diaPASEF: parallel accumulation–serial fragmentation combined with data-independent acquisition

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.

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.

Alzheimer's Disease Biomarker Analysis Using Targeted Mass Spectrometry

Alzheimer's disease (AD) is characterized by several neuropathological changes, mainly extracellular amyloid aggregates (plaques), intraneuronal inclusions of phosphorylated tau (tangles), as well as neuronal and synaptic degeneration, accompanied by tissue reactions to these processes (astrocytosis and microglial activation) that precede neuronal network disturbances in the symptomatic phase of the disease. A number of biomarkers for these brain tissue changes have been developed, mainly using immunoassays. In this review, we discuss how targeted mass spectrometry (TMS) can be used to validate and further characterize classes of biomarkers reflecting different AD pathologies, such as tau- and amyloid-beta pathologies, synaptic dysfunction, lysosomal dysregulation, and axonal damage, and the prospect of using TMS to measure these proteins in clinical research and diagnosis. TMS advantages and disadvantages in relation to immunoassays are discussed, and complementary aspects of the technologies are discussed.

Ubiquilin-4 induces immune escape in gastric cancer by activating the notch signaling pathway

PURPOSE

We aimed to investigate the role of ubiquilin-4 in predicting the immunotherapy response in gastric cancer.

METHODS

Retrospective RNA-sequencing and immunohistochemical analysis were performed for patients with gastric cancer who received programmed death-1 blockade therapy after recurrence. Multiplex immunohistochemistry identified immune cell types in gastric cancer tissues. We used immunocompetent 615 mice and immunodeficient nude mice to perform tumorigenic experiments.

RESULTS

Ubiquilin-4 expression was significantly higher in responders (p < 0.05, false discovery rate > 2.5) and showed slight superiority over programmed death ligand 1 in predicting programmed death-1 inhibitor therapy response (area under the curve: 87.08 vs. 72.50). Ubiquilin-4-high patients exhibited increased CD4+ and CD8+ T cells, T follicular helper cells, monocytes, and macrophages. Ubiquilin-4-overexpressed mouse forestomach carcinoma cells showed significantly enhanced growth in immunocompetent mice but not in immunodeficient mice. Upregulation or downregulation of ubiquilin-4 synergistically affected programmed death ligand 1 at the protein and messenger RNA levels. Functional enrichment analysis revealed significant enrichment of the Notch, JAK-STAT, and WNT signaling pathways in ubiquilin-4-high gastric cancers. Ubiquilin-4 promoted Numb degaration, activating the Notch signaling pathway and upregulating programmed death ligand 1.

CONCLUSIONS

Ubiquilin-4 may contribute to immune escape in gastric cancer by upregulating programmed death ligand 1 expression in tumor cells through Notch signaling activation. Thus, ubiquilin-4 could serve as a predictive marker for programmed death ligand 1 inhibitor therapy response in gastric cancer.

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.

Plasma proteome profiling reveals molecular mechanisms underlying the effects of daily consumption of 'Bahia' and 'Cara Cara' orange juices

Orange juice is an important food source of bioactive compounds, mainly the flavanones hesperidin and narirutin. This study aimed to investigate the underlying molecular mechanisms of action of orange juice's health properties by analyzing changes in the plasma proteome of healthy Brazilian volunteers after consuming juices made from 'Bahia' (BOJ-source of flavanones) and 'Cara Cara' (CCOJ-source of flavanones and carotenoids) oranges cultivated in Brazil. We used an untargeted proteomic approach, with a particular emphasis on the juices' effects on blood coagulant activity. We identified 247 differentially expressed proteins, of which 170 significantly increased or decreased after BOJ consumption and 145 after CCOJ. These proteins are involved in 105 processes that can significantly regulate cell adhesion, cell signaling, cell metabolism, inflammation, or others. Bioinformatic analysis evidenced proteins with major cellular regulatory capacity (e.g., FN1 and GAPDH) and predicted transcription factors (TFs) (e.g., SP1 and CEBPA) and miRNAs (e.g., miR-1-3p and miR-615-3p) that could be involved in the regulation of differentially expressed proteins. In-silico docking analyses between flavanone metabolites and TFs evidenced the higher binding capacity of narirutin phase II metabolites with akt1 and p38, interactions that suggest how the expression of genes of differentially expressed proteins were activated or inhibited. Moreover, the study shed light on proteins of coagulation cascade that presented expression modulated by both juices, proposing the modulation of blood coagulant activity as a potential benefit of OJ (mainly CCOJ) consumption. Taken together, this study revealed that BOJ and CCOJ consumption affected plasma proteome in healthy individuals, suggesting potential molecular targets and mechanisms of OJ bioactive compounds in humans.

