dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts

Caspase-2 protects against ferroptotic cell death

Caspase-2, one of the most evolutionarily conserved members of the caspase family, is an important regulator of the cellular response to oxidative stress. Given that ferroptosis is suppressed by antioxidant defense pathways, such as that involving selenoenzyme glutathione peroxidase 4 (GPX4), we hypothesized that caspase-2 may play a role in regulating ferroptosis. This study provides the first demonstration of an important and unprecedented function of caspase-2 in protecting cancer cells from undergoing ferroptotic cell death. Specifically, we show that depletion of caspase-2 leads to the downregulation of stress response genes including SESN2, HMOX1, SLC7A11, and sensitizes mutant-p53 cancer cells to cell death induced by various ferroptosis-inducing compounds. Importantly, the canonical catalytic activity of caspase-2 is not required for its role and suggests that caspase-2 regulates ferroptosis via non-proteolytic interaction with other proteins. Using an unbiased BioID proteomics screen, we identified novel caspase-2 interacting proteins (including heat shock proteins and co-chaperones) that regulate cellular responses to stress. Finally, we demonstrate that caspase-2 limits chaperone-mediated autophagic degradation of GPX4 to promote the survival of mutant-p53 cancer cells. In conclusion, we document a novel role for caspase-2 as a negative regulator of ferroptosis in cells with mutant p53. Our results provide evidence for a novel function of caspase-2 in cell death regulation and open potential new avenues to exploit ferroptosis in cancer therapy.

Robust, Precise, and Deep Proteome Profiling Using a Small Mass Range and Narrow Window Data-Independent-Acquisition Scheme

In recent years, a plethora of different data-independent acquisition methods have been developed for proteomics to cover a wide range of requirements. Current deep proteome profiling methods rely on fractionations, elaborate chromatography, and mass spectrometry setups or display suboptimal quantitative precision. We set out to develop an easy-to-use one shot DIA method that achieves high quantitative precision and high proteome coverage. We achieve this by focusing on a small mass range of 430-670 m/z using small isolation windows without overlap. With this new method, we were able to quantify >9200 protein groups in HEK lysates with an average coefficient of variance of 3.2%. To demonstrate the power of our newly developed narrow mass range method, we applied it to investigate the effect of PGC-1α knockout on the skeletal muscle proteome in mice. Compared to a standard data-dependent acquisition method, we could double proteome coverage and, most importantly, achieve a significantly higher quantitative precision, as compared to a previously proposed DIA method. We believe that our method will be especially helpful in quantifying low abundant proteins in samples with a high dynamic range. All raw and result files are available at massive.ucsd.edu (MSV000092186).

Targeting cancer-derived extracellular vesicles by combining CD147 inhibition with tissue factor pathway inhibitor for the management of urothelial cancer cells

BACKGROUND

Extracellular vesicles (EVs), including microvesicles, hold promise for the management of bladder urothelial carcinoma (BLCA), particularly because of their utility in identifying therapeutic targets and their diagnostic potential using easily accessible urine samples. Among the transmembrane glycoproteins highly enriched in cancer-derived EVs, tissue factor (TF) and CD147 have been implicated in promoting tumor progression. In this in vitro study, we explored a novel approach to impede cancer cell migration and metastasis by simultaneously targeting these molecules on urothelial cancer-derived EVs.

METHODS

Cell culture supernatants from invasive and non-invasive bladder cancer cell lines and urine samples from patients with BLCA were collected. Large, microvesicle-like EVs were isolated using sequential centrifugation and characterized by electron microscopy, nanoparticle tracking analysis, and flow cytometry. The impact of urinary or cell supernatant-derived EVs on cellular phenotypes was evaluated using cell-based assays following combined treatment with a specific CD147 inhibitor alone or in combination with a tissue factor pathway inhibitor (TFPI), an endogenous anticoagulant protein that can be released by low-molecular-weight heparins.

RESULTS

We observed that EVs obtained from the urine samples of patients with muscle-invasive BLCA and from the aggressive bladder cancer cell line J82 exhibited higher TF activity and CD147 expression levels than did their non-invasive counterparts. The shedding of GFP-tagged CD147 into isolated vesicles demonstrated that the vesicles originated from plasma cell membranes. EVs originating from invasive cancer cells were found to trigger migration, secretion of matrix metalloproteinases (MMPs), and invasion. The same induction of MMP activity was replicated using EVs obtained from urine samples of patients with invasive BLCA. EVs derived from cancer cell clones overexpressing TF and CD147 were produced in higher quantities and exhibited a higher invasive potential than those from control cancer cells. TFPI interfered with the effect when used in conjunction with the CD147 inhibitor, further suppressing homotypic EV-induced migration, MMP production, and invasion.

CONCLUSIONS

Our findings suggest that combining a CD147 inhibitor with low molecular weight heparins to induce TFPI release may be a promising therapeutic approach for urothelial cancer management. This combination can potentially suppress the tumor-promoting actions of cancer-derived microvesicle-like EVs, including collective matrix invasion.

Serum/Plasma Proteome in Non-Malignant Liver Disease

The liver is the central metabolic organ and produces 85-90% of the proteins found in plasma. Accordingly, the plasma proteome is an attractive source of liver disease biomarkers that reflects the different cell types present in this organ, as well as the processes such as responses to acute and chronic injury or the formation of an extracellular matrix. In the first part, we summarize the biomarkers routinely used in clinical evaluations and their biological relevance in the different stages of non-malignant liver disease. Later, we describe the current proteomic approaches, including mass spectrometry and affinity-based techniques, that allow a more comprehensive assessment of the liver function but also require complex data processing. The many approaches of analysis and interpretation and their potential caveats are delineated. While these advances hold the promise to transform our understanding of liver diseases and support the development and validation of new liver-related drugs, an interdisciplinary collaboration is needed.

Nanoparticle enrichment mass-spectrometry proteomics identifies protein-altering variants for precise pQTL mapping

Proteogenomics studies generate hypotheses on protein function and provide genetic evidence for drug target prioritization. Most previous work has been conducted using affinity-based proteomics approaches. These technologies face challenges, such as uncertainty regarding target identity, non-specific binding, and handling of variants that affect epitope affinity binding. Mass spectrometry-based proteomics can overcome some of these challenges. Here we report a pQTL study using the Proteograph™ Product Suite workflow (Seer, Inc.) where we quantify over 18,000 unique peptides from nearly 3000 proteins in more than 320 blood samples from a multi-ethnic cohort in a bottom-up, peptide-centric, mass spectrometry-based proteomics approach. We identify 184 protein-altering variants in 137 genes that are significantly associated with their corresponding variant peptides, confirming target specificity of co-associated affinity binders, identifying putatively causal cis-encoded proteins and providing experimental evidence for their presence in blood, including proteins that may be inaccessible to affinity-based proteomics.

