Proteomics profiling plays an important role in biomedical studies. Proteomics studies are much more complicated than genome research, mainly because of the complexity and diversity of proteomic samples. High performance liquid chromatography-mass spectrometry (HPLC-MS) is a fundamental tool in proteomics research owing to its high speed, resolution, and sensitivity. Proteomics research targets from the peptides and individual proteins to larger protein complexes, the molecular weight of which gradually increases, leading to sustained increases in structural and compositional complexity and alterations in molecular properties. Therefore, the selection of various separation strategies and stationary-phase parameters is crucial when dealing with the different targets in proteomics research for in-depth proteomics analysis. This article provides an overview of commonly used chromatographic-separation strategies in the laboratory, including reversed-phase liquid chromatography (RPLC), hydrophilic interaction liquid chromatography (HILIC), hydrophobic interaction chromatography (HIC), ion-exchange chromatography (IEC), and size-exclusion chromatography (SEC), as well as their applications and selectivity in the context of various biomacromolecules. At present, no single chromatographic or electrophoretic technology features the peak capacity required to resolve such complex mixtures into individual components. Multidimensional liquid chromatography (MDLC), which combines different orthogonal separation modes with MS, plays an important role in proteomics research. In the MDLC strategy, IEC, together with RPLC, remains the most widely used separation mode in proteomics analysis; other chromatographic methods are also frequently used for peptide/protein fractionation. MDLC technologies and their applications in a variety of proteomics analyses have undergone great development. Two strategies in MDLC separation systems are mainly used in proteomics profiling: the “bottom-up” approach and the “top-down” approach. The “shotgun” method is a typical “bottom-up” strategy that is based on the RPLC or MDLC separation of whole-protein-sample digests coupled with MS; it is an excellent technique for identifying a large number of proteins. “Top-down” analysis is based on the separation of intact proteins and provides their detailed molecular information; thus, this technique may be advantageous for analyzing the post-translational modifications (PTMs) of proteins. In this paper, the “bottom-up” “top-down” and protein-protein interaction (PPI) analyses of proteome samples are briefly reviewed. The diverse combinations of different chromatographic modes used to set up MDLC systems are described, and compatibility issues between mobile phases and analytes, between mobile phases and MS, and between mobile phases in different separation modes in multidimensional chromatography are analyzed. Novel developments in MDLC techniques, such as high-abundance protein depletion and chromatography arrays, are further discussed. In this review, the solutions proposed by researchers when encountering compatibility issues are emphasized. Moreover, the applications of HPLC-MS combined with various sample pretreatment methods in the study of exosomal and single-cell proteomics are examined. During exosome isolation, the combined use of ultracentrifugation and SEC can yield exosomes of higher purity. The use of SEC with ultra-large-pore-size packing materials (200 nm) enables the isolation of exosomal subgroups, and proteomics studies have revealed significant differences in protein composition and function between these subgroups. In the field of single-cell proteomics, researchers have addressed challenges related to reducing sample processing volumes, preventing sample loss, and avoiding contamination during sample preparation. Innovative methods and improvements, such as the utilization of capillaries for sample processing and microchips as platforms to minimize the contact area of the droplets, have been proposed. The integration of these techniques with HPLC-MS shows some progress. In summary, this article focuses on the recent advances in HPLC-MS technology for proteomics analysis and provides a comprehensive reference for future research in the field of proteomics.