Automated Structural Activity Screening of β-Diketonate Assemblies with High-Throughput Ion Mobility-Mass Spectrometry

Embracing complexity in design, metallo-supramolecular self-assembly presents an opportunity for fabricating materials of economic significance. The array of accessible supramolecules is alluring, along with favourable energy requirements. Implementation is hampered by an inability to efficiently characterise complex mixtures. The stoichiometry, size, shape, guest binding properties and reactivity of individual components and combinations thereof are inherently challenging to resolve. A large combinatorial library of four transition metals (Fe, Cu, Ni and Zn), and six β-diketonate ligands at different molar ratios and pH was robotically prepared and directly analysed over multiple timepoints with electrospray ionisation travelling wave ion mobility-mass spectrometry. The dataset was parsed for self-assembling activity without first attempting to structurally assign individual species. Self-assembling systems were readily categorised without manual data-handling, allowing efficient screening of self-assembly activity. This workflow clarifies solution phase supramolecular assembly processes without manual, bottom-up processing. The complex behaviour of the self-assembling systems was reduced to simpler qualities, which could be automatically processed.

Automated single-cell proteomics providing sufficient proteome depth to study complex biology beyond cell type classifications

Mass spectrometry (MS)-based single-cell proteomics (SCP) has gained massive attention as a viable complement to other single cell approaches. The rapid technological and computational advances in the field have pushed the boundaries of sensitivity and throughput. However, reproducible quantification of thousands of proteins within a single cell at reasonable proteome depth to characterize biological phenomena remains a challenge. To address some of those limitations we present a combination of fully automated single cell sample preparation utilizing a dedicated chip within the picolitre dispensing robot, the cellenONE. The proteoCHIP EVO 96 can be directly interfaced with the Evosep One chromatographic system for in-line desalting and highly reproducible separation with a throughput of 80 samples per day. This, in combination with the Bruker timsTOF MS instruments, demonstrates double the identifications without manual sample handling. Moreover, relative to standard high-performance liquid chromatography, the Evosep One separation provides further 2-fold improvement in protein identifications. The implementation of the newest generation timsTOF Ultra with our proteoCHIP EVO 96-based sample preparation workflow reproducibly identifies up to 4,000 proteins per single HEK-293T without a carrier or match-between runs. Our current SCP depth spans over 4 orders of magnitude and identifies over 50 biologically relevant ubiquitin ligases. We complement our highly reproducible single-cell proteomics workflow to profile hundreds of lipopolysaccharide (LPS)-perturbed THP-1 cells and identified key regulatory proteins involved in interleukin and interferon signaling. This study demonstrates that the proteoCHIP EVO 96-based SCP sample preparation with the timsTOF Ultra provides sufficient proteome depth to study complex biology beyond cell-type classifications.

Postmortem Muscle Proteome Characteristics of Silver Carp (Hypophthalmichthys molitrix): Insights from Full-Length Transcriptome and Deep 4D Label-Free Proteomic

The coverage of the protein database directly determines the results of shotgun proteomics. In this study, PacBio single-molecule real-time sequencing technology was performed on postmortem silver carp muscle transcripts. A total of 42.43 Gb clean data, 35,834 nonredundant transcripts, and 15,413 unigenes were obtained. In total, 99.32% of the unigenes were successfully annotated and assigned specific functions. PacBio long-read isoform sequencing (Iso-Seq) analysis can provide more accurate protein information with a higher proportion of complete coding sequences and longer lengths. Subsequently, 2671 proteins were identified in deep 4D proteomics informed by a full-length transcriptomics technique, which has been shown to improve the identification of low-abundance muscle proteins and potential protein isoforms. The feature of the sarcomeric protein profile and information on more than 30 major proteins in the white dorsal muscle of silver carp were reported here for the first time. Overall, this study provides valuable transcriptome data resources and the comprehensive muscle protein information detected to date for further study into the processing characteristic of early postmortem fish muscle, as well as a spectral library for data-independent acquisition and data processing. This batch of muscle-specific dependent acquisition data is available via PRIDE with identifier PXD043702.

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.

Uncovering Distinct Peptide Charging Behaviors in Electrospray Ionization Mass Spectrometry Using a Large-Scale Dataset

Electrospray ionization is a powerful and prevalent technique used to ionize analytes in mass spectrometry. The distribution of charges that an analyte receives (charge state distribution, CSD) is an important consideration for interpreting mass spectra. However, due to an incomplete understanding of the ionization mechanism, the analyte properties that influence CSDs are not fully understood. Here, we employ a machine learning-based approach and analyze CSDs of hundreds of thousands of peptides. Interestingly, half of the peptides exhibit charges that differ from what one would naively expect (the number of basic sites). We find that these peptides can be classified into two regimes (undercharging and overcharging) and that these two regimes display markedly different charging characteristics. Notably, peptides in the overcharging regime show minimal dependence on basic site count, and more generally, the two regimes exhibit distinct sequence determinants. These findings highlight the rich ionization behavior of peptides and the potential of CSDs for enhancing peptide identification.

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.

Proteomics-based mass spectrometry profiling of SARS-CoV-2 infection from human nasopharyngeal samples

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the on-going global pandemic of coronavirus disease 2019 (COVID-19) that continues to pose a significant threat to public health worldwide. SARS-CoV-2 encodes four structural proteins namely membrane, nucleocapsid, spike, and envelope proteins that play essential roles in viral entry, fusion, and attachment to the host cell. Extensively glycosylated spike protein efficiently binds to the host angiotensin-converting enzyme 2 initiating viral entry and pathogenesis. Reverse transcriptase polymerase chain reaction on nasopharyngeal swab is the preferred method of sample collection and viral detection because it is a rapid, specific, and high-throughput technique. Alternate strategies such as proteomics and glycoproteomics-based mass spectrometry enable a more detailed and holistic view of the viral proteins and host-pathogen interactions and help in detection of potential disease markers. In this review, we highlight the use of mass spectrometry methods to profile the SARS-CoV-2 proteome from clinical nasopharyngeal swab samples. We also highlight the necessity for a comprehensive glycoproteomics mapping of SARS-CoV-2 from biological complex matrices to identify potential COVID-19 markers.

