Chemical isotope labeling for quantitative proteomics

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.

6-Plex Tandem Phosphorus Tags (TPT) for Accurate Quantitative Proteomics

Isobaric chemical labeling is a widely used strategy for high-throughput quantitative proteomics based on mass spectrometry. However, commercially available reagents have high costs in applications as well as the sensitivity limitations for detection of the trace protein samples. Previously, we developed a 2-plex isobaric labeling strategy based on phosphorus chemistry for ultrasensitive proteome quantification with high accuracy. In this work, 6-plex tandem phosphorus tags (TPT) were developed with 3-fold increase in the multiplexing quantitative capacity compared to the 2-plex isobaric phosphorus reagents introduced previously. High isotope enrichment of 18O labeling was incorporated into the phosphoryl group with three exchangeable oxygen atoms by using commercially available H218O. The combinational incorporations of 18O atom in reporter ions and balance group set up the low-cost foundation for development of multiplex TPT reagents. The novel 6-plex TPT reagents could produce phosphoramidate as unique reporter ions with approximately 1 Da mass difference and thus enable 6-plex quantitative analysis in high-resolution ESI-MS/MS analysis. Using HeLa cell tryptic peptides, we concluded that 6-plex TPT reagents could facilitate large-scale accurate quantitative proteomics with very high labeling efficiency.

PCAS: An Integrated Tool for Multi-Dimensional Cancer Research Utilizing Clinical Proteomic Tumor Analysis Consortium Data

Proteomics offers a robust method for quantifying proteins and elucidating their roles in cellular functions, surpassing the insights provided by transcriptomics. The Clinical Proteomic Tumor Analysis Consortium database, enriched with comprehensive cancer proteomics data including phosphorylation and ubiquitination profiles, alongside transcriptomics data from the Genomic Data Commons, allow for integrative molecular studies of cancer. The ProteoCancer Analysis Suite (PCAS), our newly developed R package and Shinyapp, leverages these resources to facilitate in-depth analyses of proteomics, phosphoproteomics, and transcriptomics, enhancing our understanding of the tumor microenvironment through features like immune infiltration and drug sensitivity analysis. This tool aids in identifying critical signaling pathways and therapeutic targets, particularly through its detailed phosphoproteomic analysis. To demonstrate the functionality of the PCAS, we conducted an analysis of GAPDH across multiple cancer types, revealing a significant upregulation of protein levels, which is consistent with its important biological and clinical significance in tumors, as indicated in our prior research. Further experiments were used to validate the findings performed using the tool. In conclusion, the PCAS is a powerful and valuable tool for conducting comprehensive proteomic analyses, significantly enhancing our ability to uncover oncogenic mechanisms and identify potential therapeutic targets in cancer research.

Monitoring host-pathogen interactions using chemical proteomics

With the rapid emergence and the dissemination of microbial resistance to conventional chemotherapy, the shortage of novel antimicrobial drugs has raised a global health threat. As molecular interactions between microbial pathogens and their mammalian hosts are crucial to establish virulence, pathogenicity, and infectivity, a detailed understanding of these interactions has the potential to reveal novel therapeutic targets and treatment strategies. Bidirectional molecular communication between microbes and eukaryotes is essential for both pathogenic and commensal organisms to colonise their host. In particular, several devastating pathogens exploit host signalling to adjust the expression of energetically costly virulent behaviours. Chemical proteomics has emerged as a powerful tool to interrogate the protein interaction partners of small molecules and has been successfully applied to advance host-pathogen communication studies. Here, we present recent significant progress made by this approach and provide a perspective for future studies.

Improved quantification of carbonyl sub-metabolome by liquid chromatography mass spectrometry using a fragment controlled multiplexed isotopic tag

BACKGROUND

Carbonyl-containing metabolites are a class of key intermediate in metabolism, which has potentials to be biomarkers. Since their poor ionization, derivatization reagents, such as dansylhydrazine, are usually used to improve the sensitivity and/or to facilitate quantification. However, most current carbonyl derivatization reagents only have two channels, one is isotopically labeled and the other one is non-labeled. To quantify more samples in a run and using data-independent acquisition (DIA) mode to get comprehensive and unbiased mass fragmentation, we proposed a fragment-controlled isotopic tag, called DiMe-FP-NHNH2 (FP) which has five channels: Δ0, Δ3, Δ6, Δ9, and Δ12, thus up to 5 samples can be analyzed in a run.