Drug targets are biological macromolecules that bind drug molecules in vivo. Therefore, the system-wide identification of drug targets plays a vital role in fully understanding the mechanism of drug action, efficacy, and side effects. The unbiased screening of drug targets may accelerate the process of drug discovery and candidate screening. Mass spectrometry is a key tool for large-scale protein identification and accurate quantification owing to its high acquisition speed, resolution, and sensitivity. Mass spectrometry-based proteomics has been widely used for drug-target screening. It can systematically identify the protein-target landscape of a drug and elucidate drug-protein interactions. Commonly used drug-target characterization methods, such as labeling-based affinity enrichment, require the chemical derivatization of drug molecules, which is not only time-consuming but may also affect the affinity of the drug towards its targets. Furthermore, the spatial effects of the derivatization groups may block interactions between the drug and its targets. Considering the disadvantages of affinity-enrichment methods, strategies that do not require chemical derivatization have received widespread attention. Proteins may undergo denaturation, unfolding, and precipitation under different conditions such as high temperatures, extreme pH, denaturants, and mechanical stress. Binding to small-molecule drugs may alter the folding balance of target proteins. The conformational stability of target proteins can be stabilized by binding with drugs, and protein-drug complexes are more resistant than free proteins to the precipitation induced by different conditions. Based on this mechanism, various large-scale drug-target identification methods using protein precipitation have been developed by combining proteomics and mass spectrometry analysis, including thermal proteome profiling and solvent-, mechanical stress-, and pH-induced protein precipitation. These methods have been successfully applied to the characterization of small-molecule drug targets. In this review, we describe the protein precipitation-based methods used for the high-throughput discovery of drug targets and elucidation of the interactions between drugs and proteins in the past decade. We also summarize the characteristics of each method and discuss their application potential in drug-efficacy evaluation and drug discovery.

Protein phosphorylation is one of the most common and important post-translational modifications that regulates almost all life processes. In particular, protein phosphorylation regulates the development of major diseases such as tumors, neurodegenerative diseases, and diabetes. For example, excessive phosphorylation of Tau protein can cause neurofibrillary tangles, leading to Alzheimer’s disease. Therefore, large-scale methods for identifying protein phosphorylation must be developed. Rapid developmentin efficient enrichment methods and biological mass spectrometry technologies have enabled the large-scale identification of low-abundance protein O-phosphorylation modifications in, allowing for a more thorough study of their biological functions. The N-phosphorylation modifications that occur on the side-chain amino groups of histidine, arginine, and lysine have recently received increased attention. For example, the biological function of histidine phosphorylation in prokaryotes has been well studied; this type of modification regulates signal transduction and sugar metabolism. Two mammalian pHis kinases (NME1 and NME2) and three pHis phosphatases (PHPT1, LHPP, and PGAM5) have been successfully identified using various biological methods. N-Phosphorylation is involved in multiple biological processes, and its functions cannot be ignored. However, N-phosphorylation is unstable under acidic and thermal conditions owing to the poor chemical stability of the P-N bond. Unfortunately, the current O-phosphorylation enrichment method, which relies on acidic conditions, is unsuitable for N-phosphorylation enrichment, resulting in a serious lag in the large-scale identification of protein N-phosphorylation. The lack of enrichment methods has also seriously hindered studies on the biological functions of N-phosphorylation. Therefore, the development of efficient enrichment methods that target protein N-phosphorylation is an urgent undertaking. Research on N-phosphorylation proteome enrichment methods is limited, hindering functional research. Thus, summarizing such methods is necessary to promote further functional research. This article introduces the structural characteristics and reported biological functions of protein N-phosphorylation, reviews the protein N-phosphorylation modification enrichment methods developed over the past two decades, and analyzes the advantages and disadvantages of each method. In this study, both antibody-based and nonantibody-dependent methods are described in detail. Owing to the stability of the molecular structure of histidine, the antibody method is currently limited to histidine phosphorylation enrichment research. Future studies will focus on the development of new enrichment ligands. Moreover, research on ligands will promote studies on other nonconventional phosphorylation targets, such as two acyl-phosphates (pAsp, pGlu) and S-phosphate (pCys). In summary, this review provides a detailed analysis of the history and development directions of N-phosphorylation enrichment methods.

