Targeted peptide measurements in biology and medicine: best practices for mass spectrometry-based assay development using a fit-for-purpose approach

Characterization of Proteome Variation During Modern Maize Breeding

The success of modern maize breeding has been demonstrated by remarkable increases in productivity with tremendous modification of agricultural phenotypes over the last century. Although the underlying genetic changes of the maize adaptation from tropical to temperate regions have been extensively studied, our knowledge is limited regarding the accordance of protein and mRNA expression levels accompanying such adaptation. Here we conducted an integrative analysis of proteomic and transcriptomic changes in a maize association panel. The minimum extent of correlation between protein and RNA levels suggests that variation in mRNA expression is often not indicative of protein expression at a population scale. This is corroborated by the observation that mRNA- and protein-based coexpression networks are relatively independent of each other, and many pQTLs arise without the presence of corresponding eQTLs. Importantly, compared with transcriptome, the subtypes categorized by the proteome show a markedly high accuracy to resemble the genomic subpopulation. These findings suggest that proteome evolved under a greater evolutionary constraint than transcriptome during maize adaptation from tropical to temperate regions. Overall, the integrated multi-omics analysis provides a functional context to interpret gene expression variation during modern maize breeding.

A multiplexed UPLC-MS/MS assay for the simultaneous measurement of urinary safety biomarkers of drug-induced kidney injury and phospholipidosis

Drug-induced kidney injury (DIKI) is a major concern in drug risk assessment given its clinical importance and the absence of a sensitive/specific method of diagnosis. Pharmaceutical regulatory agencies have qualified and issued letters of support for new biomarkers to better evaluate DIKI in nonclinical toxicity and clinical studies. Additional efforts have focused on drug-induced phospholipidosis (DIPL) and its potential link with collateral renal damage. The combined use of urinary biomarkers is an efficient way to evaluate renal safety in nonclinical and clinical studies. Eight FDA/EMA/PMDA qualified (or supported) urinary biomarkers, including kidney injury molecule-1 (KIM-1), β2-microglobulin (B2M), clusterin (CLU), cystatin C (CysC), trefoil factor 3 (TFF3), neutrophil gelatinase-associated lipocalin (NGAL), osteopontin (OPN), and alpha-glutathione S-transferase (α-GST), were quantified by multiplex UPLC-MS/MS in a repeat dose study of gentamicin in rats. Rats administered gentamicin at 100 mg/kg/day for 2 weeks developed renal lesions detected by histopathology. Biomarkers of tubular damage (CLU, KIM-1, OPN) increased 9.8, 34.7, and 35.6-fold (relative to concurrent controls), respectively, after 2 weeks of dosing. Biomarkers of glomerular damage and/or impairment of tubular reabsorption (CysC, B2M) increased 11.7 and 22.6-fold. NGAL and α-GST increased <3-fold after 2 weeks of dosing. TFF3 was comparable to concurrent controls. The elevated biomarker concentrations met PSTC threshold criteria and were consistent with mechanisms of gentamicin nephrotoxicity. Increased urinary di-22:6-BMP indicated concomitant DIPL as confirmed by TEM. This work provides evidence supporting the combined use of the DIKI biomarker panel and di-22:6-BMP as a biomarker of DIPL in drug risk assessment.

Proteomic Characterization of Epithelial-Like Extracellular Vesicles in Advanced Endometrial Cancer

Endometrial cancer (EC) is the most frequent gynecological cancer. Tumor dissemination affecting ∼20% of EC patients is characterized at the primary carcinoma by epithelial-to-mesenchymal transition (EMT) associated with myometrial infiltration. At distant sites, the interaction of circulating tumor cells (CTCs) with the microenvironment is crucial for metastatic colonization, with a participation of the extracellular vesicles (EVs). We comprehensively approached these primary and secondary sites to study the impact of tumor EVs on the metastatic efficiency of CTCs in EC. Tumor EVs in circulation reproduce the epithelial phenotype predominant in the primary carcinoma, whereas CTCs are characterized by an EMT phenotype. We modeled this EMT-related clinical scenario in the Hec1A endometrial cell line and characterized the epithelial-like EVs in circulation by SILAC proteome analysis. The identification of proteins involved in cell-cell and cell-matrix interaction and binding, together with in vitro evidence of an improved adhesion of CTC to a functionalized endothelium, suggests a contribution of the epithelial-like EVs in the homing of CTCs at metastatic sites. Accordingly, adhesion protein LGALS3BP was found to be significantly enriched in circulating EVs from a cohort of EC patients with a high risk of recurrence by targeted proteomics (multiple reaction monitoring), highlighting its potential in liquid biopsy in EC.

Development of a Highly Multiplexed SRM Assay for Biomarker Discovery in Formalin-Fixed Paraffin-Embedded Tissues

The search for novel and clinically relevant biomarkers still represents a major clinical challenge and mass-spectrometry-based technologies are essential tools to help in this process. In this application, we demonstrate how selected reaction monitoring (SRM) can be applied in a highly multiplexed way to analyze formalin-fixed paraffin-embedded (FFPE) tissues. Such an assay can be used to analyze numerous samples for narrowing down a list of potential biomarkers to the most relevant candidates. The use of FFPE tissues is of high relevance in this context as large sample collections linked with valuable clinical information are available in hospitals around the world. Here we describe in detail how we proceeded to develop such an assay for 200 proteins in breast tumor FFPE tissues. We cover the selection of suitable peptides, which are different in FFPE compared to fresh frozen tissues and show how we deliberately biased our assay toward proteins with a high probability of being measurable in human clinical samples.

Designing an In Silico Strategy to Select Tissue-Leakage Biomarkers Using the Galaxy Framework

Knowledge-based approaches using large-scale biological ("omics") data are a powerful way to identify mechanistic biomarkers, provided that scientists have access to computational solutions even when they have little programming experience or bioinformatics support. To achieve this goal, we designed a set of tools under the Galaxy framework to allow biologists to define their own strategy for reproducible biomarker selection. These tools rely on retrieving experimental data from public databases, and applying successive filters derived from information relating to disease pathophysiology. A step-by-step protocol linking these tools was implemented to select tissue-leakage biomarker candidates of myocardial infarction. A list of 24 candidates suitable for experimental assessment by MS-based proteomics is proposed. These tools have been made publicly available at http://www.proteore.org , allowing researchers to reuse them in their quest for biomarker discovery.

