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Protein lipidation dynamically controls protein localization and function within cellular membranes. A unique form of protein O -fatty acylation in Corynebacterium , termed protein O -mycoloylation, involves the attachment of mycolic acids─unusually large and hydrophobic fatty acids─to serine residues of proteins in these organisms' outer mycomembrane. However, as with other forms of protein lipidation, the scope and functional consequences of protein O -mycoloylation are challenging to investigate due to the inherent difficulties of enriching and analyzing lipidated peptides. To facilitate the analysis of protein lipidation and enable the comprehensive profiling and site mapping of protein O -mycoloylation, we developed a chemical proteomics strategy integrating metabolic labeling, click chemistry, cleavable linkers, and a novel liquid chromatography-tandem mass spectrometry (LC-MS/MS) method employing LC separation and complementary fragmentation methods tailored to the analysis of lipophilic, MS-labile O -acylated peptides. Using these tools in the model organism Corynebacterium glutamicum , we identified approximately 30 candidate O -mycoloylated proteins, including porins, mycoloyltransferases, secreted hydrolases, and other proteins with cell envelope-related functions─consistent with a role for O -mycoloylation in targeting proteins to the mycomembrane. Site mapping revealed that many of the proteins contained multiple spatially proximal modification sites, which occurred predominantly at serine residues surrounded by conformationally flexible peptide motifs. Overall, this study (i) discloses the putative protein O -mycoloylome for the first time, (ii) yields new insights into the undercharacterized proteome of the mycomembrane, which is a hallmark of important pathogens (e.g., Corynebacterium diphtheriae , Mycobacterium tuberculosis ), and (iii) provides generally applicable chemical strategies for the proteomic analysis of protein lipidation.

Mass spectrometry-based large-scale multi-omics research has proven to be powerful in answering biological questions; nonetheless, it faces many challenges from sample preparation to downstream data integration. To efficiently extract biomolecules of different physicochemical properties, preparation of various sample type needs specific tailoring, especially of difficult ones, such as Caenorhabditis elegans . In this study, we sought to develop a multi-omics sample preparation method starting with a single set of C. elegans samples to save time, minimize variability, expand biomolecule coverage, and promote multi-omics integration. We investigated tissue disruption methods to effectively release biomolecules and optimized extraction strategies to achieve broader and more reproducible biomolecule coverage in proteomics, lipidomics, and metabolomics workflows. In our assessment, we also considered speediness and usability of the approaches. The developed method was validated through a study of 16 C. elegans samples designed to shine light on mitochondrial unfolded protein response (UPRmt), induced by three unique stressors─knocking down electron transfer chain element cco-1 , mitochondrial ribosome protein S5 mrps-5 , and antibiotic treatment Doxycycline. Our findings suggested that the method achieved great coverage of proteome, lipidome, and metabolome with high reproducibility and validated that all stressors triggered UPRmt in C. elegans , although generating unique molecular signatures. Innate immune response was activated, and triglycerides were decreased under all three stressor conditions. Additionally, Doxycycline treatment elicited more distinct proteomic, lipidomic, and metabolomic response than the other two treatments. This method has been successfully used to process Saccharomyces cerevisiae (data not shown) and can likely be applied to other organisms for multi-omics research.