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.

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.

Design principles for cyclin K molecular glue degraders

Molecular glue degraders are an effective therapeutic modality, but their design principles are not well understood. Recently, several unexpectedly diverse compounds were reported to deplete cyclin K by linking CDK12-cyclin K to the DDB1-CUL4-RBX1 E3 ligase. Here, to investigate how chemically dissimilar small molecules trigger cyclin K degradation, we evaluated 91 candidate degraders in structural, biophysical and cellular studies and reveal all compounds acquire glue activity via simultaneous CDK12 binding and engagement of DDB1 interfacial residues, in particular Arg928. While we identify multiple published kinase inhibitors as cryptic degraders, we also show that these glues do not require pronounced inhibitory properties for activity and that the relative degree of CDK12 inhibition versus cyclin K degradation is tuneable. We further demonstrate cyclin K degraders have transcriptional signatures distinct from CDK12 inhibitors, thereby offering unique therapeutic opportunities. The systematic structure-activity relationship analysis presented herein provides a conceptual framework for rational molecular glue design.

Measurement and utilization of the proteomic reactivity by mass spectrometry

Chemical proteomics, which involves studying the covalent modifications of proteins by small molecules, has significantly contributed to our understanding of protein function and has become an essential tool in drug discovery. Mass spectrometry (MS) is the primary method for identifying and quantifying protein-small molecule adducts. In this review, we discuss various methods for measuring proteomic reactivity using MS and covalent proteomics probes that engage through reactivity-driven and proximity-driven mechanisms. We highlight the applications of these methods and probes in live-cell measurements, drug target identification and validation, and characterizing protein-small molecule interactions. We conclude the review with current developments and future opportunities in the field, providing our perspectives on analytical considerations for MS-based analysis of the proteomic reactivity landscape.

Analyzing Posttranslational Modifications in Single Cells

With the rapid expansion of capabilities in the analysis of proteins in single cells, we can now identify multiple classes of protein posttranslational modifications on some of these proteins. Each new technology that has increased the number of proteins measured per cell has likewise increased our ability to identify and quantify modified peptides. In this chapter, I will discuss our current capabilities, concerns, and challenges specific to this emerging field of study and the inevitable demand for services, providing a general review of concepts that should be considered.

Optimizing the PROTREC network-based missing protein prediction algorithm

This article summarizes the PROTREC method and investigates the impact that the different hyper-parameters have on the task of missing protein prediction using PROTREC. We evaluate missing protein recovery rates using different PROTREC score selection approaches (MAX, MIN, MEDIAN, and MEAN), different PROTREC score thresholds, as well as different complex size thresholds. In addition, we included two additional cancer datasets in our analysis and introduced a new validation method to check both the robustness of the PROTREC method as well as the correctness of our analysis. Our analysis showed that the missing protein recovery rate can be improved by adopting PROTREC score selection operations of MIN, MEDIAN, and MEAN instead of the default MAX. However, this may come at a cost of reduced numbers of proteins predicted and validated. The users should therefore choose their hyper-parameters carefully to find a balance in the accuracy-quantity trade-off. We also explored the possibility of combining PROTREC with a p-value-based method (FCS) and demonstrated that PROTREC is able to perform well independently without any help from a p-value-based method. Furthermore, we conducted a downstream enrichment analysis to understand the biological pathways and protein networks within the cancerous tissues using the recovered proteins. Missing protein recovery rate using PROTREC can be improved by selecting a different PROTREC score selection method. Different PROTREC score selection methods and other hyper-parameters such as PROTREC score threshold and complex size threshold introduce accuracy-quantity trade-off. PROTREC is able to perform well independently of any filtering using a p-value-based method. Verification of the PROTREC method on additional cancer datasets. Downstream Enrichment Analysis to understand the biological pathways and protein networks in cancerous tissues.

Cerebrospinal Fluid Proteomics Identifies Potential Biomarkers for Early-Onset Alzheimer's Disease

Background

Early-onset Alzheimer's disease (EOAD) exhibits a notable degree of heterogeneity as compared to late-onset Alzheimer's disease (LOAD). The proteins and pathways contributing to the pathophysiology of EOAD still need to be completed and elucidated.

Objective

Using correlation network analysis and machine learning to analyze cerebrospinal fluid (CSF) proteomics data to identify potential biomarkers and pathways associated with EOAD.

Methods

We employed mass spectrometry to conduct CSF proteomic analysis using the data-independent acquisition method in a Chinese cohort of 139 CSF samples, including 40 individuals with normal cognition (CN), 61 patients with EOAD, and 38 patients with LOAD. Correlation network analysis of differentially expressed proteins was performed to identify EOAD-associated pathways. Machine learning assisted in identifying crucial proteins differentiating EOAD. We validated the results in an Western cohort and examined the proteins expression by enzyme-linked immunosorbent assay (ELISA) in additional 9 EOAD, 9 LOAD, and 9 CN samples from our cohort.

