Application Of Proteomics Research Articles

Recent Advancements in Proteomic Sample Preparation from Recombinant Chinese Hamster Ovary Cells.

Chinese hamster ovary (CHO) cells are the most commonly used mammalian host cell line for biopharmaceutical production because of their ability to correctly fold and post-translationally modify recombinant proteins that are compatible with human use. Proteomics, along with other "omic platforms," are being used to understand the biology of CHO cells with the ultimate aim to enhance CHO cell factories for more efficient production of biopharmaceuticals. Liquid chromatography-mass spectrometry (LC-MS) for proteomic analysis has become the standard technique for profiling proteomes. There are three stages to a typical bottom-up approach, namely, sample preparation, LC-MS/MS, and data analysis. Although there have been major advances in LC-MS/MS instrumentation and data analysis tools, sample preparation is still the crucial stage that affects the overall efficiency of any proteomic study. In this chapter, we present a comparison of a number of widely used sample preparation methods for proteomic applications, including in-solution digestion and commercially available device-based kits. All methods performed well; however, each method has inherent advantages and disadvantages.

Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabolomics

With the advent of high-throughput technologies, the field of omics has made significant strides in characterizing biological systems at various levels of complexity. Transcriptomics, proteomics, and metabolomics are the three most widely used omics technologies, each providing unique insights into different layers of a biological system. However, analyzing each omics data set separately may not provide a comprehensive understanding of the subject under study. Therefore, integrating multi-omics data has become increasingly important in bioinformatics research. In this article, we review strategies for integrating transcriptomics, proteomics, and metabolomics data, including co-expression analysis, metabolite–gene networks, constraint-based models, pathway enrichment analysis, and interactome analysis. We discuss combined omics integration approaches, correlation-based strategies, and machine learning techniques that utilize one or more types of omics data. By presenting these methods, we aim to provide researchers with a better understanding of how to integrate omics data to gain a more comprehensive view of a biological system, facilitating the identification of complex patterns and interactions that might be missed by single-omics analyses.

Mass Spectrometry-Based Proteomics for Assessing Epitranscriptomic Regulations.

Epitranscriptomics is a rapidly evolving field that explores chemical modifications in RNA and how they contribute to dynamic and reversible regulations of gene expression. These modifications, for example, N6-methyladenosine (m6A), are crucial in various RNA metabolic processes, including splicing, stability, subcellular localization, and translation efficiency of mRNAs. Mass spectrometry-based proteomics has become an indispensable tool in unraveling the complexities of epitranscriptomics, offering high-throughput, precise protein identification, and accurate quantification of differential protein expression. Over the past two decades, advances in mass spectrometry, including the improvement of high-resolution mass spectrometers and innovative sample preparation methods, have allowed researchers to perform in-depth analyses of epitranscriptomic regulations. This review focuses on the applications of bottom-up proteomics in the field of epitranscriptomics, particularly in identifying and quantifying epitranscriptomic reader, writer, and eraser (RWE) proteins and in characterizing their functions, posttranslational modifications, and interactions with other proteins. Together, by leveraging modern proteomics, researchers can gain deep insights into the intricate regulatory networks of RNA modifications, advancing fundamental biology, and fostering potential therapeutic applications.

Use of a Novel Whole Blood Separation and Transport Device for Targeted and Untargeted Proteomics.

There is significant interest in developing alternatives to traditional blood transportation and separation methods, which often require centrifugation and cold storage to preserve specimen integrity. Here we provide new performance findings that characterize a novel device that separates whole blood via lateral flow then dries the isolated components for room temperature storage and transport. Untargeted proteomics was performed on non-small cell lung cancer (NSCLC) and normal healthy plasma applied to the device or prepared neat. Significantly, proteomic profiles from the storage device were more reproducible than from neat plasma. Proteins depleted or absent in the device preparation were shown to be absorbed onto the device membrane through largely hydrophilic interactions. Use of the device did not impact proteins relevant to an NSCLC clinical immune classifier. The device was also evaluated for use in targeted proteomics experiments using multiple-reaction monitoring (MRM) mass spectrometry. Intra-specimen detection intensity for protein targets between neat and device preparations showed a strong correlation, and device variation was comparable to the neat after normalization. Inter-specimen measurements between the device and neat preparations were also highly concordant. These studies demonstrate that the lateral flow device is a viable blood separation and transportation tool for untargeted and targeted proteomics applications.

