NeuCode labels for relative protein quantification

Proteome Birthdating: A Single-Sample Approach for Measuring Global Turnover Dynamics and "Protein Age"

Within a cell, proteins have distinct and highly variable half-lives. As a result, the molecular ages of proteins can range from seconds to years. How the age of a protein influences its environmental interactions is a largely unexplored area of biology. To facilitate such studies, we recently developed a technique termed "proteome birthdating" that differentially labels proteins based on their time of synthesis. Proteome birthdating enables analyses of age distributions of the proteome by tandem mass spectrometry (LC-MS/MS) and provides a methodology for investigating the protein age selectivity of diverse cellular pathways. Proteome birthdating can also provide measurements of protein turnover kinetics from single, sequentially labeled samples. Here, we provide a practical guide for conducting proteome birthdating in in vitro model systems. The outlined workflow covers cell culture, isotopic labeling, protein extraction, enzymatic digestion, peptide cleanup, mass spectrometry, data processing, and theoretical considerations for interpretation of the resulting data. Key features • Proteome birthdating barcodes the proteome with isotopically labeled precursors based on time of synthesis or "age." • Global protein turnover kinetics can be analyzed from single, sequentially labeled biological samples. • Protein age distributions of subsets of the proteome can be analyzed (e.g., ubiquitinated proteins). • Age selectivity of protein properties, cellular pathways, or disease states can be investigated.

Implications of tissue specific STING protein flux and abundance on inflammation and the development of targeted therapeutics

Drugs targeting the ER-resident innate immune receptor Stimulator of Interferon Genes (STING) are in development for treatments of cancer and inflammatory diseases. Accurate determination of STING receptor levels in normal and disease tissue is an essential component of modeling pharmacology and drug-target disposition. Using metabolic labeling with deuterium oxide paired with high resolution mass spectrometry, we report the protein fractional synthesis rates and turnover of STING in wild-type (C57BL/6) and inflamed mice carrying the Trex1 D18N mutation (Trex1D18N) as a STING-dependent model of human Acardi-Goutiéres syndrome. Remarkably, STING protein half-life is tissue specific with the shortest half-life of 4 days in colon and lymph node and longest half-life of 24 days in skeletal muscle. Despite the relative increase in STING protein abundance in the inflamed Trex1D18N mouse, the overall kinetics of protein degradation and resynthesis was similar between Trex1D18N and WT mice. The extent of tissue specific interferon stimulated gene transcription, a hallmark of SLE linked pathophysiology, correlates with the extend of increased STING levels in Trex1D18N tissues and appears inversely proportional to the turnover rate of STING. Understanding STING's fractional protein synthesis rate and half-life provides a valuable component of quantitative modeling of drug pharmacology, dose frequency and targeting tissues of STING directed therapies.

Research Progress in Isotope Labeling/Tags-Based Protein Quantification and Metrology Technologies

Advanced liquid chromatogram-mass spectrometry (LC-MS) and automated large-scale data processing have made MS-based quantitative analysis increasingly useful for research in fields such as biology, medicine, food safety, and beyond. This is because MS-based quantitative analysis can accurately and sensitively analyze thousands of proteins and peptides in a single experiment. However, the precision, coverage, complexity, and resilience of conventional quantification methods vary as a result of the modifications to the analytic environment and the physicochemical characteristics of analytes. Therefore, specially designed approaches are necessary for sample preparation. Dozens of methods have been developed and adapted for these needs based on stable isotopic labeling or isobaric tagging, each with distinct characteristics. In this review, we will summarize the leading strategies and techniques used thus far for MS-based protein quantification as well as analyze the advantages and shortcomings of different approaches. Additionally, we provide an overview of protein metrology development.

A Tutorial Review of Labeling Methods in Mass Spectrometry-Based Quantitative Proteomics

Recent advancements in mass spectrometry (MS) have revolutionized quantitative proteomics, with multiplex isotope labeling emerging as a key strategy for enhancing accuracy, precision, and throughput. This tutorial review offers a comprehensive overview of multiplex isotope labeling techniques, including precursor-based, mass defect-based, reporter ion-based, and hybrid labeling methods. It details their fundamental principles, advantages, and inherent limitations along with strategies to mitigate the limitation of ratio-distortion. This review will also cover the applications and latest progress in these labeling techniques across various domains, including cancer biomarker discovery, neuroproteomics, post-translational modification analysis, cross-linking MS, and single-cell proteomics. This Review aims to provide guidance for researchers on selecting appropriate methods for their specific goals while also highlighting the potential future directions in this rapidly evolving field.

Proteome Birthdating Reveals Age-Selectivity of Protein Ubiquitination

Within a cell, proteins have distinct and highly variable half-lives. As a result, the molecular ages of proteins can range from seconds to years. How the age of a protein influences its environmental interactions is a largely unexplored area of biology. To investigate the age-selectivity of cellular pathways, we developed a methodology termed "proteome birthdating" that barcodes proteins based on their time of synthesis. We demonstrate that this approach provides accurate measurements of protein turnover kinetics from a single biological sample encoding multiple labeling time-points. As a first application of the birthdated proteome, we investigated the age distribution of the human ubiquitinome. Our results indicate that the vast majority of ubiquitinated proteins in a cell consist of newly synthesized proteins and that these young proteins constitute the bulk of the degradative flux through the proteasome. Rapidly ubiquitinated nascent proteins are enriched in cytosolic subunits of large protein complexes. Conversely, proteins destined for the secretory pathway and vesicular transport have older ubiquitinated populations. Our data also identify a smaller subset of older ubiquitinated cellular proteins that do not appear to be targeted to the proteasome for rapid degradation. Together, our data provide an age census of the human ubiquitinome and establish proteome birthdating as a robust methodology for investigating the protein age-selectivity of diverse cellular pathways.

