PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows

A crucial part of a successful systems biology experiment is an assay that provides reliable, quantitative measurements for each of the components in the system being studied. For proteomics to be a key part of such studies, it must deliver accurate quantification of all the components in the system for each tested perturbation without any gaps in the data. This will require a new approach to proteomics that is based on emerging targeted quantitative mass spectrometry techniques. The PeptideAtlas Project comprises a growing, publicly accessible database of peptides identified in many tandem mass spectrometry proteomics studies and software tools that allow the building of PeptideAtlas, as well as its use by the research community. Here, we describe the PeptideAtlas Project, its contents and components, and show how together they provide a unique platform to select and validate mass spectrometry targets, thereby allowing the next revolution in proteomics.     

DPHL:A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery

To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to generate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000.     

Enhancing Data Reliability in TOMAHAQ for Large‐Scale Protein Quantification

The analytical scale of most mass‐spectrometry‐based targeted proteomics assays is usually limited by assay performance and instrument utilization. A recently introduced method, called triggered by offset, multiplexed, accurate mass, high resolution, and absolute quantitation (TOMAHAQ), combines both peptide and sample multiplexing to simultaneously improve analytical scale and quantitative performance. In the present work, critical technical requirements and data analysis considerations for successful implementation of the TOMAHAQ technique based on the study of a total of 185 target peptides across over 200 clinical plasma samples are discussed. Importantly, it is observed that significant interference originate from the TMTzero reporter ion used for the synthetic trigger peptides. This interference is not expected because only TMT10plex reporter ions from the target peptides should be observed under typical TOMAHAQ conditions. In order to unlock the great promise of the technique for high throughput quantification, here a post‐acquisition data correction strategy to deconvolute the reporter ion superposition and recover reliable data is proposed.     

基于串联质谱标签法和平行反应监测技术的氟喹诺酮耐药沙门菌蛋白质组学分析

【背景】随着沙门菌对氟喹诺酮类药物的耐药性不断增强,对其耐药机理的研究显得尤为迫切和重要,蛋白质组学分析将为沙门菌的耐药机理研究提供新的靶点和方向。【目的】对鼠伤寒沙门菌诱导获得耐药性前后进行蛋白质组学分析,为深入研究沙门菌耐药机理奠定基础。【方法】用环丙沙星对鼠伤寒沙门菌ATCC13311进行耐药性诱导,利用串联质谱标签法(Tandem mass tag,TMT)对其耐药性进行差异蛋白的筛选和生物信息学分析,并选取15个差异蛋白进行平行反应监测(Parallel reaction monitoring,PRM)靶向蛋白验证。【结果】筛选出318个差异表达蛋白,其中上调159个,下调159个,涉及的KEGG通路主要包括细菌趋药性、ABC转运蛋白、双组分系统等;PRM定量到13个验证蛋白且变化趋势与TMT一致。【结论】通过TMT定量结合PRM靶向验证对鼠伤寒沙门菌耐药前后进行蛋白质组学分析,筛选出多个差异蛋白和代谢通路,包括外排泵相关蛋白、外膜蛋白、双组分相关蛋白及通路、细菌趋化性相关蛋白及通路等,为沙门菌氟喹诺酮类耐药机理的深入研究奠定了基础。     

基于质谱的选择反应监测技术相关策略和方法的研究进展

随着蛋白质组学研究的不断深入,基于质谱的选择反应监测技术(SRM)已经成为以发现生物标志物为代表的定向蛋白质组学研究的重要手段.SRM技术根据假设信息,特异性地获取符合假设条件的质谱信号,去除不符合条件的离子信号干扰,从而得到特定蛋白质的定量信息.SRM技术具有更高的灵敏度和精确性、更大的动态范围等优势.该技术可分为实验设计、数据获取和数据分析三个步骤.在这几个步骤中,最重要的是利用生物信息学手段总结当前实验数据的结果,并用机器学习方法和总结的经验规则进行SRM实验的母离子和子离子对的预测.针对数据质控和定量的生物信息学方法研究在提高SRM数据可靠性方面具有重要作用.此外,为方便SRM的研究,本文还收集、汇总了SRM技术相关的软件、工具和数据库资源.随着质谱仪器的不断发展,新的SRM实验策略以及分析方法、计算工具也应运而生.结合更优化的实验策略、方法,采用更精准的生物信息学算法和工具,SRM在未来蛋白质组学的发展中将发挥更加重要的作用.     

