Multiplexed peptide analysis using data-independent acquisition and Skyline

Improving Proteomic Identification Using Narrow Isolation Windows with Zeno SWATH Data-Independent Acquisition

Data-independent acquisition (DIA) techniques such as sequential window acquisition of all theoretical mass spectra (SWATH) acquisition have emerged as the preferred strategies for proteomic analyses. Our study optimized the SWATH-DIA method using a narrow isolation window placement approach, improving its proteomic performance. We optimized the acquisition parameter combinations of narrow isolation windows with different widths (1.9 and 2.9 Da) on a ZenoTOF 7600 (Sciex); the acquired data were analyzed using DIA-NN (version 1.8.1). Narrow SWATH (nSWATH) identified 5916 and 7719 protein groups on the digested peptides, corresponding to 400 ng of protein from mouse liver and HEK293T cells, respectively, improving identification by 7.52 and 4.99%, respectively, compared to conventional SWATH. The median coefficient of variation of the quantified values was less than 6%. We further analyzed 200 ng of benchmark samples comprising peptides from known ratios ofEscherichia coli, yeast, and human peptides using nSWATH. Consequently, it achieved accuracy and precision comparable to those of conventional SWATH, identifying an average of 95,456 precursors and 9342 protein groups across three benchmark samples, representing 12.6 and 9.63% improved identification compared to conventional SWATH. The nSWATH method improved identification at various loading amounts of benchmark samples, identifying 40.7% more protein groups at 25 ng. These results demonstrate the improved performance of nSWATH, contributing to the acquisition of deeper proteomic data from complex biological samples.

Improved detection of DNA replication fork-associated proteins

Innovative methods to retrieve proteins associated with actively replicating DNA have provided a glimpse into the molecular dynamics of replication fork stalling. We report that a combination of density-based replisome enrichment by isolating proteins on nascent DNA (iPOND2) and label-free quantitative mass spectrometry (iPOND2-DRIPPER) substantially increases both replication factor yields and the dynamic range of protein quantification. Replication protein abundance in retrieved nascent DNA is elevated up to 300-fold over post-replicative controls, and recruitment of replication stress factors upon fork stalling is observed at similar levels. The increased sensitivity of iPOND2-DRIPPER permits direct measurement of ubiquitination events without intervening retrieval of diglycine tryptic fragments of ubiquitin. Using this approach, we find that stalled replisomes stimulate the recruitment of a diverse cohort of DNA repair factors, including those associated with poly-K63-ubiquitination. Finally, we uncover the temporally controlled association of stalled replisomes with nuclear pore complex components and nuclear cytoskeleton networks.

Environment modulates protein heterogeneity through transcriptional and translational stop codon readthrough

Stop codon readthrough events give rise to longer proteins, which may alter the protein's function, thereby generating short-lasting phenotypic variability from a single gene. In order to systematically assess the frequency and origin of stop codon readthrough events, we designed a library of reporters. We introduced premature stop codons into mScarlet, which enabled high-throughput quantification of protein synthesis termination errors in E. coli using fluorescent microscopy. We found that under stress conditions, stop codon readthrough may occur at rates as high as 80%, depending on the nucleotide context, suggesting that evolution frequently samples stop codon readthrough events. The analysis of selected reporters by mass spectrometry and RNA-seq showed that not only translation but also transcription errors contribute to stop codon readthrough. The RNA polymerase was more likely to misincorporate a nucleotide at premature stop codons. Proteome-wide detection of stop codon readthrough by mass spectrometry revealed that temperature regulated the expression of cryptic sequences generated by stop codon readthrough in E. coli. Overall, our findings suggest that the environment affects the accuracy of protein production, which increases protein heterogeneity when the organisms need to adapt to new conditions.

Serum proteomic profiling of precancerous gastric lesions and early gastric cancer reveals signatures associated with systemic inflammatory response and metaplastic differentiation

The noninvasive detection technique using serum for large-scale screening is useful for the early diagnosis of gastric cancer (GC). Herein, we employed liquid chromatography mass spectrometry to determine the serum proteome signatures and related pathways in individuals with gastric precancerous (pre-GC) lesions and GC and explore the effect of Helicobacter pylori (H. pylori) infection. Differentially expressed proteins in GC and pre-GC compared with non-atrophic gastritis (NAG) group were identified. APOA4, a protein associated with metaplastic differentiation, and COMP, an extracellular matrix protein, were increased in the serum of patients with pre-GC lesions and GC. In addition, several inflammation-associated proteins, such as component C3, were decreased in the GC and pre-GC groups, which highlight a tendency for the inflammatory response to converge at the gastric lesion site during the GC cascade. Moreover, the abundance of proteins associated with oxidant detoxification was higher in the GC group compared with that in the NAG group, and these proteins were also increased in the serum of the H. pylori-positive GC group compared with that in the H. pylori-negative GC patients, reflecting the importance of oxidative stress pathways in H. pylori infection. Collectively, the findings of this study highlight pathways that play important roles in GC progression, and may provide potential diagnostic biomarkers for the detection of pre-GC lesions.

Research progress in the application of proteomics technology in brain injury

The aim of this article is to review the application progress of proteomics technology in brain injury research in recent years, point out the current problems that need to be overcome, and explore the application prospects of proteomics analysis in brain injury. This study also aims to retrieve all literature on brain injury and proteomics and summarize it. Through searching and screening, the widespread application of proteomics technology in the treatment of traumatic brain injury (TBI) and the use of a large number of TBI biomarkers were discovered. The pathways mediated by some biomarkers and the physiological and pathological mechanisms of occurrence were elucidated. The current classification of brain injury is mainly based on subjective evaluation of clinical symptoms, combined with objective imaging. However, its practical value is often limited when applied to prognosis evaluation in brain injury. Proteomics technology can make up for this deficiency and provide a reference for the prevention and treatment of brain injury.

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.

Q-RAI data-independent acquisition for lipidomic quantitative profiling

Untargeted lipidomics has been increasingly adopted for hypothesis generation in a biological context or discovery of disease biomarkers. Most of the current liquid chromatography mass spectrometry (LC-MS) based untargeted methodologies utilize a data dependent acquisition (DDA) approach in pooled samples for identification and MS-only acquisition for semi-quantification in individual samples. In this study, we present for the first time an untargeted lipidomic workflow that makes use of the newly implemented Quadrupole Resolved All-Ions (Q-RAI) acquisition function on the Agilent 6546 quadrupole time-of-flight (Q-TOF) mass spectrometer to acquire MS2 spectra in data independent acquisition (DIA) mode. This is followed by data processing and analysis on MetaboKit, a software enabling DDA-based spectral library construction and extraction of MS1 and MS2 peak areas, for reproducible identification and quantification of lipids in DIA analysis. This workflow was tested on lipid extracts from human plasma and showed quantification at MS1 and MS2 levels comparable to multiple reaction monitoring (MRM) targeted analysis of the same samples. Analysis of serum from Ceramide Synthase 2 (CerS2) null mice using the Q-RAI DIA workflow identified 88 lipid species significantly different between CerS2 null and wild type mice, including well-characterized changes previously associated with this phenotype. Our results show the Q-RAI DIA as a reliable option to perform simultaneous identification and reproducible relative quantification of lipids in exploratory biological studies.