Appendage- and Scaffold-Diverse Electrophilic and Photoreactive Probes for Integrated Phenotypic Screening-Target Identification Campaigns via a Minimalist Bifunctional Isocyanide.

One of the grand challenges in chemical biology is identifying a small-molecule modulator for all proteins within a proteome. To expand the variety and number of ligandable proteins for drug discovery, the objective of this study was to synthesize and evaluate the protein target profiles of electrophilic and photoreactive fully functionalized small-molecule probes (FFSMPs) featuring increased scaffold-, appendage-, and protein-reactive functional group (PRFG) diversity. FFSMPs contain: (1) a protein-binding motif, (2) an electrophilic or photoreactive PRFG for target protein capture, and (3) a terminal alkyne for click chemistry-based proteomic applications. These compounds can be directly applied in phenotypic screening programs to identify ligand-protein pairs in cells unbiasedly. Herein, we highlight 17 examples from 34 structurally diverse FFSMPs featuring five electrophiles, three photoreactive groups, and 15 chemical scaffolds. Essential to the synthesis of the FFSMPs was a new minimalist bifunctional isocyanide in an "isocyanide-based multicomponent reaction-Boc deprotection-arming" synthetic sequence. To the best of our knowledge, this is the first report concerning the preparation of appendage- and scaffold-diverse FFSMPs for integrated phenotypic screening-target identification campaigns with the ability to examine either electrophilic or photoreactive PRFGs. In contrast, the status quo for such studies has been appendage-diverse FFSMPs comprised of a single chemical scaffold and a single PRFG, which limits efficient target protein capture and/or chemical space sampling significantly in the quest for discovering new drug targets and/or compounds with novel mechanisms of action. Phenotypic screening of the electrophilic members of our library identified several FFSMPs with potent antiproliferative activity against MCF10CA1a breast cancer cells. One of these FFSMPs (Compound 4a) covalently targeted and potently inhibited protein disulfide isomerase A1 (PDIA1). This study supports the continued use of minimalist bifunctional isocyanides as valuable building blocks for preparing structurally diverse FFSMPs for integrated phenotypic screening-target identification campaigns.

Mass spectrometry-based proteomics for source-level attribution after DNA extraction

Biological traces recovered from crime scenes serve as vital evidence in forensic investigations. While DNA evidence is frequently used to address the sub-source level of the hierarchy of propositions, the biological source of the DNA can be highly probative at the source level. Current body fluid detection methods pose certain limitations, such as reports of false positive results from some of the presumptive and/or confirmatory tests in current use. These tests are also individual tests for the detection of one body fluid, meaning that if the sample is suspected to be a mixture of multiple body fluids, then different tests would need to be conducted to confirm the body fluid(s) present, which may exhaust small amounts of available biological trace. Proteomics applications for the identification of body fluids have been previously explored, and potential biomarkers indicative of body fluids discovered from liquid-chromatography tandem mass spectrometry (LC-MS/MS) methods have been reported. This work focuses on developing a mass spectrometry-based proteomics approach for the identification of body fluids by targeting discriminating peptide biomarkers from the non-DNA component left over after DNA extraction of samples. The non-DNA component is typically a waste product but with unappreciated evidential value. Our methodology for the purification of proteins from the post-DNA extraction waste includes an acetone precipitation and single-pot solid-phase-enhanced sample preparation (SP3) technique, microwave-assisted trypsin digestion, and LC-MS/MS analysis of the resultant peptides. Preliminary results from this proof-of-concept study include a list of potentially discriminating proteins and peptides for blood, saliva, and semen developed from the analysis of post-DNA extraction waste. Our method allows for multiple analytes to be targeted simultaneously from a DNA profiling waste stream and we anticipate that it could eventually be incorporated into standard forensic laboratory workflows.

Online Isotope Analysis of Sulfur in Proteins via Capillary Electrophoresis Coupled With Multicollector ICP-MS (CE/MC-ICP-MS): A Proof of Concept Study.