Decoding the ubiquitin landscape by cutting-edge ubiquitinomic approaches

Functional consequences of protein ubiquitination have gone far beyond the degradation regulation as was initially imagined during its discovery 40 years back. The state-of-the-art has revealed the plethora of signaling pathways that are largely regulated by ubiquitination process in eukaryotes. To no surprise, ubiquitination is often dysregulated in many human diseases, including cancer, neurodegeneration and infection. Hence it has become a major focus with high-gain research value for many investigators to unravel new proteoforms, that are the targets of this ubiquitination modification. Despite many biochemical or proteomic approaches available for ubiquitination detection, mass-spectrometry stood out to be the most efficient and transformative technology to read this complex modification script. Here in this review, we have discussed how different ubiquitin codes can be decoded qualitatively and quantitatively following various sequential proteomic approaches to date reported and indicated the current limitations with scope for improvements.

Identification of Proteomic Biomarkers of Acetaminophen-Induced Hepatotoxicity Using Stable Isotope Labeling

Quantitative proteomics approaches based on stable isotopic labeling and mass spectrometry have been widely applied to disease research, drug target discovery, biomarker identification, and systems biology. One of the notable stable isotopic labeling approaches is trypsin-catalyzed 18O/16O labeling, which has its own advantages of low sample consumption, simple labeling procedure, cost-effectiveness, and absence of chemical reactions that potentially generate by-products. In this chapter, a protocol for 18O/16O labeling-based quantitative proteomics approach is described with an application to the identification of proteomic biomarkers of acetaminophen (APAP)-induced hepatotoxicity in rats. The protocol involves first the extraction of proteins from liver tissues of control and APAP-treated rats and digestion into peptides by trypsin. After cleaning of the peptides by solid-phase extraction, equal amounts of peptides from the APAP treatment and the control groups are then subject to trypsin-catalyzed 18O/16O labeling. The labeled peptides are combined and fractionated by off-line strong cation exchange liquid chromatography (SCXLC), and each fraction is then analyzed by nanoflow reversed-phase LC coupled online with tandem mass spectrometry (RPLC-MS/MS) for identification and quantification of differential protein expression between APAP-treated rats and controls. The protocol is applicable to quantitative proteomic analysis for a variety of biological samples.

6-Plex mdSUGAR Isobaric-Labeling Guide Fingerprint Embedding for Glycomics Analysis

Glycans are vital biomolecules with diverse functions in biological processes. Mass spectrometry (MS) has become the most widely employed technology for glycomics studies. However, in the traditional data-dependent acquisition mode, only a subset of the abundant ions during MS1 scans are isolated and fragmented in subsequent MS2 events, which reduces reproducibility and prevents the measurement of low-abundance glycan species. Here, we reported a new method termed 6-plex mdSUGAR isobaric-labeling guide fingerprint embedding (MAGNI), to achieve multiplexed, quantitative, and targeted glycan analysis. The glycan peak signature was embedded by a triplicate-labeling strategy with a 6-plex mdSUGAR tag, and using ultrahigh-resolution mass spectrometers, the low-abundance glycans that carry the mass fingerprints can be recognized on the MS1 spectra through an in-house developed software tool, MAGNIFinder. These embedded unique fingerprints can guide the selection and fragmentation of targeted precursor ions and further provide rich information on glycan structures. Quantitative analysis of two standard glycoproteins demonstrated the accuracy and precision of MAGNI. Using this approach, we identified 304 N-glycans in two ovarian cancer cell lines. Among them, 65 unique N-glycans were found differentially expressed, which indicates a distinct glycosylation pattern for each cell line. Remarkably, 31 N-glycans can be quantified in only 1 × 103 cells, demonstrating the high sensitivity of our method. Taken together, our MAGNI method offers a useful tool for low-abundance N-glycan characterization and is capable of determining small quantitative differences in N-glycan profiling. Therefore, it will be beneficial to the field of glycobiology and will expand our understanding of glycosylation.

Omics and systems view of innate immune pathways

Multiomics approaches to studying systems biology are very powerful techniques that can elucidate changes in the genomic, transcriptomic, proteomic, and metabolomic levels within a cell type in response to an infection. These approaches are valuable for understanding the mechanisms behind disease pathogenesis and how the immune system responds to being challenged. With the emergence of the COVID-19 pandemic, the importance and utility of these tools have become evident in garnering a better understanding of the systems biology within the innate and adaptive immune response and for developing treatments and preventative measures for new and emerging pathogens that pose a threat to human health. In this review, we focus on state-of-the-art omics technologies within the scope of innate immunity.

Enhanced Multiplexing Technology for Proteomics

The identification of thousands of proteins and their relative levels of expression has furthered understanding of biological processes and disease and stimulated new systems biology hypotheses. Quantitative proteomics workflows that rely on analytical assays such as mass spectrometry have facilitated high-throughput measurements of proteins partially due to multiplexing. Multiplexing allows proteome differences across multiple samples to be measured simultaneously, resulting in more accurate quantitation, increased statistical robustness, reduced analysis times, and lower experimental costs. The number of samples that can be multiplexed has evolved from as few as two to more than 50, with studies involving more than 10 samples being denoted as enhanced multiplexing or hyperplexing. In this review, we give an update on emerging multiplexing proteomics techniques and highlight advantages and limitations for enhanced multiplexing strategies.