Analysis of significant protein abundance from multiple reaction-monitoring data

Background

Discovering reliable protein biomarkers is one of the most important issues in biomedical research. The ELISA is a traditional technique for accurate quantitation of well-known proteins. Recently, the multiple reaction-monitoring (MRM) mass spectrometry has been proposed for quantifying newly discovered protein and has become a popular alternative to ELISA. For the MRM data analysis, linear mixed modeling (LMM) has been used to analyze MRM data. MSstats is one of the most widely used tools for MRM data analysis that is based on the LMMs. However, LMMs often provide various significance results, depending on model specification. Sometimes it would be difficult to specify a correct LMM method for the analysis of MRM data. Here, we propose a new logistic regression-based method for Significance Analysis of Multiple Reaction Monitoring (LR-SAM).

Results

Through simulation studies, we demonstrate that LMM methods may not preserve type I error, thus yielding high false- positive errors, depending on how random effects are specified. Our simulation study also shows that the LR-SAM approach performs similarly well as LMM approaches, in most cases. However, LR-SAM performs better than the LMMs, particularly when the effects sizes of peptides from the same protein are heterogeneous. Our proposed method was applied to MRM data for identification of proteins associated with clinical responses of treatment of 115 hepatocellular carcinoma (HCC) patients with the tyrosine kinase inhibitor sorafenib. Of 124 candidate proteins, LMM approaches provided 6 results varying in significance, while LR-SAM, by contrast, yielded 18 significant results that were quite reproducibly consistent.

Conclusion

As exemplified by an application to HCC data set, LR-SAM more effectively identified proteins associated with clinical responses of treatment than LMM did.

Rapidly Assessing the Quality of Targeted Proteomics Experiments through Monitoring Stable-Isotope Labeled Standards

Targeted proteomics experiments based on selected reaction monitoring (SRM) have gained wide adoption in the use of clinical biomarkers, cellular modeling, and numerous other biological experiments due to their highly accurate and reproducible quantification. The quantitative accuracy in targeted proteomics experiments is reliant on the stable-isotope, heavy-labeled peptide standards that are spiked into a sample and used as a reference when calculating the abundance of endogenous peptides. Therefore, the quality of measurement for these standards is a critical factor in determining whether data acquisition was successful. With improved mass spectrometry (MS) instrumentation that enables the monitoring of hundreds of peptides in hundreds to thousands of samples, quality assessment is increasingly important and cannot be performed manually. We present Q4SRM, a software tool that rapidly checks the signal from all heavy-labeled peptides and flags those that fail quality-control metrics. Using four metrics, the tool detects problems with both individual SRM transitions and the collective group of transitions that monitor a single peptide. The program's speed and simplicity enable its use at the point of data acquisition and can be ideally run immediately upon the completion of a liquid chromatography-SRM-MS analysis.

TomahaqCompanion: A Tool for the Creation and Analysis of Isobaric Label Based Multiplexed Targeted Assays

Triggered by Offset, Multiplexed, Accurate mass, High resolution, and Absolute Quantitation (TOMAHAQ) is a recently introduced targeted proteomics method that combines peptide and sample multiplexing. TOMAHAQ assays enable sensitive and accurate multiplexed quantification by implementing an intricate data collection scheme that comprises multiple MSⁿ scans, mass inclusion lists, and data-driven filters. Consequently, manual creation of TOMAHAQ methods can be time-consuming and error prone, while the resulting TOMAHAQ data may not be compatible with common mass spectrometry analysis pipelines. To address these concerns we introduce TomahaqCompanion, an open-source desktop application that enables rapid creation of TOMAHAQ methods and analysis of TOMAHAQ data. Starting from a list of peptide sequences, a user can perform each step of TOMAHAQ assay development including (1) generation of priming run target list, (2) analysis of priming run data, (3) generation of TOMAHAQ method file, and (4) analysis and export of quantitative TOMAHAQ data. We demonstrate the flexibility of TomahaqCompanion by creating a variety of methods testing TOMAHAQ parameters (e.g., number of SPS notches, run length, etc.). Lastly, we analyze an interference sample comprising heavy yeast peptides, a standard human peptide mixture, TMT11-plex, and super heavy TMT (shTMT) isobaric labels to demonstrate ∼10-200 attomol limit of quantification within a complex background using TOMAHAQ.

pRAD50: a novel and clinically applicable pharmacodynamic biomarker of both ATM and ATR inhibition identified using mass spectrometry and immunohistochemistry

Background

AZD0156 and AZD6738 are potent and selective inhibitors of ataxia-telangiectasia-kinase (ATM) and ataxia-telangiectasia-mutated and Rad3-related (ATR), respectively, important sensors/signallers of DNA damage.

Methods

We used multiplexed targeted-mass-spectrometry to select pRAD50(Ser635) as a pharmacodynamic biomarker for AZD0156-mediated ATM inhibition from a panel of 45 peptides, then developed and tested a clinically applicable immunohistochemistry assay for pRAD50(Ser635) detection in FFPE tissue.

Results

We found moderate pRAD50 baseline levels across cancer indications. pRAD50 was detectable in 100% gastric cancers (n = 23), 99% colorectal cancers (n = 102), 95% triple-negative-breast cancers (TNBC) (n = 40) and 87.5% glioblastoma-multiformes (n = 16). We demonstrated AZD0156 target inhibition in TNBC patient-derived xenograft models; where AZD0156 monotherapy or post olaparib treatment, resulted in a 34–72% reduction in pRAD50. Similar inhibition of pRAD50 (68%) was observed following ATM inhibitor treatment post irinotecan in a colorectal cancer xenograft model. ATR inhibition, using AZD6738, increased pRAD50 in the ATM-proficient models whilst in ATM-deficient models the opposite was observed, suggesting pRAD50 pharmacodynamics post ATR inhibition may be ATM-dependent and could be useful to determine ATM functionality in patients treated with ATR inhibitors.

