Search

Single-cell transcriptomics has revolutionized our understanding of basic biology and disease. Since transcript levels often do not correlate with protein expression, it is crucial to complement transcriptomics approaches with proteome analyses at single-cell resolution. Despite continuous technological improvements in sensitivity, mass-spectrometry-based single-cell proteomics ultimately faces the challenge of reproducibly comparing the protein expression profiles of thousands of individual cells. Here, we combine two hitherto opposing analytical strategies, DIA and Tandem-Mass-Tag (TMT)-multiplexing, to generate highly reproducible, quantitative proteome signatures from ultralow input samples. We developed a novel, identification-independent proteomics data-analysis pipeline that allows to quantitatively compare DIA-TMT proteome signatures across hundreds of samples independent of their biological origin to identify cell types and single protein knockouts. These proteome signatures overcome the need to impute quantitative data due to accumulating detrimental amounts of missing data in standard multibatch TMT experiments. We validate our approach using integrative data analysis of different human cell lines and standard database searches for knockouts of defined proteins. Our data establish a novel and reproducible approach to markedly expand the numbers of proteins one detects from ultralow input samples., Graphical Abstract, Highlights • DIA-TMT provides reproducible, quantitative proteome signatures at high throughput. • Proteome signature inferred cell type characterization is highly accurate. • Proteome signatures accurately highlight underrepresented cell types. • ID-independent DIA-TMT is more reproducible than standard DDA acquisition strategies., In Brief Proteomics faces the challenge of reproducibly comparing the protein expression profiles across large sample cohorts. Here, we combined two hitherto opposing analytical strategies, DIA and isobaric labeling to generate highly reproducible, quantitative “proteome signatures”. These signatures decouple peptide identification from quantification to quantitatively compare hundreds of samples. DIA-TMT data provides complete quantitative signatures independent of peptide identification that distinguish cell types down to single protein knockouts in high-throughput even at ultralow input.

Drug resistance is a critical obstacle to effective treatment in patients with chronic myeloid leukemia. To understand the underlying resistance mechanisms in response to imatinib mesylate (IMA) and adriamycin (ADR), the parental K562 cells were treated with low doses of IMA or ADR for 2 months to generate derivative cells with mild, intermediate, and severe resistance to the drugs as defined by their increasing resistance index. PulseDIA-based (DIA [data-independent acquisition]) quantitative proteomics was then employed to reveal the proteome changes in these resistant cells. In total, 7082 proteins from 98,232 peptides were identified and quantified from the dataset using four DIA software tools including OpenSWATH, Spectronaut, DIA-NN, and EncyclopeDIA. Sirtuin signaling pathway was found to be significantly enriched in both ADR-resistant and IMA-resistant K562 cells. In particular, isocitrate dehydrogenase (NADP(+)) 2 was identified as a potential drug target correlated with the drug resistance phenotype, and its inhibition by the antagonist AGI-6780 reversed the acquired resistance in K562 cells to either ADR or IMA. Together, our study has implicated isocitrate dehydrogenase (NADP(+)) 2 as a potential target that can be therapeutically leveraged to alleviate the drug resistance in K562 cells when treated with IMA and ADR., Graphical Abstract, Highlights • Temporal proteomic dynamics in the imatinib or adriamycin-induced drug resistance. • Comparison of four DIA software tools (OpenSWATH, Spectronaut, DIA-NN, and EncyclopeDIA). • Sirtuin signaling pathway was significantly regulated in resistant K562 cells. • IDH2 was identified as a potential drug target correlated for resistant K562 cells., In Brief To understand the underlying resistance mechanisms in response to imatinib (IMA) and adriamycin (ADR), we explored two unique drug resistance models of K562 cells. We applied an optimized DIA–MS method to quantify 98,232 peptides from 7082 proteotypic proteins from these samples using four DIA software tools including OpenSWATH, Spectronaut, DIA-NN, and EncyclopeDIA. The sirtuin signaling pathway was found significantly regulated in both models, and IDH2 was identified as a druggable regulator of acquired drug resistance.

Single-cell tandem MS has enabled analyzing hundreds of single cells per day and quantifying thousands of proteins across the cells. The broad dissemination of these capabilities can empower the dissection of pathophysiological mechanisms in heterogeneous tissues. Key requirements for achieving this goal include robust protocols performed on widely accessible hardware, robust quality controls, community standards, and automated data analysis pipelines that can pinpoint analytical problems and facilitate their timely resolution. Toward meeting these requirements, this perspective outlines both existing resources and outstanding opportunities, such as parallelization, for catalyzing the wide dissemination of quantitative single-cell proteomics analysis that can be scaled up to tens of thousands of single cells. Indeed, simultaneous parallelization of the analysis of peptides and single cells is a promising approach for multiplicative increase in the speed of performing deep and quantitative single-cell proteomics. The community is ready to begin a virtuous cycle of increased adoption fueling the development of more technology and resources for single-cell proteomics that in turn drive broader adoption, scientific discoveries, and clinical applications., Graphical Abstract, Highlights • Wide adoption of single-cell proteomics can empower biology. • Protocols for single-cell proteomics by MS are ready for broad adoption. • Parallelizing the analysis offers multiplicative throughput gains. • Quantity controls and community standards are essential. • Single-cell proteomics offers unique perspective on biology., In Brief Single-cell proteomics will drive the next wave of single-cell biology. This requires broad adoption of existing methods, the application of rigorous quality control standards, and the continuous advancement of the technology. The advancement will be driven by numerous innovations, including highly parallelized analysis, and will increase the throughput, quantitative accuracy, and the accessibility of the single-cell proteomics.