Proteomic approaches to study ubiquitinomics

As originally described some 40 years ago, protein ubiquitination was thought to serve primarily as a static mark for protein degradation. In the ensuing years, it has become clear that 'ubiquitination' is a structurally diverse and dynamic post-translational modification and is intricately involved in a myriad of signaling pathways in all eukaryote cells. And like other key pathways in the functional proteome, ubiquitin signaling is often disrupted, sometimes severely so, in human pathophysiology. As a result of its central role in normal physiology and human disease, the ubiquitination field is now represented across the full landscape of biomedical research from fundamental structural and biochemical studies to translational and clinical research. In recent years, mass spectrometry has emerged as a powerful technology for the detection and characterization of protein ubiquitination. Herein we detail qualitative and quantitative proteomic methods using a compare/contrast approach to highlight their strengths and weaknesses.

Chemical isotope labeling for quantitative proteomics

Advancements in liquid chromatography and mass spectrometry over the last decades have led to a significant development in mass spectrometry-based proteome quantification approaches. An increasingly attractive strategy is multiplex isotope labeling, which significantly improves the accuracy, precision and throughput of quantitative proteomics in the data-dependent acquisition mode. Isotope labeling-based approaches can be classified into MS1-based and MS2-based quantification. In this review, we give an overview of approaches based on chemical isotope labeling and discuss their principles, benefits, and limitations with the goal to give insights into fundamental questions and provide a useful reference for choosing a method for quantitative proteomics. As a perspective, we discuss the current possibilities and limitations of multiplex, isotope labeling approaches for the data-independent acquisition mode, which is increasing in popularity.

mNeuCode Empowers Targeted Proteome Analysis of Arginine Dimethylation

Characterization of protein arginine dimethylation presents significant challenges due to its occurrence at the substoichiometric level. To enable a targeted MS/MS analysis of these dimethylation sites, we developed the mNeuCode (methyl-neutron-coding) tag by metabolically labeling methylarginine with stable isotopes during cell culture, which generated a diagnostic peak containing the NeuCode isotopologue signature in a high-resolution MS scan. A software tool, termed NeuCodeFinder, was developed for screening the NeuCode signatures in mass spectra. Therefore, a targeted MS/MS workflow was established for proteome-wide discovery of arginine dimethylation. The efficacy and utility were demonstrated by identifying 176 arginine dimethylation sites residing on 70 proteins in HeLa cells. Among them, 38% of the sites and 29% of the dimethylated proteins are novel, including five novel arginine dimethylation sites on the protein FAM98A, which is a substrate of protein arginine methyltransferase 1 (PRMT1). Our results show that deletion of FAM98A in HeLa cells suppressed cell migration, and importantly, dimethylation-deficient mutation suppressed this process as well. Therefore, the PRMT1-FAM98A pathway mediates cell migration possibly through dimethylation of these newly identified sites of FAM98A. Our study might drive the methodological shift from shotgun-based to targeted proteome analysis for interrogation of the substoichiometric biomolecules by using NeuCode-enabled techniques.

Proteomic analysis of antiviral innate immunity

The capacity of host cells to detect and restrict an infecting virus rests on an array of cell-autonomous antiviral effectors and innate immune receptors that can trigger inflammatory processes at tissue and organismal levels. Dynamic changes in protein abundance, subcellular localisation, post-translational modifications and interactions with other biomolecules govern these processes. Proteomics is therefore an ideal experimental tool to discover novel mechanisms of host antiviral immunity. Additional information can be gleaned both about host and virus by systematic analysis of viral immune evasion strategies. In this review, we summarise recent advances in proteomic technologies and their application to antiviral innate immunity.

High-Throughput Quantitative Glycomics Enabled by 12-plex Isobaric Multiplex Labeling Reagents for Carbonyl-Containing Compound (SUGAR) Tags

Glycans, which are widely distributed on most proteins and cell surfaces, are a class of important biomolecules playing crucial roles in various biological processes such as immune response and cellular communication. Modern mass spectrometry (MS) coupled with novel chemical probes greatly facilitates routine analysis of glycans. However, the requirement of high-throughput analysis still calls for advanced tools to be developed. Recently, we devised isobaric multiplex reagents for carbonyl-containing compound (SUGAR) tags for 4-plex N-glycan analysis. To further improve the throughput, we utilized the subtle mass differences among different isotopologues and expanded the multiplexing capacity to 12 channels, a 3-fold throughput improvement for the original SUGAR tag design and achieved high-throughput N-glycan analysis in a single LC-MS/MS injection. We then applied 12-plex SUGAR tags to profile the N-glycans in four subtypes of human Immunoglobulin G (IgG) and to investigate the N-glycan changes in the endometrial cancer cells (ECC1) treated with Atovaquone, a quinone antimicrobial medication, and a dihydroorotate dehydrogenase (DHODH) inhibitor. Data are available via ProteomeXchange with the identifier PXD038501.

Coisolation of Peptide Pairs for Peptide Identification and MS/MS-Based Quantification

Stable-isotope labeling with amino acids in cell culture (SILAC)-based metabolic labeling is a widely adopted proteomics approach that enables quantitative comparisons among a variety of experimental conditions. Despite its quantitative capacity, SILAC experiments analyzed with data-dependent acquisition (DDA) do not fully leverage peptide pair information for identification and suffer from undersampling compared to label-free proteomic experiments. Herein, we developed a DDA strategy that coisolates and fragments SILAC peptide pairs and uses y-ions for their relative quantification. To facilitate the analysis of this type of data, we adapted the Comet sequence database search engine to make use of SILAC peptide paired fragments and developed a tool to annotate and quantify MS/MS spectra of coisolated SILAC pairs. This peptide pair coisolation approach generally improved expectation scores compared to the traditional DDA approach. Fragment ion quantification performed similarly well to precursor quantification in the MS1 and achieved more quantifications. Lastly, our method enables reliable MS/MS quantification of SILAC proteome mixtures with overlapping isotopic distributions. This study shows the feasibility of the coisolation approach. Coupling this approach with intelligent acquisition strategies has the potential to improve SILAC peptide sampling and quantification.

