Automated detection of inaccurate and imprecise transitions in peptide quantification by multiple reaction monitoring mass spectrometry

Characterization of Anticancer Drug Protomers Using Electrospray Ionization and Ion Mobility Spectrometry-Mass Spectrometry

We used electrospray ionization and ion mobility spectrometry-mass spectrometry to detect and characterize the three anticancer drugs palbociclib, copanlisib, and olaparib. Ion mobility-mass spectrometry and density functional theory revealed that these compounds generate isomers during ionization (protomers) due to the presence of multiple protonation sites within their chemical structures. Our work has implications for understanding the solution- and gas-phase chemistry of these molecules during spray-based ionization processes.

Wideband PRM: Highly Accurate and Sensitive Method for High-Throughput Targeted Proteomics

Targeted mass spectrometry (MS) approaches, which are powerful methods for uniquely and confidently quantifying a specific panel of proteins in complex biological samples, play a crucial role in validating and clinically translating protein biomarkers discovered through global proteomic profiling. Common targeted MS methods, such as multiple reaction monitoring (MRM) and parallel-reaction monitoring (PRM), employ specific mass spectrometric technologies to quantify protein levels by comparing the transitions of surrogate endogenous (ENDO) peptides with those of stable isotope-labeled (SIL) peptide counterparts. These methods utilizing amino acid analyzed (AAA) SIL peptides warrant sensitive and precise measurements required for targeted MS assays. Compared with MRM, PRM provides higher experimental throughput by simultaneously acquiring all transitions of the target peptides and thereby compensates for different ion suppressions among transitions of a target peptide. However, PRM still suffers different ion suppressions between ENDO and SIL peptides due to spray instability, as the ENDO and SIL peptides were monitored at different liquid chromatography (LC) retention times. Here we introduce a new targeted MS method, termed wideband PRM (WBPRM), that is designed for high-throughput targeted MS analysis. WBPRM employs a wide isolation window for simultaneous fragmentation of both ENDO and SIL peptides along with multiplexed single ion monitoring (SIM) scans for enhanced MS sensitivity of the target peptides. Compared with PRM, WBPRM was demonstrated to provide increased sensitivity, precision, and reproducibility of quantitative measurements of target peptides with increased throughput, allowing more target peptide measurements in a shortened experiment time. WBPRM is a straightforward adaptation to a manufacturer-provided MS method, making it an easily implementable technique, particularly in complex biological samples where the demand for higher precision, sensitivity, and efficiency is paramount.

Establishment of a targeted proteomics method for the quantification of collagen chain: Revealing the chain stoichiometry of heterotypic collagen fibrils in sea cucumber

Collagen is the most abundant and important structural biomacromolecule in sea cucumbers. The sea cucumber collagen fibrils were previously confirmed to be heterotypic, nevertheless, the stoichiometry of collagen α-chains governing the complexity of collagen fibrils is still poorly understood. Herein, four representative collagen α-chains in sea cucumber including two clade A fibrillar collagens, one clade B fibrillar collagen, and one fibril-associated collagen with interrupted triple helices were selected. After the screening of signature peptides and optimization of multiple reaction monitoring (MRM) acquisition parameters including fragmentation, collision energy, and ion transition, a feasible MRM-based method was established. Consequently, the stoichiometry of the four collagen chains was determined to be approximately 100:54:3:4 based on the method. The assembly forms of sea cucumber collagen fibrils were further hypothesized according to the chain stoichiometry. This study facilitated the quantification of collagen and understanding of the collagen constituents in sea cucumber.