Targeted proteomics via selected reaction monitoring (SRM) or parallel reaction monitoring (PRM) enables fast and sensitive detection of a preselected set of target peptides. However, the number of peptides that can be monitored in conventional targeting methods is usually rather small. Recently, a series of methods has been described that employ intelligent acquisition strategies to increase the efficiency of mass spectrometers to detect target peptides. These methods are based on one of two strategies. First, retention time adjustment-based methods enable intelligent scheduling of target peptide retention times. These include Picky, iRT, as well as spike-in free real-time adjustment methods such as MaxQuant.Live. Second, in spike-in triggered acquisition methods such as SureQuant, Pseudo-PRM, TOMAHAQ, and Scout-MRM, targeted scans are initiated by abundant labeled synthetic peptides added to samples before the run. Both strategies enable the mass spectrometer to better focus data acquisition time on target peptides. This either enables more sensitive detection or a higher number of targets per run. Here, we provide an overview of available advanced targeting methods and highlight their intrinsic strengths and weaknesses and compatibility with specific experimental setups. Our goal is to provide a basic introduction to advanced targeting methods for people starting to work in this field., Graphical abstract, Highlights • Advanced acquisition methods improve focus of mass spectrometers on target peptides. • This review discusses existing methods based on two strategies. • Retention time adjustment-based methods enable intelligent scheduling of peptide RTs. • In spike-in triggered acquisition methods targeted scans are initiated by spike-ins., In Brief The analytical power of targeted proteomics depends on how efficiently the mass spectrometer detects target peptides. A number of “smart” acquisition approaches have been developed that enable more targets per run and improve analytical performance such as sensitivity, specificity, and quantitative accuracy. This review provides an introduction to these methods and highlights their inherent strengths and weaknesses.

Recent advances in efficiency and ease of implementation have rekindled interest in ion mobility spectrometry, a technique that separates gas phase ions by their size and shape and that can be hybridized with conventional LC and MS. Here, we review the recent development of trapped ion mobility spectrometry (TIMS) coupled to TOF mass analysis. In particular, the parallel accumulation–serial fragmentation (PASEF) operation mode offers unique advantages in terms of sequencing speed and sensitivity. Its defining feature is that it synchronizes the release of ions from the TIMS device with the downstream selection of precursors for fragmentation in a TIMS quadrupole TOF configuration. As ions are compressed into narrow ion mobility peaks, the number of peptide fragment ion spectra obtained in data-dependent or targeted analyses can be increased by an order of magnitude without compromising sensitivity. Taking advantage of the correlation between ion mobility and mass, the PASEF principle also multiplies the efficiency of data-independent acquisition. This makes the technology well suited for rapid proteome profiling, an increasingly important attribute in clinical proteomics, as well as for ultrasensitive measurements down to single cells. The speed and accuracy of TIMS and PASEF also enable precise measurements of collisional cross section values at the scale of more than a million data points and the development of neural networks capable of predicting them based only on peptide sequences. Peptide collisional cross section values can differ for isobaric sequences or positional isomers of post-translational modifications. This additional information may be leveraged in real time to direct data acquisition or in postprocessing to increase confidence in peptide identifications. These developments make TIMS quadrupole TOF PASEF a powerful and expandable platform for proteomics and beyond., Graphical Abstract, Highlights • TIMS is a compact, highly efficient, and flexible ion mobility device. • PASEF increases sequencing speed without compromising sensitivity. • Collisional cross-section information remains to be fully explored and utilized., In Brief The combination of ion mobility and MS is increasingly attractive in the field of proteomics research. Here, we provide a perspective on the recent development of TIMS and PASEF. TIMS offers an additional dimension of separation and collisional cross-section information, whereas PASEF greatly increases the efficiency of MS/MS experiments. As the user base broadens and computational tools evolve, a wide range of applications emerge from structural to single-cell proteomics.