Complexome Profiling-Exploring Mitochondrial Protein Complexes in Health and Disease

Complexome profiling (CP) is a state-of-the-art approach that combines separation of native proteins by electrophoresis, size exclusion chromatography or density gradient centrifugation with tandem mass spectrometry identification and quantification. Resulting data are computationally clustered to visualize the inventory, abundance and arrangement of multiprotein complexes in a biological sample. Since its formal introduction a decade ago, this method has been mostly applied to explore not only the composition and abundance of mitochondrial oxidative phosphorylation (OXPHOS) complexes in several species but also to identify novel protein interactors involved in their assembly, maintenance and functions. Besides, complexome profiling has been utilized to study the dynamics of OXPHOS complexes, as well as the impact of an increasing number of mutations leading to mitochondrial disorders or rearrangements of the whole mitochondrial complexome. Here, we summarize the major findings obtained by this approach; emphasize its advantages and current limitations; discuss multiple examples on how this tool could be applied to further investigate pathophysiological mechanisms and comment on the latest advances and opportunity areas to keep developing this methodology.

Neutron encoded derivatization of endothelial cell lysates for quantitation of aldehyde metabolites using nESI-LC-HRMS

Biological aldehydes are difficult to analyze by electrospray ionization mass spectrometry due to their poor proton affinity and low biological concentrations. Chemical derivatization with stable isotope tags is used here for sample multiplexing, increased throughput, improved signal intensity, and quantitation. Nine quaternary amine tags with mass differences as low as 0.0058 Da had no observable chromatographic shifts, small amounts of ion suppression, and minimal matrix effects. Low concentration perfluoropentanoic acid was used as an ion pairing reagent to improve the retention of derivatized aldehydes. Perfluoropentanoic acid addition showed an average of three-fold improvement in limits of detection, 50% reduction in peak width, and 2.5 fold increase in analyte retention. Analysis of fifteen tagged aldehydes yielded an average of 13 nM limit of detection, 9 %RSD, R² of 0.995, and linear dynamic range of 40-1000 nM. In a single 20 min separation, absolute quantitative data was obtained for 11 reactive aldehydes across 8 aortic endothelial cell samples. High glucose treatment produced significant changes to malondialdehyde, decanal, and (2E)-hexadecenal. These changes are consistent with glucose-induced oxidative stress. This method demonstrates that neutron encoded tagging of aldehydes is suitable for the analysis of complex samples.

Identification and characterization of post-translational modifications: Clinical implications

Post-translational modifications (PTMs) generate marginally modified isoforms of native peptides, proteins and lipoproteins thereby regulating protein functions, molecular interactions, and localization. With a key role in functional proteomics, post-translational modifications are recently also associated with the onsets and progressions of various diseases, such as cancer, cardiovascular, renal, and metabolic diseases. With the impact of post-translational modifications becoming increasingly clear, its reliable detection and quantification remain a major obstacle in the translation of these novel pathological markers into clinical diagnosis. While current antibody-based clinical diagnostics struggle to detect and quantify these marginal protein and lipoprotein alterations, state-of-the-art mass spectrometric, proteomic approaches provide the mass accuracy and resolving power necessary to isolate, identify and quantify novel and pathological post-translational modifications; however clinical translation of mass spectrometric applications are still facing major challenges. Here we review the status quo of the clinical translation of mass-spectrometric applications as novel diagnostic tools for the identification and quantification of post-translational modifications and focus on the emerging role of mass spectrometric methods in the clinical assessment of PTMs in disease states.

MSTopDiff: A Tool for the Visualization of Mass Shifts in Deconvoluted Top-Down Proteomics Data for the Database-Independent Detection of Protein Modifications

Top-down proteomics analyzes intact proteoforms with all of their post-translational modifications and genetic and RNA splice variants. In addition, modifications introduced either deliberately or inadvertently during sample preparation, that is, via oxidation, alkylation, or labeling reagents, or through the formation of noncovalent adducts (e.g., detergents) further increase the sample complexity. To facilitate the recognition of protein modifications introduced during top-down analysis, we developed MSTopDiff, a software tool with a graphical user interface written in Python, which allows one to detect protein modifications by calculating and visualizing mass differences in top-down data without the prerequisite of a database search. We demonstrate the successful application of MSTopDiff for the detection of artifacts originating from oxidation, formylation, overlabeling during isobaric labeling, and adduct formation with cations or sodium dodecyl sulfate. MSTopDiff offers several modes of data representation using deconvoluted MS1 or MS2 spectra. In addition to artificial modifications, the tool enables the visualization of biological modifications such as phosphorylation and acetylation. MSTopDiff provides an overview of the artificial and biological modifications in top-down proteomics samples, which makes it a valuable tool in quality control of standard workflows and for parameter evaluation during method development.