Conclusion

Together these data support clinical utilisation of pRAD50 as a biomarker of AZD0156 and AZD6738 pharmacology to elucidate clinical pharmacokinetic/pharmacodynamic relationships, thereby informing recommended Phase 2 dose/schedule.

Multiple-Enzyme-Digestion Strategy Improves Accuracy and Sensitivity of Label- and Standard-Free Absolute Quantification to a Level That Is Achievable by Analysis with Stable Isotope-Labeled Standard Spiking

Quantification of individual proteins is an essential task in understanding biological processes. For example, determination of concentrations of proteins transporting and metabolizing xenobiotics is a prerequisite for drug disposition predictions in humans based on in vitro data. So far, this task has frequently been accomplished by targeted proteomics. This type of analyses requires preparation of stable isotope labeled standards for each protein of interest. The selection of appropriate standard peptides is usually tedious and the number of proteins that can be studied in a single experiment by these approaches is limited. In addition, incomplete digestion of proteins often affects the accuracy of the quantification. To circumvent these constrains in proteomic protein quantification, label- and standard-free approaches, such as "total protein approach" (TPA) have been proposed. Here we directly compare an approach using stable isotope labeled (SIL) standards and TPA for quantification of transporters and enzymes in human liver samples within the same LC-MS/MS runs. We show that TPA is a convenient alternative to SIL-based methods. Optimization of the sample preparation beyond commonly used single tryptic digestion, by adding consecutive cleavage steps, improves accuracy and reproducibility of the TPA method to a level, which is achievable by analysis using stable isotope-labeled standard spiking.

Automated Plasma Glycomics with Linkage-Specific Sialic Acid Esterification and Ultrahigh Resolution MS

High-throughput mass spectrometry (MS) glycomics is an emerging field driven by technological advancements including sample preparation and data processing. Previously, we reported an automated protocol for the analysis of N-glycans released from plasma proteins that included sialic acid derivatization with linkage-specificity, namely, ethylation of α2,6-linked sialic acid residues and lactone formation of α2,3-linked sialic acids. In the current study, each step in this protocol was further optimized. Method improvements included minimizing the extent of side-reaction during derivatization, an adjusted glycan purification strategy and mass analysis of the released N-glycans by ultrahigh resolution matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance MS. The latter resolved peak overlap and simplified spectral alignment due to high mass measurement precision. Moreover, this resulted in more confident glycan assignments and improved signal-to-noise for low-abundant species. The performance of the protocol renders high-throughput applications feasible in the exciting field of clinical glycomics.

Plasma lipoproteome in Alzheimer's disease: a proof-of-concept study

Background

Although total plasma lipoproteome consists of proteins that have shown promises as biomarkers that can identify Alzheimer's disease (AD), effect sizes are modest. The objective of this study is to provide initial proof-of-concept that the plasma lipoproteome more likely differ between AD cases and controls when measured in individual plasma lipoprotein fractions than when measured as total in immunodepleted plasma.

Methods

We first developed a targeted proteomics method based on selected reaction monitoring (SRM) and liquid chromatography and tandem mass spectrometry for measurement of 120 tryptic peptides from 79 proteins that are commonly present in plasma lipoproteins. Then in a proof-of concept case-control study of 5 AD cases and 5 sex- and age-matched controls, we applied the targeted proteomic method and performed relatively quantification of 120 tryptic peptides in plasma lipoprotein fractions (fractionated by sequential gradient ultracentrifugation) and in immunodepleted plasma (of albumin and IgG). Unadjusted p values from two-sample t-tests and overall fold change was used to evaluate a peptide relative difference between AD cases and controls, with lower p values (< 0.05) or greater fold differences (> 1.05 or < 0.95) suggestive of greater peptide/protein differences.

Results

Within-day and between-days technical precisions (mean %CV [SD] of all SRM transitions) of the targeted proteomic method were 3.95% (2.65) and 9.31% (5.59), respectively. Between-days technical precisions (mean % CV [SD]) of the entire plasma lipoproteomic workflow including plasma lipoprotein fractionation was 27.90% (14.61). Ten tryptic peptides that belonged to 5 proteins in plasma lipoproteins had unadjusted p values < 0.05, compared to no peptides in immunodepleted plasma. Furthermore, 27, 32, 17, and 20 tryptic peptides in VLDL, IDL, LDL and HDL, demonstrated overall peptide fold differences > 1.05 or < 0.95, compared to only 6 tryptic peptides in immunodepleted plasma. The overall comparisons, therefore, suggested greater peptide/protein differences in plasma lipoproteome when measured in individual plasma lipoproteins than as total in immunodepleted plasma. Specifically, protein complement C3's peptide IHWESASLLR, had unadjusted p values of 0.00007, 0.00012, and 0.0006 and overall 1.25, 1.17, 1.14-fold changes in VLDL, IDL, and LDL, respectively. After positive False Discovery Rate (pFDR) adjustment, the complement C3 peptide IHWESASLLR in VLDL remained statistically different (adjusted p value < 0.05).

Discussion

The findings may warrant future studies to investigate plasma lipoproteome when measured in individual plasma lipoprotein fractions for AD diagnosis.

Liver cancer-associated changes to the proteome: what deserves clinical focus?

INTRODUCTION

Hepatocellular carcinoma (HCC) is recognized as the fifth most common neoplasm and currently represents the second leading form of cancer-related death worldwide. Despite great progress has been done in the understanding of its pathogenesis, HCC represents a heavy societal and economic burden as most patients are still diagnosed at advanced stages and the 5-year survival rate remain below 20%. Early detection and revolutionary therapies that rely on the discovery of new molecular biomarkers and therapeutic targets are therefore urgently needed to develop precision medicine strategies for a more efficient management of patients. Areas covered: This review intends to comprehensively analyse the proteomics-based research conducted in the last few years to address some of the principal still open riddles in HCC biology, based on the identification of molecular drivers of tumor progression and metastasis. Expert commentary: The technical advances in mass spectrometry experienced in the last decade have significantly improved the analytical capacity of proteome wide studies. Large-scale protein and protein variant (post-translational modifications) identification and quantification have allowed detailed dissections of molecular mechanisms underlying HCC progression and are already paving the way for the identification of clinically relevant proteins and the development of their use on patient care.