Isotope ratio analysis of sulfur in biological samples using inductively coupled plasma-mass spectrometry (ICP-MS) has gained significant interest for applications in quantitative proteomics. Advancements like coupling separation techniques with multicollector ICP-MS (MC-ICP-MS) enhance the throughput of species-specific sulfur isotope ratio measurements, fostering new avenues for studying sulfur metabolism in complex biological matrices. This proof-of-concept study investigates the feasibility of online CE/MC-ICP-MS for directly analyzing sulfur isotope ratios in proteins (albumin). Leveraging our previous work on the applicability of CE/ICP-MS for quantifying sulfur-containing biological molecules, we explore its potential for sulfur isotope analysis. Our results demonstrate that direct analysis of sulfur isotopes in albumin protein using online capillary electrophoresis MC-ICP-MS (CE/MC-ICP-MS) eliminates the need for laborious pretreatment steps, while yielding isotope ratios comparable to the reference values. Although initial precision can be improved through further system optimization and protein injection techniques, this approach paves the way for future analysis of mixtures of various biological compounds in, for example, clinical diagnosis studies.

A Proteogenomic Approach to Unveiling the Complex Biology of the Microbiome

The complex biology of the microbiome was elucidated once the genomics era began. The proteogenomic approach analyzes and integrates genetic makeup (genomics) and microbial communities′ expressed proteins (proteomics). Therefore, researchers gained insights into gene expression, protein functions, and metabolic pathways, understanding microbial dynamics and behavior, interactions with host cells, and responses to environmental stimuli. In this context, our work aims to bring together data regarding the application of genomics, proteomics, and bioinformatics in microbiome research and to provide new perspectives for applying microbiota modulation in clinical practice with maximum efficiency. This review also synthesizes data from the literature, shedding light on the potential biomarkers and therapeutic targets for various diseases influenced by the microbiome.

Automated High-Throughput Biological Sex Identification from Archeological Human Dental Enamel Using Targeted Proteomics.

Biological sex is key information for archeological and forensic studies, which can be determined by proteomics. However, the lack of a standardized approach for fast and accurate sex identification currently limits the reach of proteomics applications. Here, we introduce a streamlined mass spectrometry (MS)-based workflow for the determination of biological sex using human dental enamel. Our approach builds on a minimally invasive sampling strategy by acid etching, a rapid online liquid chromatography (LC) gradient coupled to a high-resolution parallel reaction monitoring (PRM) assay allowing for a throughput of 200 samples per day (SPD) with high quantitative performance enabling confident identification of both males and females. Additionally, we developed a streamlined data analysis pipeline and integrated it into a Shiny interface for ease of use. The method was first developed and optimized using modern teeth and then validated in an independent set of deciduous teeth of known sex. Finally, the assay was successfully applied to archeological material, enabling the analysis of over 300 individuals. We demonstrate unprecedented performance and scalability, speeding up MS analysis by 10-fold compared to conventional proteomics-based sex identification methods. This work paves the way for large-scale archeological or forensic studies enabling the investigation of entire populations rather than focusing on individual high-profile specimens. Data are available via ProteomeXchange with the identifier PXD049326.

Amino Acid Composition Determination From the Fractional Mass of Peptides

ABSTRACTA peptide's fractional mass is directly associated with its elemental composition and thus amino acid composition. Here it is demonstrated that a peptide's fractional mass alone can be a useful identifier or indicator of that composition for small to mid‐sized peptides (5–7 amino acids) and can significantly reduce the number of viable amino acid compositions for larger peptides (> 8 residues) to include or exclude certain possibilities. Separate consideration of the integer portion of the peptide's mass helps to reduce the number of possibilities where many duplicate fractional mass values are found. Adoption of this fractional mass strategy should aid approaches that are presently employed for peptide identification, including in the use of mass map data to search protein databases for proteomics applications.

Proteomics to Study Parchment Degradation – From Bulk to Spatial Analysis

Abstract Implementing biomolecular techniques in the study of written cultural heritage has led to a steady development of biocodicology and the study of DNA and proteins in parchment in the past years. In this context, matrix-assisted laser desorption/ionisation mass spectrometry (MALDI-MS) has gained increasing attention as a powerful tool to study the ancient writing support. With a particular focus on the main components of parchment – different types of collagens – proteomic applications of the technique are highlighted. The review summarises advances in biocodicological studies focusing on manufacturing aspects and conservation treatments. Herein we further investigated proteomic studies on animal skin to prove the potential of MALDI-MS imaging (MALD-MSI) to broaden our knowledge and take studies on damage assessment and degradation of parchment to another level.