Proteomic analysis of the extracellular matrix of human atherosclerotic plaques shows marked changes between plaque types

Cardiovascular disease is the leading cause of death, with atherosclerosis the major underlying cause. While often asymptomatic for decades, atherosclerotic plaque destabilization and rupture can arise suddenly and cause acute arterial occlusion or peripheral embolization resulting in myocardial infarction, stroke and lower limb ischaemia. As extracellular matrix (ECM) remodelling is associated with plaque instability, we hypothesized that the ECM composition would differ between plaques. We analyzed atherosclerotic plaques obtained from 21 patients who underwent carotid surgery following recent symptomatic carotid artery stenosis. Plaques were solubilized using a new efficient, single-step approach. Solubilized proteins were digested to peptides, and analyzed by liquid chromatography-mass spectrometry using data-independent acquisition. Identification and quantification of 4498 plaque proteins was achieved, including 354 ECM proteins, with unprecedented coverage and high reproducibility. Multidimensional scaling analysis and hierarchical clustering indicate two distinct clusters, which correlate with macroscopic plaque morphology (soft/unstable versus hard/stable), ultrasound classification (echolucent versus echogenic) and the presence of hemorrhage/ulceration. We identified 714 proteins with differential abundances between these groups. Soft/unstable plaques were enriched in proteins involved in inflammation, ECM remodelling, and protein degradation (e.g. matrix metalloproteinases, cathepsins). In contrast, hard/stable plaques contained higher levels of ECM structural proteins (e.g. collagens, versican, nidogens, biglycan, lumican, proteoglycan 4, mineralization proteins). These data indicate that a single-step proteomics method can provide unique mechanistic insights into ECM remodelling and inflammatory mechanisms within plaques that correlate with clinical parameters, and help rationalize plaque destabilization. These data also provide an approach towards identifying biomarkers for individualized risk profiling of atherosclerosis.

mTORC1 regulates the metabolic switch of postnatal cardiomyocytes during regeneration

The metabolic switch from glycolysis to fatty acid oxidation in postnatal cardiomyocytes contributes to the loss of the cardiac regenerative potential of the mammalian heart. However, the mechanisms that regulate this metabolic switch remain unclear. The protein kinase complex mechanistic target of rapamycin complex 1 (mTORC1) is a central signaling hub that regulates cellular metabolism and protein synthesis, yet its role during mammalian heart regeneration and postnatal metabolic maturation is undefined. Here, we use immunoblotting, rapamycin treatment, myocardial infarction, and global proteomics to define the role of mTORC1 in postnatal heart development and regeneration. Our results demonstrate that the activity of mTORC1 is dynamically regulated between the regenerating and the non-regenerating hearts. Acute inhibition of mTORC1 by rapamycin or everolimus reduces cardiomyocyte proliferation and inhibits neonatal heart regeneration following injury. Our quantitative proteomic analysis demonstrates that transient inhibition of mTORC1 during neonatal heart injury did not reduce protein synthesis, but rather shifts the cardiac proteome of the neonatal injured heart from glycolysis towards fatty acid oxidation. This indicates that mTORC1 inhibition following injury accelerates the postnatal metabolic switch, which promotes metabolic maturation and impedes cardiomyocyte proliferation and heart regeneration. Taken together, our results define an important role for mTORC1 in regulating postnatal cardiac metabolism and may represent a novel target to modulate cardiac metabolism and promote heart regeneration.

Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023

Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.

Protein interaction networks characterizing the A549 cells Klotho transfected are associated with activated pro-apoptotic Bim and suppressed Wnt/β-catenin signaling pathway

Invasive assays and lung tumor-bearing mice models using a human lung adenocarcinoma cell line A549 cells transfected with the Klotho (KL) gene, A549/KL cells, have confirmed that KL suppresses invasive/metastatic potential. This study aimed to identify the co-expression protein networks and proteomic profiles associated with A549/KL cells to understand how Klotho protein expression affects molecular networks associated with lung carcinoma malignancy. A two-step application of a weighted network correlation analysis to the cells' quantitative proteome datasets of a total of 6,994 proteins, identified by mass spectrometry-based proteomic analysis with data-independent acquisition (DIA), identified one network module as most significantly associated with the A549/KL trait. Upstream analyses, confirmed by western blot, implicated the pro-apoptotic Bim (Bcl-2-like protein 11) as a master regulator of molecular networks affected by Klotho. GeneMANIA interaction networks and quantitative proteome data implicated that Klotho interacts with two signaling axes: negatively with the Wnt/β-catenin axis, and positively by activating Bim. Our findings might contribute to the development of future therapeutic strategies.

Mass Spectrometry-Based Proteogenomics: New Therapeutic Opportunities for Precision Medicine

Proteogenomics refers to the integration of comprehensive genomic, transcriptomic, and proteomic measurements from the same samples with the goal of fully understanding the regulatory processes converting genotypes to phenotypes, often with an emphasis on gaining a deeper understanding of disease processes. Although specific genetic mutations have long been known to drive the development of multiple cancers, gene mutations alone do not always predict prognosis or response to targeted therapy. The benefit of proteogenomics research is that information obtained from proteins and their corresponding pathways provides insight into therapeutic targets that can complement genomic information by providing an additional dimension regarding the underlying mechanisms and pathophysiology of tumors. This review describes the novel insights into tumor biology and drug resistance derived from proteogenomic analysis while highlighting the clinical potential of proteogenomic observations and advances in technique and analysis tools.

In-depth metaproteomics analysis of tongue coating for gastric cancer: a multicenter diagnostic research study

BACKGROUND

Our previous study revealed marked differences in tongue images between individuals with gastric cancer and those without gastric cancer. However, the biological mechanism of tongue images as a disease indicator remains unclear. Tongue coating, a major factor in tongue appearance, is the visible layer on the tongue dorsum that provides a vital environment for oral microorganisms. While oral microorganisms are associated with gastric and intestinal diseases, the comprehensive function profiles of oral microbiota remain incompletely understood. Metaproteomics has unique strength in revealing functional profiles of microbiota that aid in comprehending the mechanism behind specific tongue coating formation and its role as an indicator of gastric cancer.

METHODS

We employed pressure cycling technology and data-independent acquisition (PCT-DIA) mass spectrometry to extract and identify tongue-coating proteins from 180 gastric cancer patients and 185 non-gastric cancer patients across 5 independent research centers in China. Additionally, we investigated the temporal stability of tongue-coating proteins based on a time-series cohort. Finally, we constructed a machine learning model using the stochastic gradient boosting algorithm to identify individuals at high risk of gastric cancer based on tongue-coating microbial proteins.

RESULTS

We measured 1432 human-derived proteins and 13,780 microbial proteins from 345 tongue-coating samples. The abundance of tongue-coating proteins exhibited high temporal stability within an individual. Notably, we observed the downregulation of human keratins KRT2 and KRT9 on the tongue surface, as well as the downregulation of ABC transporter COG1136 in microbiota, in gastric cancer patients. This suggests a decline in the defense capacity of the lingual mucosa. Finally, we established a machine learning model that employs 50 microbial proteins of tongue coating to identify individuals at a high risk of gastric cancer, achieving an area under the curve (AUC) of 0.91 in the independent validation cohort.

CONCLUSIONS

We characterized the alterations in tongue-coating proteins among gastric cancer patients and constructed a gastric cancer screening model based on microbial-derived tongue-coating proteins. Tongue-coating proteins are shown as a promising indicator for identifying high-risk groups for gastric cancer. Video Abstract.

Proteomics of Serum Samples for the Exploration of the Pathological Mechanism of Obstetric Antiphospholipid Syndrome

Obstetric antiphospholipid syndrome (OAPS) is a multisystem disorder characterized by thrombosis or recurrent fetal loss. In this study, we aim to explore the pathological mechanism of OAPS. Herein, we carried out data-independent acquisition (DIA) mass spectrometry quantitative proteomic analysis of serum samples from OAPS patients and healthy controls. A set of 93 differentially expressed proteins was identified, including 75 upregulated and 18 downregulated proteins compared with the levels in controls. Those proteins are enriched in KEGG pathways related to autoimmune diseases, allergic diseases, and pathogen infection. Interestingly, metabolic pathways such as fatty acid degradation and type I diabetes were enriched, indicating that OAPS is metabolic disease related. The significantly increased triglyceride also supported this idea. The differentially expressed proteins insulin-like growth factor-binding protein-1 (IGFBP-1), C-reactive protein (CRP), and ferritin light chain (FTL) were validated by ELISA. Our study presented a deep serum proteomics of OAPS and advanced our understanding of OAPS pathogenesis.

