe a novel lipidomics real-time library search (RTLS) approach, which utilizes the lipid class of real-time identifications to trigger class-targeted MSn and to improve the structural characterization of phosphotidylcholines, phosphotidylethanolamines, phosphotidylinositols, phosphotidylglycerols, phosphotidylserine, and sphingomyelins in the positive ion mode. Our class-based RTLS method demonstrates improved selectivity compared to the current methodology of triggering MSn in the presence of characteristic ions or neutral losses.

Abstract Type 2 diabetes is a challenge in modern healthcare, and animal models are necessary to identify underlying mechanisms, where we can achieve much better environmental control than what is practical in human studies. The Nile rat (Arvicanthis niloticus) develops diet-induced diabetes rapidly on a conventional rodent chow diet without genetic or chemical manipulation. Unlike common laboratory models, the outbred Nile rat model is diurnal and can progress to advanced diabetic complications, better mimicking the human condition. Some human studies indicate that compared to fasting glucose, post-prandial blood glucose is more sensitive to the initial stages of diabetes, suggesting that we should capture the non-fasted state to study early diabetes. However, it is unknown if ad libitum feeding in the Nile rats leads to increased variance thus masking diabetes-related metabolic changes in the plasma. In this study, we compared the repeatability within triplicate non-fasted or fasted plasma samples and assessed prediction of impaired glucose tolerance in fasted and non-fasted plasma. We used liquid chromatography-mass spectrometry lipidomics and polar metabolomics to measure relative metabolite abundances in the plasma samples. Metabolite measurements in non-fasted plasma were less variable than measurements in fasted plasma. We detected 66 metabolites in non-fasted plasma associated with glucose tolerance in elastic net and individual metabolite linear regression models. Low metabolite replicate variance was reproduced in a cohort of mature 30-week male and female Nile rats. Our results support using non-fasted plasma metabolomics for early detection of impaired glucose tolerance in Nile rats.