Mass spectrometry (MS)-based proteomics is a powerful technology to globally profile protein abundances, activities, interactions, and modifications. The extreme complexity of proteomics samples, which often contain hundreds of thousands of analytes, necessitates continuous development of MS techniques and instrumentation to improve speed, sensitivity, precision, and accuracy, among other analytical characteristics. Here, we systematically evaluated the Orbitrap Ascend Tribrid mass spectrometer in the context of shotgun proteomics, and we compared its performance to that of the previous generation of Tribrid instruments─the Orbitrap Eclipse. The updated architecture of the Orbitrap Ascend includes a second ion-routing multipole (IRM) in front of the redesigned C-trap/Orbitrap and a new ion funnel that allows gentler ion introduction, among other changes. These modifications in Ascend hardware configuration enabled an increase in parallelizable ion injection time during higher-energy collisional dissociation (HCD) Orbitrap tandem MS (FTMS 2 ) analysis of ∼5 ms. This enhancement was particularly valuable in the analyses of limited sample amounts, where improvements in sensitivity resulted in up to 140% increase in the number of identified tryptic peptides. Further, analysis of phosphorylated peptides enriched from the K562 human cell line yielded up to ∼50% increase in the number of unique phosphopeptides and localized phosphosites. Strikingly, we also observed a ∼2-fold boost in the number of detected N-glycopeptides, likely owing to the improvements in ion transmission and sensitivity. In addition, we performed the multiplexed quantitative proteomics analyses of TMT11-plex labeled HEK293T tryptic peptides and observed 9-14% increase in the number of quantified peptides. In conclusion, the Orbitrap Ascend consistently outperformed its predecessor the Orbitrap Eclipse in various bottom-up proteomic analyses, and we anticipate that it will generate reproducible and in-depth datasets for numerous proteomic applications.

Multi-omics analysis is a powerful and increasingly utilized approach to gain insight into complex biological systems. One major hindrance with multi-omics, however, is the lengthy and wasteful sample preparation process. Preparing samples for mass spectrometry (MS)-based multi-omics involves extraction of metabolites and lipids with organic solvents, precipitation of proteins, and overnight digestion of proteins. These existing workflows are disparate and laborious. Here, we present a simple, efficient, and unified approach to prepare lipids, metabolites, and proteins for MS analysis. Our approach, termed the Bead-enabled Accelerated Monophasic Multi-omics (BAMM) method, combines an n -butanol-based monophasic extraction with unmodified magnetic beads and accelerated protein digestion. We demonstrate that the BAMM method affords comparable depth, quantitative reproducibility, and recovery of biomolecules as state-of-the-art multi-omics methods (e.g., Matyash extraction and overnight protein digestion). However, the BAMM method only requires about 3 h to perform, which saves 11 steps and 19 h on average compared to published multi-omics methods. Furthermore, we validate the BAMM method for multiple sample types and formats (biofluid, culture plate, and pellet) and show that in all cases, it produces high biomolecular coverage and data quality.

IQGAP1 (IQ motif-containing GTPase-activating protein 1) scaffolds several signaling pathways in mammalian cells that are implicated in carcinogenesis, including the RAS and PI3K pathways that involve multiple protein kinases. IQGAP1 has been shown to promote head and neck squamous cell carcinoma (HNSCC); however, the underlying mechanism(s) remains unclear. Here, we report a mass spectrometry-based analysis identifying differences in phosphorylation of cellular proteins in vivo and in vitro in the presence or absence of IQGAP1. By comparing the esophageal phosphoproteome profiles between Iqgap1 +/+ and Iqgap1 -/- mice, we identified RNA splicing as one of the most altered cellular processes. Serine/arginine-rich splicing factor 6 (SRSF6) was the protein with the most downregulated levels of phosphorylation in Iqgap1 -/- tissue. We confirmed that the absence of IQGAP1 reduced SRSF6 phosphorylation both in vivo and in vitro . We then expanded our analysis to human normal oral keratinocytes. Again, we found factors involved in RNA splicing to be highly altered in the phosphoproteome profile upon genetic disruption of IQGAP1. Both the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Cancer Genome Atlas (TCGA) data sets indicate that phosphorylation of splicing-related proteins is important in HNSCC prognosis. The Biological General Repository for Interaction Datasets (BioGRID) repository also suggested multiple interactions between IQGAP1 and splicing-related proteins. Based on these collective observations, we propose that IQGAP1 regulates the phosphorylation of splicing proteins, which potentially affects their splicing activities and, therefore, contributes to HNSCC. Raw data are available from the MassIVE database with identifier MSV000087770.