Results

We quantified 2,168 CSF proteins. Following adjustment for age and sex, EOAD exhibited a significantly greater number of differentially expressed proteins than LOAD compared to CN. Additionally, our data indicates that EOAD may exhibit more pronounced synaptic dysfunction than LOAD. Three potential biomarkers for EOAD were identified: SH3BGRL3, LRP8, and LY6 H, of which SH3BGRL3 also accurately classified EOAD in the Western cohort. LY6 H reduction was confirmed via ELISA, which was consistent with our proteomic results.

Conclusions

This study provides a comprehensive profile of the CSF proteome in EOAD and identifies three potential EOAD biomarker proteins.

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.

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.

Brain Region Differences in α1- and α5-Subunit-Containing GABAA Receptor Proteomes Revealed with Affinity Purification and Blue Native PAGE Proteomics

GABAA receptors are the major inhibitory receptors in the brain. They are hetero-pentamers with a composition of predominantly two α, two β, and one γ or δ subunit. Of the six α subunit genes, the α5 subunit displays a limited spatial expression pattern and is known to mediate both phasic and tonic inhibition. In this study, using immunoaffinity-based proteomics, we identified the α5 subunit containing receptor complexes in the hippocampus and olfactory bulb. The α1-α5 interaction was identified in both brain regions, albeit with significantly different stoichiometries. In line with this, reverse IPs using anti-α1 antibodies showed the α5-α1 co-occurrence and validated the quantitative difference. In addition, we showed that the association of Neuroligin 2 with α1-containing receptors was much higher in the olfactory bulb than in the hippocampus, which was confirmed using blue native gel electrophoresis and quantitative mass spectrometry. Finally, immunocytochemical staining revealed a co-localization of α1 and α5 subunits in the post-synaptic puncta in the hippocampus.

Uncovering the temporal dynamics and regulatory networks of thermal stress response in a hyperthermophile using transcriptomics and proteomics

IMPORTANCE

Extreme environments provide unique challenges for life, and the study of extremophiles can shed light on the mechanisms of adaptation to such conditions. Pyrococcus furiosus, a hyperthermophilic archaeon, is a model organism for studying thermal stress response mechanisms. In this study, we used an integrated analysis of RNA-sequencing and mass spectrometry data to investigate the transcriptomic and proteomic responses of P. furiosus to heat and cold shock stress and recovery. Our results reveal the rapid and dynamic changes in gene and protein expression patterns associated with these stress responses, as well as the coordinated regulation of different gene sets in response to different stressors. These findings provide valuable insights into the molecular adaptations that facilitate life in extreme environments and advance our understanding of stress response mechanisms in hyperthermophilic archaea.

Immunopeptidomics in the Era of Single-Cell Proteomics

Immunopeptidomics, as the analysis of antigen peptides being presented to the immune system via major histocompatibility complexes (MHC), is being seen as an imperative tool for identifying epitopes for vaccine development to treat cancer and viral and bacterial infections as well as parasites. The field has made tremendous strides over the last 25 years but currently still faces challenges in sensitivity and throughput for widespread applications in personalized medicine and large vaccine development studies. Cutting-edge technological advancements in sample preparation, liquid chromatography as well as mass spectrometry, and data analysis, however, are currently transforming the field. This perspective showcases how the advent of single-cell proteomics has accelerated this transformation of immunopeptidomics in recent years and will pave the way for even more sensitive and higher-throughput immunopeptidomics analyses.

In vivo protein turnover rates in varying oxygen tensions nominate MYBBP1A as a mediator of the hyperoxia response

Oxygen deprivation and excess are both toxic. Thus, the body's ability to adapt to varying oxygen tensions is critical for survival. While the hypoxia transcriptional response has been well studied, the post-translational effects of oxygen have been underexplored. In this study, we systematically investigate protein turnover rates in mouse heart, lung, and brain under different inhaled oxygen tensions. We find that the lung proteome is the most responsive to varying oxygen tensions. In particular, several extracellular matrix (ECM) proteins are stabilized in the lung under both hypoxia and hyperoxia. Furthermore, we show that complex 1 of the electron transport chain is destabilized in hyperoxia, in accordance with the exacerbation of associated disease models by hyperoxia and rescue by hypoxia. Moreover, we nominate MYBBP1A as a hyperoxia transcriptional regulator, particularly in the context of rRNA homeostasis. Overall, our study highlights the importance of varying oxygen tensions on protein turnover rates and identifies tissue-specific mediators of oxygen-dependent responses.