Quantitative Temporal Viromics

The abundance, localization, modifications, and protein-protein interactions of many host cell and virus proteins can change dynamically throughout the course of any viral infection. Studying these changes is critical for a comprehensive understanding of how viruses replicate and cause disease, as well as for the development of antiviral therapeutics and vaccines. Previously, we developed a mass spectrometry-based technique called quantitative temporal viromics (QTV), which employs isobaric tandem mass tags (TMTs) to allow precise comparative quantification of host and virus proteomes through a whole time course of infection. In this review, we discuss the utility and applications of QTV, exemplified by numerous studies that have since used proteomics with a variety of quantitative techniques to study virus infection through time. Expected final online publication date for the Annual Review of Virology, Volume 8 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Subcellular proteomics

The eukaryotic cell is compartmentalized into subcellular niches, including membrane-bound and membrane-less organelles. Proteins localize to these niches to fulfil their function, enabling discreet biological processes to occur in synchrony. Dynamic movement of proteins between niches is essential for cellular processes such as signalling, growth, proliferation, motility and programmed cell death, and mutations causing aberrant protein localization are associated with a wide range of diseases. Determining the location of proteins in different cell states and cell types and how proteins relocalize following perturbation is important for understanding their functions, related cellular processes and pathologies associated with their mislocalization. In this Primer, we cover the major spatial proteomics methods for determining the location, distribution and abundance of proteins within subcellular structures. These technologies include fluorescent imaging, protein proximity labelling, organelle purification and cell-wide biochemical fractionation. We describe their workflows, data outputs and applications in exploring different cell biological scenarios, and discuss their main limitations. Finally, we describe emerging technologies and identify areas that require technological innovation to allow better characterization of the spatial proteome.

Phenotypic screening with target identification and validation in the discovery and development of E3 ligase modulators

The use of phenotypic screening was central to the discovery and development of novel thalidomide analogs, the IMiDs (immunomodulatory drugs) agents. With the discovery that these agents bind the E3 ligase, CRL4^CRBN, and alter its substrate specificity, there has been a great deal of endeavor to discover other small molecules that can modulate alternative E3 ligases. Furthermore, the chemical properties necessary for drug discovery and the rules by which neo-substrates are selected for degradation are being defined in the context of phenotypic alterations in specific cellular systems. This review gives a detailed summary of these recent advances and the methodologies being exploited to understand the mechanism of action of emerging protein degradation therapies.

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.

Defining the carrier proteome limit for single-cell proteomics

Single-cell proteomics by mass spectrometry (SCoPE-MS) is a recently introduced method to quantify multiplexed single-cell proteomes. While this technique has generated great excitement, the underlying technologies (isobaric labeling and mass spectrometry (MS)) have technical limitations with the potential to affect data quality and biological interpretation. These limitations are particularly relevant when a carrier proteome, a sample added at 25-500× the amount of a single-cell proteome, is used to enable peptide identifications. Here we perform controlled experiments with increasing carrier proteome amounts and evaluate quantitative accuracy, as it relates to mass analyzer dynamic range, multiplexing level and number of ions sampled. We demonstrate that an increase in carrier proteome level requires a concomitant increase in the number of ions sampled to maintain quantitative accuracy. Lastly, we introduce Single-Cell Proteomics Companion (SCPCompanion), a software tool that enables rapid evaluation of single-cell proteomic data and recommends instrument and data analysis parameters for improved data quality.

Emerging mass spectrometry-based proteomics methodologies for novel biomedical applications

Research into the basic biology of human health and disease, as well as translational human research and clinical applications, all benefit from the growing accessibility and versatility of mass spectrometry (MS)-based proteomics. Although once limited in throughput and sensitivity, proteomic studies have quickly grown in scope and scale over the last decade due to significant advances in instrumentation, computational approaches, and bio-sample preparation. Here, we review these latest developments in MS and highlight how these techniques are used to study the mechanisms, diagnosis, and treatment of human diseases. We first describe recent groundbreaking technological advancements for MS-based proteomics, including novel data acquisition techniques and protein quantification approaches. Next, we describe innovations that enable the unprecedented depth of coverage in protein signaling and spatiotemporal protein distributions, including studies of post-translational modifications, protein turnover, and single-cell proteomics. Finally, we explore new workflows to investigate protein complexes and structures, and we present new approaches for protein-protein interaction studies and intact protein or top-down MS. While these approaches are only recently incipient, we anticipate that their use in biomedical MS proteomics research will offer actionable discoveries for the improvement of human health.

Combinatory Treatment of Canavanine and Arginine Deprivation Efficiently Targets Human Glioblastoma Cells via Pleiotropic Mechanisms

Glioblastomas are the most frequent and aggressive form of primary brain tumors with no efficient cure. However, they often exhibit specific metabolic shifts that include deficiency in the biosynthesis of and dependence on certain exogenous amino acids. Here, we evaluated, in vitro, a novel combinatory antiglioblastoma approach based on arginine deprivation and canavanine, an arginine analogue of plant origin, using two human glioblastoma cell models, U251MG and U87MG. The combinatory treatment profoundly affected cell viability, morphology, motility and adhesion, destabilizing the cytoskeleton and mitochondrial network, and induced apoptotic cell death. Importantly, the effects were selective toward glioblastoma cells, as they were not pronounced for primary rat glial cells. At the molecular level, canavanine inhibited prosurvival kinases such as FAK, Akt and AMPK. Its effects on protein synthesis and stress response pathways were more complex and dependent on exposure time. We directly observed canavanine incorporation into nascent proteins by using quantitative proteomics. Although canavanine in the absence of arginine readily incorporated into polypeptides, no motif preference for such incorporation was observed. Our findings provide a strong rationale for further developing the proposed modality based on canavanine and arginine deprivation as a potential antiglioblastoma metabolic therapy independent of the blood-brain barrier.