Highly multiplexed targeted proteomics using precise control of peptide retention time

Large-scale proteomics applications using SRM analysis on triple quadrupole mass spectrometers present new challenges to LC-MS/MS experimental design. Despite the automation of building large-scale LC-SRM methods, the increased numbers of targeted peptides can compromise the balance between sensitivity and selectivity. To facilitate large target numbers, time-scheduled SRM transition acquisition is performed. Previously published results have demonstrated incorporation of a well-characterized set of synthetic peptides enabled chromatographic characterization of the elution profile for most endogenous peptides. We have extended this application of peptide trainer kits to not only build SRM methods but to facilitate real-time elution profile characterization that enables automated adjustment of the scheduled detection windows. Incorporation of dynamic retention time adjustments better facilitate targeted assays lasting several days without the need for constant supervision. This paper provides an overview of how the dynamic retention correction approach identifies and corrects for commonly observed LC variations. This adjustment dramatically improves robustness in targeted discovery experiments as well as routine quantification experiments.     

Targeted proteomics of solid cancers: from quantification of known biomarkers towards reading the digital proteome maps

The concept of personalized medicine includes novel protein biomarkers that are expected to improve the early detection, diagnosis and therapy monitoring of malignant diseases. Tissues, biofluids, cell lines and xenograft models are the common sources of biomarker candidates that require verification of clinical value in independent patient cohorts. Targeted proteomics – based on selected reaction monitoring, or data extraction from data-independent acquisition based digital maps – now represents a promising mass spectrometry alternative to immunochemical methods. To date, it has been successfully used in a high number of studies answering clinical questions on solid malignancies: breast, colorectal, prostate, ovarian, endometrial, pancreatic, hepatocellular, lung, bladder and others. It plays an important role in functional proteomic experiments that include studying the role of post-translational modifications in cancer progression. This review summarizes verified biomarker candidates successfully quantified by targeted proteomics in this field and directs the readers who plan to design their own hypothesis-driven experiments to appropriate sources of methods and knowledge.     

Quality assessment and interference detection in targeted mass spectrometry data using machine learning

Advances in the field of targeted proteomics and mass spectrometry have significantly improved assay sensitivity and multiplexing capacity. The high-throughput nature of targeted proteomics experiments has increased the rate of data production, which requires development of novel analytical tools to keep up with data processing demand. Currently, development and validation of targeted mass spectrometry assays require manual inspection of chromatographic peaks from large datasets to ensure quality, a process that is time consuming, prone to inter- and intra-operator variability and limits the efficiency of data interpretation from targeted proteomics analyses. To address this challenge, we have developed TargetedMSQC, an R package that facilitates quality control and verification of chromatographic peaks from targeted proteomics datasets. This tool calculates metrics to quantify several quality aspects of a chromatographic peak, e.g. symmetry, jaggedness and modality, co-elution and shape similarity of monitored transitions in a peak group, as well as the consistency of transitions’ ratios between endogenous analytes and isotopically labeled internal standards and consistency of retention time across multiple runs. The algorithm takes advantage of supervised machine learning to identify peaks with interference or poor chromatography based on a set of peaks that have been annotated by an expert analyst. Using TargetedMSQC to analyze targeted proteomics data reduces the time spent on manual inspection of peaks and improves both speed and accuracy of interference detection. Additionally, by allowing the analysts to customize the tool for application on different datasets, TargetedMSQC gives the users the flexibility to define the acceptable quality for specific datasets. Furthermore, automated and quantitative assessment of peak quality offers a more objective and systematic framework for high throughput analysis of targeted mass spectrometry assay datasets and is a step towards more robust and faster assay implementation.     