Evaluation of Serum Proteome Sample Preparation Methods to Support Clinical Proteomics Applications.

Serum contains several proteins that are associated with disease-related processes. Mass spectrometry (MS)-based proteomics approaches greatly facilitate serum protein biomarker development. However, the serum proteome complexity presents a technical challenge for the accurate, sensitive, and reproducible quantification of proteins by MS. Thus, efficient sample preparation methods are of critical importance for serum proteome analyses. In this study, we evaluated the technical performance of two serum proteome sample preparation methods using sera from patients with high-grade serous ovarian cancer and patients with benign nongynecological conditions with a goal of providing insight into their compatibility with clinical proteomics workflows. One method entailed the use of immobilized trypsin (SMART Digest Trypsin) with RapiGest SF, an acid-labile surfactant designed to enhance the in-solution enzymatic digestion of proteins. The other method incorporated a commercially available sample preparation kit, iST-BCT, which contains standardized reagents. Significantly higher protein sequence coverage, albeit with lower digestion efficiency, was obtained with the immobilized trypsin + RapiGest SF workflow, whereas the iST-BCT workflow was quicker and had marginally better reproducibility. Protein relative abundance analysis revealed that the serum proteomes clustered primarily by the sample processing workflow and secondarily by disease state. We conducted a time course study to determine whether differences in the relative abundance of diagnostic high-grade serous ovarian cancer serum protein biomarker candidates were biased according to the duration of enzymatic digestion. Our results highlight the importance of optimizing enzymatic digestion kinetics according to the peptide targets of interest while considering the sensitivity of the downstream analytical method utilized in clinical proteomics workflows designed to measure biomarkers.

From Nanozymes to Multi-Purpose Nanomaterials: The Potential of Metal-Organic Frameworks for Proteomics Applications.

Metal-organic frameworks (MOFs) have the potential to revolutionize the biotechnological and medical landscapes due to their easily tunable crystalline porous structure. Herein, the study presents MOFs' potential impact on proteomics, unveiling the diverse roles MOFs can play to boost it. Although MOFs are excellent catalysts in other scientific disciplines, their role as catalysts in proteomics applications remains largely underexplored, despite protein cleavage being of crucial importance in proteomics protocols. Additionally, the study discusses evolving MOF materials that are tailored for proteomics, showcasing their structural diversity and functional advantages compared to other types of materials used for similar applications. MOFs can be developed to seamlessly integrate into proteomics workflows due to their tunable features, contributing to protein separation, peptide enrichment, and ionization for mass spectrometry. This review is meant as a guide to help bridge the gap between material scientists, engineers, and MOF chemists and on the other side researchers in biology or bioinformatics working in proteomics.

Proteome Dynamics in iPSC-Derived Human Dopaminergic Neurons

Dopaminergic neurons participate in fundamental physiological processes and are the cell type primarily affected in Parkinson's disease. Their analysis is challenging due to the intricate nature of their function, involvement in diverse neurological processes, and heterogeneity and localization in deep brain regions. Consequently, most of the research on the protein dynamics of dopaminergic neurons has been performed in animal cells exvivo. Here we use iPSC-derived human mid-brain-specific dopaminergic neurons to study general features of their proteome biology and provide datasets for protein turnover and dynamics, including a human axonal translatome. We cover the proteome to a depth of 9409 proteins and use dynamic SILAC to measure the half-life of more than 4300 proteins. We report uniform turnover rates of conserved cytosolic protein complexes such as the proteasome and map the variable rates of turnover of the respiratory chain complexes in these cells. We use differential dynamic SILAC labeling in combination with microfluidic devices to analyze local protein synthesis and transport between axons and soma. We report 105 potentially novel axonal markers and detect translocation of 269 proteins between axons and the soma in the time frame of our analysis (120h). Importantly, we provide evidence for local synthesis of 154 proteins in the axon and their retrograde transport to the soma, among them several proteins involved in RNA editing such as ADAR1 and the RNA helicase DHX30, involved in the assembly of mitochondrial ribosomes. Our study provides a workflow and resource for the future applications of quantitative proteomics in iPSC-derived human neurons.

Machine Learning Strategies to Tackle Data Challenges in Mass Spectrometry-Based Proteomics.