RESULTS

The most important improvement is that the FP tag can produce multiple characteristic signals in tandem mass, diagnostic ions and neutral losses, which helps to selectively detect aldehydes/ketones for targeted and untargeted analysis. To exhibit all capabilities of the FP tag, we mimicked an untargeted metabolomics experiment, which comprises two steps. First, discovery step, using Data-Independent Analysis (SWATH-MS) and the labeling of two channels (Δ0 and Δ3), we picked out aldehyde/ketone from the pooled urine samples based on three characteristic signals, including isotope patterns, diagnostic ions, and neutral losses. Second, five-plex quantification, relative and absolute quantification were achieved in a single LC-MS analysis. Notably, because of different nominal masses, the FP tag can be used on any low or high resolution mass spectrometers.

SIGNIFICANCE

The benefits and performance of the FP tag are demonstrated by the analysis of urine samples collected from patients from a prostate cancer study, in which more than a thousand features were found based on MS1 fingerprint, but only around 120 aldehyde/ketone candidates were confirmed with characteristic signals and nine of which were quantified showing significant differences from healthy and reference urine samples.

Characterization and determination of casein glycomacropeptide in dairy products by UHPLC-MS/MS based on its characteristic peptide

The ultra-high-performance liquid chromatography tandem mass spectrometry (UHPLC-MS) method was built to quantify the casein glycomacropeptide (CGMP) in bovine dairy products accurately based on targeted proteomics. Qualitative analysis of theoretical peptides was carried out using high-resolution mass spectrometry (HRMS) and protein software. Isotope-labeled characteristic peptides were acquired via the labeled amino acid condensation method to correct the matrix effects. Peptide MAIPPK was the representative characteristic peptide for distinguishing the CGMP from κ-casein through trypsin digestion. After optimizing the pre-treatment conditions, the final 8% oxidant concentration was selected and the 10% formic acid concentration with 2.5 h oxidation time. Moreover, the results of methodological verification showed that the recovery rate was 103.7%, meanwhile the precision of inter-day and intra-day was less than 5%. In conclusion, the research demonstrated the characteristic peptide MAIPPK could quantitatively applied to detect CGMP in dairy products.

Mass spectrometry-based proteomics approaches to interrogate skeletal muscle adaptations to exercise

Acute exercise and chronic exercise training elicit beneficial whole-body changes in physiology that ultimately depend on profound alterations to the dynamics of tissue-specific proteins. Since the work accomplished during exercise owes predominantly to skeletal muscle, it has received the majority of interest from exercise scientists that attempt to unravel adaptive mechanisms accounting for salutary metabolic effects and performance improvements that arise from training. Contemporary scientists are also beginning to use mass spectrometry-based proteomics, which is emerging as a powerful approach to interrogate the muscle protein signature in a more comprehensive manner. Collectively, these technologies facilitate the analysis of skeletal muscle protein dynamics from several viewpoints, including changes to intracellular proteins (expression proteomics), secreted proteins (secretomics), post-translational modifications as well as fiber-, cell-, and organelle-specific changes. This review aims to highlight recent literature that has leveraged new workflows and advances in mass spectrometry-based proteomics to further our understanding of training-related changes in skeletal muscle. We call attention to untapped areas in skeletal muscle proteomics research relating to exercise training and metabolism, as well as basic points of contention when applying mass spectrometry-based analyses, particularly in the study of human biology. We further encourage researchers to couple the hypothesis-generating and descriptive nature of omics data with functional analyses that propel our understanding of the complex adaptive responses in skeletal muscle that occur with acute and chronic exercise.

Measurement and utilization of the proteomic reactivity by mass spectrometry

Chemical proteomics, which involves studying the covalent modifications of proteins by small molecules, has significantly contributed to our understanding of protein function and has become an essential tool in drug discovery. Mass spectrometry (MS) is the primary method for identifying and quantifying protein-small molecule adducts. In this review, we discuss various methods for measuring proteomic reactivity using MS and covalent proteomics probes that engage through reactivity-driven and proximity-driven mechanisms. We highlight the applications of these methods and probes in live-cell measurements, drug target identification and validation, and characterizing protein-small molecule interactions. We conclude the review with current developments and future opportunities in the field, providing our perspectives on analytical considerations for MS-based analysis of the proteomic reactivity landscape.

Integration of data-independent acquisition (DIA) with co-fractionation mass spectrometry (CF-MS) to enhance interactome mapping capabilities

Proteomics technologies are continually advancing, providing opportunities to develop stronger and more robust protein interaction networks (PINs). In part, this is due to the ever-growing number of high-throughput proteomics methods that are available. This review discusses how data-independent acquisition (DIA) and co-fractionation mass spectrometry (CF-MS) can be integrated to enhance interactome mapping abilities. Furthermore, integrating these two techniques can improve data quality and network generation through extended protein coverage, less missing data, and reduced noise. CF-DIA-MS shows promise in expanding our knowledge of interactomes, notably for non-model organisms (NMOs). CF-MS is a valuable technique on its own, but upon the integration of DIA, the potential to develop robust PINs increases, offering a unique approach for researchers to gain an in-depth understanding into the dynamics of numerous biological processes.