Isobaric Matching between Runs and Novel PSM-Level Normalization in MaxQuant Strongly Improve Reporter Ion-Based Quantification

Isobaric labeling has the promise of combining high sample multiplexing with precise quantification. However, normalization issues and the missing value problem of complete n-plexes hamper quantification across more than one n-plex. Here, we introduce two novel algorithms implemented in MaxQuant that substantially improve the data analysis with multiple n-plexes. First, isobaric matching between runs makes use of the three-dimensional MS1 features to transfer identifications from identified to unidentified MS/MS spectra between liquid chromatography–mass spectrometry runs in order to utilize reporter ion intensities in unidentified spectra for quantification. On typical datasets, we observe a significant gain in MS/MS spectra that can be used for quantification. Second, we introduce a novel PSM-level normalization, applicable to data with and without the common reference channel. It is a weighted median-based method, in which the weights reflect the number of ions that were used for fragmentation. On a typical dataset, we observe complete removal of batch effects and dominance of the biological sample grouping after normalization. Furthermore, we provide many novel processing and normalization options in Perseus, the companion software for the downstream analysis of quantitative proteomics results. All novel tools and algorithms are available with the regular MaxQuant and Perseus releases, which are downloadable at http://maxquant.org.

Analyzing Ribosome Remodeling in Health and Disease

Increasing evidence suggests that ribosomes actively regulate protein synthesis. However, much of this evidence is indirect, leaving this layer of gene regulation largely unexplored, in part due to methodological limitations. Indeed, evidence is reviewed demonstrating that commonly used methods, such as transcriptomics, are inadequate because the variability in mRNAs coding for ribosomal proteins (RP) does not necessarily correspond to RP variability. Thus protein remodeling of ribosomes should be investigated by methods that allow direct quantification of RPs, ideally of isolated ribosomes. Such methods are reviewed, focusing on mass spectrometry and emphasizing method-specific biases and approaches to control these biases. It is argued that using multiple complementary methods can help reduce the danger of interpreting reproducible systematic biases as evidence for ribosome remodeling.

21-plex DiLeu Isobaric Tags for High-Throughput Quantitative Proteomics

Isobaric tags enable multiplexed quantitative analysis of many biological samples in a single LC-MS/MS experiment. As a cost-effective alternative to expensive commercial isobaric tagging reagents, we developed our own custom N,N-dimethylleucine "DiLeu" isobaric tags for quantitative proteomics. Here, we present a new generation of DiLeu tags that achieves 21-plex quantification in high-resolution HCD MS/MS spectra via distinct reporter ions that differ in mass from each other by a minimum of 3 mDa. The 21-plex set retains the compact tag structure and existing isotopologues of the 12-plex set but includes nine new reporter variants formulated with unique configurations of ¹³C, ¹⁵N, and ²H stable isotopes, each synthesized in-house via a stepwise N-monomethylation synthesis strategy using readily available reagents. Thus, multiplexing capacity is expanded significantly, while preserving the performance and low cost of the previous implementation. We show that 21-plex DiLeu tags generate strong reporter ions following HCD fragmentation of labeled peptides acquired on Orbitrap platforms at a minimum of 60,000 resolving power (at 400 m/z), and we demonstrate accurate 21-plex quantification of labeled K562 human cell line protein digests via single-shot nanoLC-MS/MS analysis on a Q Exactive HF system.

Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis

Recent advances in mass spectrometry (MS)-based proteomics have enabled tremendous progress in the understanding of cellular mechanisms, disease progression, and the relationship between genotype and phenotype. Though many popular bioinformatics methods in proteomics are derived from other omics studies, novel analysis strategies are required to deal with the unique characteristics of proteomics data. In this review, we discuss the current developments in the bioinformatics methods used in proteomics and how they facilitate the mechanistic understanding of biological processes. We first introduce bioinformatics software and tools designed for mass spectrometry-based protein identification and quantification, and then we review the different statistical and machine learning methods that have been developed to perform comprehensive analysis in proteomics studies. We conclude with a discussion of how quantitative protein data can be used to reconstruct protein interactions and signaling networks.

MS1-Level Proteome Quantification Platform Allowing Maximally Increased Multiplexity for SILAC and In Vitro Chemical Labeling

Quantitative proteomic platforms based on precursor intensity in mass spectrometry (MS1-level) uniquely support in vivo metabolic labeling with superior quantification accuracy but suffer from limited multiplexity (≤3-plex) and frequent missing quantities. Here we present a new MS1-level quantification platform that allows maximal multiplexing with high quantification accuracy and precision for the given labeling scheme. The platform currently comprises 6-plex in vivo SILAC or in vitro diethylation labeling with a dedicated algorithm and is also expandable to higher multiplexity (e.g., nine-plex for SILAC). For complex samples with broad dynamic ranges such as total cell lysates, our platform performs highly accurately and free of missing quantities. Furthermore, we successfully applied our method to measure protein synthesis rate under heat shock response in human cells by 6-plex pulsed SILAC experiments, demonstrating the unique biological merits of our in vivo platform to disclose translational regulations for cellular response to stress.

High-throughput quantitative top-down proteomics

Top-down mass spectrometry (MS) analyzes intact proteins at the proteoform level, which allows researchers to better understand the functions of protein modifications. Recently, top-down proteomics has increased in popularity due to advancements in high-resolution mass spectrometers, increased efficiency in liquid chromatography (LC) separation, and advances in data analysis software. Some unique protein proteoforms, which have been distinguished using top-down MS, have even been shown to exhibit marked variation in biological function compared to similar proteoforms. However, the qualitative identification of a particular proteoform may not be enough to determine the biological relevance of that proteoform. Quantitative top-down MS methods have been notably applied to the study of the differing biological functions of protein proteoforms and have allowed researchers to explore proteomes at the proteoform, rather than the peptide, level. Here, we review the top-down MS methods that have been used to quantitatively identify intact proteins, discuss current applications of quantitative top-down MS analysis, and present new areas where quantitative top-down MS analysis may be implemented.