Fast proteomics with dia-PASEF and analytical flow-rate chromatography

Increased throughput in proteomic experiments can improve accessibility of proteomic platforms, reduce costs, and facilitate new approaches in systems biology and biomedical research. Here we propose combination of analytical flow rate chromatography with ion mobility separation of peptide ions, data-independent acquisition, and data analysis with the DIA-NN software suite, to achieve high-quality proteomic experiments from limited sample amounts, at a throughput of up to 400 samples per day. For instance, when benchmarking our workflow using a 500-μL/min flow rate and 3-min chromatographic gradients, we report the quantification of 5211 proteins from 2 μg of a mammalian cell-line standard at high quantitative accuracy and precision. We further used this platform to analyze blood plasma samples from a cohort of COVID-19 inpatients, using a 3-min chromatographic gradient and alternating column regeneration on a dual pump system. The method delivered a comprehensive view of the COVID-19 plasma proteome, allowing classification of the patients according to disease severity and revealing plasma biomarker candidates.

Unbiased Quantitative Proteomics of Organoid Models of Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is a lethal solid malignancy with many patients succumbing to the disease within 6 months of diagnosis. The mechanisms that underlie PDAC initiation and progression are poorly understood. Current treatment options are primarily limited to chemotherapy, which is often provided with palliative intent. Unfortunately, there are no robust biomarkers to guide treatment selection or monitor treatment response. This is concerning given the increasing incidence of this cancer. We and others have generated organoid models to explore the biology underlying PDAC with the goal of identifying new therapeutic targets. Here we provide protocols to generate a preclinical PDAC organoid model and methods to use these to define the proteomic landscape of this cancer.

An Updated Guide to the Identification, Quantitation, and Imaging of the Crustacean Neuropeptidome

Crustaceans serve as a useful, simplified model for studying peptides and neuromodulation, as they contain numerous neuropeptide homologs to mammals and enable electrophysiological studies at the single-cell and neural circuit levels. Crustaceans contain well-defined neural networks, including the stomatogastric ganglion, oesophageal ganglion, commissural ganglia, and several neuropeptide-rich organs such as the brain, pericardial organs, and sinus glands. As existing mass spectrometry (MS) methods are not readily amenable to neuropeptide studies, there is a great need for optimized sample preparation, data acquisition, and data analysis methods. Herein, we present a general workflow and detailed methods for MS-based neuropeptidomic analysis of crustacean tissue samples and circulating fluids. In conjunction with profiling, quantitation can also be performed with isotopic or isobaric labeling. Information regarding the localization patterns and changes of peptides can be studied via mass spectrometry imaging. Combining these sample preparation strategies and MS analytical techniques allows for a multi-faceted approach to obtaining deep knowledge of crustacean peptidergic signaling pathways.

Mind bomb 2 limits inflammatory dermatitis in Sharpin mutant mice independently of cell death

Skin inflammation is a complex process implicated in various dermatological disorders. The chronic proliferative dermatitis (cpd) phenotype driven by the cpd mutation (cpdm) in the Sharpin gene is characterized by dermal inflammation and epidermal abnormalities. Tumour necrosis factor (TNF) and caspase-8-driven cell death causes the pathogenesis of Sharpincpdm mice; however, the role of mind bomb 2 (MIB2), a pro-survival E3 ubiquitin ligase involved in TNF signaling, in skin inflammation remains unknown. Here, we demonstrate that MIB2 antagonizes inflammatory dermatitis in the context of the cpd mutation. Surprisingly, the role of MIB2 in limiting skin inflammation is independent of its known pro-survival function and E3 ligase activity. Instead, MIB2 enhances the production of wound-healing molecules, granulocyte colony-stimulating factor, and Eotaxin, within the skin. This discovery advances our comprehension of inflammatory cytokines and chemokines associated with cpdm pathogenesis and highlights the significance of MIB2 in inflammatory skin disease that is independent of its ability to regulate TNF-induced cell death.

Mass spectrometry-based proteomics approaches to interrogate skeletal muscle adaptations to exercise

Acute exercise and chronic exercise training elicit beneficial whole-body changes in physiology that ultimately depend on profound alterations to the dynamics of tissue-specific proteins. Since the work accomplished during exercise owes predominantly to skeletal muscle, it has received the majority of interest from exercise scientists that attempt to unravel adaptive mechanisms accounting for salutary metabolic effects and performance improvements that arise from training. Contemporary scientists are also beginning to use mass spectrometry-based proteomics, which is emerging as a powerful approach to interrogate the muscle protein signature in a more comprehensive manner. Collectively, these technologies facilitate the analysis of skeletal muscle protein dynamics from several viewpoints, including changes to intracellular proteins (expression proteomics), secreted proteins (secretomics), post-translational modifications as well as fiber-, cell-, and organelle-specific changes. This review aims to highlight recent literature that has leveraged new workflows and advances in mass spectrometry-based proteomics to further our understanding of training-related changes in skeletal muscle. We call attention to untapped areas in skeletal muscle proteomics research relating to exercise training and metabolism, as well as basic points of contention when applying mass spectrometry-based analyses, particularly in the study of human biology. We further encourage researchers to couple the hypothesis-generating and descriptive nature of omics data with functional analyses that propel our understanding of the complex adaptive responses in skeletal muscle that occur with acute and chronic exercise.

Detection of Protein Tyrosine Phosphatase Interacting Partners by Mass Spectrometry

Unraveling interacting partners of protein tyrosine (Tyr) phosphatases is considered a key aspect in resolving the regulation of signaling cascades either in a pathological or in developmental context. Mass spectrometry (MS)-based protein identification has emerged as the major approach in this arena, complemented by the development of novel biochemical methodologies for sample preparation. In this chapter, we highlight two methods that, combined with mass spectrometry, may help the investigator create an interactome map for the phosphatase of interest within a specific biological context.

Loss of lysosomal acid lipase results in mitochondrial dysfunction and fiber switch in skeletal muscles of mice

OBJECTIVE

Lysosomal acid lipase (LAL) is the only enzyme known to hydrolyze cholesteryl esters (CE) and triacylglycerols in lysosomes at an acidic pH. Despite the importance of lysosomal hydrolysis in skeletal muscle (SM), research in this area is limited. We hypothesized that LAL may play an important role in SM development, function, and metabolism as a result of lipid and/or carbohydrate metabolism disruptions.

RESULTS

Mice with systemic LAL deficiency (Lal-/-) had markedly lower SM mass, cross-sectional area, and Feret diameter despite unchanged proteolysis or protein synthesis markers in all SM examined. In addition, Lal-/- SM showed increased total cholesterol and CE concentrations, especially during fasting and maturation. Regardless of increased glucose uptake, expression of the slow oxidative fiber marker MYH7 was markedly increased in Lal-/-SM, indicating a fiber switch from glycolytic, fast-twitch fibers to oxidative, slow-twitch fibers. Proteomic analysis of the oxidative and glycolytic parts of the SM confirmed the transition between fast- and slow-twitch fibers, consistent with the decreased Lal-/- muscle size due to the "fiber paradox". Decreased oxidative capacity and ATP concentration were associated with reduced mitochondrial function of Lal-/- SM, particularly affecting oxidative phosphorylation, despite unchanged structure and number of mitochondria. Impairment in muscle function was reflected by increased exhaustion in the treadmill peak effort test in vivo.