Advancements in Sample Preparation Methods for the Chromatographic and Mass Spectrometric Determination of Zearalenone and Its Metabolites in Food: An Overview

Zearalenone and its metabolites are mycotoxins generated by Fusarium species while crops are growing and can typically be found in various foods, posing a risk to human health. Governments have implemented stricter regulations concerning the permissible levels of zearalenone in food products to safeguard public health. Stricter regulations on zearalenone levels in food have been implemented. However, detecting zearalenone and its metabolites remains challenging due to sample complexity and interference. Surprisingly few reviews of sample preparation methods for zearalenone in food have appeared in the past decade. In this overview, we outline the most recent developments in the sample pre-treatment technology of zearalenone and its metabolites in food samples based on chromatography-mass spectrometry methods since 2012. This review covers some prominent technologies, such as liquid-liquid extraction-based methods, solid-phase extraction-based methods, and QuEChERS (quick, easy, cheap, effective, rugged, and safe) extraction, providing valuable insights into their advantages and limitations for potential applications. The assessment of the methods discussed, along with an overview of current challenges and prospects, will guide researchers in advancing the field and ensuring safer food quality for consumers worldwide.

Non-Canonical Amino Acids in Analyses of Protease Structure and Function

All known organisms encode 20 canonical amino acids by base triplets in the genetic code. The cellular translational machinery produces proteins consisting mainly of these amino acids. Several hundred natural amino acids serve important functions in metabolism, as scaffold molecules, and in signal transduction. New side chains are generated mainly by post-translational modifications, while others have altered backbones, such as the β- or γ-amino acids, or they undergo stereochemical inversion, e.g., in the case of D-amino acids. In addition, the number of non-canonical amino acids has further increased by chemical syntheses. Since many of these non-canonical amino acids confer resistance to proteolytic degradation, they are potential protease inhibitors and tools for specificity profiling studies in substrate optimization and enzyme inhibition. Other applications include in vitro and in vivo studies of enzyme kinetics, molecular interactions and bioimaging, to name a few. Amino acids with bio-orthogonal labels are particularly attractive, enabling various cross-link and click reactions for structure-functional studies. Here, we cover the latest developments in protease research with non-canonical amino acids, which opens up a great potential, e.g., for novel prodrugs activated by proteases or for other pharmaceutical compounds, some of which have already reached the clinical trial stage.

Revealing Intramolecular Isotope Effects with Chemical-Bond Precision

Isotope substitution of a molecule not only changes its vibrational frequencies but also changes its vibrational distributions in real-space. Quantitatively measuring the isotope effects inside a polyatomic molecule requires both energy and spatial resolutions at the single-bond level, which has been a long-lasting challenge in macroscopic techniques. By achieving ångström resolution in tip-enhanced Raman spectroscopy (TERS), we record the corresponding local vibrational modes of pentacene and its fully deuterated form, enabling us to identify and measure the isotope effect of each vibrational mode. The measured frequency ratio ν_H/ν_D varies from 1.02 to 1.33 in different vibrational modes, indicating different isotopic contributions of H/D atoms, which can be distinguished from TERS maps in real-space and well described by the potential energy distribution simulations. Our study demonstrates that TERS can serve as a non-destructive and highly sensitive methodology for isotope detection and recognition with chemical-bond precision.

Deep Learning-Based Label-Free Surface-Enhanced Raman Scattering Screening and Recognition of Small-Molecule Binding Sites in Proteins

Identification of small-molecule binding sites in proteins is of great significance in analysis of protein function and drug design. Modified sites can be recognized via proteolytic cleavage followed by liquid chromatography-mass spectrometry (LC-MS); however, this has always been impeded by the complexity of peptide mixtures and the elaborate synthetic design for tags. Here, we demonstrate a novel technique for identifying protein binding sites using a deep learning-based label-free surface-enhanced Raman scattering (SERS) screening (DLSS) strategy. In DLSS, the deep learning model that was trained with large SERS signals could detect signal features of small molecules with high accuracy (>99%). Without any secondary tag, the small molecules are directly complexed with proteins. After proteolysis and LC, SERS signals of all LC fractions are collected and input into the model, whereby the fractions containing the small-molecule-modified peptides can be recognized by the model and sent to MS/MS to identify the binding site(s). By using an automated DLSS system, we successfully identified the modification sites of fomepizole in alcohol dehydrogenase, which is coordinated with zinc along with three peptides. We also showed that the DLSS strategy works for identification of amino-acid residues that covalently bond with ibrutinib in Bruton tyrosine kinase. These results suggest that the DLSS strategy, which provides high molecular recognition capability to LC-MS analysis, has potential in drug discovery, proteomics, and metabolomics.