We report both the design of a high-throughput MICROFASP (a miniaturized filter aided sample preparation) system and its use for the comprehensive proteomic analysis of single blastomeres isolated from 50-cell stage Xenopus laevis embryos (∼200 ng of yolk-free protein/blastomere). A single run of the MICROFASP system was used to process 146 of these blastomeres in parallel. Three samples failed to generate signals presumably due to membrane clogging. Two cells were lost due to operator error. Of the surviving samples, 32 were analyzed using a Q Exactive HF mass spectrometer in survey experiments (data not included). The 109 remaining blastomeres were analyzed using a capillary LC-ESI-MS/MS system coupled to an Orbitrap Fusion Lumos mass spectrometer, which identified a total of 4189 protein groups and 40,998 unique peptides. On average, 3468 ± 229 protein groups and 14,525 ± 2437 unique peptides were identified from each blastomere, which is the highest throughput and deepest proteome coverage to date of single blastomeres at this stage of development. We also compared two dissociation buffers, Newport and calcium-magnesium-free (CMFM) buffers; the two buffers generated similar numbers of protein identifications (3615 total protein IDs from use of the Newport dissociation buffer and 3671 total protein IDs from use of the CMFM buffer).

Shotgun proteomics techniques infer the presence and quantity of proteins using peptide proxies produced by cleavage of the proteome with a protease. Most protein quantitation strategies assume that multiple peptides derived from a protein will behave quantitatively similar across treatment groups, but this assumption may be false due to (1) heterogeneous proteoforms and (2) technical artifacts. Here we describe a strategy called peptide correlation analysis (PeCorA) that detects quantitative disagreements between peptides mapped to the same protein. PeCorA fits linear models to assess whether a peptide's change across treatment groups differs from all other peptides assigned to the same protein. PeCorA revealed that ∼15% of proteins in a mouse microglia stress data set contain at least one discordant peptide. Inspection of the discordant peptides shows the utility of PeCorA for the direct and indirect detection of regulated post-translational modifications (PTMs) and also for the discovery of poorly quantified peptides. The exclusion of poorly quantified peptides before protein quantity summarization decreased false-positives in a benchmark data set. Finally, PeCorA suggests that the inactive isoform of prothrombin, a coagulation cascade protease, is more abundant in plasma from COVID-19 patients relative to non-COVID-19 controls. PeCorA is freely available as an R package that works with arbitrary tables of quantified peptides.

Mass spectrometry (MS) serves as the centerpiece technology for proteome, lipidome, and metabolome analysis. To gain a better understanding of the multifaceted networks of myriad regulatory layers in complex organisms, integration of different multiomic layers is increasingly performed, including joint extraction methods of diverse biomolecular classes and comprehensive data analyses of different omics. Despite the versatility of MS systems, fractured methodology drives nearly all MS laboratories to specialize in analysis of a single ome at the exclusion of the others. Although liquid chromatography-mass spectrometry (LC-MS) analysis is similar for different biomolecular classes, the integration on the instrument level is lagging behind. The recent advancements in high flow proteomics enable us to take a first step towards integration of protein and lipid analysis. Here, we describe a technology to achieve broad and deep coverage of multiple molecular classes simultaneously through multi-omic single-shot technology (MOST), requiring only one column, one LC-MS instrument, and a simplified workflow. MOST achieved great robustness and reproducibility. Its application to a Saccharomyces cerevisiae study consisting of 20 conditions revealed 2842 protein groups and 325 lipids and potential molecular relationships.