Proteomic discovery of prognostic protein biomarkers for persisting problems after mild traumatic brain injury

Some individuals with mild traumatic brain injury (mTBI), also known as concussion, have neuropsychiatric and physical problems that last longer than a few months. Symptoms following mTBI are not only impacted by the kind and severity of the injury but also by the post-injury experience and the individual's responses to it, making the persistence of mTBI particularly difficult to predict. We aimed to identify prognostic blood-based protein biomarkers predicting 6-month outcomes, in light of the clinical course after the injury, in a longitudinal mTBI cohort (N = 42). Among 420 target proteins quantified by multiple-reaction monitoring-mass spectrometry assays of blood samples, 31, 43, and 15 proteins were significantly associated with the poor recovery of neuropsychological symptoms at < 72 h, 1 week, and 1 month after the injury, respectively. Sequential associations among clinical assessments (depressive symptoms and cognitive function) affecting the 6-month outcomes were evaluated. Then, candidate biomarker proteins indirectly affecting the outcome via neuropsychological symptoms were identified. Using the identified proteins, prognostic models that can predict the 6-month outcome of mTBI were developed. These protein biomarkers established in the context of the clinical course of mTBI may have potential for clinical application.

Targeted proteomics data interpretation with DeepMRM

Targeted proteomics is widely utilized in clinical proteomics; however, researchers often devote substantial time to manual data interpretation, which hinders the transferability, reproducibility, and scalability of this approach. We introduce DeepMRM, a software package based on deep learning algorithms for object detection developed to minimize manual intervention in targeted proteomics data analysis. DeepMRM was evaluated on internal and public datasets, demonstrating superior accuracy compared with the community standard tool Skyline. To promote widespread adoption, we have incorporated a stand-alone graphical user interface for DeepMRM and integrated its algorithm into the Skyline software package as an external tool.

Review of the Use of Liquid Chromatography-Tandem Mass Spectrometry in Clinical Laboratories: Part II-Operations

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is increasingly utilized in clinical laboratories because it has advantages in terms of specificity and sensitivity over other analytical technologies. These advantages come with additional responsibilities and challenges given that many assays and platforms are not provided to laboratories as a single kit or device. The skills, staff, and assays used in LC-MS/MS are internally developed by the laboratory, with relatively few exceptions. Hence, a laboratory that deploys LC-MS/MS assays must be conscientious of the practices and procedures adopted to overcome the challenges associated with the technology. This review discusses the post-development landscape of LC-MS/MS assays, including validation, quality assurance, operations, and troubleshooting. The content knowledge of LC-MS/MS users is quite broad and deep and spans multiple scientific fields, including biology, clinical chemistry, chromatography, engineering, and MS. However, there are no formal academic programs or specific literature to train laboratory staff on the fundamentals of LC-MS/MS beyond the reports on method development. Therefore, depending on their experience level, some readers may be familiar with aspects of the laboratory practices described herein, while others may be not. This review endeavors to assemble aspects of LC-MS/MS operations in the clinical laboratory to provide a framework for the thoughtful development and execution of LC-MS/MS applications.

Plasma protein biomarker model for screening Alzheimer disease using multiple reaction monitoring-mass spectrometry

Alzheimer disease (AD) is a leading cause of dementia that has gained prominence in our aging society. Yet, the complexity of diagnosing AD and measuring its invasiveness poses an obstacle. To this end, blood-based biomarkers could mitigate the inconveniences that impede an accurate diagnosis. We developed models to diagnose AD and measure the severity of neurocognitive impairment using blood protein biomarkers. Multiple reaction monitoring-mass spectrometry, a highly selective and sensitive approach for quantifying targeted proteins in samples, was used to analyze blood samples from 4 AD groups: cognitive normal control, asymptomatic AD, prodromal AD), and AD dementia. Multimarker models were developed using 10 protein biomarkers and apolipoprotein E genotypes for amyloid beta and 10 biomarkers with Korean Mini-Mental Status Examination (K-MMSE) score for predicting Alzheimer disease progression. The accuracies for the AD classification model and AD progression monitoring model were 84.9% (95% CI 82.8 to 87.0) and 79.1% (95% CI 77.8 to 80.5), respectively. The models were more accurate in diagnosing AD, compared with single APOE genotypes and the K-MMSE score. Our study demonstrates the possibility of predicting AD with high accuracy by blood biomarker analysis as an alternative method of screening for AD.