Over 170 chemical modifications have been discovered in various types of ribonucleic acids (RNAs), including messenger RNA (mRNA), ribosomal RNA (rRNA), transfer RNA (tRNA), and small nuclear RNA (snRNA). These RNA modifications play crucial roles in a wide range of biological processes such as gene expression regulation, RNA stability maintenance, and protein translation. RNA modifications represent a new dimension of gene expression regulation known as the “epitranscriptome”. The discovery of RNA modifications and the relevant writers, erasers, and readers provides an important basis for studies on the dynamic regulation and physiological functions of RNA modifications. Owing to the development of detection technologies for RNA modifications, studies on RNA epitranscriptomes have progressed to the single-base resolution, multilayer, and full-coverage stage. Transcriptome-wide methods help discover new RNA modification sites and are of great importance for elucidating the molecular regulatory mechanisms of epitranscriptomics, exploring the disease associations of RNA modifications, and understanding their clinical applications. The existing RNA modification sequencing technologies can be categorized according to the pretreatment approach and sequencing principle as direct high-throughput sequencing, antibody-enrichment sequencing, enzyme-assisted sequencing, chemical labeling-assisted sequencing, metabolic labeling sequencing, and nanopore sequencing technologies. These methods, as well as studies on the functions of RNA modifications, have greatly expanded our understanding of epitranscriptomics. In this review, we summarize the recent progress in RNA modification detection technologies, focusing on the basic principles, advantages, and limitations of different methods. Direct high-throughput sequencing methods do not require complex RNA pretreatment and allow for the mapping of RNA modifications using conventional RNA sequencing methods. However, only a few RNA modifications can be analyzed by high-throughput sequencing. Antibody enrichment followed by high-throughput sequencing has emerged as a crucial approach for mapping RNA modifications, significantly advancing the understanding of RNA modifications and their regulatory functions in different species. However, the resolution of antibody-enrichment sequencing is limited to approximately 100-200 bp. Although chemical crosslinking techniques can achieve single-base resolution, these methods are often complex, and the specificity of the antibodies used in these methods has raised concerns. In particular, the issue of off-target binding by the antibodies requires urgent attention. Enzyme-assisted sequencing has improved the accuracy of the localization analysis of RNA modifications and enables stoichiometric detection with single-base resolution. However, the enzymes used in this technique show poor reactivity, specificity, and sequence preference. Chemical labeling sequencing has become a widely used approach for profiling RNA modifications, particularly by altering reverse transcription (RT) signatures such as RT stops, misincorporations, and deletions. Chemical-assisted sequencing provides a sequence-independent RNA modification detection strategy that enables the localization of multiple RNA modifications. Additionally, when combined with the biotin-streptavidin affinity method, low-abundance RNA modifications can be enriched and detected. Nevertheless, the specificity of many chemical reactions remains problematic, and the development of specific reaction probes for particular modifications should continue in the future to achieve the precise localization of RNA modifications. As an indirect localization method, metabolic labeling sequencing specifically localizes the sites at which modifying enzymes act, which is of great significance in the study of RNA modification functions. However, this method is limited by the intracellular labeling of RNA and cannot be applied to biological samples such as clinical tissues and blood samples. Nanopore sequencing is a direct RNA-sequencing method that does not require RT or the polymerase chain reaction (PCR). However, challenges in analyzing the data obtained from nanopore sequencing, such as the high rate of false positives, must be resolved. Discussing sequencing analysis methods for various types of RNA modifications is instructive for the future development of novel RNA modification mapping technologies, and will aid studies on the functions of RNA modifications across the entire transcriptome.

Glycomics, an emerging “omics” technology that was developed after genomics and proteomics, is a discipline that studies the composition, structure, and functions of glycomes in cells, tissues, and organisms. Glycomics plays key roles in understanding the laws of major life activities, disease prevention and treatment, and drug quality control and development. At present, the structural analysis of glycans relies mainly on mass spectrometry. However, glycans have low abundance in biological samples. In addition, factors such as variable monosaccharide compositions, differences in glycosidic bond positions and modes, diverse branching structures, contribute to the complexity of the compositions and structures of glycans, posing great challenges to glycomics research. Liquid chromatography can effectively remove matrix interferences and enhance glycan separation to improve the mass spectrometric response of glycans. Thus, liquid chromatography and liquid chromatography coupled with mass spectrometry are important technical tools that have been actively applied to solve these problems; these technologies play indispensable roles in glycomics research. Different studies have highlighted similarities and differences in the applications of various types of liquid chromatography, which also reflects the versatility and flexibility of this technology.