Sliding Windows in Ion Mobility (SWIM): A New Approach to Increase the Resolving Power in Trapped Ion Mobility-Mass Spectrometry Hyphenated with Chromatography

Over the past decade, the separation efficiency achieved by linear IMS instruments has increased substantially, with state-of-the-art IM technologies, such as the trapped ion mobility (TIMS), the cyclic traveling wave ion mobility (cTWIMS), and the structure for lossless ion manipulation (SLIM) platforms commonly demonstrating resolving powers in excess of 200. However, for complex sample analysis that require front end separation, the achievement of such high resolving power in TIMS is significantly hampered, since the ion mobility range must be broad enough to analyze all the classes of compounds of interest, whereas the IM analysis time must be short enough to cope with the time scale of the preseparation technique employed. In this paper, we introduce the concept of sliding windows in ion mobility (SWIM) for chromatography hyphenated TIMS applications that bypasses the need to use a wide and fixed IM range by using instead narrow and mobile ion mobility windows that adapt to the analytes' ion mobility during chromatographic separation. GC-TIMS-MS analysis of a mixture of 174 standards from several halogenated persistent organic pollutant (POP) classes, including chlorinated and brominated dioxins, biphenyls, and PBDEs, demonstrated that the average IM resolving power could be increased up to 40% when the SWIM mode was used, thereby greatly increasing the method selectivity for the analysis of complex samples.

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.

Secreted HLA-Fc fusion profiles immunopeptidome in hypoxic PDAC and cellular senescence

Human leukocyte antigens (HLA) present peptides largely from intracellular proteins on cell surfaces. As these complexes can serve as biomarkers in disease, proper identification of peptides derived from disease-associated antigens and the corresponding presenting HLA is important for the design and execution of therapeutic strategies. Yet, current mass spectrometry methods for immunopeptidomic profiling require large and complex sample inputs, hindering the study of certain disease phenotypes and lowering confidence in peptide and allele identification. Here, we describe a secreted HLA (sHLA)-Fc fusion construct for simple single HLA allele profiling in hypoxic pancreatic ductal adenocarcinoma (PDAC) and cellular senescence. This method streamlines sample preparation, enables temporal control, and provides allele-restricted target identification. Over 30,000 unique HLA-associated peptides were identified across 2 different HLA alleles and 7 cell lines, with ∼9,300 peptides newly discovered. The sHLA-Fc fusion capture technology holds the potential to expedite immunopeptidomics and advance therapeutic interest in HLA-peptide complexes.

Guidelines for mouse and human DC functional assays

This article is part of the Dendritic Cell Guidelines article series, which provides a collection of state-of-the-art protocols for the preparation, phenotype analysis by flow cytometry, generation, fluorescence microscopy, and functional characterization of mouse and human dendritic cells (DC) from lymphoid organs and various non-lymphoid tissues. Recent studies have provided evidence for an increasing number of phenotypically distinct conventional DC (cDC) subsets that on one hand exhibit a certain functional plasticity, but on the other hand are characterized by their tissue- and context-dependent functional specialization. Here, we describe a selection of assays for the functional characterization of mouse and human cDC. The first two protocols illustrate analysis of cDC endocytosis and metabolism, followed by guidelines for transcriptomic and proteomic characterization of cDC populations. Then, a larger group of assays describes the characterization of cDC migration in vitro, ex vivo, and in vivo. The final guidelines measure cDC inflammasome and antigen (cross)-presentation activity. While all protocols were written by experienced scientists who routinely use them in their work, this article was also peer-reviewed by leading experts and approved by all co-authors, making it an essential resource for basic and clinical DC immunologists.

H3K27me3 spreading organizes canonical PRC1 chromatin architecture to regulate developmental programs

Polycomb Repressive Complex 2 (PRC2)-mediated histone H3K27 tri-methylation (H3K27me3) recruits canonical PRC1 (cPRC1) to maintain heterochromatin. In early development, polycomb-regulated genes are connected through long-range 3D interactions which resolve upon differentiation. Here, we report that polycomb looping is controlled by H3K27me3 spreading and regulates target gene silencing and cell fate specification. Using glioma-derived H3 Lys-27-Met (H3K27M) mutations as tools to restrict H3K27me3 deposition, we show that H3K27me3 confinement concentrates the chromatin pool of cPRC1, resulting in heightened 3D interactions mirroring chromatin architecture of pluripotency, and stringent gene repression that maintains cells in progenitor states to facilitate tumor development. Conversely, H3K27me3 spread in pluripotent stem cells, following neural differentiation or loss of the H3K36 methyltransferase NSD1, dilutes cPRC1 concentration and dissolves polycomb loops. These results identify the regulatory principles and disease implications of polycomb looping and nominate histone modification-guided distribution of reader complexes as an important mechanism for nuclear compartment organization.

Highlights

The confinement of H3K27me3 at PRC2 nucleation sites without its spreading correlates with increased 3D chromatin interactions.The H3K27M oncohistone concentrates canonical PRC1 that anchors chromatin loop interactions in gliomas, silencing developmental programs.Stem and progenitor cells require factors promoting H3K27me3 confinement, including H3K36me2, to maintain cPRC1 loop architecture.The cPRC1-H3K27me3 interaction is a targetable driver of aberrant self-renewal in tumor cells.