Empowering peptidomics: utilizing computational tools and approaches

Bioinformatics plays a critical role in the advancement of peptidomics by providing powerful tools for data analysis, interpretation and integration. Peptidomics is concerned with the study of peptides, short chains of amino acids with diverse biological functions. This area includes peptide identification and characterization, database construction, de novo sequencing, functional annotation, omics data integration and systems biology. Artificial intelligence techniques, such as machine learning and natural language processing, aid in the interpretation of peptide sequence data and the generation of biological insights. By using bioinformatics approaches, peptidomics researchers can accelerate peptide discovery, understand their functions and gain insights into complex molecular interactions.

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.

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 .

Deep learning from harmonized peptide libraries enables retention time prediction of diverse post translational modifications

In proteomics experiments, peptide retention time (RT) is an orthogonal property to fragmentation when assessing detection confidence. Advances in deep learning enable accurate RT prediction for any peptide from sequence alone, including those yet to be experimentally observed. Here we present Chronologer, an open-source software tool for rapid and accurate peptide RT prediction. Using new approaches to harmonize and false-discovery correct across independently collected datasets, Chronologer is built on a massive database with >2.2 million peptides including 10 common post-translational modification (PTM) types. By linking knowledge learned across diverse peptide chemistries, Chronologer predicts RTs with less than two-thirds the error of other deep learning tools. We show how RT for rare PTMs, such as OGlcNAc, can be learned with high accuracy using as few as 10-100 example peptides in newly harmonized datasets. This iteratively updatable workflow enables Chronologer to comprehensively predict RTs for PTM-marked peptides across entire proteomes.

Quantitative Proteomics for the Development and Manufacturing of Human-Induced Pluripotent Stem Cell-Derived Neural Stem Cells Using Data-Independent Acquisition Mass Spectrometry

Human-induced pluripotent stem cell (iPSC)-derived neural stem cells (NSCs) have several potential applications in regenerative medicine. A deep understanding of stem cell characteristics is critical for developing appropriate products for use in the clinic. This study aimed to develop approaches for characterizing iPSC-derived NSCs. Data-independent acquisition mass spectrometry (DIA-MS) was used to obtain temporal proteomic profiles of differentiating cells. Principal component analysis of the proteome profiles allowed for the discrimination of cells cultured for different periods. Cells were characterized by Gene Ontology analysis to annotate the upregulated proteins based on their functions. We found that trophoblast glycoprotein (TPBG), a membrane glycoprotein that inhibits the Wnt/β-catenin pathway, was elevated in NSC and that silencing TPBG promoted proliferation rather than neuronal differentiation. Treatment with Wnt/β-catenin pathway activators and inhibitors showed that modulating the Wnt/β-catenin pathway is crucial for differentiation into NSC. These results suggest that the level of TPBG is critical for differentiation into NSC, and TPBG is a potentially critical quality attribute of differentiating cells. In summary, DIA-MS-based proteomics is a promising multi-attribute method for characterizing stem cell-derived products.

Identification of Five Gelatins Based on Marker Peptides from Type I Collagen by Mass Spectrum in Multiple Reaction Monitoring Mode

In this study, a novel pseudo-targeted peptidomics strategy, integrating the transition list generated by an in-house software (Pep-MRMer) and the retention time transfer by high-abundance ion-based retention time calibration (HAI-RT-cal), was developed to screen marker peptides of gelatins from five closely related animal species, including porcine, bovine, horse, mule, and donkey. Five marker peptides were screened from the molecular phenotypic differences of type I collagen. Furthermore, a simple and robust 10 min multiple reaction monitoring (MRM) method was established and performed well in distinguishing different gelatins, particularly in discerning horse-hide gelatin (HHG) and mule-hide gelatin (MHG) from donkey-hide gelatin (DHG). The market investigation revealed the serious adulteration of DHG. Meantime, the pseudo-targeted peptidomics could be used to screen marker peptides of other gelatin foods.

Complementary hepatic metabolomics and proteomics reveal the adaptive mechanisms of dairy cows to the transition period

The transition period from late pregnancy to early lactation is a vital time of the lifecycle of dairy cows due to the marked metabolic challenges. Besides, the liver is the pivot point of metabolism in cattle. Nevertheless, the hepatic physiological molecular adaptation during the transition period has not been elucidated, especially from the metabolomics and proteomics view. Therefore, the present study aims to investigate the hepatic metabolic alterations in transition cows by using integrative metabolomics and proteomics methods. Gas chromatography quadrupole-time-of-flight mass spectrometry-based metabolomics and data-independent acquisition-based quantitative proteomics methods were used to analyze liver tissues collected from 8 healthy multiparous Holstein dairy cows 21 d before and after calving. In total, 44 metabolites and 250 proteins were identified as differentially expressed from 233 metabolites and 3,539 proteins detected from the liver biopsies during the transition period. Complementary functional analysis of different metabolites and proteins indicated the upregulated gluconeogenesis, tricarboxylic acid cycles, AA degradation, fatty acid oxidation, AMP-activated protein kinase signaling pathway, peroxisome proliferator-activated receptor signaling pathway, and ribosome proteins in postpartum dairy cows. In terms of the metabolites and proteins, glucose-6-phosphate, fructose-6-phosphate, carnitine palmitoyltransferase 1A, and phosphoenolpyruvate carboxykinase played a significant role in these pathways. The upregulated oxidative status may be accompanied by the pathways mentioned above. In addition, the upregulated glucagon and insulin signaling pathways also indicated the significant requirement for glucose in postpartum dairy cows. These outcomes, from the view of global metabolites and proteins, may present a better comprehension of the biology of the transition period, which can be helpful in further developing nutritional regulation strategies targeting the liver to help cows overcome this metabolically challenging time.

Multi-omics analyses reveal stone cell distribution pattern in pear fruit

Stone cells are the brachysclereid cells in pear (Pyrus) fruit, consisting almost entirely of lignified secondary cell walls. They are distributed mainly near the fruit core and spread radially in the whole fruit. However, the development of stone cells has not been comprehensively characterized, and little is known about the regulation of stone cell formation at the transcriptomic, proteomic, and metabolomic levels. In the present study, we performed phenomic analysis on the stone cells and their associated vascular bundles distributed near the fruit cores. Transcriptomic, proteomic, and metabolomic analyses revealed a significant positive regulation of biological processes which contribute to the lignification and lignin deposition in stone cells near the fruit core, including sucrose metabolism and phenylalanine, tyrosine, tryptophan, and phenylalanine biosynthesis. We found many metabolites generated from the phenylpropanoid pathway contributing to the cell wall formation of stone cells near the fruit core. Furthermore, we identified a key transcription factor, PbbZIP48, which was highly expressed near the fruit core and was shown to regulate lignin biosynthesis in stone cells. In conclusion, the present study provides insight into the mechanism of lignified stone cell formation near the pear fruit core at multiple levels.

Comparative proteomic analysis of seminal plasma exosomes in buffalo with high and low sperm motility

BACKGROUND

Exosomes are nanosized membranous vesicles secreted by various types of cells, which facilitate intercellular communication by transporting bioactive compounds. Exosomes are abundant in biological fluids including semen, and their protein composition and the potential of seminal plasma exosomes (SPEs) as fertility biomarkers were elucidated in humans, however, little information is available regarding buffalo (Bubalus bubalis). Here, we examined protein correlation between spermatozoa, seminal plasma (SP), and SPEs, and we compared and analyzed protein differences between high-motility (H-motility) and low-motility (L-motility) SPEs in buffalo.