Stress and injury to the retinal pigment epithelium (RPE) often lead to dedifferentiation and epithelial-to-mesenchymal transition (EMT). These processes have been implicated in several retinal diseases, including proliferative vitreoretinopathy, diabetic retinopathy, and age-related macular degeneration. Despite the importance of RPE-EMT and the large body of data characterizing malignancy-related EMT, comprehensive proteomic studies to define the protein changes and pathways underlying RPE-EMT have not been reported. This study sought to investigate the temporal protein expression changes that occur in a human-induced pluripotent stem cell–based RPE-EMT model. We utilized multiplexed isobaric tandem mass tag labeling followed by high-resolution tandem MS for precise and in-depth quantification of the RPE-EMT proteome. We have identified and quantified 7937 protein groups in our tandem mass tag–based MS analysis. We observed a total of 532 proteins that are differentially regulated during RPE-EMT. Furthermore, we integrated our proteomic data with prior transcriptomic (RNA-Seq) data to provide additional insights into RPE-EMT mechanisms. To validate these results, we have performed a label-free single-shot data-independent acquisition MS study. Our integrated analysis indicates both the commonality and uniqueness of RPE-EMT compared with malignancy-associated EMT. Our comparative analysis also revealed that multiple age-related macular degeneration–associated risk factors are differentially regulated during RPE-EMT. Together, our integrated dataset provides a comprehensive RPE-EMT atlas and resource for understanding the molecular signaling events and associated biological pathways that underlie RPE-EMT onset. This resource has already facilitated the identification of chemical modulators that could inhibit RPE-EMT, and it will hopefully aid in ongoing efforts to develop EMT inhibition as an approach for the treatment of retinal disease., Graphical Abstract, Highlights • Proteomics data were integrated with prior transcriptomic (RNA-Seq) data on RPE-EMT. • Dysregulated RPE-EMT proteome shares commonality with malignancy-associated EMT. • Altered RPE-EMT proteome signatures correlated with known AMD-associated risk factors. • Protein kinases and phosphatases crosstalk modulate RPE-EMT., In Brief EMT can play a role in retinal diseases. Here, we present a comprehensive proteomic analysis aimed at defining the temporal protein expression changes associated with EMT of stem cell–derived retinal pigment epithelial cells. Tandem mass tag and direct data-independent acquisition MS approaches were performed after inducing RPE-EMT by enzymatic dissociation. We present integration of our proteomic data with prior transcriptomic (RNA-Seq) data to provide additional insights into the RPE-EMT progression.

Signet ring cell carcinoma (SRCC) is a histological subtype of gastric cancer with distinct features in multiple aspects compared with adenocarcinomas (ACs). The lack of a systematic molecular overview of this disease has led to slow progress in its clinical practice. In the present proteomics study, gastric tissues were collected from tumors and adjacent tissues, including 14 SRCCs and 34 ACs, and laser capture microdissection (LCM) was employed to eradicate the cellular heterogeneity of the tissues. The proteomes of tissues were profiled by data-independent acquisition (DIA) mass spectrometry (MS). Based on the over 6000 proteins quantified, univariate analysis and pathway enrichment revealed that some proteins and pathways demonstrated differences between SRCC and ACs. Importantly, the upregulation of a majority of complement-related proteins was notable for SRCC but not for ACs. A hypothesis, based on the proteomics evidence, was proposed that the complement cascade was evoked in the SRCC microenvironment upon infiltration, and the SRCC cells survived the complement cytotoxicity by secreting endogenous negative regulators. Moreover, an attempt was made to establish appropriate cell models for gastric SRCC through proteomic comparison of the 15 gastric cell lines and gastric tumors. The predictions of a supervised classifier suggested that none of these gastric cell lines qualified to mimic SRCC. This study discovered that the complement cascade is activated at a higher level in gastric SRCC than in ACs., Graphical Abstract, Highlights • LCM-DIA extracted unprecedented proteomic details of gastric in different subtypes. • Complement cascade was found to be an SRCC-specific pathway for the first time. • Gastric cell lines were evaluated based on proteomic features for the first time. • Re-analyzable DIA data collected provide rich opportunity for future research., In Brief This study took advantages of LCM and DIA-MS, generating a data set in the context of different subtypes of gastric tumors, globally and precisely. It was discovered for the first time that the complement cascade in SRCC tumors was specifically activated compared with AC.

Data-independent acquisition (DIA) is now an emerging method in bottom–up proteomics and capable of achieving deep proteome coverage and accurate label-free quantification. However, for post-translational modifications, such as glycosylation, DIA methodology is still in the early stage of development. The full characterization of glycoproteins requires site-specific glycan identification as well as subsequent quantification of glycan structures at each site. The tremendous complexity of glycosylation represents a significant analytical challenge in glycoproteomics. This review focuses on the development and perspectives of DIA methodology for N- and O-linked glycoproteomics and posits that DIA-based glycoproteomics could be a method of choice to address some of the challenging aspects of glycoproteomics. First, the current challenges in glycoproteomics and the basic principles of DIA are briefly introduced. DIA-based glycoproteomics is then summarized and described into four aspects based on the actual samples. Finally, we discussed the important challenges and future perspectives in the field. We believe that DIA can significantly facilitate glycoproteomic studies and contribute to the development of future advanced tools and approaches in the field of glycoproteomics., Graphical Abstract, Highlights • Protein glycosylation and challenges in glycoproteomics. • Data-independent acquisition for deglycosylated and intact N-linked glycopeptides. • Unbiased screening of oxonium ions from all glycopeptide precursors. • Glyco–data-independent acquisition on mucin-type O-glycopeptides., In Brief As a highly abundant and diverse post-translational modification, protein glycosylation is challenging to characterize in various approaches including MS. In MS-based proteomics, data-independent acquisition (DIA) has been advanced rapidly and showed outstanding analytical performances. DIA now started to be applied in different facets of glycoproteomics, including deglycosylated and intact N-linked and O-linked glycopeptides, and screening of oxonium ions. We summarized current applications of DIA in glycoproteomics and discussed its limitations and perspectives.