CONCLUSION

We conclude that whole-body loss of LAL is associated with a profound remodeling of the muscular phenotype, manifested by fiber type switch and a decline in muscle mass, most likely due to dysfunctional mitochondria and impaired energy metabolism, at least in mice.

Characterization of Synthetic Peptides by Mass Spectrometry

In the quality control of synthetic peptides, mass spectroscopy (MS) serves as an optimal method for evaluating authenticity and integrity. Typically, the sequence of a synthetic peptide is already established, thereby directing the focus of analysis towards validating its identity and purity. This chapter outlines straightforward methodologies for conducting MS analyses specifically tailored for synthetic peptides.

Phosphorylation Sites of the Gastric Inhibitory Polypeptide Receptor (GIPR) Revealed by Trapped-Ion-Mobility Spectrometry Coupled to Time-of-Flight Mass Spectrometry (TIMS-TOF MS)

The gastric inhibitory polypeptide receptor (GIPR), a G protein-coupled receptor (GPCR) that regulates glucose metabolism and insulin secretion, is a target for the development of therapeutic agents to address type 2 diabetes and obesity. Signal transduction processes mediated by GPCR activation typically result in receptor phosphorylation, but very little is known about GIPR phosphorylation. Mass spectrometry (MS) is a powerful tool for detecting phosphorylation and other post-translational modifications of proteins and for identifying modification sites. However, applying MS methods to GPCRs is challenging because the native expression levels are low and the hydrophobicity of these proteins complicates isolation and enrichment. Here we use a widely available technique, trapped-ion-mobility spectrometry coupled to time-of-flight mass spectrometry (TIMS-TOF MS), to characterize the phosphorylation status of the GIPR. We identified eight serine residues that are phosphorylated, one in an intracellular loop and the remainder in the C-terminal domain. Stimulation with the native agonist GIP enhanced phosphorylation at four of these sites. For comparison, we evaluated tirzepatide (TZP), a dual agonist of the glucagon-like peptide-1 (GLP-1) receptor and the GIPR that has recently been approved for the treatment of type 2 diabetes. Stimulation with TZP enhanced phosphorylation at the same four sites that were enhanced with GIP; however, TZP also enhanced phosphorylation at a fifth site that is unique to this synthetic agonist. This work establishes an important and accessible tool for the characterization of signal transduction via the GIPR and reveals an unanticipated functional difference between GIP and TZP.

Integrative deep learning framework predicts lipidomics-based investigation of preservatives on meat nutritional biomarkers and metabolic pathways

Preservatives are added as antimicrobial agents to extend the shelf life of meat. Adding preservatives to meat products can affect their flavor and nutrition. This review clarifies the effects of preservatives on metabolic pathways and network molecular transformations in meat products based on lipidomics, metabolomics and proteomics analyses. Preservatives change the nutrient content of meat products via altering ionic strength and pH to influence enzyme activity. Ionic strength in salt triggers muscle triglyceride hydrolysis by causing phosphorylation and lipid droplet splitting in adipose tissue hormone-sensitive lipase and triglyceride lipase. DisoLipPred exploiting deep recurrent networks and transfer learning can predict the lipid binding trend of each amino acid in the disordered region of input protein sequences, which could provide omics analyses of biomarkers metabolic pathways in meat products. While conventional meat quality assessment tools are unable to elucidate the intrinsic mechanisms and pathways of variables in the influences of preservatives on the quality of meat products, the promising application of omics techniques in food analysis and discovery through multimodal learning prediction algorithms of neural networks (e.g., deep neural network, convolutional neural network, artificial neural network) will drive the meat industry to develop new strategies for food spoilage prevention and control.

Mobility-Modulated Sequential Dissociation Analysis Enables Structural Lipidomics in Mass Spectrometry Imaging

Spatial lipidomics based on mass spectrometry imaging (MSI) is a powerful tool for fundamental biology studies and biomarker discovery. But the structure-resolving capability of MSI is limited because of the lack of multiplexed tandem mass spectrometry (MS/MS) method, primarily due to the small sample amount available from each pixel and the poor ion usage in MS/MS analysis. Here, we report a mobility-modulated sequential dissociation (MMSD) strategy for multiplex MS/MS imaging of distinct lipids from biological tissues. With ion mobility-enabled data-independent acquisition and automated spectrum deconvolution, MS/MS spectra of a large number of lipid species from each tissue pixel are acquired, at no expense of imaging speed. MMSD imaging is highlighted by MS/MS imaging of 24 structurally distinct lipids in the mouse brain and the revealing of the correlation of a structurally distinct phosphatidylethanolamine isomer (PE 18 : 1_18 : 1) from a human hepatocellular carcinoma (HCC) tissue. Mapping of structurally distinct lipid isomers is now enabled and spatial lipidomics becomes feasible for MSI.

MultiPro: DDA-PASEF and diaPASEF acquired cell line proteomic datasets with deliberate batch effects

Mass spectrometry-based proteomics plays a critical role in current biological and clinical research. Technical issues like data integration, missing value imputation, batch effect correction and the exploration of inter-connections amongst these technical issues, can produce errors but are not well studied. Although proteomic technologies have improved significantly in recent years, this alone cannot resolve these issues. What is needed are better algorithms and data processing knowledge. But to obtain these, we need appropriate proteomics datasets for exploration, investigation, and benchmarking. To meet this need, we developed MultiPro (Multi-purpose Proteome Resource), a resource comprising four comprehensive large-scale proteomics datasets with deliberate batch effects using the latest parallel accumulation-serial fragmentation in both Data-Dependent Acquisition (DDA) and Data Independent Acquisition (DIA) modes. Each dataset contains a balanced two-class design based on well-characterized and widely studied cell lines (A549 vs K562 or HCC1806 vs HS578T) with 48 or 36 biological and technical replicates altogether, allowing for investigation of a multitude of technical issues. These datasets allow for investigation of inter-connections between class and batch factors, or to develop approaches to compare and integrate data from DDA and DIA platforms.

Unveiling the Influence of Tumor Microenvironment and Spatial Heterogeneity on Temozolomide Resistance in Glioblastoma Using an Advanced Human In Vitro Model of the Blood-Brain Barrier and Glioblastoma

Glioblastoma (GBM) is the most common primary malignant brain cancer in adults with a dismal prognosis. Temozolomide (TMZ) is the first-in-line chemotherapeutic; however, resistance is frequent and multifactorial. While many molecular and genetic factors have been linked to TMZ resistance, the role of the solid tumor morphology and the tumor microenvironment, particularly the blood-brain barrier (BBB), is unknown. Here, the authors investigate these using a complex in vitro model for GBM and its surrounding BBB. The model recapitulates important clinical features such as a dense tumor core with tumor cells that invade along the perivascular space; and a perfusable BBB with a physiological permeability and morphology that is altered in the presence of a tumor spheroid. It is demonstrated that TMZ sensitivity decreases with increasing cancer cell spatial organization, and that the BBB can contribute to TMZ resistance. Proteomic analysis with next-generation low volume sample workflows of these cultured microtissues revealed potential clinically relevant proteins involved in tumor aggressiveness and TMZ resistance, demonstrating the utility of complex in vitro models for interrogating the tumor microenvironment and therapy validation.