Four-dimensional proteomics analysis of human cerebrospinal fluid with trapped ion mobility spectrometry using PASEF

A quadrupole time-of-flight mass spectrometer coupled with a trapped ion mobility spectrometry (timsTOF) operated in parallel accumulation-serial fragmentation (PASEF) mode has recently emerged as a platform capable of providing four-dimensional (4D) features comprising of elution time, collision cross section (CCS), mass-to-charge ratio, and intensity of peptides. The PASEF mode provides ∼100% ion sampling efficiency both in data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes without sacrificing sensitivity. In addition, targeted measurements using PASEF integrated parallel reaction monitoring (PRM) mode have also been described. However, only limited number of studies have used timsTOF for analysis of clinical samples. Although Orbitrap mass spectrometers have been used for biomarker discovery from cerebrospinal fluid (CSF) in a variety of neurological diseases, these Orbitrap-derived datasets cannot readily be applied for driving experiments on timsTOF mass spectrometers. We generated a catalog of peptides and proteins in human CSF in DDA mode on a timsTOF mass spectrometer and used these data to build a spectral library. This strategy allowed us to use elution times and ion mobility values from the spectral library to design PRM experiments for quantifying previously discovered biomarkers from CSF samples in Alzheimer's disease. When the same samples were analyzed using a DIA approach combined with a spectral library search, a higher number of proteins were identified than in a library-free approach. Overall, we have established a spectral library of CSF as a resource and demonstrated its utility for PRM and DIA studies, which should facilitate studies of neurological disorders.     

What is targeted proteomics? A concise revision of targeted acquisition and targeted data analysis in mass spectrometry

Targeted proteomics has gained significant popularity in mass spectrometry‐based protein quantification as a method to detect proteins of interest with high sensitivity, quantitative accuracy and reproducibility. However, with the emergence of a wide variety of targeted proteomics methods, some of them with high‐throughput capabilities, it is easy to overlook the essence of each method and to determine what makes each of them a targeted proteomics method. In this viewpoint, we revisit the main targeted proteomics methods and classify them in four categories differentiating those methods that perform targeted data acquisition from targeted data analysis, and those methods that are based on peptide ion data (MS1 targeted methods) from those that rely on the peptide fragments (MS2 targeted methods).     

基于质谱的植物蛋白质组学研究方法

基于质谱的植物蛋白质组学研究方法,从定性和定量蛋白质组学两个方向进行了归纳总结,并对近年来出现的靶向蛋白质组学、DIA/SWATH技术、化学蛋白质组学,以及多组学联合分析等蛋白质组学研究的新技术、新方法和新应用进行了综述。     

Recent advances in mass spectrometry: data independent analysis and hyper reaction monitoring

New mass spectrometry (MS) methods, collectively known as data independent analysis and hyper reaction monitoring, have recently emerged. These methods hold promises to address the shortcomings of data-dependent analysis and selected reaction monitoring (SRM) employed in shotgun and targeted proteomics, respectively. They allow MS analyses of all species in a complex sample indiscriminately, or permit SRM-like experiments conducted with full high-resolution product ion spectra, potentially leading to higher sequence coverage or analytical selectivity. These methods include MS^E, all-ion fragmentation, Fourier transform-all reaction monitoring, SWATH Acquisition, multiplexed MS/MS, pseudo-SRM (pSRM) and parallel reaction monitoring (PRM). In this review, the strengths and pitfalls of these methods are discussed and illustrated with examples. In essence, the suitability of the use of each method is contingent on the biological questions posed. Although these methods do not fundamentally change the shape of proteomics, they are useful additional tools that should expedite biological discoveries.     

Shortlisting SARS‐CoV‐2 Peptides for Targeted Studies from Experimental Data‐Dependent Acquisition Tandem Mass Spectrometry Data

Detection of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is a crucial tool for fighting the COVID‐19 pandemic. This dataset brief presents the exploration of a shotgun proteomics dataset acquired on SARS‐CoV‐2 infected Vero cells. Proteins from inactivated virus samples were extracted, digested with trypsin, and the resulting peptides were identified by data‐dependent acquisition tandem mass spectrometry. The 101 peptides reporting for six viral proteins were specifically analyzed in terms of their analytical characteristics, species specificity and conservation, and their proneness to structural modifications. Based on these results, a shortlist of 14 peptides from the N, S, and M main structural proteins that could be used for targeted mass‐spectrometry method development and diagnostic of the new SARS‐CoV‐2 is proposed and the best candidates are commented.     