Mass spectrometry (MS) is the state-of-the-art methodology for capturing the breadth and depth of the immunopeptidome across human leukocyte antigen (HLA) allotypes and cell types. The majority of studies in the immunopeptidomics field are discovery driven. Hence, data-dependent tandem MS (MS/MS) acquisition (DDA) is widely used, as it generates high-quality references of peptide fingerprints. However, DDA suffers from the stochastic selection of abundant ions that impairs sensitivity and reproducibility. In contrast, in data-independent acquisition (DIA), the systematic fragmentation and acquisition of all fragment ions within given isolation m/z windows yield a comprehensive map for a given sample. However, many DIA approaches commonly require generating comprehensive DDA-based spectrum libraries, which can become impractical for studying noncanonical and personalized neoantigens. Because the amount of HLA peptides eluted from biological samples such as small tissue biopsies is typically not sufficient for acquiring both meaningful DDA data necessary for generating comprehensive spectral libraries and DIA MS measurements, the implementation of DIA in the immunopeptidomics translational research domain has remained limited. We implemented a DIA immunopeptidomics workflow and assessed its sensitivity and accuracy by matching DIA data against libraries with growing complexity—from sample-specific libraries to libraries combining 2 to 40 different immunopeptidomics samples. Analyzing DIA immunopeptidomics data against a complex multi-HLA spectral library resulted in a two-fold increase in peptide identification compared with sample-specific library and in a three-fold increase compared with DDA measurements, yet with no detrimental effect on the specificity. Furthermore, we demonstrated the implementation of DIA for sensitive personalized neoantigen discovery through the analysis of DIA data with predicted MS/MS spectra of clinically relevant HLA ligands. We conclude that a comprehensive multi-HLA library for DIA approach in combination with MS/MS prediction is highly advantageous for clinical immunopeptidomics, especially when low amounts of biological samples are available., Graphical Abstract, Highlights • So far, DIA in the immunopeptidomics translational research has been limited. • DIA immunopeptidomics data were analyzed against a complex multi-HLA spectral library. • This resulted in improved sensitivity with no detrimental effect on the specificity. • We implemented DIA for clinical antigen discovery combined with predicted MS/MS spectra., In Brief The implementation of DIA in the immunopeptidomics translational research domain has remained limited because the amount of HLA peptides eluted from clinical samples is typically not sufficient for acquiring both meaningful DDA data for generating comprehensive spectral libraries and DIA MS measurements. We implemented a DIA immunopeptidomics workflow and assessed its sensitivity and accuracy with libraries of growing complexity and multi-HLA libraries. In addition, we demonstrated the analysis of DIA data with predicted MS/MS spectra of clinically relevant HLA ligands.

Traditional Chinese medicine (TCM) has been an indispensable source of drugs for curing various human diseases. However, the inherent chemical diversity and complexity of TCM restricted the safety and efficacy of its usage. Over the past few decades, the combination of liquid chromatography with mass spectrometry has contributed greatly to the TCM qualitative analysis. And novel approaches have been continuously introduced to improve the analytical performance, including both the data acquisition methods to generate a large and informative dataset, and the data post-processing tools to extract the structure-related MS information. Furthermore, the fast-developing computer techniques and big data analytics have markedly enriched the data processing tools, bringing benefits of high efficiency and accuracy. To provide an up-to-date review of the latest techniques on the TCM qualitative analysis, multiple data-independent acquisition methods and data-dependent acquisition methods (precursor ion list, dynamic exclusion, mass tag, precursor ion scan, neutral loss scan, and multiple reaction monitoring) and post-processing techniques (mass defect filtering, diagnostic ion filtering, neutral loss filtering, mass spectral trees similarity filter, molecular networking, statistical analysis, database matching, etc.) were summarized and categorized. Applications of each technique and integrated analytical strategies were highlighted, discussion and future perspectives were proposed as well., Graphical abstract This review summarized the mechanisms and applications of various data acquisition and data post-processing techniques of liquid chromatography−mass spectrometry (LC−MS) in qualitative analysis of traditional Chinese medicine (TCM).Image 1

The increasing consumption of high-fat foods combined with a lack of exercise is a major contributor to the burden of obesity in humans. Aerobic exercise such as running is known to provide metabolic benefits, but how the overconsumption of a high-fat diet (HFD) and exercise interact is not well characterized at the molecular level. Here, we examined the plasma proteome in mice for the effects of aerobic exercise as both a treatment and as a preventative regimen for animals on either a HFD or a healthy control diet. This analysis detected large changes in the plasma proteome induced by the HFD, such as increased abundance of SERPINA7, ALDOB, and downregulation of SERPINA1E and complement factor D (CFD; adipsin). Some of these changes were significantly reverted using exercise as a preventative measure but not as a treatment regimen. To determine if either the intensity or duration of exercise influenced the outcome, we compared high-intensity interval training and endurance running. Endurance running slightly outperformed high-intensity interval training exercise, but overall, both provided similar reversion in abundance of plasma proteins modulated by the HFD, including SERPINA7, apolipoprotein E, SERPINA1E, and CFD. Finally, we compared the changes induced by overconsumption of a HFD with previous data from mice fed on an isocaloric high-saturated fatty acid or polyunsaturated fatty acid diet. This identified several common changes, including not only increased apolipoprotein C-II and apolipoprotein E but also highlighted changes specific for overconsumption of a HFD (fructose-bisphosphate aldolase B, SERPINA7, and CFD), saturated fatty acid–based diets (SERPINA1E), or polyunsaturated fatty acid–based diets (haptoglobin). Together, these data highlight the importance of early intervention with exercise to revert HFD-induced phenotypes and suggest some of the molecular mechanisms leading to the changes in the plasma proteome generated by HFD consumption. Web-based interactive visualizations are provided for this dataset (larancelab.com/hfd-exercise), which give insight into diet and exercise phenotypic interactions on the plasma proteome., Graphical Abstract, Highlights • Plasma proteomics reveals 30% of proteins change in response to a high-fat diet (HFD). • Running exercise applied from the beginning of the HFD reverts some of these changes. • High-intensity interval training and endurance training provide similar benefits. • Some proteins were regulated by either HFD overconsumption or diet fat profile., In Brief Exercise is known to provide benefits, but how a high-fat diet (HFD) and exercise interact is not well characterized. Here, we examined the plasma proteome in mice for the effects of exercise as both a treatment and a preventative regimen for animals on either a HFD or a healthy control diet. Many changes in the plasma proteome were induced by the HFD, and these were significantly reverted using exercise as a preventative measure but not as a treatment regimen.