Deep topographic proteomics of a human brain tumour

The spatial organisation of cellular protein expression profiles within tissue determines cellular function and is key to understanding disease pathology. To define molecular phenotypes in the spatial context of tissue, there is a need for unbiased, quantitative technology capable of mapping proteomes within tissue structures. Here, we present a workflow for spatially-resolved, quantitative proteomics of tissue that generates maps of protein abundance across tissue slices derived from a human atypical teratoid-rhabdoid tumour at three spatial resolutions, the highest being 40 µm, to reveal distinct abundance patterns of thousands of proteins. We employ spatially-aware algorithms that do not require prior knowledge of the fine tissue structure to detect proteins and pathways with spatial abundance patterns and correlate proteins in the context of tissue heterogeneity and cellular features such as extracellular matrix or proximity to blood vessels. We identify PYGL, ASPH and CD45 as spatial markers for tumour boundary and reveal immune response-driven, spatially-organised protein networks of the extracellular tumour matrix. Overall, we demonstrate spatially-aware deep proteo-phenotyping of tissue heterogeneity, to re-define understanding tissue biology and pathology at the molecular level.

On-tissue dataset-dependent MALDI-TIMS-MS2 bioimaging

Trapped ion mobility spectrometry (TIMS) adds an additional separation dimension to mass spectrometry (MS) imaging, however, the lack of fragmentation spectra (MS2) impedes confident compound annotation in spatial metabolomics. Here, we describe spatial ion mobility-scheduled exhaustive fragmentation (SIMSEF), a dataset-dependent acquisition strategy that augments TIMS-MS imaging datasets with MS2 spectra. The fragmentation experiments are systematically distributed across the sample and scheduled for multiple collision energies per precursor ion. Extendable data processing and evaluation workflows are implemented into the open source software MZmine. The workflow and annotation capabilities are demonstrated on rat brain tissue thin sections, measured by matrix-assisted laser desorption/ionisation (MALDI)-TIMS-MS, where SIMSEF enables on-tissue compound annotation through spectral library matching and rule-based lipid annotation within MZmine and maps the (un)known chemical space by molecular networking. The SIMSEF algorithm and data analysis pipelines are open source and modular to provide a community resource.

HDAC3 and HDAC8 PROTAC dual degrader reveals roles of histone acetylation in gene regulation

HDAC3 and HDAC8 have critical biological functions and represent highly sought-after therapeutic targets. Because histone deacetylases (HDACs) have a very conserved catalytic domain, developing isozyme-selective inhibitors remains challenging. HDAC3/8 also have deacetylase-independent activity, which cannot be blocked by conventional enzymatic inhibitors. Proteolysis-targeting chimeras (PROTACs) can selectively degrade a target enzyme, abolishing both enzymatic and scaffolding function. Here, we report a novel HDAC3/8 dual degrader YX968 that induces highly potent, rapid, and selective degradation of both HDAC3/8 without triggering pan-HDAC inhibitory effects. Unbiased quantitative proteomic experiments confirmed its high selectivity. HDAC3/8 degradation by YX968 does not induce histone hyperacetylation and broad transcriptomic perturbation. Thus, histone hyperacetylation may be a major factor for altering transcription. YX968 promotes apoptosis and kills cancer cells with a high potency in vitro. YX968 thus represents a new probe for dissecting the complex biological functions of HDAC3/8.

4D DIA-PRM proteomic study identifying modulated pathways and biomarkers associated with pelvic organ prolapse

Pelvic organ prolapse (POP) is a highly disabling condition that negatively affects the quality of life of millions of women worldwide. However, the underlying mechanisms associated with the development and progression of the disease remain poorly understood. Here, an untargeted four-dimensional data-independent acquisition (4D DIA)-based proteomics approach was applied to vaginal wall tissue samples from POP (n = 19) and control (n = 8) patients to identify potential diagnostic biomarker(s) for POP and examine the molecular mechanisms underlying the disease. Of the 5713 tissue proteins that were detected, 1957 proteins were significantly changed in POP patients. Further bioinformatics analysis revealed that multiple biological processes including protein digestion & absorption, retrograde endocannabinoid signaling, tyrosine metabolism, and nucleotide metabolism were significantly enriched and associated with the pathogenesis of POP. Interestingly, 16 of these differentially expressed proteins associated with four pathways were also identified by targeted parallel reaction monitoring (PRM) proteomics analysis on the same 27 tissue samples. Changes in 94 % (15/16) of these proteins were consistent with the 4D DIA data. Furthermore, most proteins displayed good diagnostic accuracy with high area under the curve (AUC) values (AUC＞0.8). Specifically, five proteins including ELN, COL6A2, ENTPD1, AOC3, and COX7A2 distinguished between POP and control patients with very high accuracy (AUC ≥ 0.95) in both 4D DIA and PRM analyses, and may therefore be potential diagnostic biomarkers for POP. In summary, the present study not only provided several potential biomarker(s) for effective POP diagnosis, but extended our knowledge of the key regulatory pathways associated with the disease.

A framework for ultra-low-input spatial tissue proteomics

Spatial proteomics combining microscopy-based cell phenotyping with ultrasensitive mass-spectrometry-based proteomics is an emerging and powerful concept to study cell function and heterogeneity in (patho)physiology. However, optimized workflows that preserve morphological information for phenotype discovery and maximize proteome coverage of few or even single cells from laser microdissected tissue are currently lacking. Here, we report a robust and scalable workflow for the proteomic analysis of ultra-low-input archival material. Benchmarking in murine liver resulted in up to 2,000 quantified proteins from single hepatocyte contours and nearly 5,000 proteins from 50-cell regions. Applied to human tonsil, we profiled 146 microregions including T and B lymphocyte niches and quantified cell-type-specific markers, cytokines, and transcription factors. These data also highlighted proteome dynamics within activated germinal centers, illuminating sites undergoing B cell proliferation and somatic hypermutation. This approach has broad implications in biomedicine, including early disease profiling and drug target and biomarker discovery. A record of this paper's transparent peer review process is included in the supplemental information.