Mass Spectrometry Approaches to Glycomic and Glycoproteomic Analyses

Glycomic and glycoproteomic analyses involve the characterization of oligosaccharides (glycans) conjugated to proteins. Glycans are produced through a complicated nontemplate driven process involving the competition of enzymes that extend the nascent chain. The large diversity of structures, the variations in polarity of the individual saccharide residues, and the poor ionization efficiencies of glycans all conspire to make the analysis arguably much more difficult than any other biopolymer. Furthermore, the large number of glycoforms associated with a specific protein site makes it more difficult to characterize than any post-translational modification. Nonetheless, there have been significant progress, and advanced separation and mass spectrometry methods have been at its center and the main reason for the progress. While glycomic and glycoproteomic analyses are still typically available only through highly specialized laboratories, new software and workflow is making it more accessible. This review focuses on the role of mass spectrometry and separation methods in advancing glycomic and glycoproteomic analyses. It describes the current state of the field and progress toward making it more available to the larger scientific community.

[Quantitative targeted screening of proteins associated with lung adenocarcinoma cancer by the method of selected reaction monitoring]

In the present study, we applied selected reaction monitoring (SRM) to a group of proteins that were previously reported to be associated with lung cancer (Novikova S.E. et al. (2017) Biomeditsinskaya khimiya, 63, 181-210. [1]). Measurements were performed on 59 plasma samples. These samples included: 23 samples of plasma of patients diagnosed with lung adenocarcinoma (LAC), 11 samples of plasma of patients diagnosed with squamous cell lung carcinoma (SqCC), 25 samples of donors with no previous history of oncological diseases, and one pooled sample from each of the above group. As a result of the SRM measurements 52 proteins were detected at least in one individual plasma sample. Statistical analysis showed that there were two groups confidently differentiated by the concentration value of 8 proteins wherein 5 proteins displayed increased level (P00738, P26639, P21926, P08603, P51149) in LAC group and 3 proteins (P51884, O15162, Q8N2K0) indicated diminishing the concentration level towards the control level. Data on protein concentrations obtained for LAC and SqCC did not distinguish the samples by statistical clustering analysis. These potential biomarkers can be used for further development of methods for early diagnostics of lung cancer.

Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial

Many research questions in fields such as personalized medicine, drug screens or systems biology depend on obtaining consistent and quantitatively accurate proteomics data from many samples. SWATH-MS is a specific variant of data-independent acquisition (DIA) methods and is emerging as a technology that combines deep proteome coverage capabilities with quantitative consistency and accuracy. In a SWATH-MS measurement, all ionized peptides of a given sample that fall within a specified mass range are fragmented in a systematic and unbiased fashion using rather large precursor isolation windows. To analyse SWATH-MS data, a strategy based on peptide-centric scoring has been established, which typically requires prior knowledge about the chromatographic and mass spectrometric behaviour of peptides of interest in the form of spectral libraries and peptide query parameters. This tutorial provides guidelines on how to set up and plan a SWATH-MS experiment, how to perform the mass spectrometric measurement and how to analyse SWATH-MS data using peptide-centric scoring. Furthermore, concepts on how to improve SWATH-MS data acquisition, potential trade-offs of parameter settings and alternative data analysis strategies are discussed.

Plasma Proteomics for Epidemiology: Increasing Throughput With Standard-Flow Rates

BACKGROUND

Mass spectrometry is selective and sensitive, permitting routine quantification of multiple plasma proteins. However, commonly used nanoflow liquid chromatography (LC) approaches hamper sample throughput, reproducibility, and robustness. For this reason, most publications using plasma proteomics to date are small in study size.

METHODS AND RESULTS

Here, we tested a standard-flow LC mass spectrometry (MS) method using multiple reaction monitoring for the application to large epidemiological cohorts. We have reduced the LC-MS run time to almost a third of the nanoflow LC-MS approach. On the basis of a comparison of the quantification of 100 plasma proteins in >1500 LC-MS runs, the SD range of the retention time during continuous operation was substantially lower with the standard-flow LC-MS (<0.05 minutes) compared with the nanoflow LC-MS method (0.26-0.44 minutes). In addition, the standard-flow LC method also offered less variation in protein measurements. However, 5× more sample volume was required to achieve similar sensitivity. Two different commercial multiple reaction monitoring kits and an antibody-based multiplexing kit were used to compare the apolipoprotein measurements in a subset of samples. In general, good agreement was observed between the 2 multiple reaction monitoring kits, but some of the multiple reaction monitoring-based measurements differed from antibody-based assays.

CONCLUSIONS

The multiplexing capability of LC-MS combined with a standard-flow method increases throughput and reduces the costs of large-scale protein measurements in epidemiological cohorts, but protein rather than peptide standards will be required for defined absolute proteoform quantification.

Proteoform Analysis to Fulfill Unmet Clinical Needs and Reach Global Standardization of Protein Measurands in Clinical Chemistry Proteomics

In clinical testing of protein markers, structure variants of the measurand are often not taken into account. This heterogeneous character of protein measurands in immunoassays often renders test standardization impossible. Consequently, test results from different methods can lead to underdiagnosis or overdiagnosis and, thus, undertreatment or overtreatment of patients. The systematic structural analysis of protein isoforms has been coined proteoform profiling and is performed through mass spectrometry-based proteomics strategies. Knowledge on proteoforms allows refining existing uni-marker tests and moreover has great potential to contribute to the urgent need for new tests to predict prognosis and severity of diseases.