Using proteomics for stratification and risk prediction in patients with solid tumors

Proteomics, the study of proteins and their functions, has greatly evolved due to advances in analytical chemistry and computational biology. Unlike genomics or transcriptomics, proteomics captures the dynamic and diverse nature of proteins, which play crucial roles in cellular processes. This is exemplified in cancer, where genomic and transcriptomic information often falls short in reflecting actual protein expression and interactions. Liquid chromatography-mass spectrometry (LC-MS) is pivotal in proteomic data generation, enabling high-throughput analysis of protein samples. The MS-based workflow involves protein digestion, chromatographic separation, ionization, and fragmentation, leading to peptide identification and quantification. Computational biostatistics, particularly using tools in R (R Foundation for Statistical Computing, Vienna, Austria; www.R-project.org ), aid in data analysis, revealing protein expression patterns and correlations with clinical variables. Proteomic studies can be explorative, aiming to characterize entire proteomes, or targeted, focusing on specific proteins of interest. The integration of proteomics with genomics addresses database limitations and enhances peptide identification. Case studies in intrahepatic cholangiocarcinoma, glioblastoma multiforme, and pancreatic ductal adenocarcinoma highlight proteomics' clinical applications, from subtyping cancers to identifying diagnostic markers. Moreover, proteomic data augment molecular tumor boards by providing deeper insights into pathway activities and genomic mutations, supporting personalized treatment decisions. Overall, proteomics contributes significantly to advancing our understanding of cellular biology and improving clinical care.

Exploring novel ANGICon-EIPs through ameliorated peptidomics techniques: Can deep learning strategies as a core breakthrough in peptide structure and function prediction?

Dairy-derived angiotensin-I-converting enzyme inhibitory peptides (ANGICon-EIPs) have been regarded as a relatively safe supplementary diet-therapy strategy for individuals with hypertension, and short-chain peptides may have more relevant antihypertensive benefits due to their direct intestinal absorption. Our previous explorations have confirmed that endogenous goat milk short-chain peptides are also an essential source of ANGICon-EIPs. Nonetheless, there are limited explorations on endogenous ANGICon-EIPs owing to the limitations of the extraction and enrichment of endogenous peptides, currently. This review outlined ameliorated pre-treatment strategies, data acquisition methods, and tools for the prediction of peptide structure and function, aiming to provide creative ideas for discovering novel ANGICon-EIPs. Currently, deep learning-based peptide structure and function prediction algorithms have achieved significant advancements. The convolutional neural network (CNN) and peptide sequence-based multi-label deep learning approach for determining the multi-functionalities of bioactive peptides (MLBP) can predict multiple peptide functions with absolute true value and accuracy of 0.699 and 0.708, respectively. Utilizing peptide sequence input, torsion angles, and inter-residue distance to train neural networks, APPTEST predicted the average backbone root mean square deviation (RMSD) value of peptide (5-40 aa) structures as low as 1.96 Å. Overall, with the exploration of more neural network architectures, deep learning could be considered a critical research tool to reduce the cost and improve the efficiency of identifying novel endogenous ANGICon-EIPs.

Targeting Glial Cells by Organic Anion-Transporting Polypeptide 1C1 (OATP1C1)-Utilizing l-Thyroxine-Derived Prodrugs

OATP1C1 (organic anion-transporting polypeptide 1C1) transports thyroid hormones, particularly thyroxine (T4), into human astrocytes. In this study, we investigated the potential of utilizing OATP1C1 to improve the delivery of anti-inflammatory drugs into glial cells. We designed and synthesized eight novel prodrugs by incorporating T4 and 3,5-diiodo-l-tyrosine (DIT) as promoieties to selected anti-inflammatory drugs. The prodrug uptake in OATP1C1-expressing human U-87MG glioma cells demonstrated higher accumulation with T4 promoiety compared to those with DIT promoiety or the parent drugs themselves. In silico models of OATP1C1 suggested dynamic binding for the prodrugs, wherein the pose changed from vertical to horizontal. The predicted binding energies correlated with the transport profiles, with T4 derivatives exhibiting higher binding energies when compared to prodrugs with a DIT promoiety. Interestingly, the prodrugs also showed utilization of oatp1a4/1a5/1a6 in mouse primary astrocytes, which was further supported by docking studies and a great potential for improved brain drug delivery.

Functional Consequences of Shifting Transcript Boundaries in Glucose Starvation

Glucose is a major source of carbon and essential for the survival of many organisms, ranging from yeast to human. A sudden 60-fold reduction of glucose in exponentially growing fission yeast induces transcriptome-wide changes in gene expression. This regulation is multilayered, and the boundaries of transcripts are known to vary, with functional consequences at the protein level. By combining direct RNA sequencing with 5'-CAGE and short-read sequencing, we accurately defined the 5'- and 3'-ends of transcripts that are both poly(A) tailed and 5'-capped in glucose starvation, followed by proteome analysis. Our results confirm previous experimentally validated loci with alternative isoforms and reveal several transcriptome-wide patterns. First, we show that sense-antisense gene pairs are more strongly anticorrelated when a time lag is taken into account. Second, we show that the glucose starvation response initially elicits a shortening of 3'-UTRs and poly(A) tails, followed by a shortening of the 5'-UTRs at later time points. These result in domain gains and losses in proteins involved in the stress response. Finally, the relatively poor overlap both between differentially expressed genes (DEGs), differential transcript usage events (DTUs), and differentially detected proteins (DDPs) highlight the need for further study on post-transcriptional regulation mechanisms in glucose starvation.

Spatial proteo-transcriptomic profiling reveals the molecular landscape of borderline ovarian tumors and their invasive progression

Serous borderline tumors (SBT) are epithelial neoplastic lesions of the ovaries that commonly have a good prognosis. In 10-15% of cases, however, SBT will recur as low-grade serous cancer (LGSC), which is deeply invasive and responds poorly to current standard chemotherapy1,2,3. While genetic alterations suggest a common origin, the transition from SBT to LGSC remains poorly understood4. Here, we integrate spatial proteomics5 with spatial transcriptomics to elucidate the evolution from SBT to LGSC and its corresponding metastasis at the molecular level in both the stroma and the tumor. We show that the transition of SBT to LGSC occurs in the epithelial compartment through an intermediary stage with micropapillary features (SBT-MP), which involves a gradual increase in MAPK signaling. A distinct subset of proteins and transcripts was associated with the transition to invasive tumor growth, including the neuronal splicing factor NOVA2, which was limited to expression in LGSC and its corresponding metastasis. An integrative pathway analysis exposed aberrant molecular signaling of tumor cells supported by alterations in angiogenesis and inflammation in the tumor microenvironment. Integration of spatial transcriptomics and proteomics followed by knockdown of the most altered genes or pharmaceutical inhibition of the most relevant targets confirmed their functional significance in regulating key features of invasiveness. Combining cell-type resolved spatial proteomics and transcriptomics allowed us to elucidate the sequence of tumorigenesis from SBT to LGSC. The approach presented here is a blueprint to systematically elucidate mechanisms of tumorigenesis and find novel treatment strategies.

Integration of data-independent acquisition (DIA) with co-fractionation mass spectrometry (CF-MS) to enhance interactome mapping capabilities

Proteomics technologies are continually advancing, providing opportunities to develop stronger and more robust protein interaction networks (PINs). In part, this is due to the ever-growing number of high-throughput proteomics methods that are available. This review discusses how data-independent acquisition (DIA) and co-fractionation mass spectrometry (CF-MS) can be integrated to enhance interactome mapping abilities. Furthermore, integrating these two techniques can improve data quality and network generation through extended protein coverage, less missing data, and reduced noise. CF-DIA-MS shows promise in expanding our knowledge of interactomes, notably for non-model organisms (NMOs). CF-MS is a valuable technique on its own, but upon the integration of DIA, the potential to develop robust PINs increases, offering a unique approach for researchers to gain an in-depth understanding into the dynamics of numerous biological processes.