Sparkling wine is an alcoholic beverage enjoyed around the world. The sensory properties of sparkling wine depend on a complex interplay between the chemical and biochemical components in the final product. Glycoproteins have been linked to positive and negative qualities in sparkling wine, but the glycosylation profiles of sparkling wine have not been previously investigated in detail. We analyzed the glycoproteome of sparkling wines using protein- and glycopeptide-centric approaches. We developed an automated workflow that created ion libraries to analyze sequential window acquisition of all theoretical mass spectra data-independent acquisition mass spectrometry data based on glycopeptides identified by Byonic (Protein Metrics; version 2.13.17). We applied our workflow to three pairs of experimental sparkling wines to assess the effects of aging on lees and of different yeast strains used in the liqueur de tirage for secondary fermentation. We found that aging a cuvée on lees for 24 months compared with 8 months led to a dramatic decrease in overall protein abundance and an enrichment in large glycans at specific sites in some proteins. Secondary fermentation of a Riesling wine with Saccharomyces cerevisiae yeast strain Siha4 produced more yeast proteins and glycoproteins than with S. cerevisiae yeast strain DV10. The abundance and glycosylation profiles of grape glycoproteins were also different between grape varieties. To our knowledge, this work represents the first in-depth study into protein- and peptide-specific glycosylation in sparkling wines and describes a quantitative glycoproteomic sequential window acquisition of all theoretical mass spectra/data-independent acquisition workflow that is broadly applicable to other sample types., Graphical Abstract, Highlights • Development of an automated glycoproteomic sequential window acquisition of all theoretical mass spectra workflow. • Application to three pairs of commercial-scale experimental sparkling wines. • Decreased protein abundance in cuvée during the aging process. • Different yeast strains produce varying levels of yeast proteins., In Brief Glycoproteins in sparkling wine are especially important for its sensory properties. Here, we developed a novel quantitative glycoproteomic approach to investigate the glycoproteomes of sparkling wine. The protein- and glycopeptide-centric approaches enabled robust quantification from small volumes of sparkling wine and highlighted key changes that may contribute to sparkling wine quality.

Histone post-translational modifications (hPTMs) are epigenetic marks that strongly affect numerous processes, including cell cycling and protein interactions. They have been studied by both antibody- and MS-based methods for years, but the analyses are still challenging, mainly because of the diversity of histones and their modifications arising from high contents of reactive amine groups in their amino acid sequences. Here, we introduce use of trimethylacetic anhydride (TMA) as a new reagent for efficient histone derivatization, which is a requirement for bottom–up proteomic hPTM analysis. TMA can derivatize unmodified amine groups of lysine residues and amine groups generated at peptide N-termini by trypsin digestion. The derivatization is facilitated by microwave irradiation, which also reduces incubation times to minutes. We demonstrate that histone derivatization with TMA reliably provides high yields of fully derivatized peptides and thus is an effective alternative to conventional methods. TMA afforded more than 98% and 99% labeling efficiencies for histones H4 and H3, respectively, thereby enabling accurate quantification of peptide forms. Trimethylacetylation substantially improves chromatographic separation of peptide forms, which is essential for direct quantification based on signals extracted from MS1 data. For this purpose, software widely applied by the proteomics community can be used without additional computational development. Thorough comparison with widely applied propionylation highlights the advantages of TMA-based histone derivatization for monitoring hPTMs in biological samples., Graphical abstract, Highlights • Microwave-assisted labeling of histones using TMA for bottom–up proteomics. • TMA enables discrimination of isobaric peptides by improved chromatographic behavior. • TMA facilitates histone quantification from MS1-level spectral information. • TMA provides excellent performance for monitoring hPTM pattern in the tissues., In Brief The discrimination of isobaric peptides represents a common challenge in histone characterization. This study reports TMA as a novel derivatization reagent for bottom–up proteomics of histone proteoforms. TMA substantially improves separation of positional isomers, which is a prerequisite for identification and quantification of post-translationally modified histone forms.