Clinical Proteomics: Closing the Gap from Discovery to Implementation

Clinical proteomics, the application of proteome analysis to serve a clinical purpose, represents a major field in the area of proteome research. Over 1000 manuscripts on this topic are published each year, with numbers continuously increasing. However, the anticipated outcome, the transformation of the reported findings into improvements in patient management, is not immediately evident. In this article, the value and validity of selected clinical proteomics findings are investigated, and it is assessed how far implementation has progressed. A main conclusion from this assessment is that to achieve implementation, well-powered clinical studies are required in the appropriate population, addressing a specific clinical need and with a clear context-of-use. Efforts toward implementation, to be feasible, must be supported by the key players in science: publishers and funders. The authors propose a change on objectives, from additional discovery studies toward studies aiming at validation of the plethora of potential biomarkers that have been described, to demonstrate practical value of clinical proteomics. All elements required, potential biomarkers, technologies, and bio-banked samples are available (based on today's literature), hence a change in focus from discovery toward validation and application is not only urgently necessary, but also possible based on resources available today.

A large-scale and robust dynamic MRM study of colorectal cancer biomarkers

Over the past 20 years, mass spectrometry (MS) has emerged as a dynamic tool for proteomics biomarker discovery. However, published MS biomarker candidates often do not translate to the clinic, failing during attempts at independent replication. The cause can be shortcomings in study design, sample quality, assay quantitation, and/or quality/process control. To address these shortcomings, we developed an MS workflow in accordance with Tier 2 measurement requirements for targeted peptides, defined by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) "fit-for-purpose" approach, using dynamic multiple reaction monitoring (dMRM), which measures specific peptide transitions during predefined retention time (RT) windows. We describe the development of a robust multipex dMRM assay measuring 641 proteotypic peptides from 392 colorectal cancer (CRC) related proteins, and the procedures to track and handle sample processing and instrument variation over a four-month study, during which the assay measured blood samples from 1045 patients with CRC symptoms. After data collection, transitions were filtered by signal quality metrics before entering receiver operating characteristic (ROC) analysis. The results demonstrated CRC signal carried by 127 proteins in the symptomatic population. The workflow might be further developed to build Tier 1 assays for clinical tests identifying symptomatic individuals at elevated risk of CRC.

SIGNIFICANCE

We developed a dMRM MS method with the rigor of a Tier 2 assay as defined by the CPTAC 'fit for purpose approach' [1]. Using quality and process control procedures, the assay was used to quantify 641 proteotypic peptides representing 392 CRC-related proteins in plasma from 1045 CRC-symptomatic patients. To our knowledge, this is the largest MRM method applied to the largest study to date. The results showed that 127 of the proteins carried univariate CRC signal in the symptomatic population. This large number of single biomarkers bodes well for future development of multivariate classifiers to distinguish CRC in the symptomatic population.

A Multiplexed Mass Spectrometry-Based Assay for Robust Quantification of Phosphosignaling in Response to DNA Damage

A lack of analytically robust and multiplexed assays has hampered studies of the large, branched phosphosignaling network responsive to DNA damage. To address this need, we developed and fully analytically characterized a 62-plex assay quantifying protein expression and post-translational modification (phosphorylation and ubiquitination) after induction of DNA damage. The linear range was over 3 orders of magnitude, the median inter-assay variability was 10% CV and the vast majority (∼85%) of assays were stable after extended storage. The multiplexed assay was applied in proof-of-principle studies to quantify signaling after exposure to genotoxic stress (ionizing radiation and 4-nitroquinoline 1-oxide) in immortalized cell lines and primary human cells. The effects of genomic variants and pharmacologic kinase inhibition (ATM/ATR) were profiled using the assay. This study demonstrates the utility of a quantitative multiplexed assay for studying cellular signaling dynamics, and the potential application to studies on inter-individual variation in the radiation response.

Manual method of visually identifying candidate signals for a targeted peptide

The purpose of this study is to improve peptide signal identification in groups of extracted ion chromatograms (XICs) obtained with the liquid chromatography-selected reaction monitoring (LC-SRM) technique and a triple quadrupole mass spectrometer (QqQ) operating in one of the supported multiple reaction monitoring (MRM) modes. The imperfection of quadrupole mass analyzers causes ion interference, which impedes the identification of peptide signals as chromatographic peak groups in relevant retention time intervals. To investigate this problem in depth, the QqQ conversion of the eluate into XIC groups was considered as the consecutive transformations of the particles' abundances as the corresponding functions of retention time. In this study, the hypothesis that, during this conversion, the same chromatographic profile should be preserved as an implicit sign in each chromatographic peak of the signal was confirmed for peptides. To examine chromatographic profiles, continuous transformations of XIC groups were derived and implemented in srm2prot Express software (s2pe, http://msr.ibmc.msk.ru/s2pe). Because of ion interference, several peptide-like signals may appear in one XIC group. Therefore, these signals must be considered candidates for a targeted peptide's signal and should be resolved after identification. The theoretical investigation of intensity functions as XICs that are not distorted by noise produced three rules for Identifying Candidate Signals for a targeted Peptide (ICSP, http://msr.ibmc.msk.ru/ICSP) that constitute the proposed manual visual method. We theoretically and experimentally compared this method with the conventional semiempirical intuitive technique and found that the former significantly streamlines peptide signal identification and avoids typical errors.

Targeted Proteomics Guided by Label-free Quantitative Proteome Analysis in Saliva Reveal Transition Signatures from Health to Periodontal Disease

Periodontal diseases are among the most prevalent worldwide, but largely silent, chronic diseases. They affect the tooth-supporting tissues with multiple ramifications on life quality. Their early diagnosis is still challenging, due to lack of appropriate molecular diagnostic methods. Saliva offers a non-invasively collectable reservoir of clinically relevant biomarkers, which, if utilized efficiently, could facilitate early diagnosis and monitoring of ongoing disease. Despite several novel protein markers being recently enlisted by discovery proteomics, their routine diagnostic application is hampered by the lack of validation platforms that allow for rapid, accurate and simultaneous quantification of multiple proteins in large cohorts. Here we carried out a pipeline of two proteomic platforms; firstly, we applied open ended label-free quantitative (LFQ) proteomics for discovery in saliva (n = 67, including individuals with health, gingivitis, and periodontitis), followed by selected-reaction monitoring (SRM)-targeted proteomics for validation in an independent cohort (n = 82). The LFQ platform led to the discovery of 119 proteins with at least 2-fold significant difference between health and disease. The 65 proteins chosen for the subsequent SRM platform included 50 functionally related proteins derived from the significantly enriched processes of the LFQ data, 11 from literature-mining, and four house-keeping ones. Among those, 60 were reproducibly quantifiable proteins (92% success rate), represented by a total of 143 peptides. Machine-learning modeling led to a narrowed-down panel of five proteins of high predictive value for periodontal diseases with maximum area under the receiver operating curve >0.97 (higher in disease: Matrix metalloproteinase-9, Ras-related protein-1, Actin-related protein 2/3 complex subunit 5; lower in disease: Clusterin, Deleted in Malignant Brain Tumors 1). This panel enriches the pool of credible clinical biomarker candidates for diagnostic assay development. Yet, the quantum leap brought into the field of periodontal diagnostics by this study is the application of the biomarker discovery-through-verification pipeline, which can be used for validation in further cohorts.