RESULTS

SPEs were concentrated and purified by ultracentrifugation combined with sucrose density gradient centrifugation, followed by verification using western blotting, nanoparticle tracking analysis, and transmission electron microscopy. Protein composition in spermatozoa, SP and SPEs, and protein difference in H- and L-motility SPEs were identified by LC-MS/MS proteomic analysis and were functionally analyzed through comprehensive bioinformatics. Many SPEs proteins originated from spermatozoa and SP, and nearly one third were also present in spermatozoa and SP. A series of proteins associated with reproductive processes including sperm capacitation, spermatid differentiation, fertilization, sperm-egg recognition, membrane fusion, and acrosome reaction were integrated in a functional network. Comparative proteomic analyses showed 119 down-regulated and 41 up-regulated proteins in L-motility SPEs, compared with H-motility SPEs. Gene Ontology (GO) enrichment of differentially expressed proteins (DEPs) showed that most differential proteins were located in sperm and vesicles, with activities of hydrolase and metalloproteinase, and were involved in sperm-egg recognition, fertilization, single fertilization, and sperm-zona pellucida binding processes, etc. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that differential proteins were mainly involved in the PPRP signaling pathway, calcium signaling pathway, and cAMP signaling pathway, among others. Furthermore, 6 proteins associated with reproduction were validated by parallel reaction monitoring analysis.

CONCLUSION

This study provides a comprehensive description of the seminal plasma exosome proteome and may be of use for further screening of biomarkers associated with male infertility.

Elamipretide effects on the skeletal muscle phosphoproteome in aged female mice

The age-related decline in skeletal muscle mass and function is known as sarcopenia. Sarcopenia progresses based on complex processes involving protein dynamics, cell signaling, oxidative stress, and repair. We have previously found that 8-week treatment with elamipretide improves skeletal muscle function, reverses redox stress, and restores protein S-glutathionylation changes in aged female mice. This study tested whether 8-week treatment with elamipretide also affects global phosphorylation in skeletal muscle consistent with functional improvements and S-glutathionylation. Using female 6-7-month-old mice and 28-29-month-old mice, we found that phosphorylation changes did not relate to S-glutathionylation modifications, but that treatment with elamipretide did partially reverse age-related changes in protein phosphorylation in mouse skeletal muscle.

Data-independent acquisition mass spectrometry identification of extracellular vesicle biomarkers for gastric adenocarcinoma

Early diagnosis of gastric adenocarcinoma (GAC) can effectively prevent the progression of the disease and significantly improve patient survival. Currently, protein markers in clinical practice barely meet patient needs; it is therefore imperative to develop new diagnostic biomarkers with high sensitivity and specificity. In this study, we extracted extracellular vesicles (EV) from the sera of 33 patients with GAC and 19 healthy controls, then applied data-independent acquisition (DIA) mass spectrometry to measure protein expression profiles. Differential protein expression analysis identified 23 proteins showing expression patterns across different cancer stages, from which 15 proteins were selected as candidate biomarkers for GAC diagnosis. From this subset of 15 proteins, up to 6 proteins were iteratively selected as features and logistic regression was used to distinguish patients from healthy controls. Furthermore, serum-derived EV from a new cohort of 12 patients with gastric cancer and 18 healthy controls were quantified using the same method. A classification panel consisting of GSN, HP, ORM1, PIGR, and TFRC showed the best performance, with a sensitivity and negative predictive value (NPV) of 0.83 and 0.82. The area under curve (AUC) of the receiver operating characteristic (ROC) is 0.80. Finally, to facilitate the diagnosis of advanced stage GAC, we identified a 3-protein panel consisting of LYZ, SAA1, and F12 that showed reasonably good performance with an AUC of 0.83 in the validation dataset. In conclusion, we identified new protein biomarker panels from serum EVs for early diagnosis of gastric cancer that worth further validation.

Proteomic analysis reveals the mechanism of green regulation in garlic puree induced by purple light stress

Greening is an undesirable appearance in garlic puree during processing. Our previous study indicated that purple light could induce the greening changes in garlic. In order to investigate the mechanism of green regulation in garlic puree, purple light-induced greening and nongreening garlic puree were used as materials to investigate the differentially expressed proteins (DEPs) by sodium dodecyl-sulfate polyacrylamide gel electrophoresis and data-independent acquisition (DIA) technology. The results showed that a total of 186 DEPs were detected by DIA, with 73 DEPs were up-regulated in greening garlic puree and 113 of them were down-regulated in greening garlic puree. Most DEPs were belonged to 20 functional categories, and mainly participated in post-translational modification and transport of proteins, molecular chaperones (12.93%) and signal transduction mechanisms (10.20%), energy production and transformation (6.80%), carbohydrate transport and metabolism (5.44%) and amino acid transport and metabolism (4.08%), indicating that the biological metabolic pathway, metabolic direction, and metabolic strength efficiency significantly changed in garlic puree after greening. Besides, the physiological and biochemical experiments showed that purple light significantly induced the γ-glutathione transpeptidase activity and prompted the conversion of thiosulfinate into garlic green pigment. This study explained the general molecular mechanism of greening changes of garlic puree in response to purple light. Practical Application Greening is an undesirable appearance in garlic puree during processing, which deteriorate the qualities of garlic. This study provides a comprehensive understanding of green regulation in garlic puree based on proteomics analysis.

Protein Contaminants Matter: Building Universal Protein Contaminant Libraries for DDA and DIA Proteomics

Mass spectrometry-based proteomics is constantly challenged by the presence of contaminant background signals. In particular, protein contaminants from reagents and sample handling are almost impossible to avoid. For data-dependent acquisition (DDA) proteomics, an exclusion list can be used to reduce the influence of protein contaminants. However, protein contamination has not been evaluated and is rarely addressed in data-independent acquisition (DIA). How protein contaminants influence proteomic data is also unclear. In this study, we established new protein contaminant FASTA and spectral libraries that are applicable to all proteomic workflows and evaluated the impact of protein contaminants on both DDA and DIA proteomics. We demonstrated that including our contaminant libraries can reduce false discoveries and increase protein identifications, without influencing the quantification accuracy in various proteomic software platforms. With the pressing need to standardize proteomic workflow in the research community, we highly recommend including our contaminant FASTA and spectral libraries in all bottom-up proteomic data analysis. Our contaminant libraries and a step-by-step tutorial to incorporate these libraries in various DDA and DIA data analysis platforms can be valuable resources for proteomic researchers, freely accessible at https://github.com/HaoGroup-ProtContLib.

Serum plays an important role in reprogramming the seasonal transcriptional profile of brown bear adipocytes

Understanding how metabolic reprogramming happens in cells will aid the progress in the treatment of a variety of metabolic disorders. Brown bears undergo seasonal shifts in insulin sensitivity, including reversible insulin resistance in hibernation. We performed RNA-sequencing on brown bear adipocytes and proteomics on serum to identify changes possibly responsible for reversible insulin resistance. We observed dramatic transcriptional changes, which depended on both the cell and serum season of origin. Despite large changes in adipocyte gene expression, only changes in eight circulating proteins were identified as related to the seasonal shifts in insulin sensitivity, including some that have not previously been associated with glucose homeostasis. The identified serum proteins may be sufficient for shifting hibernation adipocytes to an active-like state.