In this review, we first discuss the enrichment methods for glycans and their applications in glycomics research from the perspective of chromatographic separation mechanisms. We then compare the advantages and disadvantages of these methods. Some glycan-enrichment modes include affinity, hydrophilic interactions, size exclusion, and porous graphitized carbon adsorption. A number of newly developed materials exhibit excellent glycan-enrichment ability. We enumerate the separation mechanisms of reversed-phase high performance liquid chromatography (RP-HPLC), high performance anion-exchange chromatography (HPAEC), hydrophilic interaction chromatography (HILIC), and porous graphitic carbon (PGC) chromatography in the separation and analysis of glycans, and describe the applications of these methods in the separation of glycans, glycoconjugates, and glyco-derivatives. Among these methods, HILIC and PGC chromatography are the most widely used, whereas HPAEC and RP-HPLC are less commonly used. The HILIC and RP-HPLC modes are often used for the separation of derived glycans. The ionization efficiency and detectability of glycans are significantly improved after derivatization. However, the derivatization process is relatively cumbersome, and byproducts inevitably affect the accuracy and completeness of the detection results. HPAEC and PGC chromatography exhibit good separation effects on nonderivative glycans, but issues related to the detection integrity of low-abundance glycans owing to their poor detection effect continue to persist. Therefore, the appropriate analytical method for a specific sample or target analyte or mutual verification must be selected.

Finally, we highlight the research progress in various chromatographic methods coupled with mass spectrometry for glycomics analysis. Significant progress has been made in glycomics research in recent years owing to advancements in the development of chromatographic separation techniques. However, several significant challenges remain. As the development of novel separation materials and methods continues, chromatographic techniques may be expected to play a critical role in future glycomics research.

Microorganisms are closely associated with human diseases and health. Understanding the composition and function of microbial communities requires extensive research. Metaproteomics has recently become an important method for throughout and in-depth study of microorganisms. However, major challenges in terms of sample processing, mass spectrometric data acquisition, and data analysis limit the development of metaproteomics owing to the complexity and high heterogeneity of microbial community samples. In metaproteomic analysis, optimizing the preprocessing method for different types of samples and adopting different microbial isolation, enrichment, extraction, and lysis schemes are often necessary. Similar to those for single-species proteomics, the mass spectrometric data acquisition modes for metaproteomics include data-dependent acquisition (DDA) and data-independent acquisition (DIA). DIA can collect comprehensive peptide information from a sample and holds great potential for future development. However, data analysis for DIA is challenged by the complexity of metaproteome samples, which hinders the deeper coverage of metaproteomes. The most important step in data analysis is the construction of a protein sequence database. The size and completeness of the database strongly influence not only the number of identifications, but also analyses at the species and functional levels. The current gold standard for metaproteome database construction is the metagenomic sequencing-based protein sequence database. A public database-filtering method based on an iterative database search has been proven to have strong practical value. The peptide-centric DIA data analysis method is a mainstream data analysis strategy. The development of deep learning and artificial intelligence will greatly promote the accuracy, coverage, and speed of metaproteomic analysis. In terms of downstream bioinformatics analysis, a series of annotation tools that can perform species annotation at the protein, peptide, and gene levels has been developed in recent years to determine the composition of microbial communities. The functional analysis of microbial communities is a unique feature of metaproteomics compared with other omics approaches. Metaproteomics has become an important component of the multi-omics analysis of microbial communities, and has great development potential in terms of depth of coverage, sensitivity of detection, and completeness of data analysis.

Mass spectrometry imaging (MSI) is a promising method for characterizing the spatial distribution of compounds. Given the diversified development of acquisition methods and continuous improvements in the sensitivity of this technology, both the total amount of generated data and complexity of analysis have exponentially increased, rendering increasing challenges of data postprocessing, such as large amounts of noise, background signal interferences, as well as image registration deviations caused by sample position changes and scan deviations, and etc. Deep learning (DL) is a powerful tool widely used in data analysis and image reconstruction. This tool enables the automatic feature extraction of data by building and training a neural network model, and achieves comprehensive and in-depth analysis of target data through transfer learning, which has great potential for MSI data analysis. This paper reviews the current research status, application progress and challenges of DL in MSI data analysis, focusing on four core stages: data preprocessing, image reconstruction, cluster analysis, and multimodal fusion. The application of a combination of DL and mass spectrometry imaging in the study of tumor diagnosis and subtype classification is also illustrated. This review also discusses trends of development in the future, aiming to promote a better combination of artificial intelligence and mass spectrometry technology.