Nicotinamide phosphoribosyltransferase (NAMPT) converts nicotinamide to NAD+. As low hepatic NAD+ levels have been linked to the development of nonalcoholic fatty liver disease, we hypothesized that ablation of hepatic Nampt would affect susceptibility to liver injury in response to diet-induced metabolic stress. Following 3 weeks on a low-methionine and choline-free 60% high-fat diet, hepatocyte-specific Nampt knockout (HNKO) mice accumulated less triglyceride than WT littermates but had increased histological scores for liver inflammation, necrosis, and fibrosis. Surprisingly, liver injury was also observed in HNKO mice on the purified control diet. This HNKO phenotype was associated with decreased abundance of mitochondrial proteins, especially proteins involved in oxidoreductase activity. High-resolution respirometry revealed lower respiratory capacity in purified control diet–fed HNKO liver. In addition, fibrotic area in HNKO liver sections correlated negatively with hepatic NAD+, and liver injury was prevented by supplementation with NAD+ precursors nicotinamide riboside and nicotinic acid. MS-based proteomic analysis revealed that nicotinamide riboside supplementation rescued hepatic levels of oxidoreductase and OXPHOS proteins. Finally, single-nucleus RNA-Seq showed that transcriptional changes in the HNKO liver mainly occurred in hepatocytes, and changes in the hepatocyte transcriptome were associated with liver necrosis. In conclusion, HNKO livers have reduced respiratory capacity, decreased abundance of mitochondrial proteins, and are susceptible to fibrosis because of low NAD+ levels. Our data suggest a critical threshold level of hepatic NAD+ that determines the predisposition to liver injury and supports that NAD+ precursor supplementation can prevent liver injury and nonalcoholic fatty liver disease progression.

We have previously shown that multimers of plasma pentraxin-3 (PTX3) were predictive of survival in patients with sepsis. To characterize the release kinetics and cellular source of plasma protein changes in sepsis, serial samples were obtained from healthy volunteers (n = 10; three time points) injected with low-dose endotoxin (lipopolysaccharide [LPS]) and analyzed using data-independent acquisition MS. The human plasma proteome response was compared with an LPS-induced endotoxemia model in mice. Proteomic analysis of human plasma revealed a rapid neutrophil degranulation signature, followed by a rise in acute phase proteins. Changes in circulating PTX3 correlated with increases in neutrophil-derived proteins following LPS injection. Time course analysis of the plasma proteome in mice showed a time-dependent increase in multimeric PTX3, alongside increases in neutrophil-derived myeloperoxidase (MPO) upon LPS treatment. The mechanisms of oxidation-induced multimerization of PTX3 were explored in two genetic mouse models: MPO global knock-out (KO) mice and LysM Cre Nox2 KO mice, in which NADPH oxidase 2 (Nox2) is only deficient in myeloid cells. Nox2 is the enzyme responsible for the oxidative burst in neutrophils. Increases in plasma multimeric PTX3 were not significantly different between wildtype and MPO or LysM Cre Nox2 KO mice. Thus, PTX3 may already be stored and released in a multimeric form. Through in vivo neutrophil depletion and multiplexed vascular proteomics, PTX3 multimer deposition within the aorta was confirmed to be neutrophil dependent. Proteomic analysis of aortas from LPS-injected mice returned PTX3 as the most upregulated protein, where multimeric PTX3 was deposited as early as 2 h post-LPS along with other neutrophil-derived proteins. In conclusion, the rise in multimeric PTX3 upon LPS injection correlates with neutrophil-related protein changes in plasma and aortas. MPO and myeloid Nox2 are not required for the multimerization of PTX3; instead, neutrophil extravasation is responsible for the LPS-induced deposition of multimeric PTX3 in the aorta., Graphical Abstract, Highlights • Multimer formation of pentraxin-3 (PTX3) has been linked to outcome in sepsis. • PTX3 plasma levels have been correlated to increases in neutrophil-derived proteins. • Neutrophil-derived myeloperoxidase and NADPH oxidase 2 are not responsible for PTX3 oxidation. • PTX3 multimer deposition within the aorta is neutrophil dependent., In Brief Circulating proteome alterations, in both humans and mice, in response to endotoxin were mapped utilizing proteomic approaches. Multimeric pentraxin-3 (PTX3) plasma and aortic levels mimicked changes observed in proteins of known neutrophil origin in response to endotoxin. Genetic mouse models ruled out myeloperoxidase and NADPH oxidase 2 as drivers of PTX3 oxidation; however, neutrophil extravasation promotes PTX3 deposition within the vasculature. The results reveal a link between the systemic inflammatory response and the neutrophil-dependent vascular deposition of multimeric PTX3.

Growing implications of glycosylation in physiological occurrences and human disease have prompted intensive focus on revealing glycomic perturbations through absolute and relative quantification. Empowered by seminal methodologies and increasing capacity for detection, identification, and characterization, the past decade has provided a significant increase in the number of suitable strategies for glycan and glycopeptide quantification. Mass-spectrometry-based strategies for glycomic quantitation have grown to include metabolic incorporation of stable isotopes, deposition of mass difference and mass defect isotopic labels, and isobaric chemical labeling, providing researchers with ample tools for accurate and robust quantitation. Beyond this, workflows have been designed to harness instrument capability for label-free quantification, and numerous software packages have been developed to facilitate reliable spectrum scoring. In this review, we present and highlight the most recent advances in chemical labeling and associated techniques for glycan and glycopeptide quantification., Graphical Abstract, Highlights • Novel glycomic applications of label-free, metabolic, isotopic, and isobaric labeling quantitation • Informatic tools for investigative quantitative glycomics • Critical considerations for entry or expansion of quantitative glycomics • Introduction of synthetically facile, cost-effective labeling technology, In Brief Recent years have seen an explosion in novel strategies for quantitative glycomics and glycoproteomics. Whether through metabolic incorporation of stable isotopes, deposition of custom isotopic labels, or high-throughput isobaric chemical tags, these numerous novel strategies provide ease of access to glycomic and glycoproteomic investigation. This review highlights the recent innovations in labeling methods, label-free strategies, acquisition modes, and bioinformatic tools for glycan and glycopeptide quantitation, while providing critical evaluations and technical considerations to enable effective analysis.