Evaluating Combined Precursor Isotopic Labeling and Isobaric Tagging Performance on Orbitraps To Study the Peripheral Proteome of Alzheimer's Disease

Combined precursor isotopic labeling and isobaric tagging (cPILOT) is an enhanced multiplexing strategy currently capable of analyzing up to 24 samples simultaneously. This capability is especially helpful when studying multiple tissues and biological replicates in models of disease, such as Alzheimer's disease (AD). Here, cPILOT was used to study proteomes from heart, liver, and brain tissues in a late-stage amyloid precursor protein/presenilin-1 (APP/PS-1) human transgenic double-knock-in mouse model of AD. The original global cPILOT assay developed on an Orbitrap Velos instrument was transitioned to an Orbitrap Fusion Lumos instrument. The advantages of faster scan rates, lower limits of detection, and synchronous precursor selection on the Fusion Lumos afford greater numbers of isobarically tagged peptides to be quantified in comparison to the Orbitrap Velos. Parameters such as LC gradient, m/z isolation window, dynamic exclusion, targeted mass analyses, and synchronous precursor scan were optimized leading to >600 000 PSMs, corresponding to 6074 proteins. Overall, these studies inform of system-wide changes in brain, heart, and liver proteins from a mouse model of AD.

Mass Spectrometry-Based Analysis of Time-Resolved Proteome Quantification

The aspect of time is essential in biological processes and thus it is important to be able to monitor signaling molecules through time. Proteins are key players in cellular signaling and they respond to many stimuli and change their expression in many time-dependent processes. Mass spectrometry (MS) is an important tool for studying proteins, including their posttranslational modifications and their interaction partners-both in qualitative and quantitative ways. In order to distinguish the different trends over time, proteins, modification sites, and interacting proteins must be compared between different time points, and therefore relative quantification is preferred. In this review, the progress and challenges for MS-based analysis of time-resolved proteome dynamics are discussed. Further, aspects on model systems, technologies, sampling frequencies, and presentation of the dynamic data are discussed.

Improved Sensitivity for Protein Turnover Quantification by Monitoring Immonium Ion Isotopologue Abundance

We describe an analytical strategy allowing for the direct quantification of stable isotope label incorporation in newly synthesized proteins following administration of the stable isotope tracer deuterium oxide. We present a demonstration of coupling high-resolution mass spectrometry, metabolic stable isotope labeling, and MS/MS-based isotopologue quantification for the measurement of protein turnover. Stable isotope labeling with deuterium oxide, followed by immonium ion isotopologue quantification, is a more sensitive strategy for determining protein fractional synthesis rates compared to peptide centric mass isotopomer distribution analysis approaches when labeling time and/or stable isotope tracer exposure is limited and, as such, offers a great advantage for human studies.

High-Resolution Enabled 5-plex Mass Defect-Based N, N-Dimethyl Leucine Tags for Quantitative Proteomics

A mass defect-based labeling strategy provides high accuracy as an MS¹-centric quantification method, avoiding the ratio compression that affects isobaric label-based reporter ion quantification. We have developed cost-effective 5-plex mass defect N, N-dimethyl leucine (mdDiLeu) tags for quantification of various biological samples with increased multiplexing at a given resolving power afforded by the addition of mass difference isotopologues. The combination of mass difference and mass defect produces two labeled peak clusters separated by 5 Da in MS¹ spectra that are detected as five isotopic peaks at high resolution with mass differences of 15, 17, and 18 mDa per tag. Synthesis of each of the 5-plex mdDiLeu tags is accomplished by a single straightforward reaction step, making it accessible to any lab. To demonstrate 5-plex mdDiLeu for quantitative proteomics, we perform proof-of-principle experiments of mdDiLeu-labeled Saccharomyces cerevisiae lysate digest on an Orbitrap Fusion Lumos mass spectrometer.

PKA and HOG signaling contribute separable roles to anaerobic xylose fermentation in yeast engineered for biofuel production

Lignocellulosic biomass offers a sustainable source for biofuel production that does not compete with food-based cropping systems. Importantly, two critical bottlenecks prevent economic adoption: many industrially relevant microorganisms cannot ferment pentose sugars prevalent in lignocellulosic medium, leaving a significant amount of carbon unutilized. Furthermore, chemical biomass pretreatment required to release fermentable sugars generates a variety of toxins, which inhibit microbial growth and metabolism, specifically limiting pentose utilization in engineered strains. Here we dissected genetic determinants of anaerobic xylose fermentation and stress tolerance in chemically pretreated corn stover biomass, called hydrolysate. We previously revealed that loss-of-function mutations in the stress-responsive MAP kinase HOG1 and negative regulator of the RAS/Protein Kinase A (PKA) pathway, IRA2, enhances anaerobic xylose fermentation. However, these mutations likely reduce cells' ability to tolerate the toxins present in lignocellulosic hydrolysate, making the strain especially vulnerable to it. We tested the contributions of Hog1 and PKA signaling via IRA2 or PKA negative regulatory subunit BCY1 to metabolism, growth, and stress tolerance in corn stover hydrolysate and laboratory medium with mixed sugars. We found mutations causing upregulated PKA activity increase growth rate and glucose consumption in various media but do not have a specific impact on xylose fermentation. In contrast, mutation of HOG1 specifically increased xylose usage. We hypothesized improving stress tolerance would enhance the rate of xylose consumption in hydrolysate. Surprisingly, increasing stress tolerance did not augment xylose fermentation in lignocellulosic medium in this strain background, suggesting other mechanisms besides cellular stress limit this strain's ability for anaerobic xylose fermentation in hydrolysate.

