A Versatile Isobaric Tag Enables Proteome Quantification in Data-Dependent and Data-Independent Acquisition Modes

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.

Enhancing coverage of annotated compounds in traditional Chinese medicine formulas: Integrating MSE and Fast-DDA molecular network with AntDAS-Case study of Xiao Jian Zhong Tang

The chemical characterisation of traditional Chinese medicine formulas (TCMFs) using mass spectrometry poses notable challenges owing to their complex and diverse chemical compositions. While acquisition modes such as data-dependent acquisition (DDA) and data-independent acquisition (DIA) offer new insights, DDA's tendency to overlook low-abundance ions and DIA's complicated data processing, particularly in matching MS1 and MS2 information, limit the effective annotation of valuable compounds in TCMFs. Herein, we present a new integrated strategy to enhance the coverage of annotated compounds in TCMFs, using Xiao Jian Zhong Tang (XJZ) as a case study. First, we characterised the components of XJZ through UNIFI software in Fast-DDA and DIA modes. We then summarised the diagnostic ions and substituent information of the identified compounds based on the Fast-DDA data, integrating molecular networks and AntDAS to predict unknown components and uncover potential components. Ultimately, we characterised a total of 785 components in XJZ, including 43 that were unique to XJZ when compared to the individual herbs involved. The presence of these new components may result from the recombination of substituents during compatibility. In conclusion, this new integrated strategy facilitates more in-depth characterisation of components in TCMFs, providing a new direction for exploring the compatibility principles among TCMFs.

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.

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.

A review of the current state of single-cell proteomics and future perspective

Single-cell methodologies and technologies have started a revolution in biology which until recently has primarily been limited to deep sequencing and imaging modalities. With the advent and subsequent torrid development of single-cell proteomics over the last 5 years, despite the fact that proteins cannot be amplified like transcripts, it has now become abundantly clear that it is a worthy complement to single-cell transcriptomics. In this review, we engage in an assessment of the current state of the art of single-cell proteomics including workflow, sample preparation techniques, instrumentation, and biological applications. We investigate the challenges associated with working with very small sample volumes and the acute need for robust statistical methods for data interpretation. We delve into what we believe is a promising future for biological research at single-cell resolution and highlight some of the exciting discoveries that already have been made using single-cell proteomics, including the identification of rare cell types, characterization of cellular heterogeneity, and investigation of signaling pathways and disease mechanisms. Finally, we acknowledge that there are a number of outstanding and pressing problems that the scientific community vested in advancing this technology needs to resolve. Of prime importance is the need to set standards so that this technology becomes widely accessible allowing novel discoveries to be easily verifiable. We conclude with a plea to solve these problems rapidly so that single-cell proteomics can be part of a robust, high-throughput, and scalable single-cell multi-omics platform that can be ubiquitously applied to elucidating deep biological insights into the diagnosis and treatment of all diseases that afflict us.

Robust dimethyl-based multiplex-DIA doubles single-cell proteome depth via a reference channel

Single-cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. It is currently limited in proteomic depth, throughput, and robustness, which we address here by a streamlined multiplexed workflow using data-independent acquisition (mDIA). We demonstrate automated and complete dimethyl labeling of bulk or single-cell samples, without losing proteomic depth. Lys-N digestion enables five-plex quantification at MS1 and MS2 level. Because the multiplexed channels are quantitatively isolated from each other, mDIA accommodates a reference channel that does not interfere with the target channels. Our algorithm RefQuant takes advantage of this and confidently quantifies twice as many proteins per single cell compared to our previous work (Brunner et al, PMID 35226415), while our workflow currently allows routine analysis of 80 single cells per day. Finally, we combined mDIA with spatial proteomics to increase the throughput of Deep Visual Proteomics seven-fold for microdissection and four-fold for MS analysis. Applying this to primary cutaneous melanoma, we discovered proteomic signatures of cells within distinct tumor microenvironments, showcasing its potential for precision oncology.

Collision energies: Optimization strategies for bottom-up proteomics

Mass-spectrometry coupled to liquid chromatography is an indispensable tool in the field of proteomics. In the last decades, more and more complex and diverse biochemical and biomedical questions have arisen. Problems to be solved involve protein identification, quantitative analysis, screening of low abundance modifications, handling matrix effect, and concentrations differing by orders of magnitude. This led the development of more tailored protocols and problem centered proteomics workflows, including advanced choice of experimental parameters. In the most widespread bottom-up approach, the choice of collision energy in tandem mass spectrometric experiments has outstanding role. This review presents the collision energy optimization strategies in the field of proteomics which can help fully exploit the potential of MS based proteomics techniques. A systematic collection of use case studies is then presented to serve as a starting point for related further scientific work. Finally, this article discusses the issue of comparing results from different studies or obtained on different instruments, and it gives some hints on methodology transfer between laboratories based on measurement of reference species.