Mass spectrometry is the premier tool for identifying and quantifying protein phosphorylation on a global scale. Analysis of phosphopeptides requires enrichment, and even after the samples remain highly complex and exhibit broad dynamic range of abundance. Achieving maximal depth of coverage for phosphoproteomics therefore typically necessitates offline liquid chromatography prefractionation, a time-consuming and laborious approach. Here, we incorporate a recently commercialized aerodynamic high-field asymmetric waveform ion mobility spectrometry (FAIMS) device into the phosphoproteomic workflow. We characterize the effects of phosphorylation on the FAIMS separation, describe optimized compensation voltage settings for unlabeled phosphopeptides, and demonstrate the advantages of FAIMS-enabled gas-phase fractionation. Standard FAIMS single-shot analyses identified around 15-20% additional phosphorylation sites than control experiments without FAIMS. In comparison to liquid chromatography prefractionation, FAIMS experiments yielded similar or superior results when analyzing up to four discrete gas-phase fractions. Although using FAIMS led to a modest reduction in the precision of quantitative measurements when using label-free approaches, the data collected with FAIMS yielded a 26% increase in total reproducible measurements. Overall, we conclude that the new FAIMS technology is a valuable addition to any phosphoproteomic workflow, with greater benefits emerging from longer analyses and higher amounts of material.

To cope with sudden changes in the external environment, the budding yeast Saccharomyces cerevisiae orchestrates a multifaceted response that spans many levels of physiology. Several studies have interrogated the transcriptome response to endoplasmic reticulum (ER) stress and the role of regulators such as the Ire1 kinase and Hac1 transcription factors. However, less is known about responses to ER stress at other levels of physiology. Here, we used quantitative phosphoproteomics and computational network inference to uncover the yeast phosphoproteome response to the reducing agent dithiothreitol (DTT) and the upstream signaling network that controls it. We profiled wild-type cells and mutants lacking IRE1 or MAPK kinases MKK1 and MKK2 , before and at various times after DTT treatment. In addition to revealing downstream targets of these kinases, our inference approach predicted new regulators in the DTT response, including cell-cycle regulator Cdc28 and osmotic-response kinase Rck2, which we validated computationally. Our results also revealed similarities and surprising differences in responses to different stress conditions, especially in the response of protein kinase A targets. These results have implications for the breadth of signaling programs that can give rise to common stress response signatures.

Monoclonal antibodies (mAbs) are important therapeutic glycoproteins, but their large size and structural complexity make them difficult to rapidly characterize. Top-down mass spectrometry (MS) has the potential to overcome challenges of other common approaches by minimizing sample preparation and preserving endogenous modifications. However, comprehensive mAb characterization requires generation of many, well-resolved fragments and remains challenging. While ETD retains modifications and cleaves disulfide bonds-making it attractive for mAb characterization-it can be less effective for precursors having high m / z values. Activated ion electron transfer dissociation (AI-ETD) uses concurrent infrared photoactivation to promote product ion generation and has proven effective in increasing sequence coverage of intact proteins. Here, we present the first application of AI-ETD to mAb sequencing. For the standard NIST mAb, we observe a high degree of complementarity between fragments generated using standard ETD with a short reaction time and AI-ETD with a long reaction time. Most importantly, AI-ETD reveals disulfide-bound regions that have been intractable, thus far, for sequencing with top-down MS. We conclude AI-ETD has the potential to rapidly and comprehensively analyze intact mAbs.

Extreme sample complexity is an inherent challenge in shotgun proteomics that positions quality of chromatographic separations as one of the key determinants of attainable proteome coverage. In search of better separations, macroscopic physical characteristics of capillary columns, i.e., length and properties of stationary phase particles, are typically considered and optimized, while significance of packing bed morphology is frequently underappreciated. Here, we describe a technology that enables packing of capillary columns at excess of 30,000 psi and demonstrate that such columns exhibit reduced backpressure and remarkably reproducible chromatographic performance, improved on average by 23%. These enhancements afford up to 35% increase in the depth of commonplace bottom-up proteomic analyses, owning to augmented sensitivity and resolution of peptide separations and improvements in spectral quality. Our findings strongly corroborate advantages of ultra-high pressure packing of capillary columns for diverse shotgun proteomic workflows.