Data-Driven Tool for Cross-Run Ion Selection and Peak-Picking in Quantitative Proteomics with Data-Independent Acquisition LC-MS/MS

Proteomics provides molecular bases of biology and disease, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a platform widely used for bottom-up proteomics. Data-independent acquisition (DIA) improves the run-to-run reproducibility of LC-MS/MS in proteomics research. However, the existing DIA data processing tools sometimes produce large deviations from true values for the peptides and proteins in quantification. Peak-picking error and incorrect ion selection are the two main causes of the deviations. We present a cross-run ion selection and peak-picking (CRISP) tool that utilizes the important advantage of run-to-run consistency of DIA and simultaneously examines the DIA data from the whole set of runs to filter out the interfering signals, instead of only looking at a single run at a time. Eight datasets acquired by mass spectrometers from different vendors with different types of mass analyzers were used to benchmark our CRISP-DIA against other currently available DIA tools. In the benchmark datasets, for analytes with large content variation among samples, CRISP-DIA generally resulted in 20 to 50% relative decrease in error rates compared to other DIA tools, at both the peptide precursor level and the protein level. CRISP-DIA detected differentially expressed proteins more efficiently, with 3.3 to 90.3% increases in the numbers of true positives and 12.3 to 35.3% decreases in the false positive rates, in some cases. In the real biological datasets, CRISP-DIA showed better consistencies of the quantification results. The advantages of assimilating DIA data in multiple runs for quantitative proteomics were demonstrated, which can significantly improve the quantification accuracy.

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.

Proteomics and Peptidomics As a Tool to Compare the Proteins and Endogenous Peptides in Human, Cow, and Donkey Milk

Cow's milk is the most widely used ingredient in infant formulas. However, its specific protein composition can cause allergic reactions. Finding alternatives to replace cow's milk and fill the nutritional gap with human milk is essential for the health of infants. Proteomic and peptidomic techniques have supported the elucidation of milk's nutritional ingredients. Recently, omics approaches have attracted increasing interest in the investigation of milk because of their high throughput, precision, sensitivity, and reproducibility. This review offers a significant overview of recent developments in proteomics and peptidomics used to study the differences in human, cow, and donkey milk. All three types of milks were identified to have critical biological functions in human health, particularly in infants. Donkey milk proteins were closer in composition to human milk, were less likely to cause allergic reactions, and may be developed as novel raw materials for formula milk powders.

CysQuant: Simultaneous quantification of cysteine oxidation and protein abundance using data dependent or independent acquisition mass spectrometry

Protein cysteinyl thiols are susceptible to reduction-oxidation reactions that can influence protein function. Accurate quantification of cysteine oxidation is therefore crucial for decoding protein redox regulation. Here, we present CysQuant, a novel approach for simultaneous quantification of cysteine oxidation degrees and protein abundancies. CysQuant involves light/heavy iodoacetamide isotopologues for differential labeling of reduced and reversibly oxidized cysteines analyzed by data-dependent acquisition (DDA) or data-independent acquisition mass spectrometry (DIA-MS). Using plexDIA with in silico predicted spectral libraries, we quantified an average of 18% cysteine oxidation in Arabidopsis thaliana by DIA-MS, including a subset of highly oxidized cysteines forming disulfide bridges in AlphaFold2 predicted structures. Applying CysQuant to Arabidopsis seedlings exposed to excessive light, we successfully quantified the well-established increased reduction of Calvin-Benson cycle enzymes and discovered yet uncharacterized redox-sensitive disulfides in chloroplastic enzymes. Overall, CysQuant is a highly versatile tool for assessing the cysteine modification status that can be widely applied across various mass spectrometry platforms and organisms.

Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape

The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.

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.

Aberrant mitochondrial dynamics contributes to diaphragmatic weakness induced by mechanical ventilation

In critical care patients, the ""temporary inactivity of the diaphragm caused by mechanical ventilation (MV) triggers a series of events leading to diaphragmatic dysfunction and atrophy, commonly known as ventilator-induced diaphragm dysfunction (VIDD). While mitochondrial dysfunction related to oxidative stress is recognized as a crucial factor in VIDD, the exact molecular mechanism remains poorly understood. In this study, we observe that 6 h of MV triggers aberrant mitochondrial dynamics, resulting in a reduction in mitochondrial size and interaction, associated with increased expression of dynamin-related protein 1 (DRP1). This effect can be prevented by P110, a molecule that inhibits the recruitment of DRP1 to the mitochondrial membrane. Furthermore, isolated mitochondria from the diaphragms of ventilated patients exhibited increased production of reactive oxygen species (ROS). These mitochondrial changes were associated with the rapid oxidation of type 1 ryanodine receptor (RyR1) and a decrease in the stabilizing subunit calstabin 1. Subsequently, we observed that the sarcoplasmic reticulum (SR) in the ventilated diaphragms showed increased calcium leakage and reduced contractile function. Importantly, the mitochondrial fission inhibitor P110 effectively prevented all of these alterations. Taken together, the results of our study illustrate that MV leads, in the diaphragm, to both mitochondrial fragmentation and dysfunction, linked to the up-/down-regulation of 320 proteins, as assessed through global comprehensive quantitative proteomics analysis, primarily associated with mitochondrial function. These outcomes underscore the significance of developing compounds aimed at modulating the balance between mitochondrial fission and fusion as potential interventions to mitigate VIDD in human patients.