Secreted proteins in plasma and placenta as novel non-invasive biomarkers for intrahepatic cholestasis of pregnancy: A case-control study

Background

Intrahepatic cholestasis of pregnancy (ICP) is likely to lead to unfavorable consequences. Total bile acid (TBA) is thought to be the sole ICP indicator available as of now, but it comes with some kind of restrictions in terms of sensitivity and specificity. We were endeavoring to find potential diagnostic biomarkers for ICP in this investigation.

Methods

This case-control study with a prospective nature included 40 females in the stage of pregnancy who were diagnosed with ICP. It also included another 20 females who were also pregnant but with sound physical condition(control). Placental and plasma samples were collected from all females that were in the stage of pregnancy, except for 20 ICP patients, in which only plasma was collected. We used four-dimensional data-independent acquisition followed by enzyme-linked immunosorbent assay and immunohistochemistry to identify and validate plasma and placental profiles in ICP patients and controls. Bioinformatics was adopted in an effort to demonstrate the relevant biological processes and signalling pathways. Correlation analysis was used to analyse the consistency of tissue and plasma protein expression and the correlation between sequencing and experimental results.

Results

The expression levels of nectin-1 (NECTIN1), Kunitz-type protease inhibitor 1 (SPINT1), and inter-alpha-trypsin inhibitor heavy chain H3 (ITIH3) were remarkably higher in ICP patients than in controls. However, heparin cofactor 2 (SERPIND1) expression levels in female participants in the stage of pregnancy who were diagnosed with ICP were remarkably lower than those pregnant females with good physical fitness. In addition to the negative correlation between SERPIND1 and TBA, NECTIN1, SPINT1, and ITIH3 expression positively correlated with TBA. Area under the receiver operating characteristic curve (AUC) values of 0.7925, 0.8313, 0.8163, and 0.9025, respectively, were used to assess the diagnostic accuracies of NECTIN1, SPINT1, ITIH3, and SERPIND1. AUC (0.9438) was considerably greater when NECTIN1, SPINT1, and SERPIND1 were integrated, according to binary logistic regression. The AUC of the ROC curve for various combinations of SERPIND1 and other indicators was higher than itself, thus providing a more reliable ICP diagnosis. Furthermore, according to the bioinformatics analysis, the NECTIN1, SPINT1, ITIH3, and SERPIND1 were identified as secreted proteins because they were localized in the extracellular region.

Conclusions

This research discovered new non-invasive ICP indicators. On top of this, it sheds new light on the crucial diagnostic function of secreted proteins in ICP.

Integrated Proteomics Identifies Troponin I Isoform Switch as a Regulator of a Sarcomere-Metabolism Axis During Cardiac Regeneration

Adult mammalian cardiomyocytes have limited proliferative potential, and after myocardial infarction (MI), injured cardiac tissue is replaced with fibrotic scar rather than with functioning myocardium. In contrast, the neonatal mouse heart possesses a regenerative capacity governed by cardiomyocyte proliferation; however, a metabolic switch from glycolysis to fatty acid oxidation during postnatal development results in loss of this regenerative capacity. Interestingly, a sarcomere isoform switch also takes place during postnatal development where slow skeletal troponin I (ssTnI) is replaced with cardiac troponin I (cTnI). In this study, we first employ integrated quantitative bottom-up and top-down proteomics to comprehensively define the proteomic and sarcomeric landscape during postnatal heart maturation. Utilizing a cardiomyocyte-specific ssTnI transgenic mouse model, we found that ssTnI overexpression increased cardiomyocyte proliferation and the cardiac regenerative capacity of the postnatal heart following MI compared to control mice by histological analysis. Our global proteomic analysis of ssTnI transgenic mice following MI reveals that ssTnI overexpression induces a significant shift in the cardiac proteomic landscape. This shift is characterized by an upregulation of key proteins involved in glycolytic metabolism. Collectively, our data suggest that the postnatal TnI isoform switch may play a role in the metabolic shift from glycolysis to fatty acid oxidation during postnatal maturation. This underscores the significance of a sarcomere-metabolism axis during cardiomyocyte proliferation and heart regeneration.

Elucidating the cellular determinants of targeted membrane protein degradation by lysosome-targeting chimeras

Targeted protein degradation can provide advantages over inhibition approaches in the development of therapeutic strategies. Lysosome-targeting chimeras (LYTACs) harness receptors, such as the cation-independent mannose 6-phosphate receptor (CI-M6PR), to direct extracellular proteins to lysosomes. In this work, we used a genome-wide CRISPR knockout approach to identify modulators of LYTAC-mediated membrane protein degradation in human cells. We found that disrupting retromer genes improved target degradation by reducing LYTAC recycling to the plasma membrane. Neddylated cullin-3 facilitated LYTAC-complex lysosomal maturation and was a predictive marker for LYTAC efficacy. A substantial fraction of cell surface CI-M6PR remains occupied by endogenous M6P-modified glycoproteins. Thus, inhibition of M6P biosynthesis increased the internalization of LYTAC-target complexes. Our findings inform design strategies for next-generation LYTACs and elucidate aspects of cell surface receptor occupancy and trafficking.

Increasing taxonomic and functional characterization of host-microbiome interactions by DIA-PASEF metaproteomics

Introduction

Metaproteomics is a rapidly advancing field that offers unique insights into the taxonomic composition and the functional activity of microbial communities, and their effects on host physiology. Classically, data-dependent acquisition (DDA) mass spectrometry (MS) has been applied for peptide identification and quantification in metaproteomics. However, DDA-MS exhibits well-known limitations in terms of depth, sensitivity, and reproducibility. Consequently, methodological improvements are required to better characterize the protein landscape of microbiomes and their interactions with the host.

Methods

We present an optimized proteomic workflow that utilizes the information captured by Parallel Accumulation-Serial Fragmentation (PASEF) MS for comprehensive metaproteomic studies in complex fecal samples of mice.

Results and discussion

We show that implementing PASEF using a DDA acquisition scheme (DDA-PASEF) increased peptide quantification up to 5 times and reached higher accuracy and reproducibility compared to previously published classical DDA and data-independent acquisition (DIA) methods. Furthermore, we demonstrate that the combination of DIA, PASEF, and neuronal-network-based data analysis, was superior to DDA-PASEF in all mentioned parameters. Importantly, DIA-PASEF expanded the dynamic range towards low-abundant proteins and it doubled the quantification of proteins with unknown or uncharacterized functions. Compared to previous classical DDA metaproteomic studies, DIA-PASEF resulted in the quantification of up to 4 times more taxonomic units using 16 times less injected peptides and 4 times shorter chromatography gradients. Moreover, 131 additional functional pathways distributed across more and even uniquely identified taxa were profiled as revealed by a peptide-centric taxonomic-functional analysis. We tested our workflow on a validated preclinical mouse model of neuropathic pain to assess longitudinal changes in host-gut microbiome interactions associated with pain - an unexplored topic for metaproteomics. We uncovered the significant enrichment of two bacterial classes upon pain, and, in addition, the upregulation of metabolic activities previously linked to chronic pain as well as various hitherto unknown ones. Furthermore, our data revealed pain-associated dynamics of proteome complexes implicated in the crosstalk between the host immune system and the gut microbiome. In conclusion, the DIA-PASEF metaproteomic workflow presented here provides a stepping stone towards a deeper understanding of microbial ecosystems across the breadth of biomedical and biotechnological fields.