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Hibernation in grizzly bears is marked by insulin resistance

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Bear adipocytes were stimulated with active and hibernating bear blood serum

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Serum elicited dramatic gene expression responses related to insulin signaling

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Eight serum proteins were implicated in driving this transcriptional response

Exploration of potential mechanism of interleukin-33 up-regulation caused by 1,4-naphthoquinone black carbon in RAW264.7 cells

BACKGROUND

As air pollution has been paid more attention to by public in recent years, effects and mechanism in particulate matter-triggered health problems become a focus of research. Lysosomes and mitochondria play an important role in regulation of inflammation. Interleukin-33 (IL-33) has been proved to promote inflammation in our previous studies. In this research, macrophage cell line RAW264.7 was used to explore the potential mechanism of upregulation of IL-33 induced by 1,4-naphthoquinone black carbon (1,4-NQ-BC), and to explore changes of lysosomes and mitochondria during the process.

RESULTS

50 μg/mL 1,4-NQ-BC exposure for 24 h dramatically increased expression of IL-33 in RAW264.7 cells. Lysosomal membrane permeability was damaged by 1,4-NQ-BC treatment, and higher mitochondrial membrane potential and ROS level were induced by 1,4-NQ-BC. The results of proteomics suggested that expression of ferritin light chain was increased after cells were challenged with 1,4-NQ-BC, and it was verified by Western blot. Meanwhile, expressions of p62 and LC3B-II were increased by 50 μg/mL 1,4-NQ-BC in RAW264.7 cells. Ultimately, expression of IL-33 could return to same level as control in cells treated with 50 μg/mL 1,4-NQ-BC and 50 μM deferoxamine combined.

CONCLUSIONS

1,4-NQ-BC induces IL-33 upregulation in RAW264.7 cells, and it is responsible for higher lysosomal membrane permeability and ROS level, lower mitochondrial membrane potential, and inhibition of autophagy. Ferritin light chain possibly plays an important role in the upregulation of IL-33 evoked by 1,4-NQ-BC.

Histone Acid Extraction and High Throughput Mass Spectrometry to Profile Histone Modifications in Arabidopsis thaliana

Histone post-translational modifications (PTMs) play important roles in many biological processes, including gene regulation and chromatin dynamics, and are thus of high interest across many fields of biological research. Chromatin immunoprecipitation coupled with sequencing (ChIP-seq) is a powerful tool to profile histone PTMs in vivo. This method, however, is largely dependent on the specificity and availability of suitable commercial antibodies. While mass spectrometry (MS)-based proteomic approaches to quantitatively measure histone PTMs have been developed in mammals and several other model organisms, such methods are currently not readily available in plants. One major challenge for the implementation of such methods in plants has been the difficulty in isolating sufficient amounts of pure, high-quality histones, a step rendered difficult by the presence of the cell wall. Here, we developed a high-yielding histone extraction and purification method optimized for Arabidopsis thaliana that can be used to obtain high-quality histones for MS. In contrast to other methods used in plants, this approach is relatively simple, and does not require membranes or additional specialized steps, such as gel excision or chromatography, to extract highly purified histones. We also describe methods for producing MS-ready histone peptides through chemical labeling and digestion. Finally, we describe an optimized method to quantify and analyze the resulting histone PTM data using a modified version of EpiProfile 2.0 for Arabidopsis. In all, the workflow described here can be used to measure changes to histone PTMs resulting from various treatments, stresses, and time courses, as well as in different mutant lines. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Nuclear isolation and histone acid extraction Basic Protocol 2: Peptide labeling, digestion, and desalting Basic Protocol 3: Histone HPLC-MS/MS and data analysis.

Utilizing Skyline to analyze lipidomics data containing liquid chromatography, ion mobility spectrometry and mass spectrometry dimensions

Lipidomics studies suffer from analytical and annotation challenges because of the great structural similarity of many of the lipid species. To improve lipid characterization and annotation capabilities beyond those afforded by traditional mass spectrometry (MS)-based methods, multidimensional separation methods such as those integrating liquid chromatography, ion mobility spectrometry, collision-induced dissociation and MS (LC-IMS-CID-MS) may be used. Although LC-IMS-CID-MS and other multidimensional methods offer valuable hydrophobicity, structural and mass information, the files are also complex and difficult to assess. Thus, the development of software tools to rapidly process and facilitate confident lipid annotations is essential. In this Protocol Extension, we use the freely available, vendor-neutral and open-source software Skyline to process and annotate multidimensional lipidomic data. Although Skyline ( https://skyline.ms/skyline.url ) was established for targeted processing of LC-MS-based proteomics data, it has since been extended such that it can be used to analyze small-molecule data as well as data containing the IMS dimension. This protocol uses Skyline's recently expanded capabilities, including small-molecule spectral libraries, indexed retention time and ion mobility filtering, and provides a step-by-step description for importing data, predicting retention times, validating lipid annotations, exporting results and editing our manually validated 500+ lipid library. Although the time required to complete the steps outlined here varies on the basis of multiple factors such as dataset size and familiarity with Skyline, this protocol takes ~5.5 h to complete when annotations are rigorously verified for maximum confidence.

Targeted proteomics on its way to discovery

For a long time, targeted and discovery proteomics covered different corners of the detection spectrum, with targeted proteomics focused on small target sets. This changed with the recent advances in highly multiplexed analysis. While discovery proteomics still pushes higher numbers of identified and quantified proteins, the advances in targeted proteomics rose to cover large parts of less complex proteomes or proteomes with low protein detection counts due to dynamic range restrictions, like the blood proteome. These new developments will impact, especially on the field of biomarker discovery and the possibility of using targeted proteomics for diagnostic purposes. This article is protected by copyright. All rights reserved.

Proteome changes associated with the VEGFR pathway and immune system in diabetic macular edema patients at different diabetic retinopathy stages

PURPOSE

Diabetic macular edema (DME) is a major cause of vision loss in all stages of diabetic retinopathy (DR). However, there is limited recognition of aqueous humor (AH) proteome profiles of DME patients at different DR stages. In this study, we aimed to investigate the AH proteome changes between DME patients at the nonproliferative diabetic retinopathy (NPDR) stage and those at the proliferative diabetic retinopathy (PDR) stage.

METHODS

A label-free data-independent acquisition based liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis was performed to profile the abundances of AH proteins in 73 eyes from DME patients at different DR stages. Enzyme-linked immunosorbent assay (ELISA) was used to confirm the proteomics results with AH samples from non-diabetic patients and DME patients at the NPDR or PDR stage.

RESULTS

LC-MS/MS results showed significantly changed expression of 308 proteins between DME patients in the NPDR and PDR groups. Compared to the NPDR group, the proteins relatively up-regulated in the PDR group are involved in the immune system and/or negative regulation of the cell cycle, while proteins relatively down-regulated in the PDR group are associated with the vascular endothelial growth factor receptor (VEGFR) pathway and/or metabolism. ELISA results further verified the proteomic result of down-regulated expression of the immune-associated protein cystatin C (CST3) in the PDR group compared to that in the NPDR and non-diabetic groups.