Missing values weaken the power of label-free quantitative proteomic experiments to uncover true quantitative differences between biological samples or experimental conditions. Match-between-runs (MBR) has become a common approach to mitigate the missing value problem, where peptides identified by tandem mass spectra in one run are transferred to another by inference based on m/z, charge state, retention time, and ion mobility when applicable. Though tolerances are used to ensure such transferred identifications are reasonably located and meet certain quality thresholds, little work has been done to evaluate the statistical confidence of MBR. Here, we present a mixture model-based approach to estimate the false discovery rate (FDR) of peptide and protein identification transfer, which we implement in the label-free quantification tool IonQuant. Using several benchmarking datasets generated on both Orbitrap and timsTOF mass spectrometers, we demonstrate superior performance of IonQuant with FDR-controlled MBR compared with MaxQuant (19–38 times faster; 6–18% more proteins quantified and with comparable or better accuracy). We further illustrate the performance of IonQuant and highlight the need for FDR-controlled MBR, in two single-cell proteomics experiments, including one acquired with the help of high-field asymmetric ion mobility spectrometry separation. Fully integrated in the FragPipe computational environment, IonQuant with FDR-controlled MBR enables fast and accurate peptide and protein quantification in label-free proteomics experiments., Graphical Abstract, Highlights • A mixture-model approach controls the false discovery rate of match-between-runs. • The method is implemented in IonQuant. • Experiments with various data types show high sensitivity and accuracy of IonQuant., In Brief Match-between-runs is a powerful approach to mitigate the missing value problem in label-free quantification. It transfers features identified by MS/MS from one run to the other, but previously, there was no false discovery rate control over this process. We present a mixture model–based approach to estimate and control the false discovery rate, which we have implemented in IonQuant. We demonstrate the sensitivity, accuracy, and speed of IonQuant using proteomics data from timsTOF, Orbitrap, and Orbitrap coupled to FAIMS.

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’., Graphical Abstract, Highlights • Efficient indexing of billions of five-dimensional data points in seconds. • Fast accession of arbitrary LC-TIMS-QTOF data slices in milliseconds. • Easy and fast visualization of LC-TIMS-QTOF data along multiple axes. • Freely available GUI, CLI, and Python module on Windows, Linux, and macOS., In Brief AlphaTims efficiently indexes five-dimensional LC-TIMS-Q-TOF data containing billions of datapoints in a matter of seconds. Owing to this indexing, retrieving the retention time, ion mobility, quadrupole m/z, TOF m/z, and intensity values of millions of ions along arbitrary dimensions takes only milliseconds. Subsequent visualization of these coordinates is then achieved with similar timings. AlphaTims is a freely available open-source package with a graphical user interface, command-line interface, or Python module for all major operating systems.

Regulation of gene expression is essential for the functioning of all eukaryotic organisms. Understanding gene expression regulation requires determining which proteins interact with regulatory elements in chromatin. MS-based analysis of chromatin has emerged as a powerful tool to identify proteins associated with gene regulation, as it allows studying protein function and protein complex formation in their in vivo chromatin-bound context. Total chromatin isolated from cells can be directly analyzed using MS or further fractionated into transcriptionally active and inactive chromatin prior to MS-based analysis. Newly formed chromatin that is assembled during DNA replication can also be specifically isolated and analyzed. Furthermore, capturing specific chromatin domains facilitates the identification of previously unknown transcription factors interacting with these domains. Finally, in recent years, advances have been made toward identifying proteins that interact with a single genomic locus of interest. In this review, we highlight the power of chromatin proteomics approaches and how these provide complementary alternatives compared with conventional affinity purification methods. Furthermore, we discuss the biochemical challenges that should be addressed to consolidate and expand the role of chromatin proteomics as a key technology in the context of gene expression regulation and epigenetics research in health and disease., Graphical abstract, Highlights • An overview of proteomics methods to study chromatin and gene regulation. • Strength and limitations of the different approaches are highlighted. • An outlook on the outstanding challenges for chromatin proteomics. • Future directions for chromatin proteomics are discussed., In Brief MS-based analysis of chromatin has emerged as a powerful tool to identify proteins associated with gene regulation. Total chromatin isolated from cells can be directly analyzed using MS, further fractionated into transcriptionally active and inactive chromatin, enriched for specific compartment or regions, and potentially used for single-locus isolation. This review highlights recent advances and discusses current challenges that should be addressed to further advance the field of chromatin proteomics.