Targeted mass spectrometry enables robust quantification of FANCD2 mono-ubiquitination in response to DNA damage

The Fanconi anemia pathway is an important coordinator of DNA repair pathways and is particularly relevant to repair of DNA inter-strand crosslinks. Central to the pathway is monoubiquitination of FANCD2, requiring the function of multiple proteins in an upstream Fanconi core complex. We present development and analytical characterization of a novel assay for quantification of unmodified and monoubiquitinated FANCD2 proteoforms, based on peptide immunoaffinity enrichment and targeted multiple reaction monitoring mass spectrometry (immuno-MRM). The immuno-MRM assay is analytically characterized using fit-for-purpose method validation. The assay linear range is >3 orders of magnitude with total repeatability <16% CV. In proof-of-principle experiments, we demonstrate application of the multiplex assay by quantifying the FANCD2 proteoforms following mitomycin-c treatment in an isogenic pair of FancA-corrected and uncorrected cell lines, as well as primary peripheral blood mononuclear cells from Fanconi Anemia patients. Additionally, we demonstrate detection of endogenous FANCD2 monoubiquitination in human breast cancer tissue. The immuno-MRM assay provides a potential functional diagnostic for patients with Fanconi Anemia with defects in the upstream FA complex or FANCD2, and a potential test for predicting sensitivity to DNA cross-linking agents in human cancers.

ExSTA: External Standard Addition Method for Accurate High-Throughput Quantitation in Targeted Proteomics Experiments

PURPOSE

Targeted proteomics using MRM with stable-isotope-labeled internal-standard (SIS) peptides is the current method of choice for protein quantitation in complex biological matrices. Better quantitation can be achieved with the internal standard-addition method, where successive increments of synthesized natural form (NAT) of the endogenous analyte are added to each sample, a response curve is generated, and the endogenous concentration is determined at the x-intercept. Internal NAT-addition, however, requires multiple analyses of each sample, resulting in increased sample consumption and analysis time.

EXPERIMENTAL DESIGN

To compare the following three methods, an MRM assay for 34 high-to-moderate abundance human plasma proteins is used: classical internal SIS-addition, internal NAT-addition, and external NAT-addition-generated in buffer using NAT and SIS peptides. Using endogenous-free chicken plasma, the accuracy is also evaluated.

RESULTS

The internal NAT-addition outperforms the other two in precision and accuracy. However, the curves derived by internal vs. external NAT-addition differ by only ≈3.8% in slope, providing comparable accuracies and precision with good CV values.

CONCLUSIONS AND CLINICAL RELEVANCE

While the internal NAT-addition method may be "ideal", this new external NAT-addition can be used to determine the concentration of high-to-moderate abundance endogenous plasma proteins, providing a robust and cost-effective alternative for clinical analyses or other high-throughput applications.

Recent advances and opportunities in proteomic analyses of tumour heterogeneity

Solid tumour malignancies comprise a highly variable admixture of tumour and non-tumour cellular populations, forming a complex cellular ecosystem and tumour microenvironment. This tumour heterogeneity is not incidental, and is known to correlate with poor patient prognosis for many cancer types. Indeed, non-malignant cell populations, such as vascular endothelial and immune cells, are known to play key roles supporting and, in some cases, driving aggressive tumour biology, and represent targets of emerging therapeutics, such as antiangiogenesis and immune checkpoint inhibitors. The biochemical interplay between these cellular populations and how they contribute to molecular tumour heterogeneity remains enigmatic, particularly from the perspective of the tumour proteome. This review focuses on recent advances in proteomic methods, namely imaging mass spectrometry, single-cell proteomic techniques, and preanalytical sample processing, that are uniquely positioned to enable detailed analysis of discrete cellular populations within tumours to improve our understanding of tumour proteomic heterogeneity. This review further emphasizes the opportunity afforded by the application of these techniques to the analysis of tumour heterogeneity in formalin-fixed paraffin-embedded archival tumour tissues, as these represent an invaluable resource for retrospective analyses that is now routinely accessible, owing to recent technological and methodological advances in tumour tissue proteomics. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Protein biomarkers for subtyping breast cancer and implications for future research

INTRODUCTION

Breast cancer subtypes are currently defined by a combination of morphologic, genomic, and proteomic characteristics. These subtypes provide a molecular portrait of the tumor that aids diagnosis, prognosis, and treatment escalation/de-escalation options. Gene expression signatures describing intrinsic breast cancer subtypes for predicting risk of recurrence have been rapidly adopted in the clinic. Despite the use of subtype classifications, many patients develop drug resistance, breast cancer recurrence, or therapy failure. Areas covered: This review provides a summary of immunohistochemistry, reverse phase protein array, mass spectrometry, and integrative studies that are revealing differences in biological functions within and between breast cancer subtypes. We conclude with a discussion of rigor and reproducibility for proteomic-based biomarker discovery. Expert commentary: Innovations in proteomics, including implementation of assay guidelines and standards, are facilitating refinement of breast cancer subtypes. Proteomic and phosphoproteomic information distinguish biologically functional subtypes, are predictive of recurrence, and indicate likelihood of drug resistance. Actionable, activated signal transduction pathways can now be quantified and characterized. Proteomic biomarker validation in large, well-designed studies should become a public health priority to capitalize on the wealth of information gleaned from the proteome.