CONCLUSIONS

In this study, we reported for the first time the decreased abundances of AH proteins associated with the VEGFR pathway and both down- and up-regulated expression of AH proteins associated with the immune system in the PDR group compared to that in the NPDR group. Furthermore, we found negative correlations of immune-associated protein, CST3 concentration in AH with DR severity and central retinal thickness, suggesting CST3 as a promising target independent of the VEGFR pathway in DME-involved DR treatment.

Data-Independent Acquisition Protease-Multiplexing Enables Increased Proteome Sequence Coverage Across Multiple Fragmentation Modes

The use of multiple proteases has been shown to increase protein sequence coverage in proteomics experiments; however, due to the additional analysis time required, it has not been widely adopted in routine data-dependent acquisition (DDA) proteomic workflows. Alternatively, data-independent acquisition (DIA) has the potential to analyze multiplexed samples from different protease digests, but has been primarily optimized for fragmenting tryptic peptides. Here we evaluate a DIA multiplexing approach that combines three proteolytic digests (Trypsin, AspN, and GluC) into a single sample. We first optimize data acquisition conditions for each protease individually with both the canonical DIA fragmentation mode (beam type CID), as well as resonance excitation CID, to determine optimal consensus conditions across proteases. Next, we demonstrate that application of these conditions to a protease-multiplexed sample of human peptides results in similar protein identifications and quantitative performance as compared to trypsin alone, but enables up to a 63% increase in peptide detections, and a 45% increase in nonredundant amino acid detections. Nontryptic peptides enabled noncanonical protein isoform determination and resulted in 100% sequence coverage for numerous proteins, suggesting the utility of this approach in applications where sequence coverage is critical, such as protein isoform analysis.

Data-Independent Acquisition Enables Robust Quantification of 400 Proteins in Non-Depleted Canine Plasma

Mass spectrometry-based plasma proteomics offers a major advance for biomarker discovery in the veterinary field, which has traditionally been limited to quantification of a small number of proteins using biochemical assays. The development of foundational data and tools related to sequential window acquisition of all theoretical mass spectra (SWATH)-mass spectrometry has allowed for quantitative profiling of a significant number of plasma proteins in humans and several animal species. Enabling SWATH in dogs enhances human biomedical research as a model species, and significantly improves diagnostic and disease monitoring capability. In this study, a comprehensive peptide spectral library specific to canine plasma proteome was developed and evaluated using SWATH for protein quantification in non-depleted dog plasma. Specifically, plasma samples were subjected to various orthogonal fractionation and digestion techniques, and peptide fragmentation data corresponding to over 420 proteins was collected. Subsequently, a SWATH-based assay was introduced that leveraged the developed resource and that enabled reproducible quantification of 400 proteins in non-depleted plasma samples corresponding to various disease conditions. The ability to profile the abundance of such a significant number of plasma proteins using a single method in dogs has the potential to accelerate biomarker discovery studies in this species.

Effects of Multispecies Probiotic on Intestinal Microbiota and Mucosal Barrier Function of Neonatal Calves Infected With E. coli K99

Altered gut microbiota are implicated in inflammatory neonatal calf diarrhea caused by E. coli K99. Beneficial probiotics are used to modulate gut microbiota. However, factors that mediate host-microbe interactions remain unclear. We evaluated the effects of a combination of multispecies probiotics (MSP) on growth, intestinal epithelial development, intestinal immune function and microbiota of neonatal calves infected with E. coli K99. Twelve newborn calves were randomly assigned as follows: C (control, without MSP); D (E. coli O78:K99 + gentamycin); and P (E. coli O78:K99 + supplemental MSP). All groups were studied for 21 d. MSP supplementation significantly (i) changed fungal Chao1 and Shannon indices of the intestine compared with group D; (ii) reduced the relative abundance of Bacteroides and Actinobacteria, while increasing Bifidobacteria, Ascomycetes, and Saccharomyces, compared with groups C and D; (iii) improved duodenal and jejunal mucosal SIgA and total Short Chain Fatty Acids (SCFA) concentrations compared with group D; (iv) increased relative ZO-1 and occludin mRNA expression in jejunal mucosa compared with group D; and (v) enhanced intestinal energy metabolism and defense mechanisms of calves by reducing HSP90 expression in E. coli K99, thereby alleviating the inflammatory response and promoting recovery of mucosal function. Our research may provide direct theoretical support for future applications of MSP in ruminant production.

Dynamic FMR1 granule phase switch instructed by m6A modification contributes to maternal RNA decay

Maternal RNA degradation is critical for embryogenesis and is tightly controlled by maternal RNA-binding proteins. Fragile X mental-retardation protein (FMR1) binds target mRNAs to form ribonucleoprotein (RNP) complexes/granules that control various biological processes, including early embryogenesis. However, how FMR1 recognizes target mRNAs and how FMR1-RNP granule assembly/disassembly regulates FMR1-associated mRNAs remain elusive. Here we show that Drosophila FMR1 preferentially binds mRNAs containing m6A-marked “AGACU” motif with high affinity to contributes to maternal RNA degradation. The high-affinity binding largely depends on a hydrophobic network within FMR1 KH2 domain. Importantly, this binding greatly induces FMR1 granule condensation to efficiently recruit unmodified mRNAs. The degradation of maternal mRNAs then causes granule de-condensation, allowing normal embryogenesis. Our findings reveal that sequence-specific mRNAs instruct FMR1-RNP granules to undergo a dynamic phase-switch, thus contributes to maternal mRNA decay. This mechanism may represent a general principle that regulated RNP-granules control RNA processing and normal development.

Maternal RNA degradation is critical for embryogenesis and is tightly controlled by maternal RNA-binding proteins. Here the authors show that a subset of m6A-modified mRNAs regulates the dynamics of RNA-granules, thus contributes to maternal mRNA decay.

Rapid Targeted Quantitation of Protein Overexpression with Direct Infusion Shotgun Proteome Analysis (DISPA-PRM)

While much effort has been placed on comprehensive quantitative proteome analysis, certain applications demand the measurement of only a few target proteins from complex systems. Traditional approaches to targeted proteomics rely on nanoliquid chromatography (nLC) and targeted mass spectrometry (MS) methods, e.g., parallel reaction monitoring (PRM). However, the time requirement for nLC can limit the throughput of targeted proteomics. To achieve rapid and high-throughput targeted methods, here we show that nLC separations can be eliminated and replaced with direct infusion shotgun proteome analysis (DISPA) using high-field asymmetric waveform ion mobility spectrometry (FAIMS) with PRM. We demonstrate the application of DISPA-PRM for rapid targeted quantification of bacterial enzymes utilized in the production of biofuels by monitoring temporal expression in 72 metabolically engineered bacterial cultures in less than 2.5 h, with a measured dynamic range >1200-fold. We conclude that DISPA-PRM presents a valuable innovative tool with results comparable to nLC-MS/MS, enabling fast and rapid detection of targeted proteins in complex mixtures.