Data-Driven Optimization of DIA Mass Spectrometry by DO-MS

Mass spectrometry (MS) enables specific and accurate quantification of proteins with ever-increasing throughput and sensitivity. Maximizing this potential of MS requires optimizing data acquisition parameters and performing efficient quality control for large datasets. To facilitate these objectives for data-independent acquisition (DIA), we developed a second version of our framework for data-driven optimization of MS methods (DO-MS). The DO-MS app v2.0 (do-ms.slavovlab.net) allows one to optimize and evaluate results from both label-free and multiplexed DIA (plexDIA) and supports optimizations particularly relevant to single-cell proteomics. We demonstrate multiple use cases, including optimization of duty cycle methods, peptide separation, number of survey scans per duty cycle, and quality control of single-cell plexDIA data. DO-MS allows for interactive data display and generation of extensive reports, including publication of quality figures that can be easily shared. The source code is available at github.com/SlavovLab/DO-MS.

Exploration of cell state heterogeneity using single-cell proteomics through sensitivity-tailored data-independent acquisition

Single-cell resolution analysis of complex biological tissues is fundamental to capture cell-state heterogeneity and distinct cellular signaling patterns that remain obscured with population-based techniques. The limited amount of material encapsulated in a single cell however, raises significant technical challenges to molecular profiling. Due to extensive optimization efforts, single-cell proteomics by Mass Spectrometry (scp-MS) has emerged as a powerful tool to facilitate proteome profiling from ultra-low amounts of input, although further development is needed to realize its full potential. To this end, we carry out comprehensive analysis of orbitrap-based data-independent acquisition (DIA) for limited material proteomics. Notably, we find a fundamental difference between optimal DIA methods for high- and low-load samples. We further improve our low-input DIA method by relying on high-resolution MS1 quantification, thus enhancing sensitivity by more efficiently utilizing available mass analyzer time. With our ultra-low input tailored DIA method, we are able to accommodate long injection times and high resolution, while keeping the scan cycle time low enough to ensure robust quantification. Finally, we demonstrate the capability of our approach by profiling mouse embryonic stem cell culture conditions, showcasing heterogeneity in global proteomes and highlighting distinct differences in key metabolic enzyme expression in distinct cell subclusters.

Recent applications of quantitative mass spectrometry in biopharmaceutical process development and manufacturing

Biopharmaceutical products have seen rapid growth over the past few decades and continue to dominate the global pharmaceutical market. Aligning with the quality by design (QbD) framework and realization, recent advances in liquid chromatography-mass spectrometry (LC-MS) instrumentation and related techniques have enhanced biopharmaceutical characterization capabilities and have supported an increased development of biopharmaceutical products. Beyond its routine qualitative characterization, the quantitative feature of LC-MS has unique applications in biopharmaceutical process development and manufacturing. This review describes the recent applications and implications of the advancement of quantitative MS methods in biopharmaceutical process development, and characterization of biopharmaceutical product, product-related variants, and process-related impurities. We also provide insights on the emerging applications of quantitative MS in the lifecycle of biopharmaceutical product development including quality control in the Good Manufacturing Practice (GMP) environment and process analytical technology (PAT) practices during process development and manufacturing. Through collaboration with instrument and software vendors and regulatory agencies, we envision broader adoption of phase-appropriate quantitative MS-based methods for the analysis of biopharmaceutical products, which in turn has the potential to enable manufacture of higher quality products for patients.

Protocol for deep proteomic profiling of formalin-fixed paraffin-embedded specimens using a spectral library-free approach

Formalin-fixed paraffin-embedded (FFPE) samples are valuable archived bio-specimens of individuals and are commonly used in biomedical research. Here, we present a protocol for deep proteomic profiling of FFPE specimens using a spectral library-free approach. We describe steps for FFPE tissue collection, tissue lysis, homogenization, protein lysate cleanup, on-beads digestion, and de-salting. We then detail data acquisition and statistical analysis. This protocol is highly sensitive, reproducible, and applicable for high-throughput proteomic profiling and can be used on various types of specimens.

mTORC1 Regulates the Metabolic Switch of Postnatal Cardiomyocytes During Regeneration

The metabolic switch from glycolysis to fatty acid oxidation in postnatal cardiomyocytes contributes to the loss of the cardiac regenerative potential of the mammalian heart. However, the mechanisms that regulate this metabolic switch remain unclear. The protein kinase complex mechanistic target of rapamycin complex 1 (mTORC1) is a central signaling hub that regulates cellular metabolism and protein synthesis, yet its role during mammalian heart regeneration and postnatal metabolic maturation is undefined. Here, we use immunoblotting, rapamycin treatment, myocardial infarction, and global proteomics to define the role of mTORC1 in postnatal heart development and regeneration. Our results demonstrate that the activity of mTORC1 is dynamically regulated between the regenerating and the non-regenerating hearts. Acute inhibition of mTORC1 by rapamycin or everolimus reduces cardiomyocyte proliferation and inhibits neonatal heart regeneration following injury. Our quantitative proteomic analysis demonstrates that transient inhibition of mTORC1 during neonatal heart injury did not reduce protein synthesis, but rather shifts the cardiac proteome of the neonatal injured heart from glycolysis towards fatty acid oxidation. This indicates that mTORC1 inhibition following injury accelerates the postnatal metabolic switch, which promotes metabolic maturation and impedes cardiomyocyte proliferation and heart regeneration. Taken together, our results define an important role for mTORC1 in regulating postnatal cardiac metabolism and may represent a novel target to modulate cardiac metabolism and promote heart regeneration.

A Complete Workflow for High Throughput Human Single Skeletal Muscle Fiber Proteomics

Skeletal muscle is a major regulatory tissue of whole-body metabolism and is composed of a diverse mixture of cell (fiber) types. Aging and several diseases differentially affect the various fiber types, and therefore, investigating the changes in the proteome in a fiber-type specific manner is essential. Recent breakthroughs in isolated single muscle fiber proteomics have started to reveal heterogeneity among fibers. However, existing procedures are slow and laborious, requiring 2 h of mass spectrometry time per single muscle fiber; 50 fibers would take approximately 4 days to analyze. Thus, to capture the high variability in fibers both within and between individuals requires advancements in high throughput single muscle fiber proteomics. Here we use a single cell proteomics method to enable quantification of single muscle fiber proteomes in 15 min total instrument time. As proof of concept, we present data from 53 isolated skeletal muscle fibers obtained from two healthy individuals analyzed in 13.25 h. Adapting single cell data analysis techniques to integrate the data, we can reliably separate type 1 and 2A fibers. Ninety-four proteins were statistically different between clusters indicating alteration of proteins involved in fatty acid oxidation, oxidative phosphorylation, and muscle structure and contractile function. Our results indicate that this method is significantly faster than prior single fiber methods in both data collection and sample preparation while maintaining sufficient proteome depth. We anticipate this assay will enable future studies of single muscle fibers across hundreds of individuals, which has not been possible previously due to limitations in throughput.

Data-Driven Optimization of DIA Mass Spectrometry by DO-MS

Mass spectrometry (MS) enables specific and accurate quantification of proteins with ever increasing throughput and sensitivity. Maximizing this potential of MS requires optimizing data acquisition parameters and performing efficient quality control for large datasets. To facilitate these objectives for data independent acquisition (DIA), we developed a second version of our framework for data-driven optimization of mass spectrometry methods (DO-MS). The DO-MS app v2.0 ( do-ms.slavovlab.net ) allows to optimize and evaluate results from both label free and multiplexed DIA (plexDIA) and supports optimizations particularly relevant for single-cell proteomics. We demonstrate multiple use cases, including optimization of duty cycle methods, peptide separation, number of survey scans per duty cycle, and quality control of single-cell plexDIA data. DO-MS allows for interactive data display and generation of extensive reports, including publication quality figures, that can be easily shared. The source code is available at: github.com/SlavovLab/DO-MS .