Dynamic changes in the structures and interactions of proteins are closely correlated with their biological functions. However, the precise detection and analysis of these molecules are challenging. Native mass spectrometry (nMS) introduces proteins or protein complexes into the gas phase by electrospray ionization, and then performs MS analysis under near-physiological conditions that preserve the folded state of proteins and their complexes in solution. nMS can provide information on stoichiometry, assembly, and dissociation constants by directly determining the relative molecular masses of protein complexes through high-resolution MS. It can also integrate various MS dissociation technologies, such as collision-induced dissociation (CID), surface-induced dissociation (SID), and ultraviolet photodissociation (UVPD), to analyze the conformational changes, binding interfaces, and active sites of protein complexes, thereby revealing the relationship between their interactions and biological functions. UVPD, especially 193 nm excimer laser UVPD, is a rapidly evolving MS dissociation method that can directly dissociate the covalent bonds of protein backbones with a single pulse. It can generate different types of fragment ions, while preserving noncovalent interactions such as hydrogen bonds within these ions, thereby enabling the MS analysis of protein structures with single-amino-acid-site resolution. This review outlines the applications and recent progress of nMS and UVPD in protein dynamic structure and interaction analyses. It covers the nMS techniques used to analyze protein-small-molecule ligand interactions, the structures of membrane proteins and their complexes, and protein-protein interactions. The discussion on UVPD includes the analysis of gas-phase protein structures and interactions, as well as alterations in protein dynamic structures, and interactions resulting from mutations and ligand binding. Finally, this review describes the future development prospects for protein analysis by nMS and new-generation advanced extreme UV light sources with higher brightness and shorter pulses.

Tyrosine phosphorylation, a common post-translational modification process for proteins, is involved in a variety of biological processes. However, the abundance of tyrosine-phosphorylated proteins is very low, making their identification by mass spectrometry (MS) is difficult; thus, milligrams of the starting material are often required for their enrichment. For example, tyrosine phosphorylation plays an important role in T cell signal transduction. However, the number of primary T cells derived from biological tissue samples is very small, and these cells are difficult to culture and expand; thus, the study of T cell signal transduction is usually carried out on immortalized cell lines, which can be greatly expanded. However, the data from immortalized cell lines cannot fully mimic the signal transduction processes observed in the real physiological state, and they usually lead to conclusions that are quite different from those of primary T cells. Therefore, a highly sensitive proteomic method was developed for studying tyrosine phosphorylation modification signals in primary T cells. To address the issue of the limited T cells numbers, a comprehensive protocol was first optimized for the isolation, activation, and expansion of primary T cells from mouse spleen. CD3⁺ primary T cells were successfully sorted; more than 91% of the T cells collected were well activated on day 2, and the number of T cells expanded to over 7-fold on day 4. Next, to address the low abundance of tyrosine-phosphorylated proteins, we used SH2-superbinder affinity enrichment and immobilized Ti⁴⁺affinity chromatography (Ti⁴⁺-IMAC) to enrich the tyrosine-phosphorylated polypeptides of primary T cells that were co-stimulated with anti-CD3 and anti-CD28. These polypeptides were resolved using nanoscale liquid chromatography-tandem mass spectrometry (nanoLC-MS/MS). Finally, 282 tyrosine phosphorylation sites were successfully identified in 1 mg of protein, including many tyrosine phosphorylation sites on the immunoreceptor tyrosine-based activation motif (ITAM) in the intracellular region of the T cell receptor membrane protein CD3, as well as the phosphotyrosine sites of ZAP70, LAT, VAV1, and other proteins related to signal transduction under costimulatory conditions. In summary, to solve the technical problems of the limited number of primary cells, low abundance of tyrosine-phosphorylated proteins, and difficulty of detection by MS, we developed a comprehensive proteomic method for the in-depth analysis of tyrosine phosphorylation modification signals in primary T cells. This protocol may be applied to map signal transduction networks that are closely related to physiological states.