Depletion of mitochondrial methionine adenosyltransferase α1 triggers mitochondrial dysfunction in alcohol-associated liver disease

MATα1 catalyzes the synthesis of S-adenosylmethionine, the principal biological methyl donor. Lower MATα1 activity and mitochondrial dysfunction occur in alcohol-associated liver disease. Besides cytosol and nucleus, MATα1 also targets the mitochondria of hepatocytes to regulate their function. Here, we show that mitochondrial MATα1 is selectively depleted in alcohol-associated liver disease through a mechanism that involves the isomerase PIN1 and the kinase CK2. Alcohol activates CK2, which phosphorylates MATα1 at Ser114 facilitating interaction with PIN1, thereby inhibiting its mitochondrial localization. Blocking PIN1-MATα1 interaction increased mitochondrial MATα1 levels and protected against alcohol-induced mitochondrial dysfunction and fat accumulation. Normally, MATα1 interacts with mitochondrial proteins involved in TCA cycle, oxidative phosphorylation, and fatty acid β-oxidation. Preserving mitochondrial MATα1 content correlates with higher methylation and expression of mitochondrial proteins. Our study demonstrates a role of CK2 and PIN1 in reducing mitochondrial MATα1 content leading to mitochondrial dysfunction in alcohol-associated liver disease.

Does Data-Independent Acquisition Data Contain Hidden Gems? A Case Study Related to Alzheimer's Disease

One of the potential benefits of using data-independent acquisition (DIA) proteomics protocols is that information not originally targeted by the study may be present and discovered by subsequent analysis. Herein, we reanalyzed DIA data originally recorded for global proteomic analysis to look for isomerized peptides, which occur as a result of spontaneous chemical modifications to long-lived proteins. Examination of a large set of human brain samples revealed a striking relationship between Alzheimer's disease (AD) status and isomerization of aspartic acid in a peptide from tau. Relative to controls, a surprising increase in isomer abundance was found in both autosomal dominant and sporadic AD samples. To explore potential mechanisms that might account for these observations, quantitative analysis of proteins related to isomerization repair and autophagy was performed. Differences consistent with reduced autophagic flux in AD-related samples relative to controls were found for numerous proteins, including most notably p62, a recognized indicator of autophagic inhibition. These results suggest, but do not conclusively demonstrate, that lower autophagic flux may be strongly associated with loss of function in AD brains. This study illustrates that DIA data may contain unforeseen results of interest and may be particularly useful for pilot studies investigating new research directions. In this case, a promising target for future investigations into the therapy and prevention of AD has been identified.

Automating Assignment, Quantitation, and Biological Annotation of Redox Proteomics Datasets with ProteoSushi

Redox proteomics plays an increasingly important role characterizing the cellular redox state and redox signaling networks. As these datasets grow larger and identify more redox regulated sites in proteins, they provide a systems-wide characterization of redox regulation across cellular organelles and regulatory networks. However, these large proteomic datasets require substantial data processing and analysis in order to fully interpret and comprehend the biological impact of oxidative posttranslational modifications. We therefore developed ProteoSushi, a software tool to biologically annotate and quantify redox proteomics and other modification-specific proteomics datasets. ProteoSushi can be applied to differentially alkylated samples to assay overall cysteine oxidation, chemically labeled samples such as those used to profile the cysteine sulfenome, or any oxidative posttranslational modification on any residue.Here we demonstrate how to use ProteoSushi to analyze a large, public cysteine redox proteomics dataset. ProteoSushi assigns each modified peptide to shared proteins and genes, sums or averages signal intensities for each modified site of interest, and annotates each modified site with the most up-to-date biological information available from UniProt. These biological annotations include known functional roles or modifications of the site, the protein domain(s) that the site resides in, the protein's subcellular location and function, and more.

Proteomic Analysis of Human Neural Stem Cell Differentiation by SWATH-MS

The unique properties of stem cells to self-renew and differentiate hold great promise in disease modelling and regenerative medicine. However, more information about basic stem cell biology and thorough characterization of available stem cell lines is needed. This is especially essential to ensure safety before any possible clinical use of stem cells or partially committed cell lines. As proteins are the key effector molecules in the cell, the proteomic characterization of cell lines, cell compartments or cell secretome and microenvironment is highly beneficial to answer above mentioned questions. Nowadays, method of choice for large-scale discovery-based proteomic analysis is mass spectrometry (MS) with data-independent acquisition (DIA). DIA is a robust, highly reproducible, high-throughput quantitative MS approach that enables relative quantification of thousands of proteins in one sample. In the current protocol, we describe a specific variant of DIA known as SWATH-MS for characterization of neural stem cell differentiation. The protocol covers the whole process from cell culture, sample preparation for MS analysis, the SWATH-MS data acquisition on TTOF 5600, the complete SWATH-MS data processing and quality control using Skyline software and the basic statistical analysis in R and MSstats package. The protocol for SWATH-MS data acquisition and analysis can be easily adapted to other samples amenable to MS-based proteomics.

l-Asparaginase and HCP quantification by SWATH LC-MS/MS for new and improved purification step in Erwinia chrysanthemil-asparaginase manufacture

Erwinase® or Erwinaze® are the proprietary names for the L-asparaginase enzyme derived from Erwinia chrysanthemi.L-asparaginase is an integral part of the treatment of Acute Lymphoblastic Leukaemia (ALL) in children and adolescents. E. chrysanthemiL-asparaginase was first developed in the early 1970s at Porton Down and is currently manufactured by Porton Biopharma Ltd. One of the early purification steps during E. chrysanthemiL-asparaginase manufacture, involves use of batch cation exchange carboxymethyl resin, and alternatives to this older technology are currently under investigation using mass spectrometry to understand the impact of resin changes on the impurity profile. In this study, a novel SWATH library was developed for E. chrysanthemi proteome and used to evaluate this potential process change on product yield and host cell protein (HCP) profile and clearance. An ELISA assay is currently used as a quality control release test for quantifying HCPs at the Drug Substance (DS) stage, but these early extract samples are too crude for interference-free analysis by ELISA. Given that ELISA assay could not be used in the assessment of new resin options, SWATH LC-MS/MS analysis proved to be pivotal in selecting a resin for further scale-up and implementation. The data quantified that L-asparaginase from the new process step was 2.28-fold higher in concentration than in legacy-process samples. The new step, using a modern ion exchanger, was at least equivalent and in some cases outperformed the legacy resin step in terms of HCP clearance for 78.2% of total HCPs (528 of 675 total proteins).

Spacer length and serum protein adsorption affect active targeting of trastuzumab-modified nanoparticles

Receptor-mediated active targeting of nanocarriers is a widely investigated approach to specifically address cancerous cells and tissues in the human body. The idea is to use these formulations as drug carriers with enhanced specificity and therefore reduced systemic side effects. Until today a big obstacle to reach this goal remains the adsorption of serum proteins to the nanocarrier's surface after contact with biological fluids. In this context different nanoparticle characteristics could be beneficial for effective active targeting after formation of a protein corona which need to be identified. In this study trastuzumab was used as an active targeting ligand which was covalently attached to human serum albumin nanoparticles. For coupling reaction different molecular weight spacers were used and resulting physicochemical nanoparticle characteristics were evaluated. The in vitro cell association of the different nanoparticle formulations was tested in cell culture experiments with or without fetal bovine serum. For specific receptor-mediated cell interaction SK-BR-3 breast cancer cells with human epidermal growth factor receptor 2 (HER2) overexpression were used. MCF-7 breast cancer cells with normal HER2 expression served as control. Furthermore, serum protein adsorption on respective nanoparticles was characterized. The qualitative and quantitative composition of the protein corona was analyzed by SDS-PAGE and LC-MS/MS and the influence of protein adsorption on active targeting capability was determined.