Glycosylation is a prevalent, yet heterogeneous modification with a broad range of implications in molecular biology. This heterogeneity precludes enrichment strategies that can be universally beneficial for all glycan classes. Thus, choice of enrichment strategy has profound implications on experimental outcomes. Here we review common enrichment strategies used in modern mass spectrometry–based glycoproteomic experiments, including lectins and other affinity chromatographies, hydrophilic interaction chromatography and its derivatives, porous graphitic carbon, reversible and irreversible chemical coupling strategies, and chemical biology tools that often leverage bioorthogonal handles. Interest in glycoproteomics continues to surge as mass spectrometry instrumentation and software improve, so this review aims to help equip researchers with the necessary information to choose appropriate enrichment strategies that best complement these efforts., Graphical Abstract, Highlights • Glycosylation is complex and often requires enrichment prior to analysis • Review of common enrichment strategies for mass spectrometry–based glycoproteomics • Enrichment methods have practical considerations and experimental implications • Appropriate enrichment strategies will complement developments in mass spectrometry, In Brief Interest in mass spectrometry–based glycoproteomics analysis is increasing because of recent advances in instrumentation and data analysis tools. Such studies can provide a wealth of information across a wide spectrum of glycan classes and biological systems. However, many studies require the choice of an enrichment strategy for glycosylated species prior to analysis to obtain the maximum amount of analytical information. Here, common enrichment strategies are reviewed with strengths and weaknesses, and the practical considerations for various methods are discussed.

Distinguishing between Zika and dengue virus infections is critical for accurate treatment, but we still lack detailed understanding of their impact on their host. To identify new protein signatures of the two infections, we used next-generation proteomics to profile 122 serum samples from 62 Zika and dengue patients. We quantified >500 proteins and identified 13 proteins that were significantly differentially expressed (adjusted p-value < 0.05). These proteins typically function in infection and wound healing, with several also linked to pregnancy and brain function. We successfully validated expression differences with Carbonic Anhydrase 2 in both the original and an independent sample set. Three of the differentially expressed proteins, i.e., Fibrinogen Alpha, Platelet Factor 4 Variant 1, and Pro-Platelet Basic Protein, predicted Zika virus infection at a ∼70% true-positive and 6% false-positive rate. Further, we showed that intraindividual temporal changes in protein signatures can disambiguate diagnoses and serve as indicators for past infections. Taken together, we demonstrate that serum proteomics can provide new resources that serve to distinguish between different viral infections., Graphical abstract, Highlights • Analysis of human serum samples with extreme protein abundance ranges • Unique protein signatures for Zika and dengue virus infection • Temporal changes in protein signatures as indicators for past infections • Machine learning to account for confounding factors, In Brief Differentiation between the mosquito-borne Zika and dengue Flavivirus infections is clinically important for correct treatment, but remains challenging. We used mass spectrometry to quantify expression levels for 277 proteins measured in serum samples from 62 patients, providing a resource to the community. We identified 13 proteins with significant differential expression between the closely related types of infections. Most of the proteins link to pregnancy and brain function. We also identified expression signatures that mark ambiguous infections with respect to temporal differences.

Graphical abstract, Highlights • Top 3 label-free quantification is compatible with DMSO in the LC mobile phases. • Significantly different populations of peptides identified in the presence of DMSO. • The precision of protein quantification and the number of proteins quantified improve with DMSO., Dimethyl sulfoxide (DMSO) has been advocated as a beneficial additive to electrospray solvents for peptide analysis due to the improved ionisation efficiency conferred. Previous reports have shown that the resultant improvements in peptide ion signal intensities are non-uniform. As a result, it was hypothesised that inclusion of DMSO in electrospray solvents could be detrimental to the outcome of intensity-based label-free absolute quantification approaches, specifically the top 3 method. The effect of DMSO as a mobile phase additive in top 3 label-free quantification was therefore evaluated. We show that inclusion of DMSO enhances data quality, improving the precision and number of proteins quantified, with no significant change to the quantification values observed in its absence.

Evidence of the involvement of epigenetics in pathologies such as cancer, diabetes, and neurodegeneration has increased global interest in epigenetic modifications. For nearly thirty years, it has been known that cancer cells exhibit abnormal DNA methylation patterns. In contrast, the large-scale analysis of histone post-translational modifications (hPTMs) has lagged behind because classically, histone modification analysis has relied on site specific antibody-based techniques. Mass spectrometry (MS) is a technique that holds the promise to picture the histone code comprehensively in a single experiment. Therefore, we developed an MS-based method that is capable of tracking all possible hPTMs in an untargeted approach. In this way, trends in single and combinatorial hPTMs can be reported and enable prediction of the epigenetic toxicity of compounds. Moreover, this method is based on the use of human cells to provide preliminary data, thereby omitting the need to sacrifice laboratory animals. Improving the workflow and the user-friendliness in order to become a high throughput, easily applicable, toxicological screening assay is an ongoing effort. Still, this novel toxicoepigenetic assay and the data it generates holds great potential for, among others, pharmaceutical industry, food science, clinical diagnostics and, environmental toxicity screening. •There is a growing interest in epigenetic modifications, and more specifically in histone post-translational modifications (hPTMs).•We describe an MS-based workflow that is capable of tracking all possible hPTMs in an untargeted approach that makes use of human cells.•Improving the workflow and the user-friendliness in order to become a high throughput, easily applicable, toxicological screening assay is an ongoing effort., Graphical abstract Image, graphical abstract