Mass spectrometry-based targeted quantitative proteomics: achieving sensitive and reproducible detection of proteins

Traditional shotgun proteomics used to detect a mixture of hundreds to thousands of proteins through mass spectrometric analysis, has been the standard approach in research to profile protein content in a biological sample which could lead to the discovery of new (and all) protein candidates with diagnostic, prognostic, and therapeutic values. In practice, this approach requires significant resources and time, and does not necessarily represent the goal of the researcher who would rather study a subset of such discovered proteins (including their variations or posttranslational modifications) under different biological conditions. In this context, targeted proteomics is playing an increasingly important role in the accurate measurement of protein targets in biological samples in the hope of elucidating the molecular mechanism of cellular function via the understanding of intricate protein networks and pathways. One such (targeted) approach, selected reaction monitoring (or multiple reaction monitoring) mass spectrometry (MRM-MS), offers the capability of measuring multiple proteins with higher sensitivity and throughput than shotgun proteomics. Developing and validating MRM-MS-based assays, however, is an extensive and iterative process, requiring a coordinated and collaborative effort by the scientific community through the sharing of publicly accessible data and datasets, bioinformatic tools, standard operating procedures, and well characterized reagents.     

Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques

Introduction: While selected/multiple-reaction monitoring (SRM or MRM) is considered the gold standard for quantitative protein measurement, emerging data-independent acquisition (DIA) using high-resolution scans have opened a new dimension of high-throughput, comprehensive quantitative proteomics. These newer methodologies are particularly well suited for discovery of biomarker candidates from human disease samples, and for investigating and understanding human disease pathways.

Areas covered: This article reviews the current state of targeted and untargeted DIA mass spectrometry-based proteomic workflows, including SRM, parallel-reaction monitoring (PRM) and untargeted DIA (e.g., SWATH). Corresponding bioinformatics strategies, as well as application in biological and clinical studies are presented.

Expert commentary: Nascent application of highly-multiplexed untargeted DIA, such as SWATH, for accurate protein quantification from clinically relevant and disease-related samples shows great potential to comprehensively investigate biomarker candidates and understand disease.     

When peptides fly: Advances in Drosophila proteomics

In the past decade, improvements in genome annotation, protein fractionation methods and mass spectrometry instrumentation resulted in rapid growth of Drosophila proteomics. This review presents the current status of proteomics research in the fly. Areas that have seen major advances in recent years include efforts to map and catalog the Drosophila proteome and high-throughput as well as targeted studies to analyze protein–protein interactions and post-translational modifications. Stable isotope labeling of flies and other applications of quantitative proteomics have opened up new possibilities for functional analyses. It is clear that proteomics is becoming an indispensable tool in Drosophila systems biology research that adds a unique dimension to studying gene function.     

Automated selected reaction monitoring software for accurate label-free protein quantification

Selected reaction monitoring (SRM) is a mass spectrometry method with documented ability to quantify proteins accurately and reproducibly using labeled reference peptides. However, the use of labeled reference peptides becomes impractical if large numbers of peptides are targeted and when high flexibility is desired when selecting peptides. We have developed a label-free quantitative SRM workflow that relies on a new automated algorithm, Anubis, for accurate peak detection. Anubis efficiently removes interfering signals from contaminating peptides to estimate the true signal of the targeted peptides. We evaluated the algorithm on a published multisite data set and achieved results in line with manual data analysis. In complex peptide mixtures from whole proteome digests of Streptococcus pyogenes we achieved a technical variability across the entire proteome abundance range of 6.5-19.2%, which was considerably below the total variation across biological samples. Our results show that the label-free SRM workflow with automated data analysis is feasible for large-scale biological studies, opening up new possibilities for quantitative proteomics and systems biology.     