Quantitative proteomic analysis of hepatic tissue in allotetraploid hybridized from red crucian carp and common carp identified crucial proteins and pathways associated with metabolism and growth rate

Allotetraploid is a new species produced by distant hybridization between red crucian carp (Carassius auratus red var., abbreviated as RCC) and common carp (Cyprinus carpio L., abbreviated as CC). There is a significant difference in growth rate between allotetraploid and its parents. However, the underlying molecular mechanism is largely unknown. In this study, to find direct evidence associated with metabolism and growth rate in protein level, we performed quantitative proteomics analysis on liver tissues between allotetraploid and its parents. A total of 2502 unique proteins were identified and quantified by SWATH-MS in our proteomics profiling. Subsequently, comprehensive bioinformatics analyses including gene ontology enrichment analysis, pathway and network analysis, and protein-protein interaction analysis (PPI) were conducted based on differentially expressed proteins (DEPs) between allotetraploid and its parents. The results revealed several significant DEPs involved in metabolism pathways in liver. More specifically, the integrative analysis highlighted that the DEPs ACSBG1, OAT, and LDHBA play vital roles in metabolism pathways including "pentose phosphate pathway," "TCA cycle," and "glycolysis and gluconeogenesis." These could directly affect the growth rate in fresh water fishes by regulating the metabolism, utilization, and exchange of substance and energy. Since the liver is the central place for metabolism activity in animals, we firstly established the comprehensive and quantitative proteomics knowledge base for liver tissue from freshwater fishes, our study may serve as an irreplaceable reference for further studies regarding fishes' culture and growth.

Extensive and Accurate Benchmarking of DIA Acquisition Methods and Software Tools Using a Complex Proteomic Standard

Over the past decade, the data-independent acquisition mode has gained popularity for broad coverage of complex proteomes by LC-MS/MS and quantification of low-abundance proteins. However, there is no consensus in the literature on the best data acquisition parameters and processing tools to use for this specific application. Here, we present the most comprehensive comparison of DIA workflows on Orbitrap instruments published so far in the field of proteomics. Using a standard human 48 proteins mixture (UPS1-Sigma) at 8 different concentrations in an E. coli proteome background, we tested 36 workflows including 4 different DIA window acquisition schemes and 6 different software tools (DIA-NN, DIA-Umpire, OpenSWATH, ScaffoldDIA, Skyline, and Spectronaut) with or without the use of a DDA spectral library. On the basis of the number of proteins identified, quantification linearity and reproducibility, as well as sensitivity and specificity in 28 pairwise comparisons of different UPS1 concentrations, we summarize the major considerations and propose guidelines for choosing the DIA workflow best suited for LC-MS/MS proteomic analyses. Our 96 DIA raw files and software outputs have been deposited on ProteomeXchange for testing or developing new DIA processing tools.

Multiomics analysis of kernel development in response to short-term heat stress at the grain formation stage in waxy maize

Understanding the adaptive changes in maize kernels under high-temperature stress during grain formation stage is critical for developing strategies to alleviate the negative effects on yield and quality. In this study, we subjected waxy maize (Zea mays L. sinensis Kulesh) to four different temperature regimes from 1-15 d after pollination (DAP), namely normal day/normal night (control), hot day/normal night, normal day/hot night, and hot day/hot night. Compared to the control, the three high-temperature treatments inhibited kernel development and starch deposition. To understand how the kernels responded to high-temperature stress, their transcriptomes, proteomes, and metabolomes were studied at 10 DAP and 25 DAP. This showed that genes and proteins related to kernel development and starch deposition were up- and down-regulated, respectively, at 10 DAP, but this pattern was reversed at 25 DAP. Metabolome profiling under high-temperature stress showed that the accumulation patterns of metabolites at 10 DAP and 25 DAP were inversely related. Our multiomics analyses indicated that the response to high-temperature stress of signaling pathways mediated by auxin, abscisic acid, and salicylic acid was more active at 10 DAP than at 25 DAP. These results confirmed that high-temperature stress during early kernel development has a carry-over effect on later development. Taken together, our multiomics profiles of developing kernels under high-temperature stress provide insights into the processes that underlie maize yield and quality under high-temperature conditions.

AlphaTims: Indexing Trapped Ion Mobility Spectrometry-TOF Data for Fast and Easy Accession and Visualization

High-resolution MS-based proteomics generates large amounts of data, even in the standard LC-tandem MS configuration. Adding an ion mobility dimension vastly increases the acquired data volume, challenging both analytical processing pipelines and especially data exploration by scientists. This has necessitated data aggregation, effectively discarding much of the information present in these rich datasets. Taking trapped ion mobility spectrometry (TIMS) on a quadrupole TOF (Q-TOF) platform as an example, we developed an efficient indexing scheme that represents all data points as detector arrival times on scales of minutes (LC), milliseconds (TIMS), and microseconds (TOF). In our open-source AlphaTims package, data are indexed, accessed, and visualized by a combination of tools of the scientific Python ecosystem. We interpret unprocessed data as a sparse four-dimensional matrix and use just-in-time compilation to machine code with Numba, accelerating our computational procedures by several orders of magnitude while keeping to familiar indexing and slicing notations. For samples with more than six billion detector events, a modern laptop can load and index raw data in about a minute. Loading is even faster when AlphaTims has already saved indexed data in an HDF5 file, a portable scientific standard used in extremely large-scale data acquisition. Subsequently, data accession along any dimension and interactive visualization happens in milliseconds. We have found AlphaTims to be a key enabling tool to explore high-dimensional LC-TIMS-Q-TOF data and have made it freely available as an open-source Python package with a stand-alone graphical user interface at https://github.com/MannLabs/alphatims or as part of the AlphaPept 'ecosystem'.

Advances in quantification and analysis of the celiac-related immunogenic potential of gluten

Gluten-free products have emerged in response to the increasing prevalence of gluten-related disorders, namely celiac disease. Therefore, the quantification of gluten in products intended for consumption by individuals who may suffer from these pathologies must be accurate and reproducible, in a way that allows their proper labeling and protects the health of consumers. Immunochemical methods have been the methods of choice for quantifying gluten, and several kits are commercially available. Nevertheless, they still face problems such as the initial extraction of gluten in complex matrices or the use of a standardized reference material to validate the results. Lately, other methodologies relying mostly on mass spectrometry-based techniques have been explored, and that may allow, in addition to quantitative analysis, the characterizationof gluten proteins. On the other hand, although the level of 20 mg/kg of gluten detected by these methods is sufficient for a product to be considered gluten-free, its immunogenic potential for celiac patients has not been clinically validated. In this sense, in vitro and in vivo models, such as the organoid technology applied in gut-on-chip devices and the transgenic humanized mouse models, respectively, are being developed for investigating both the gluten-induced pathogenesis and the treatment of celiac disease. Due to the ubiquitous nature of gluten in the food industry, as well as the increased prevalence of gluten-related disorders, here we intend to summarize the available methods for gluten quantification in food matrices and for the evaluation of its immunogenic potential concerning the development of novel therapies for celiac disease to highlight active research and discuss knowledge gaps and current challenges in this field.