Mass Spectrometry Analysis of Lysine Posttranslational Modifications of Tau Protein from Alzheimer's Disease Brain

Recent advances in mass spectrometry (MS)-based proteomics have greatly facilitated the robust identification and quantification of posttranslational modifications (PTMs), including those that are present at substoichiometric site occupancies. The abnormal posttranslational modification and accumulation of the microtubule-associated protein tau has been implicated in the pathogenesis of Alzheimer's disease (AD), and it is thought that the primary mode of regulation of tau occurs through PTMs. Several studies have been published regarding tau phosphorylation; however, other tau PTMs such as ubiquitylation, acetylation, methylation, oxidation, sumoylation, nitration, and glycosylation have not been analyzed as extensively. The comprehensive detection and delineation of these PTMs is critical for drug target discovery and validation. Lysine-directed PTMs including ubiquitylation, acetylation, and methylation play key regulatory roles with respect to the rates of tau turnover and aggregation. MS-based analytical approaches have been used to gain insight into the tau lysine-directed PTM signature that is most closely associated with neurofibrillary lesion formation. This chapter provides details pertaining to the liquid chromatography tandem mass spectrometry (LC-MS/MS)-based analysis of the lysine-directed posttranslational modification of tau.

The Expressed Genome in Cardiovascular Diseases and Stroke: Refinement, Diagnosis, and Prediction: A Scientific Statement From the American Heart Association

There have been major advances in our knowledge of the contribution of DNA sequence variations to cardiovascular disease and stroke. However, the inner workings of the body reflect the complex interplay of factors beyond the DNA sequence, including epigenetic modifications, RNA transcripts, proteins, and metabolites, which together can be considered the "expressed genome." The emergence of high-throughput technologies, including epigenomics, transcriptomics, proteomics, and metabolomics, is now making it possible to address the contributions of the expressed genome to cardiovascular disorders. This statement describes how the expressed genome can currently and, in the future, potentially be used to diagnose diseases and to predict who will develop diseases such as coronary artery disease, stroke, heart failure, and arrhythmias.

Multiple Reaction Monitoring for the Quantitation of Serum Protein Glycosylation Profiles: Application to Ovarian Cancer

Protein glycosylation fingerprints are widely recognized as potential markers for disease states, and indeed differential glycosylation has been identified in multiple types of autoimmune diseases and several types of cancer. However, releasing the glycans leave the glycoproteins unknown; therefore, there exists a need for high-throughput methods that allow quantification of site- and protein-specific glycosylation patterns from complex biological mixtures. In this study, a targeted multiple reaction monitoring (MRM)-based method for the protein- and site-specific quantitation involving serum proteins immunoglobulins A, G and M, alpha-1-antitrypsin, transferrin, alpha-2-macroglobulin, haptoglobin, alpha-1-acid glycoprotein and complement C3 was developed. The method is based on tryptic digestion of serum glycoproteins, followed by immediate reverse phase UPLC-QQQ-MS analysis of glycopeptides. To quantitate protein glycosylation independent of the protein serum concentration, a nonglycosylated peptide was also monitored. Using this strategy, 178 glycopeptides and 18 peptides from serum glycoproteins are analyzed with good repeatability (interday CVs of 3.65-21-92%) in a single 17 min run. To assess the potential of the method, protein glycosylation was analyzed in serum samples from ovarian cancer patients and controls. A training set consisting of 40 cases and 40 controls was analyzed, and differential analyses were performed to identify aberrant glycopeptide levels. All findings were validated in an independent test set (n = 44 cases and n = 44 controls). In addition to the differential glycosylation on the immunoglobulins, which was reported previously, aberrant glycosylation was also observed on each of the glycoproteins, which could be corroborated in the test set. This report shows the development of a method for targeted protein- and site-specific glycosylation analysis and the potential of such methods in biomarker development.

Variance component analysis to assess protein quantification in biomarker discovery. Application to MALDI-TOF mass spectrometry

Controlling the technological variability on an analytical chain is critical for biomarker discovery. The sources of technological variability should be modeled, which calls for specific experimental design, signal processing, and statistical analysis. Furthermore, with unbalanced data, the various components of variability cannot be estimated with the sequential or adjusted sums of squares of usual software programs. We propose a novel approach to variance component analysis with application to the matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) technology and use this approach for protein quantification by a classical signal processing algorithm and two more recent ones (BHI-PRO 1 and 2). Given the high technological variability, the quantification failed to restitute the known quantities of five out of nine proteins present in a controlled solution. There was a linear relationship between protein quantities and peak intensities for four out of nine peaks with all algorithms. The biological component of the variance was higher with BHI-PRO than with the classical algorithm (80-95% with BHI-PRO 1, 79-95% with BHI-PRO 2 vs. 56-90%); thus, BHI-PRO were more efficient in protein quantification. The technological component of the variance was higher with the classical algorithm than with BHI-PRO (6-25% vs. 2.5-9.6% with BHI-PRO 1 and 3.5-11.9% with BHI-PRO 2). The chemical component was also higher with the classical algorithm (3.6-18.7% vs. < 3.5%). Thus, BHI-PRO were better in removing noise from signal when the expected peaks are detected. Overall, either BHI-PRO algorithm may reduce the technological variance from 25 to 10% and thus improve protein quantification and biomarker validation.

New Strategies and Challenges in Lung Proteomics and Metabolomics. An Official American Thoracic Society Workshop Report

This document presents the proceedings from the workshop entitled, "New Strategies and Challenges in Lung Proteomics and Metabolomics" held February 4th-5th, 2016, in Denver, Colorado. It was sponsored by the National Heart Lung Blood Institute, the American Thoracic Society, the Colorado Biological Mass Spectrometry Society, and National Jewish Health. The goal of this workshop was to convene, for the first time, relevant experts in lung proteomics and metabolomics to discuss and overcome specific challenges in these fields that are unique to the lung. The main objectives of this workshop were to identify, review, and/or understand: (1) emerging technologies in metabolomics and proteomics as applied to the study of the lung; (2) the unique composition and challenges of lung-specific biological specimens for metabolomic and proteomic analysis; (3) the diverse informatics approaches and databases unique to metabolomics and proteomics, with special emphasis on the lung; (4) integrative platforms across genetic and genomic databases that can be applied to lung-related metabolomic and proteomic studies; and (5) the clinical applications of proteomics and metabolomics. The major findings and conclusions of this workshop are summarized at the end of the report, and outline the progress and challenges that face these rapidly advancing fields.