Organic acid metabolites exhibit acidic properties. These metabolites serve as intermediates in major carbon metabolic pathways and are involved in several biochemical pathways, including the tricarboxylic acid (TCA) cycle and glycolysis. They also regulate cellular activity and play crucial roles in epigenetics, tumorigenesis, and cellular signal transduction. Knowledge of the binding proteins of organic acid metabolites is crucial for understanding their biological functions. However, identifying the binding proteins of these metabolites has long been a challenging task owing to the transient and weak nature of their interactions. Moreover, traditional methods are unsuitable for the structural modification of the ligands of organic acid metabolites because these metabolites have simple and similar structures. Even minor structural modifications can significantly affect protein interactions. Thermal proteome profiling (TPP) provides a promising avenue for identifying binding proteins without the need for structural modifications. This approach has been successfully applied to the identification of the binding proteins of several metabolites. In this study, we investigated the binding proteins of two TCA cycle intermediates, i.e., succinate and fumarate, and lactate, an end-product of glycolysis, using the matrix thermal shift assay (mTSA) technique. This technique involves combining single-temperature (52 ℃) TPP and dose-response curve analysis to identify ligand-binding proteins with high levels of confidence and determine the binding affinity between ligands and proteins. To this end, HeLa cells were lysed, followed by protein desalting to remove endogenous metabolites from the cell lysates. The desalted cell lysates were treated with fumarate or succinate at final concentrations of 0.004, 0.04, 0.4, and 2 mmol/L in the experimental groups or 2 mmol/L sodium chloride in the control group. Considering that the cellular concentration of lactate can be as high as 2-30 mmol/L, we then applied lactate at final concentrations of 0.2, 1, 5, 10, and 25 mmol/L in the experimental groups or 25 mmol/L sodium chloride in the control group. Using high-sensitivity mass spectrometry coupled with data-independent acquisition (DIA) quantification, we quantified 5870, 5744, and 5816 proteins in succinate, fumarate, and lactate mTSA experiments, respectively. By setting stringent cut-off values (i.e., significance of changes in protein thermal stability (p-value)<0.001 and quality of the dose-response curve fitting (square of Pearson’s correlation coefficient, R²)>0.95), multiple binding proteins for these organic acid metabolites from background proteins were confidently determined. Several known binding proteins were identified, notably fumarate hydratase (FH) as a binding protein for fumarate, and α-ketoglutarate-dependent dioxygenase (FTO) as a binding protein for both fumarate and succinate. Additionally, the affinity data for the interactions between these metabolites and their binding proteins were obtained, which closely matched those reported in the literature. Interestingly, ornithine aminotransferase (OAT), which is involved in amino acid biosynthesis, and 3-mercaptopyruvate sulfurtransferase (MPST), which acts as an antioxidant in cells, were identified as lactate-binding proteins. Subsequently, an orthogonal assay technique developed in our laboratory, the solvent-induced precipitation (SIP) technique, was used to validate the mTSA results. SIP identified OAT as the top target candidate, validating the mTSA-based finding that OAT is a novel lactate-binding protein. Although MPST was not identified as a lactate-binding protein by SIP, statistical analysis of MPST in the mTSA experiments with 10 or 25 mmol/L lactate revealed that MPST is a lactate-binding protein with a high level of confidence. Peptide-level empirical Bayes t-tests combined with Fisher’s exact test also supported the conclusion that MPST is a lactate-binding protein. Lactate is structurally similar to pyruvate, the known binding protein of MPST. Therefore, assuming that lactate could potentially occupy the binding site of pyruvate on MPST. Overall, the novel binding proteins identified for lactate suggest their potential involvement in amino acid synthesis and redox balance regulation.