Simultaneous determination of eight tryptic peptides in musk using high-performance liquid chromatography coupled with tandem mass spectrometry

In comparison of herbal medicines, less attention has been paid onto animal medicines, partially attributing to the protein-enriched property. Particularly, it is still challenging to conduct quality evaluation for the animal medicines because of the lack of a fit-for-purpose analytical tool. Herein, an attempt was made to propose a workflow allowing the quality assessment of animal medicines by LC-MS/MS, and musk that is one of the most precious traditional Chinese medicines was employed as a representative case for utility illustration. After the extraction of protein from musk with a well-defined protocol, tryptic digestion was conducted to hydrolyze proteins into peptides, and the peptide-enriched sample was subjected to nanoLC-Orbitrap MS measurement. The tandem mass spectral dataset was retrieved in Human Swiss-Prot FASTA database, and the sequences together with the sources of 733 tryptic peptides, in total, were annotated. Because of the abundant distributions, eight peptides were chosen as the analytes for quantitative measurements, and their quantitative MS parameters, such as ion transitions and collision energies, were rapid optimized in an authentic compound-free manner using online energy-resolved MS (ER-MS). On the other side, the annotated peptides were structurally consolidated via synthesizing reference peptides. When the synthetic peptides were applied for parameter optimization with the authentic compound-dependent manner, the values were almost identical with those from online ER-MS measurements. After being validated with diverse assays, the developed method was applied for the simultaneous determination of eight peptides in 28 batches of musk samples, including captive (C1-C18) and wild ones (W1-W10). Significant differences took place for the content patterns of concerned tryptic peptides between the captive and wild musk samples. Trace distributions occurred for DVDAAYMNK in most batches. Captive samples were rich of QSLEASLAETEGR, TLLDIDNTR, and EVATNSELVQSGK, whereas wild samples were able to accumulate YLGYLEQLLR. Together, the present study provided a meaningful approach for the quality evaluation of musk, as well as other peptide-enriched animal medicines, even if the absences of authentic peptides.

Proteomic characterization of the human lens and Cataractogenesis

INTRODUCTION

The goal of this review is to highlight the triumphs and frontiers in measurement of the lens proteome as it relates to onset of age-related nuclear cataract. As global life expectancy increases, so too does the frequency of age-related nuclear cataracts. Molecular therapeutics do not exist for delay or relief of cataract onset in humans. Since lens fiber cells are incapable of protein synthesis after initial maturation, age-related changes in proteome composition and post-translational modification accumulation can be measured with various techniques. Several of these modifications have been associated with cataract onset.

AREAS COVERED

We discuss the impact of long-lived proteins on the lens proteome and lens homeostasis as well as proteomic techniques that may be used to measure proteomes at various levels of proteomic specificity and spatial resolution.

EXPERT OPINION

There is clear evidence that several proteome modifications are correlated with cataract formation. Past studies should be enhanced with cutting-edge, spatially resolved mass spectrometry techniques to enhance the specificity and sensitivity of modification detection as it relates to cataract formation.

Complement pathway changes at age 12 are associated with psychotic experiences at age 18 in a longitudinal population-based study: evidence for a role of stress

The complement cascade is a major component of the immune defence against infection, and there is increasing evidence for a role of dysregulated complement in major psychiatric disorders. We undertook a directed proteomic analysis of the complement signalling pathway (n = 29 proteins) using data-independent acquisition. Participants were recruited from the UK avon longitudinal study of parents and children (ALSPAC) cohort who participated in psychiatric assessment interviews at ages 12 and 18. Protein expression levels at age 12 among individuals who reported psychotic experiences (PEs) at age 18 (n = 64) were compared with age-matched controls (n = 67). Six out of the 29 targeted complement proteins or protein subcomponents were significantly upregulated following correction for multiple comparisons (VTN↑, C1RL↑, C8B↑, C8A↑, CFH↑, and C5↑). We then undertook an unbiased plasma proteomic analysis of mice exposed to chronic social stress and observed dysregulation of 11 complement proteins, including three that were altered in the same direction in individuals with PE (C1R↑, CFH↑, and C5↑). Our findings indicate that dysregulation of the complement protein pathway in blood is associated with incidence of psychotic experiences and that these changes may reflect exposure to stress.

Development of an Ocean Protein Portal for Interactive Discovery and Education

Proteins are critical in catalyzing chemical reactions, forming key cellular structures, and in regulating cellular processes. Investigation of marine microbial proteins by metaproteomics methods enables the discovery of numerous aspects of microbial biogeochemical processes. However, these datasets present big data challenges as they often involve many samples collected across broad geospatial and temporal scales, resulting in thousands of protein identifications, abundances, and corresponding annotation information. The Ocean Protein Portal (OPP) was created to enable data sharing and discovery among multiple scientific domains and serve both research and education functions. The portal focuses on three use case questions: "Where is my protein of interest?", "Who makes it?", and "How much is there?" and provides profile and section visualizations, real-time taxonomic analysis, and links to metadata, sequence analysis, and other external resources to enable connections to be made between biogeochemical and proteomics datasets.

Quantitative Mass Spectrometry-Based Proteomics: An Overview

In recent decades, mass spectrometry has moved more than ever before into the front line of protein-centered research. After being established at the qualitative level, the more challenging question of quantification of proteins and peptides using mass spectrometry has become a focus for further development. In this chapter, we discuss and review actual strategies and problems of the methods for the quantitative analysis of peptides, proteins, and finally proteomes by mass spectrometry. The common themes, the differences, and the potential pitfalls of the main approaches are presented in order to provide a survey of the emerging field of quantitative, mass spectrometry-based proteomics.

Mass defect-based carbonyl activated tags (mdCATs) for multiplex data-independent acquisition proteome quantification

A novel eight-plex mass-defect-based carbonyl activated tag (mdCAT) has been designed for DIA quantification for the first time.

Recent Developments in Data Independent Acquisition (DIA) Mass Spectrometry: Application of Quantitative Analysis of the Brain Proteome

Mass spectrometry is the driving force behind current brain proteome analysis. In a typical proteomics approach, a protein isolate is digested into tryptic peptides and then analyzed by liquid chromatography–mass spectrometry. The recent advancements in data independent acquisition (DIA) mass spectrometry provide higher sensitivity and protein coverage than the classic data dependent acquisition. DIA cycles through a pre-defined set of peptide precursor isolation windows stepping through 400–1,200 m/z across the whole liquid chromatography gradient. All peptides within an isolation window are fragmented simultaneously and detected by tandem mass spectrometry. Peptides are identified by matching the ion peaks in a mass spectrum to a spectral library that contains information of the peptide fragment ions' pattern and its chromatography elution time. Currently, there are several reports on DIA in brain research, in particular the quantitative analysis of cellular and synaptic proteomes to reveal the spatial and/or temporal changes of proteins that underlie neuronal plasticity and disease mechanisms. Protocols in DIA are continuously improving in both acquisition and data analysis. The depth of analysis is currently approaching proteome-wide coverage, while maintaining high reproducibility in a stable and standardisable MS environment. DIA can be positioned as the method of choice for routine proteome analysis in basic brain research and clinical applications.