Characterization of the Molecular Mechanisms Underlying Glucose Stimulated Insulin Secretion from Isolated Pancreatic β-cells Using Post-translational Modification Specific Proteomics (PTMomics)

Normal pancreatic islet β-cells (PBCs) abundantly secrete insulin in response to elevated blood glucose levels, in order to maintain an adequate control of energy balance and glucose homeostasis. However, the molecular mechanisms underlying the insulin secretion are unclear. Improving our understanding of glucose-stimulated insulin secretion (GSIS) mechanisms under normal conditions is a prerequisite for developing better interventions against diabetes. Here, we aimed at identifying novel signaling pathways involved in the initial release of insulin from PBCs after glucose stimulation using quantitative strategies for the assessment of phosphorylated proteins and sialylated N-linked (SA) glycoproteins.Islets of Langerhans derived from newborn rats with a subsequent 9-10 days of maturation in vitro were stimulated with 20 mm glucose for 0 min (control), 5 min, 10 min, and 15 min. The isolated islets were subjected to time-resolved quantitative phosphoproteomics and sialiomics using iTRAQ-labeling combined with enrichment of phosphorylated peptides and formerly SA glycopeptides and high-accuracy LC-MS/MS. Using bioinformatics we analyzed the functional signaling pathways during GSIS, including well-known insulin secretion pathways. Furthermore, we identified six novel activated signaling pathways (e.g. agrin interactions and prolactin signaling) at 15 min GSIS, which may increase our understanding of the molecular mechanism underlying GSIS. Moreover, we validated some of the regulated phosphosites by parallel reaction monitoring, which resulted in the validation of eleven new phosphosites significantly regulated on GSIS. Besides protein phosphorylation, alteration in SA glycosylation was observed on several surface proteins on brief GSIS. Interestingly, proteins important for cell-cell interaction, cell movement, cell-ECM interaction and Focal Adhesion (e.g. integrins, semaphorins, and plexins) were found regulated at the level of sialylation, but not in protein expression. Collectively, we believe that this comprehensive Proteomics and PTMomics survey of signaling pathways taking place during brief GSIS of primary PBCs is contributing to understanding the complex signaling underlying GSIS.

Why Are the Correlations between mRNA and Protein Levels so Low among the 275 Predicted Protein-Coding Genes on Human Chromosome 18?

In this work targeted (selected reaction monitoring, SRM, PASSEL: PASS00697) and panoramic (shotgun LC-MS/MS, PRIDE: PXD00244) mass-spectrometric methods as well as transcriptomic analysis of the same samples using RNA-Seq and PCR methods (SRA experiment IDs: SRX341198, SRX267708, SRX395473, SRX390071) were applied for quantification of chromosome 18 encoded transcripts and proteins in human liver and HepG2 cells. The obtained data was used for the estimation of quantitative mRNA-protein ratios for the 275 genes of the selected chromosome in the selected tissues. The impact of methodological limitations of existing analytical proteomic methods on gene-specific mRNA-protein ratios and possible ways of overcoming these limitations for detection of missing proteins are also discussed.

Emerging Diagnostic and Therapeutic Potentials of Human Hair Proteomics

The use of noninvasive human substrates to interrogate pathophysiological conditions has become essential in the post- Human Genome Project era. Due to its high turnover rate, and its long term capability to incorporate exogenous and endogenous substances from the circulation, hair testing is emerging as a key player in monitoring long term drug compliance, chronic alcohol abuse, forensic toxicology, and biomarker discovery, among other things. Novel high-throughput 'omics based approaches like proteomics have been underutilized globally in comprehending human hair morphology and its evolving use as a diagnostic testing substrate in the era of precision medicine. There is paucity of scientific evidence that evaluates the difference in drug incorporation into hair based on lipid content, and very few studies have addressed hair growth rates, hair forms, and the biological consequences of hair grooming or bleaching. It is apparent that protein-based identification using the human hair proteome would play a major role in understanding these parameters akin to DNA single nucleotide polymorphism profiling, up to single amino acid polymorphism resolution. Hence, this work seeks to identify and discuss the progress made thus far in the field of molecular hair testing using proteomic approaches, and identify ways in which proteomics would improve the field of hair research, considering that the human hair is mostly composed of proteins. Gaps in hair proteomics research are identified and the potential of hair proteomics in establishing a historic medical repository of normal and disease-specific proteome is also discussed.

Green-fruited Solanum habrochaites lacks fruit-specific carotenogenesis due to metabolic and structural blocks

Members of the tomato clade exhibit a wide diversity in fruit color, but the mechanisms governing inter-species diversity of coloration are largely unknown. The carotenoid profiles, carotenogenic gene expression and proteome profiles of green-fruited Solanum habrochaites (SH), orange-fruited S. galapagense, and red-fruited S. pimpinellifolium were compared with cultivated tomato [S. lycopersicum cv. Ailsa Craig (SL)] to decipher the molecular basis of coloration diversity. Green-fruited SH, though it showed normal expression of chromoplast-specific phytoene synthase1 and lycopene β-cyclase genes akin to orange/red-fruited species, failed to accumulate lycopene and β-carotene. The SH phytoene synthase1 cDNA encoded an enzymatically active protein, whereas the lycopene β-cyclase cDNA was barely active. Consistent with its green-fruited nature, SH's fruits retained chloroplast structure and PSII activity, and had impaired chlorophyll degradation with high pheophorbide a levels. Comparison of the fruit proteomes with SL revealed retention of the proteome complement related to photosynthesis in SH. Targeted peptide monitoring revealed a low abundance of key carotenogenic and sequestration proteins in SH compared with tomato. The green-fruitedness of SH appears to stem from blocks at several critical steps regulating fruit-specific carotenogenesis namely the absence of chloroplast to chromoplast transformation, block in carotenoid biosynthesis, and a dearth of carotenoid sequestering proteins.