In computational proteomics, machine learning (ML) has emerged as a vital tool for enhancing data analysis. Despite significant advancements, the diversity of ML model architectures and the complexity of proteomics data present substantial challenges in the effective development and evaluation of these tools. Here, we highlight the necessity for high-quality, comprehensive data sets to train ML models and advocate for the standardization of data to support robust model development. We emphasize the instrumental role of key data sets like ProteomeTools and MassIVE-KB in advancing ML applications in proteomics and discuss the implications of data set size on model performance, highlighting that larger data sets typically yield more accurate models. To address data scarcity, we explore algorithmic strategies such as self-supervised pretraining and multitask learning. Ultimately, we hope that this discussion can serve as a call to action for the proteomics community to collaborate on data standardization and collection efforts, which are crucial for the sustainable advancement and refinement of ML methodologies in the field.

Comparative Analysis of Digestion Methods for Bile Proteomics: The Key to Unlocking Biliary Biomarker Potential.

Bile's potential to reflect the health of the biliary system has led to increased attention, with proteomic analysis offering deeper understanding of biliary diseases and potential biomarkers. With the emergence of normothermic machine perfusion (NMP), bile can be easily collected and analyzed. However, the composition of bile can make the application of proteomics challenging. This study systematically evaluated various trypsin digestion methods to optimize proteomics of bile from human NMP livers. Bile was collected from 12 human donor livers that were accepted for transplantation after the NMP viability assessment. We performed tryptic digestion using six different methods: in-gel, in-solution, S-Trap, SMART, EasyPep, and filter-aided sample purification, with or without additional precipitation before digestion. Proteins were analyzed using untargeted proteomics. Methods were assessed for total protein IDs, variation, and protein characteristics to determine the most optimal method. Methods involving precipitation surpassed crude methods in protein identifications (4500 vs 3815) except for in-gel digestion. Filtered data (40%) resulted in 3192 versus 2469 for precipitated and crude methods, respectively. We found minimal differences in mass, cellular components, or hydrophobicity of proteins between methods. Intermethod variability was notably diverse, with in-gel, in-solution, and EasyPep outperforming others. Age-related biological comparisons revealed upregulation of metabolic-related processes in younger donors and immune response and cell cycle-related processes in older donors. Variability between methods emphasizes the importance of cross-validation across multiple analytical approaches to ensure robust analysis. We recommend the in-gel crude method for its simplicity and efficiency, avoiding additional precipitation steps. Sample processing speed, cost, cleanliness, and reproducibility should be considered when a digestion method is selected for bile proteomics.

Multi-omics analysis identified extracellular vesicles as biomarkers for cardiovascular diseases

Cell-derived extracellular vesicles (EVs) have emerged as a promising non-invasive liquid biopsy technique due to their accessibility and their ability to encapsulate and transport diverse biomolecules. EVs have garnered substantial research interest, notably in cardiovascular diseases (CVDs), where their roles in pathophysiology and as diagnostic and prognostic biomarkers are increasingly recognized. This review provides a comprehensive overview of EVs, starting with their origins, followed by the techniques used for their isolation and characterization. We explore the diverse cargo of EVs, including nucleic acids, proteins, lipids, and metabolites, highlighting their roles in intercellular communication and as potential biomarkers. We then delve into the application of genomics, transcriptomics, proteomics, and metabolomics in the analysis of EVs, particularly within the context of CVDs. Finally, we discuss how integrated multi-omics approaches are unveiling novel biomarkers, offering fresh insights into the diagnosis and prognosis of CVDs. This review underscores the growing importance of EVs in clinical diagnostics and the potential of multi-omics to propel future advancements in CVD biomarker discovery.

Current application of proteomics in the veterinary field – a short summary and literature review

The biomedical research field’s continuous search for innovative technologies has led to improved disease diagnosis, novel drug discovery, and therapeutic interventions, all aimed at enhancing animal and human health alike. In this context, proteomics has emerged as a critical new technology, focusing on detailed examinations of protein composition, abundance, structure, function, and interactions. Proteomics, the study of the entire set ofproteins expressed by an organism, plays a crucial role in disease diagnosis, drug discovery, and therapeutic interventions in both human and animal health. Proteomics in veterinary medicine and animal health is an evolving field that holds significant promise for fundamental and applied discoveries related to the biology and pathology of domestic and companion species. It encompasses a broad spectrum of applications, including disease diagnosis, comparative medicine, pharmacology, nutrition, reproductive biology, livestock production,and pathology. By analysing protein profiles and interactions, proteomics contributes to enhancing animal health, welfare, and productivity across various fields of veterinary research and practice. Experimental proteomics in domestic animals offers advantages over the use of rodents, such as the ability to conduct multipletime-series samplings of biological samples for extensive analysis, allowing for the investigation of experimental and natural disease processes. This review highlights the current application of proteomics in veterinary medicine, focusing on its potential to advance diagnostics, research, and treatments in veterinary science. The current limitations of proteomics are also discussed.