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.

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.

Evaluating the Performance of the Astral Mass Analyzer for Quantitative Proteomics Using Data-Independent Acquisition

We evaluate the quantitative performance of the newly released Asymmetric Track Lossless (Astral) analyzer. Using data-independent acquisition, the Thermo Scientific Orbitrap Astral mass spectrometer quantifies 5 times more peptides per unit time than state-of-the-art Thermo Scientific Orbitrap mass spectrometers, which have long been the gold standard for high-resolution quantitative proteomics. Our results demonstrate that the Orbitrap Astral mass spectrometer can produce high-quality quantitative measurements across a wide dynamic range. We also use a newly developed extracellular vesicle enrichment protocol to reach new depths of coverage in the plasma proteome, quantifying over 5000 plasma proteins in a 60 min gradient with the Orbitrap Astral mass spectrometer.

One-Pot Analytical Pipeline for Efficient and Sensitive Proteomic Analysis of Extracellular Vesicles

Extracellular vesicle (EV) proteomics emerges as an effective tool for discovering potential biomarkers for disease diagnosis, monitoring, and therapeutics. However, the current workflow of mass spectrometry-based EV proteome analysis is not fully compatible in a clinical setting due to inefficient EV isolation methods and a tedious sample preparation process. To streamline and improve the efficiency of EV proteome analysis, here we introduce a one-pot analytical pipeline integrating a robust EV isolation approach, EV total recovery and purification (EVtrap), with in situ protein sample preparation, to detect urinary EV proteome. By incorporating solvent-driven protein capture and fast on-bead digestion, the one-pot pipeline enabled the whole EV proteome analysis to be completed within one day. In comparison with the existing workflow, the one-pot pipeline was able to obtain better peptide yield and identify the equivalent number of unique EV proteins from 1 mL of urine. Finally, we applied the one-pot pipeline to profile proteomes in urinary EVs of bladder cancer patients. A total of 2774 unique proteins were identified in 53 urine samples using a 15 min gradient library-free data-independent acquisition method. Taken altogether, our novel one-pot analytical pipeline demonstrated its potential for routine and robust EV proteomics in biomedical applications.

Translating Senotherapeutic Interventions into the Clinic with Emerging Proteomic Technologies

Cellular senescence is a state of irreversible growth arrest with profound phenotypic changes, including the senescence-associated secretory phenotype (SASP). Senescent cell accumulation contributes to aging and many pathologies including chronic inflammation, type 2 diabetes, cancer, and neurodegeneration. Targeted removal of senescent cells in preclinical models promotes health and longevity, suggesting that the selective elimination of senescent cells is a promising therapeutic approach for mitigating a myriad of age-related pathologies in humans. However, moving senescence-targeting drugs (senotherapeutics) into the clinic will require therapeutic targets and biomarkers, fueled by an improved understanding of the complex and dynamic biology of senescent cell populations and their molecular profiles, as well as the mechanisms underlying the emergence and maintenance of senescence cells and the SASP. Advances in mass spectrometry-based proteomic technologies and workflows have the potential to address these needs. Here, we review the state of translational senescence research and how proteomic approaches have added to our knowledge of senescence biology to date. Further, we lay out a roadmap from fundamental biological discovery to the clinical translation of senotherapeutic approaches through the development and application of emerging proteomic technologies, including targeted and untargeted proteomic approaches, bottom-up and top-down methods, stability proteomics, and surfaceomics. These technologies are integral for probing the cellular composition and dynamics of senescent cells and, ultimately, the development of senotype-specific biomarkers and senotherapeutics (senolytics and senomorphics). This review aims to highlight emerging areas and applications of proteomics that will aid in exploring new senescent cell biology and the future translation of senotherapeutics.

Spatial single-cell mass spectrometry defines zonation of the hepatocyte proteome

Single-cell proteomics by mass spectrometry is emerging as a powerful and unbiased method for the characterization of biological heterogeneity. So far, it has been limited to cultured cells, whereas an expansion of the method to complex tissues would greatly enhance biological insights. Here we describe single-cell Deep Visual Proteomics (scDVP), a technology that integrates high-content imaging, laser microdissection and multiplexed mass spectrometry. scDVP resolves the context-dependent, spatial proteome of murine hepatocytes at a current depth of 1,700 proteins from a cell slice. Half of the proteome was differentially regulated in a spatial manner, with protein levels changing dramatically in proximity to the central vein. We applied machine learning to proteome classes and images, which subsequently inferred the spatial proteome from imaging data alone. scDVP is applicable to healthy and diseased tissues and complements other spatial proteomics and spatial omics technologies.