Characterization and determination of bovine immunoglobulin G subtypes in milk and dairy products by UPLC-MS

In this study, ultra-high performance liquid chromatography mass spectrometry (UPLC-MS) method was established for the characterization and quantitative determination of immunoglobulin G (IgG) subtypes (IgG1, IgG2, IgG3) in bovine dairy products. High-resolution mass spectrometry (HRMS) was applied to qualitatively confirm the theoretical peptides with specificity, enzymatic hydrolysis curve and stability among in heavy chain constant (CH1, CH2 and CH3) regions. The characteristic peptides VHNEGLPAPIVR, EPSVFIFPPKPK, GLPAPIVR, VVSALR were screened to quantitative analysis bovine IgG1, IgG2, IgG3 and the total amount of bovine IgG1 and IgG3, respectively. Isotope-labeled peptides were obtained by isotope dimethylation reaction, which aimed to correct the matrix effects. The results showed that the recovery was between 98.7% and 103.5%, and the precision of inter-day and intra-day was less than 6.8%. Moreover, this method had good linearity (R² ≥ 0.999). Therefore, this research provided an effective method for quantitatively detecting bovine IgG subtypes in milk and dairy products.

Catch, Modify and Analyze: Methods of Chemoselective Modification of Cysteine-Containing Peptides

One effective solution in the analysis of complex mixtures, including protein or cell hydrolysates, is based on chemoselective derivatization of a selected group of compounds by using selective tags to facilitate detection. Another method is based on the capture of the desired compounds by properly designed solid supports, resulting in sample enrichment. Cysteine is one of the rarest amino acids, but at least one cysteine residue is present in more than 91% of human proteins, which clearly confirms its important role in biological systems. Some cysteine-containing peptides may serve as significant molecular biomarkers, which may emerge as key indices in the management of patients with particular diseases. In the current review, we describe recent advances in the development of cysteine-containing peptide modification techniques based on solution and solid phase derivatization and enrichment strategies.

Label-Free Physical Techniques and Methodologies for Proteins Detection in Microfluidic Biosensor Structures

Proteins in biological fluids (blood, urine, cerebrospinal fluid) are important biomarkers of various pathological conditions. Protein biomarkers detection and quantification have been proven to be an indispensable diagnostic tool in clinical practice. There is a growing tendency towards using portable diagnostic biosensor devices for point-of-care (POC) analysis based on microfluidic technology as an alternative to conventional laboratory protein assays. In contrast to universally accepted analytical methods involving protein labeling, label-free approaches often allow the development of biosensors with minimal requirements for sample preparation by omitting expensive labelling reagents. The aim of the present work is to review the variety of physical label-free techniques of protein detection and characterization which are suitable for application in micro-fluidic structures and analyze the technological and material aspects of label-free biosensors that implement these methods. The most widely used optical and impedance spectroscopy techniques: absorption, fluorescence, surface plasmon resonance, Raman scattering, and interferometry, as well as new trends in photonics are reviewed. The challenges of materials selection, surfaces tailoring in microfluidic structures, and enhancement of the sensitivity and miniaturization of biosensor systems are discussed. The review provides an overview for current advances and future trends in microfluidics integrated technologies for label-free protein biomarkers detection and discusses existing challenges and a way towards novel solutions.

On the Road to Accurate Protein Biomarkers in Prostate Cancer Diagnosis and Prognosis: Current Status and Future Advances

Prostate cancer (PC) is a leading cause of morbidity and mortality among men worldwide. Molecular biomarkers work in conjunction with existing clinicopathologic tools to help physicians decide who to biopsy, re-biopsy, treat, or re-treat. The past decade has witnessed the commercialization of multiple PC protein biomarkers with improved performance, remarkable progress in proteomic technologies for global discovery and targeted validation of novel protein biomarkers from clinical specimens, and the emergence of novel, promising PC protein biomarkers. In this review, we summarize these advances and discuss the challenges and potential solutions for identifying and validating clinically useful protein biomarkers in PC diagnosis and prognosis. The identification of multi-protein biomarkers with high sensitivity and specificity, as well as their integration with clinicopathologic parameters, imaging, and other molecular biomarkers, bodes well for optimal personalized management of PC patients.