Exploiting ion-mobility mass spectrometry for unraveling proteome complexity

Ion mobility spectrometry-mass spectrometry (IMS-MS) is experiencing rapid growth in proteomic studies, driven by its enhancements in dynamic range and throughput, increasing the quantitation precision, and the depth of proteome coverage. The core principle of ion mobility spectrometry is to separate ions in an inert gas under the influence of an electric field based on differences in drift time. This minireview provides an introduction to IMS operation modes and a description of advantages and limitations is presented. Moreover, the principles of trapped IMS-MS (TIMS-MS), including parallel accumulation-serial fragmentation are discussed. Finally, emerging applications linked to TIMS focusing on sample throughput (in clinical proteomics) and sensitivity (single-cell proteomics) are reviewed, and the possibilities of intact protein analysis are discussed.

Easy, Robust, and Repeatable Online Acid Cleavage of Proteins in Mobile Phase for Fast Quantitative LC-MS Bottom-Up Protein Analysis─Application for Ricin Detection

Sample preparation involving the cleavage of proteins into peptides is the first critical step for successful bottom-up proteomics and protein analyses. Time- and labor-intensiveness are among the bottlenecks of the commonly used methods for protein sample preparation. Here, we report a fast online method for postinjection acid cleavage of proteins directly in the mobile phase typically used for LC-MS analyses in proteomics. The chemical cleavage is achieved in 0.1% formic acid within 35 s in a capillary heated to 195 °C installed upstream of the analytical column, enabling the generated peptides to be separated. The peptides generated by the optimized method covered the entire sequence except for one amino acid of trastuzumab used for the method development. The qualitative results are extraordinarily stable, even over a long period of time. Moreover, the method is also suitable for accurate and repeatable quantification. The procedure requires only one manual step, significantly decreasing sample transfer losses. To demonstrate its practical utility, we tested the method for the fast detection of ricin. Ricin can be unambiguously identified from an injection of 10 ng, and the results can be obtained within 7-8 min after receiving a suspicious sample. Because no sophisticated accessories and no additional reagents are needed, the method can be seamlessly transferred to any laboratory for high-throughput proteomic workflows.

Data-Dependent Acquisition with Precursor Coisolation Improves Proteome Coverage and Measurement Throughput for Label-Free Single-Cell Proteomics

We combined efficient sample preparation and ultra-low-flow liquid chromatography with a newly developed data acquisition and analysis scheme termed wide window acquisition (WWA) to quantify >3,000 proteins from single cells in rapid label-free analyses. WWA employs large isolation windows to intentionally co-isolate and co-fragment adjacent precursors along with the selected precursor. Optimized WWA increased the number of MS2-identified proteins by ≈40 % relative to standard data-dependent acquisition. For a 40-min LC gradient operated at ≈15 nL/min, we identified an average of 3,524 proteins per single-cell-sized aliquot of protein digest. Reducing the active gradient to 20 min resulted in a modest 10 % decrease in proteome coverage. Using this platform, we compared protein expression between single HeLa cells having an essential autophagy gene, atg9a, knocked out, with their isogenic WT parental line. Similar proteome coverage was observed, and 268 proteins were significantly up- or downregulated. Protein upregulation primarily related to innate immunity, vesicle trafficking and protein degradation.

Right in two: capabilities of ion mobility spectrometry for untargeted metabolomics

This mini review focuses on the opportunities provided by current and emerging separation techniques for mass spectrometry metabolomics. The purpose of separation technologies in metabolomics is primarily to reduce complexity of the heterogeneous systems studied, and to provide concentration enrichment by increasing sensitivity towards the quantification of low abundance metabolites. For this reason, a wide variety of separation systems, from column chemistries to solvent compositions and multidimensional separations, have been applied in the field. Multidimensional separations are a common method in both proteomics applications and gas chromatography mass spectrometry, allowing orthogonal separations to further reduce analytical complexity and expand peak capacity. These applications contribute to exponential increases in run times concomitant with first dimension fractionation followed by second dimension separations. Multidimensional liquid chromatography to increase peak capacity in metabolomics, when compared to the potential of running additional samples or replicates and increasing statistical confidence, mean that uptake of these methods has been minimal. In contrast, in the last 15 years there have been significant advances in the resolution and sensitivity of ion mobility spectrometry, to the point where high-resolution separation of analytes based on their collision cross section approaches chromatographic separation, with minimal loss in sensitivity. Additionally, ion mobility separations can be performed on a chromatographic timescale with little reduction in instrument duty cycle. In this review, we compare ion mobility separation to liquid chromatographic separation, highlight the history of the use of ion mobility separations in metabolomics, outline the current state-of-the-art in the field, and discuss the future outlook of the technology. "Where there is one, you're bound to divide it. Right in two", James Maynard Keenan.

MSBooster: improving peptide identification rates using deep learning-based features

Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, for rescoring peptide-to-spectrum matches using additional features incorporating deep learning-based predictions of peptide properties, such as LC retention time, ion mobility, and MS/MS spectra. We demonstrate the utility of MSBooster, in tandem with MSFragger and Percolator, in several different workflows, including nonspecific searches (immunopeptidomics), direct identification of peptides from data independent acquisition data, single-cell proteomics, and data generated on an ion mobility separation-enabled timsTOF MS platform. MSBooster is fast, robust, and fully integrated into the widely used FragPipe computational platform.

Towards the Definition of the Molecular Hallmarks of Idiopathic Membranous Nephropathy in Serum Proteome: A DIA-PASEF Approach

Idiopathic membranous nephropathy (IMN) is a pathologically defined disorder of the glomerulus, primarily responsible for nephrotic syndromes (NS) in nondiabetic adults. The underlying molecular mechanisms are still not completely clarified. To explore possible molecular and functional signatures, an optimised mass spectrometry (MS) method based on next-generation data-independent acquisition combined with ion-mobility was applied to serum of patients affected by IMN (n = 15) or by other glomerulopathies (PN) (n = 15). The statistical comparison highlighted a panel of 57 de-regulated proteins with a significant increase in lipoprotein-related proteins (APOC1, APOB, APOA1, APOL1 and LCAT) and a substantial quantitative alteration of key serpins (including A4, D1, A7, A6, F2, F1 and 1) possibly associated with IMN or NS and podocyte stress. A critical dysregulation in metabolisms of lipids (e.g., VLDL assembly and clearance) likely to be related to known hyperlipidemia in IMN, along with involvement of non-classical complement pathways and a putative enrolment of ficolin-2 in sustaining the activation of the lectin-mediated complement system have been pinpointed. Moreover, mannose receptor CD206 (MRC1-down in IMN) and biotinidase (BTD-up in IMN) are able alone to accurately distinguish IMN vs. PN. To conclude, our work provides key proteomic insights into the IMN complexity, opening the way to an efficient stratification of MN patients.

Evaluation of the Orbitrap Ascend Tribrid Mass Spectrometer for Shotgun Proteomics

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 (FTMS2) 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.

Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform

Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. Different from most existing methods, MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. To streamline the analysis of DIA data and enable easy reproducibility, we integrate MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data-dependent acquisition (DDA), or both data types combined. We compare MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, DIA-NN library-free, and MaxDIA. We demonstrate the fast, sensitive, and accurate performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.