Protein citrullination is an irreversible post-translational modification process regulated by peptidylarginine deiminases (PADs) in the presence of Ca²⁺. This process is closely related to the occurrence and development of autoimmune diseases, cancers, neurological disorders, cardiovascular and cerebrovascular diseases, and other major diseases. The analysis of protein citrullination by biomass spectrometry confronts great challenges owing to its low abundance, lack of affinity tags, small mass-to-charge ratio change, and susceptibility to isotopic and deamidation interferences. The methods commonly used to study the protein citrullination mainly involve the chemical derivatization of the urea group of the guanine side chain of the peptide to increase the mass-to-charge ratio difference of the citrullinated peptide. Affinity-enriched labels are then introduced to effectively improve the sensitivity and accuracy of protein citrullination by mass spectrometry. 2,3-Butanedione or phenylglyoxal compounds are often used as derivatization reagents to increase the mass-to-charge ratio difference of the citrullinated peptide, and the resulting derivatives have been observed to contain α-dicarbonyl structures. To date, however, no relevant studies on the reactivity of dicarbonyl compounds with citrullinated peptides have been reported. In this study, we determined whether six α-dicarbonyl and two β-dicarbonyl compounds undergo derivatization reactions with standard citrullinated peptides using matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS). Among the α-dicarbonyl compounds, 2,3-butanedione and glyoxal reacted efficiently with several standard citrullinated peptides, but yielded a series of by-products. Phenylglyoxal, methylglyoxal, 1,2-cyclohexanedione, and 1,10-phenanthroline-5,6-dione also derivated efficiently with standard citrullinated peptides, generating a single derivative. Thus, a new derivatization method that could yield a single derivative was identified. Among the β-dicarbonyl compounds, 1,3-cyclohexanedione and 2,4-pentanedione successfully reacted with the standard citrullinated peptides, and generated a single derivative. However, their reaction efficiency was very low, indicating that the β-dicarbonyl compounds are unsuitable for the chemical derivatization of citrullinated peptides. The above results indicate that the α-dicarbonyl structure is necessary for realizing the efficient and specific chemical derivatization of citrullinated peptides. Moreover, the side chains of the α-dicarbonyl structure determine the structure of the derivatives, derivatization efficiency, and generation (or otherwise) of by-products. Therefore, the specific enrichment and precise identification of citrullinated peptides can be achieved by synthesizing α-dicarbonyl structured compounds containing affinity tags. The proposed method enables the identification of citrullinated proteins and their modified sites by MS, thereby providing a better understanding of the distribution of citrullinated proteins in different tissues. The findings will be beneficial for studies on the mechanism of action of citrullinated proteins in a variety of diseases.

Lysine (K) is widely used in the design of lysine-targeted crosslinkers, structural elucidation of protein complexes, and analysis of protein-protein interactions. In “shotgun” proteomics, which is based on liquid chromatography-tandem mass spectrometry (LC-MS/MS), proteins from complex samples are enzymatically digested, generating thousands of peptides and presenting significant challenges for the direct analysis of K-containing peptides. In view of the lack of effective methods for the enrichment of K-containing peptides, this work developed a method which based on a hydrophobic-tag-labeling reagent C10-S-S-NHS and reversed-phase chromatography (termed as HYTARP) to achieve the efficient enrichment and identification of K-containing peptides from complex samples. The C10-S-S-NHS synthesized in this work successfully labeled standard peptides containing various numbers of K and the labeling efficiency achieved up to 96% for HeLa cell protein tryptic digests. By investigating the retention behavior of these labeled peptides in C18 RP column, we found that most K-labeled peptides were eluted once when acetonitrile percentage reached 57.6% (v/v). Further optimization of the elution gradient enabled the efficient separation and enrichment of the K-labeled peptides in HeLa digests via a stepwise elution gradient. The K-labeled peptides accounted for 90% in the enriched peptides, representing an improvement of 35% compared with the number of peptides without the enrichment. The dynamic range of proteins quantified from the enriched K-containing peptides spans 5-6 orders of magnitude, and realized the detection of low-abundance proteins in the complex sample. In summary, the HYTARP strategy offers a straightforward and effective approach for reducing sample complexity and improving the identification coverage of K-containing peptides and low-abundance proteins.