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.

Strategies for Increasing the Depth and Throughput of Protein Analysis by plexDIA

Accurate protein quantification is key to identifying protein markers, regulatory relationships between proteins, and pathophysiological mechanisms. Realizing this potential requires sensitive and deep protein analysis of a large number of samples. Toward this goal, proteomics throughput can be increased by parallelizing the analysis of both precursors and samples using multiplexed data independent acquisition (DIA) implemented by the plexDIA framework: https://plexDIA.slavovlab.net. Here we demonstrate the improved precisions of retention time estimates within plexDIA and how this enables more accurate protein quantification. plexDIA has demonstrated multiplicative gains in throughput, and these gains may be substantially amplified by improving the multiplexing reagents, data acquisition, and interpretation. We discuss future directions for advancing plexDIA, which include engineering optimized mass-tags for high-plexDIA, introducing isotopologous carriers, and developing algorithms that utilize the regular structures of plexDIA data to improve sensitivity, proteome coverage, and quantitative accuracy. These advances in plexDIA will increase the throughput of functional proteomic assays, including quantifying protein conformations, turnover dynamics, modifications states and activities. The sensitivity of these assays will extend to single-cell analysis, thus enabling functional single-cell protein analysis.

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.

Increasing the throughput of sensitive proteomics by plexDIA

Current mass spectrometry methods enable high-throughput proteomics of large sample amounts, but proteomics of low sample amounts remains limited in depth and throughput. To increase the throughput of sensitive proteomics, we developed an experimental and computational framework, called plexDIA, for simultaneously multiplexing the analysis of peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing proteome coverage or quantitative accuracy. By using three-plex non-isobaric mass tags, plexDIA enables quantification of threefold more protein ratios among nanogram-level samples. Using 1-hour active gradients, plexDIA quantified ~8,000 proteins in each sample of labeled three-plex sets and increased data completeness, reducing missing data more than twofold across samples. Applied to single human cells, plexDIA quantified ~1,000 proteins per cell and achieved 98% data completeness within a plexDIA set while using ~5 minutes of active chromatography per cell. These results establish a general framework for increasing the throughput of sensitive and quantitative protein analysis.

Recent advances in isobaric labeling and applications in quantitative proteomics

Mass spectrometry (MS) has emerged at the forefront of quantitative proteomic techniques. Liquid chromatography-mass spectrometry (LC-MS) can be used to determine abundances of proteins and peptides in complex biological samples. Several methods have been developed and adapted for accurate quantification based on chemical isotopic labeling. Among various chemical isotopic labeling techniques, isobaric tagging approaches rely on the analysis of peptides from MS2-based quantification rather than MS1-based quantification. In this review, we will provide an overview of several isobaric tags along with some recent developments including complementary ion tags, improvements in sensitive quantitation of analytes with lower abundance, strategies to increase multiplexing capabilities, and targeted analysis strategies. We will also discuss limitations of isobaric tags and approaches to alleviate these restrictions through bioinformatic tools and data acquisition methods. This review will highlight several applications of isobaric tags, including biomarker discovery and validation, thermal proteome profiling, cross-linking for structural investigations, single-cell analysis, top-down proteomics, along with applications to different molecules including neuropeptides, glycans, metabolites, and lipids, while providing considerations and evaluations to each application.

Proteome Informatics in Tibetan Sheep (Ovis aries) Testes Suggest the Crucial Proteins Related to Development and Functionality

Testis has an indispensable function in male reproduction of domestic animals. Tibetan sheep (Ovis aries) is a locally adapted breed of sheep raised in the Qinghai-Tibet Plateau, with outsized roles in providing the livelihood for millions of residents. Nevertheless, less is known on how protein expression and their functional roles in developmental testes of such breed limit their use in breeding efforts. In this study, we obtained comprehensive protein profiles from testes of Tibetan sheep at three developmental stages (including pre-puberty, post-puberty, and adulthood) using data-independent acquisition-based proteomic strategy to quantitatively identify the differentially abundant proteins (DAPs) associated with testicular development and function and to unravel the molecular basis of spermatogenesis. A total of 6,221 proteins were differentially expressed in an age-dependent manner. The reliability of the gene expression abundance was corroborated by quantitative PCR and targeted parallel reaction monitoring. These DAPs were significantly enriched to biological processes concerning spermatid development and sperm deformation, mitosis, glycolytic process, cell-cell/extracellular matrix (ECM) junctions, cell proliferation, apoptosis, and migration and to the pathways including, developmental process and sexual reproduction-related (such as VEGF, estrogen, insulin, GnRH, Hippo, PI3K-Akt, mTOR, MAPK, and AMPK), and testicular cell events-related pathways (such as tight/gap/adherens junctions, ECM-receptor interaction, regulation of actin cytoskeleton, glycolysis, cell cycle, and meiosis). Based on these bioinformatics analysis, we constructed four protein–protein interaction network, among which the proteins are involved in mitosis, meiosis, spermiogenesis, and testicular microenvironment, respectively. Altogether, these bioinformatics-based sequencing results suggest that many protein-coding genes were expressed in a development-dependent manner in Tibetan sheep testes to contribute to the testicular cell development and their surrounding microenvironment remodeling at various stages of spermatogenesis. These findings have important implications for further understanding of the mechanisms underlying spermatogenesis in sheep and even other plateau-adapted animals.