An assessment of current bioinformatic solutions for analyzing LC-MS data acquired by selected reaction monitoring technology

Selected reaction monitoring (SRM) is an accurate quantitative technique, typically used for small-molecule mass spectrometry (MS). SRM has emerged as an important technique for targeted and hypothesis-driven proteomic research, and is becoming the reference method for protein quantification in complex biological samples. SRM offers high selectivity, a lower limit of detection and improved reproducibility, compared to conventional shot-gun-based tandem MS (LC-MS/MS) methods. Unlike LC-MS/MS, which requires computationally intensive informatic postanalysis, SRM requires preacquisition bioinformatic analysis to determine proteotypic peptides and optimal transitions to uniquely identify and to accurately quantitate proteins of interest. Extensive arrays of bioinformatics software tools, both web-based and stand-alone, have been published to assist researchers to determine optimal peptides and transition sets. The transitions are oftentimes selected based on preferred precursor charge state, peptide molecular weight, hydrophobicity, fragmentation pattern at a given collision energy (CE), and instrumentation chosen. Validation of the selected transitions for each peptide is critical since peptide performance varies depending on the mass spectrometer used. In this review, we provide an overview of open source and commercial bioinformatic tools for analyzing LC-MS data acquired by SRM.     

TMT‐Based Proteomics Profiling of Bovine Liver Underscores Protein Markers of Anabolic Treatments

Illegal use of growth promoter compounds in food production exposes consumers to health risk. Surveillance of such practices is based on direct detection of drugs or related metabolites by HPLC‐MS/MS. Screening strategies focusing on indirect biological responses are considered promising tools to improve surveillance. In this study, an untargeted shotgun proteomics approach based on tandem mass tags (TMTs) is carried out to identify proteins altered in bovine liver after different anabolic treatments. Three controlled pharmacological treatments with dexamethasone, a combination of dexamethasone and clenbuterol, or a combination of sexual steroids (trenbolone and estradiol) are analyzed. Untargeted TMT analysis of liver digests by high resolution MS allowed for the relative quantification of proteins. Thanks to partial least squarediscriminant analysis, a set of proteins capable to classify animals treated with dexamethasone alone (11 proteins), or in combination with clenbuterol (13 proteins) are identified. No significant difference is found upon administration of sexual steroids. After relative quantification of candidate markers by parallel reaction monitoring (PRM), two predictive models are trained to validate protein markers. Finally, an independent animal set of control bulls and bulls treated with dexamethasone is analyzed by PRM to further validate a predictive model giving an accuracy of 100%.     

The Plasma Proteome: High Abundance versus Low Abundance

The field of proteomics is rapidly turning towards targeted mass spectrometry (MS) methods to quantify putative markers or known proteins of biological interest. Historically, the enzyme-linked immunosorbent assay (ELISA) has been used for targeted protein analysis, but, unfortunately, it is limited by the excessive time required for antibody preparation, as well as concerns over selectivity. Despite the ability of proteomics to deliver increasingly quantitative measurements, owing to limited sensitivity, the leads generated are in the microgram per milliliter range. This stands in stark contrast to ELISA, which is capable of quantifying proteins at low picogram per milliliter levels. To bridge this gap, targeted liquid chromatography (LC) tandem MS (MS/MS) analysis of tryptic peptide surrogates using selected reaction monitoring detection has emerged as a viable option for rapid quantification of target proteins. The precision of this approach has been enhanced by the use of stable isotope-labeled peptide internal standards to compensate for variation in recovery and the influence of differential matrix effects. Unfortunately, the complexity of proteinaceous matrices, such as plasma, limits the usefulness of this approach to quantification in the mid-nanogram per milliliter range (medium-abundance proteins). This article reviews the current status of LC/MS/MS using selected reaction monitoring for protein quantification, and specifically considers the use of a single antibody to achieve superior enrichment of either the protein target or the released tryptic peptide. Examples of immunoaffinity-assisted LC/MS/MS are reviewed that demonstrate quantitative analysis of low-abundance proteins (subnanogram per milliliter range). A strategy based on this technology is proposed for the expedited evaluation of novel protein biomarkers, which relies on the synergy created from the complementary nature of MS and ELISA.