A Review on Quantitative Multiplexed Proteomics

Over the last few decades, mass spectrometry-based proteomics has become an increasingly powerful tool that is now able to routinely detect and quantify thousands of proteins. A major advance for global protein quantification was the introduction of isobaric tags, which, in a single experiment, enabled the global quantification of proteins across multiple samples. Herein, these methods are referred to as multiplexed proteomics. The principles, advantages, and drawbacks of various multiplexed proteomics techniques are discussed and compared with alternative approaches. We also discuss how the emerging combination of multiplexing with targeted proteomics might enable the reliable and high-quality quantification of very low abundance proteins across multiple conditions. Lastly, we suggest that fusing multiplexed proteomics with data-independent acquisition approaches might enable the comparison of hundreds of different samples without missing values, while maintaining the superb measurement precision and accuracy obtainable with isobaric tag quantification.

Identification and Quantification of Proteoforms by Mass Spectrometry

A proteoform is a defined form of a protein derived from a given gene with a specific amino acid sequence and localized post-translational modifications. In top-down proteomic analyses, proteoforms are identified and quantified through mass spectrometric analysis of intact proteins. Recent technological developments have enabled comprehensive proteoform analyses in complex samples, and an increasing number of laboratories are adopting top-down proteomic workflows. In this review, some recent advances are outlined and current challenges and future directions for the field are discussed.

Decoupling Yeast Cell Division and Stress Defense Implicates mRNA Repression in Translational Reallocation during Stress

Stress tolerance and rapid growth are often competing interests in cells. Upon severe environmental stress, many organisms activate defense systems concurrent with growth arrest. There has been debate as to whether aspects of the stress-activated transcriptome are regulated by stress or an indirect byproduct of reduced proliferation. For example, stressed Saccharomyces cerevisiae cells mount a common gene expression program called the environmental stress response (ESR) [1] comprised of ∼300 induced (iESR) transcripts involved in stress defense and ∼600 reduced (rESR) mRNAs encoding ribosomal proteins (RPs) and ribosome biogenesis factors (RiBi) important for division. Because ESR activation also correlates with reduced growth rate in nutrient-restricted chemostats and prolonged G1 in slow-growing mutants, an alternate proposal is that the ESR is simply a consequence of reduced division [2-5]. A major challenge is that past studies did not separate effects of division arrest and stress defense; thus, the true responsiveness of the ESR-and the purpose of stress-dependent rESR repression in particular-remains unclear. Here, we decoupled cell division from the stress response by following transcriptome, proteome, and polysome changes in arrested cells responding to acute stress. We show that the ESR cannot be explained by changes in growth rate or cell-cycle phase during stress acclimation. Instead, failure to repress rESR transcripts reduces polysome association of induced transcripts, delaying production of their proteins. Our results suggest that stressed cells alleviate competition for translation factors by removing mRNAs and ribosomes from the translating pool, directing translational capacity toward induced transcripts to accelerate protein production.

Computational Methods for Understanding Mass Spectrometry–Based Shotgun Proteomics Data

Computational proteomics is the data science concerned with the identification and quantification of proteins from high-throughput data and the biological interpretation of their concentration changes, posttranslational modifications, interactions, and subcellular localizations. Today, these data most often originate from mass spectrometry–based shotgun proteomics experiments. In this review, we survey computational methods for the analysis of such proteomics data, focusing on the explanation of the key concepts. Starting with mass spectrometric feature detection, we then cover methods for the identification of peptides. Subsequently, protein inference and the control of false discovery rates are highly important topics covered. We then discuss methods for the quantification of peptides and proteins. A section on downstream data analysis covers exploratory statistics, network analysis, machine learning, and multiomics data integration. Finally, we discuss current developments and provide an outlook on what the near future of computational proteomics might bear.

Relative and Absolute Quantitation in Mass Spectrometry-Based Proteomics

Mass spectrometry-based quantitative proteomics is a powerful tool for gaining insights into function and dynamics of biological systems. However, peptides with different sequences have different ionization efficiencies, and their intensities in a mass spectrum are not correlated with their abundances. Therefore, various label-free or stable isotope label-based quantitation methods have emerged to assist mass spectrometry to perform comparative proteomic experiments, thus enabling nonbiased identification of thousands of proteins differentially expressed in healthy versus diseased cells. Here, we discuss the most widely used label-free and metabolic-, enzymatic-, and chemical labeling-based proteomic strategies for relative and absolute quantitation. We summarize the specific strengths and weaknesses of each technique in terms of quantification accuracy, proteome coverage, multiplexing capability, and robustness. Applications of each strategy for solving specific biological complexities are also presented.

Quantitative Glycomic Analysis by Mass-Defect-Based Dimethyl Pyrimidinyl Ornithine (DiPyrO) Tags and High-Resolution Mass Spectrometry

We recently developed a novel amine-reactive mass-defect-based chemical tag, dimethyl pyrimidinyl ornithine (DiPyrO), for quantitative proteomic analysis at the MS¹ level. In this work, we further extend the application of the DiPyrO tag, which provides amine group reactivity, optical detection capability, and improved electrospray sensitivity, to quantify N-linked glycans enzymatically released from glycoproteins in the glycosylamine form. Duplex DiPyrO tags that differ in mass by 45.3 mDa were used to label the glycosylamine moieties of freshly released N-glycosylamines from glycoprotein standards and human serum proteins. We demonstrate that both MALDI-LTQ-Orbitrap and nano-HILIC LC/MS/MS Fusion Lumos Orbitrap platforms are capable of resolving the singly or multiply charged N-glycans labeled with mass-defect DiPyrO tags. Dynamic range of quantification, based on MS¹ peak intensities, was evaluated across 2 orders of magnitude. With optimized N-glycan release conditions, glycosylamine labeling conditions, and MS acquisition parameters, the N-glycan profiles and abundances in human serum proteins of cancer patients before and after chemotherapy were compared. Moreover, this study also opens a door for using well-developed amine-reactive tags for relative quantification of glycans, which could be widely applied.