HRPIF data mining based on data-dependent/independent acquisition for Rhei Radix et Rhizoma metabolite screening in rats

In traditional Chinese medicine (TCM), components with identical nuclei often share structural similarity, indicating the possibility of similar second-level mass spectrometry (MS/MS) fragments. High-resolution product-ion filter (HRPIF) technique can be utilized to identify metabolites, with similar fragments, in vivo. In principle, this technique applies to TCM; however, its application has been restricted due to the limitations of traditional MS/MS data acquisition. Therefore, a novel analysis strategy, based on data-dependent acquisition (DDA) and data-independent acquisition (DIA) datasets, has been developed for the determination of template product ions and efficient non-targeted identification of TCM-related components in vivo by HRPIF and background subtraction (BS). This DDA-DIA combination strategy, taking Rhei Radix et Rhizoma as a test case, identified 71 anthraquinone prototype components in vitro (36 of which were discovered for the first time), and 45 related components in vivo, confirming glucuronidation and sulfation as the main reactions. The developed strategy could rapidly identify TCM-related components in vivo with high sensitivity, indicating the immense importance of this novel HRPIF data mining technology in TCM analysis.

Developing mass spectrometry for the quantitative analysis of neuropeptides

INTRODUCTION

Neuropeptides are signaling molecules originating in the neuroendocrine system that can act as neurotransmitters and hormones in many biochemical processes. Their exact function is difficult to characterize, however, due to dependence on concentration, post-translational modifications, and the presence of other comodulating neuropeptides. Mass spectrometry enables sensitive, accurate, and global peptidomic analyses that can profile neuropeptide expression changes to understand their roles in many biological problems, such as neurodegenerative disorders and metabolic function.

AREAS COVERED

We provide a brief overview of the fundamentals of neuropeptidomic research, limitations of existing methods, and recent progress in the field. This review is focused on developments in mass spectrometry and encompasses labeling strategies, post-translational modification analysis, mass spectrometry imaging, and integrated multi-omic workflows, with discussion emphasizing quantitative advancements.

EXPERT OPINION

Neuropeptidomics is critical for future clinical research with impacts in biomarker discovery, receptor identification, and drug design. While advancements are being made to improve sensitivity and accuracy, there is still room for improvement. Better quantitative strategies are required for clinical analyses, and these methods also need to be amenable to mass spectrometry imaging, post-translational modification analysis, and multi-omics to facilitate understanding and future treatment of many diseases.

The Isotopic Ac-IP Tag Enables Multiplexed Proteome Quantification in Data-Independent Acquisition Mode

Data-independent acquisition (DIA) is an increasingly used approach for quantitative proteomics. However, most current isotope labeling strategies are not suitable for DIA as they lead to more complex MS2 spectra or severe ratio distortion. As a result, DIA suffers from a lower throughput than data-dependent acquisition (DDA) due to a lower level of multiplexing. Herein, we synthesized an isotopically labeled acetyl-isoleucine-proline (Ac-IP) tag for multiplexed quantification in DIA. Differentially labeled peptides have distinct precursor ions carrying the quantitative information but identical MS2 spectra since the isotopically labeled Ac-Ile part leaves as a neutral loss upon collision-induced dissociation, while fragmentation of the peptide backbone generates regular fragment ions for identification. The Ac-IP-labeled samples can be analyzed using general DIA liquid chromatography–mass spectrometry settings, and the data obtained can be processed with established approaches. Relative quantification requires deconvolution of the isotope envelope of the respective precursor ions. Suitability of the Ac-IP tag is demonstrated with a triplex-labeled yeast proteome spiked with bovine serum albumin that was mixed at 10:5:1 ratios, resulting in measured ratios of 9.7:5.3:1.1.