Application of urine proteomics in the diagnosis and treatment effectiveness monitoring of early-stage Mycosis Fungoides

BackgroundMycosis fungoides (MF) is the most common type of cutaneous T cell lymphoma. As the early clinical manifestations of MF are non-specific (e.g., erythema or plaques), it is often misdiagnosed as inflammatory skin conditions (e.g., atopic dermatitis, psoriasis, and pityriasis rosea), resulting in delayed treatment. As there are no effective biological markers for the early detection and management of MF, the aim of the present study was to perform a proteomic analysis of urine samples (as a non-invasive protein source) to identify reliable MF biomarkers.MethodsThirteen patients with early-stage MF were administered a subcutaneous injection of interferon α-2a in combination with phototherapy for 6 months. The urine proteome of patients with early-stage MF before and after treatment was compared against that of healthy controls by liquid chromatography-tandem mass spectrometry. The differentially expressed proteins were subjected to Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Clusters of Orthologous Groups analyses. For validation, the levels of the selected proteins were evaluated by enzyme-linked immunosorbent assay (ELISA).ResultsWe identified 41 differentially expressed proteins (11 overexpressed and 30 underexpressed) between untreated MF patients and healthy control subjects. The proteins were mainly enriched in focal adhesion, endocytosis, and the PI3K-Akt, phospholipase D, MAPK, and calcium signaling pathways. The ELISA results confirmed that the urine levels of Serpin B5, epidermal growth factor (EGF), and Ras homologous gene family member A (RhoA) of untreated MF patients were significantly lower than those of healthy controls. After 6 months of treatment, however, there was no significant difference in the urine levels of Serpin B5, EGF, and RhoA between MF patients and healthy control subjects. The area under the receiver operating characteristic curve values for Serpin B5, EGF, and RhoA were 0.817, 0.900, and 0.933, respectively.ConclusionsThis study showed that urine proteomics represents a valuable tool for the study of MF, as well as identified potential new biomarkers (Serpin B5, EGF, and RhoA), which could be used in its diagnosis and management.

Top-down Proteomics for the Characterization and Quantification of Calreticulin Arginylation.

Arginylation installed by arginyltransferase 1 (ATE1) features an addition of arginine (Arg) to the reactive amino acids (e.g., Glu and Asp) at the protein N-terminus or side chain. Systemic removal of arginylation after ATE1 knockout (KO) in mouse models resulted in heart defects leading to embryonic lethality. The biological importance of arginylation has motivated the discovery of arginylation sites on proteins using bottom-up approaches. While bottom-up proteomics is powerful in localizing peptide arginylation, it lacks the ability to quantify proteoforms at the protein level. Here we developed a top-down proteomics workflow for characterizing and quantifying calreticulin (CALR) arginylation. To generate fully arginylated CALR (R-CALR), we have inserted an R residue after the signaling peptide (AA1-17). Upon overexpression in ATE1 KO cells, CALR and R-CALR were purified by affinity purification and analyzed by LCMS in positive mode. Both proteoforms showed charge states ranging from 27-68 with charge 58 as the most intense charge state. Their MS2 spectra from electron-activated dissociation (EAD) showed preferential fragmentation at the protein N-terminals which yielded sufficient c ions facilitating precise localization of the arginylation sites. The calcium-binding domain (CBD) gave minimum characteristic ions possibly due to the abundant presence of >100 D and E residues. Ultraviolet photodissociation (UVPD) compared with EAD and ETD significantly improved the sequence coverage of CBD. This method can identify and quantify CALR arginylation at absence, endogenous (low), and high levels. To our knowledge, our work is the first application of top-down proteomics in characterizing post-translational arginylation in vitro and in vivo.