Proteomic discovery of non-invasive biomarkers of localized prostate cancer using mass spectrometry

Prostate cancer is the second most frequently diagnosed non-skin cancer in men worldwide. Patient outcomes are remarkably heterogeneous and the best existing clinical prognostic tools such as International Society of Urological Pathology Grade Group, pretreatment serum PSA concentration and T-category, do not accurately predict disease outcome for individual patients. Thus, patients newly diagnosed with prostate cancer are often overtreated or undertreated, reducing quality of life and increasing disease-specific mortality. Biomarkers that can improve the risk stratification of these patients are, therefore, urgently needed. The ideal biomarker in this setting will be non-invasive and affordable, enabling longitudinal evaluation of disease status. Prostatic secretions, urine and blood can be sources of biomarker discovery, validation and clinical implementation, and mass spectrometry can be used to detect and quantify proteins in these fluids. Protein biomarkers currently in use for diagnosis, prognosis and relapse-monitoring of localized prostate cancer in fluids remain centred around PSA and its variants, and opportunities exist for clinically validating novel and complimentary candidate protein biomarkers and deploying them into the clinic.

Inclusive Quantification Assay of Serum Des-γ-Carboxyprothrombin Proteoforms for Hepatocellular Carcinoma Surveillance by Targeted Mass Spectrometry

Hepatocellular carcinoma (HCC) is a malignant cancer with one of the highest mortality rates. Des-γ-carboxyprothrombin (DCP) is an HCC serologic surveillance marker that can complement the low sensitivity of alpha-fetoprotein (AFP). DCP exists in the blood as a mixture of proteoforms from an impaired carboxylation process at glutamic acid (Glu) residues within the N-terminal domain. The heterogeneity of DCP may affect the accuracy of measurements because DCP levels are commonly determined using an immunoassay that relies on antibody reactivity to an epitope in the DCP molecule. In this study, we aimed to improve the DCP measurement assay by applying a mass spectrometry (MS)-based approach for a more inclusive quantification of various DCP proteoforms. We developed a multiple-reaction monitoring-MS (MRM-MS) assay to quantify multiple noncarboxylated peptides included in the various des-carboxylation states of DCP. We performed the MRM-MS assay in 300 patients and constructed a robust diagnostic model that simultaneously monitored three noncarboxylated peptides. The MS-based quantitative assay for DCP had reliable surveillance power, which was evident from the area under the receiver operating characteristic curve (AUROC) values of 0.874 and 0.844 for the training and test sets, respectively. It was equivalent to conventional antibody-based quantification, which had AUROC values at the optimal cutoff (40 mAU/mL) of 0.743 and 0.704 for the training and test sets, respectively. The surveillance performance of the MS-based DCP assay was validated using an independent validation set consisting of 318 patients from an external cohort, resulting in an AUROC value of 0.793. Conclusion: Due to cost effectiveness and high reproducibility, the quantitative DCP assay using the MRM-MS method is superior to antibody-based quantification and has equivalent performance.

Quantitative Proteomic Approach for Discriminating Major Depressive Disorder and Bipolar Disorder by Multiple Reaction Monitoring-Mass Spectrometry

Because major depressive disorder (MDD) and bipolar disorder (BD) manifest with similar symptoms, misdiagnosis is a persistent issue, necessitating their differentiation through objective methods. This study was aimed to differentiate between these disorders using a targeted proteomic approach. Multiple reaction monitoring-mass spectrometry (MRM-MS) analysis was performed to quantify protein targets regarding the two disorders in plasma samples of 270 individuals (90 MDD, 90 BD, and 90 healthy controls (HCs)). In the training set (72 MDD and 72 BD), a generalizable model comprising nine proteins was developed. The model was evaluated in the test set (18 MDD and 18 BD). The model demonstrated a good performance (area under the curve (AUC) >0.8) in discriminating MDD from BD in the training (AUC = 0.84) and test sets (AUC = 0.81) and in distinguishing MDD from BD without current hypomanic/manic/mixed symptoms (90 MDD and 75 BD) (AUC = 0.83). Subsequently, the model demonstrated excellent performance for drug-free MDD versus BD (11 MDD and 10 BD) (AUC = 0.96) and good performance for MDD versus HC (AUC = 0.87) and BD versus HC (AUC = 0.86). Furthermore, the nine proteins were associated with neuro, oxidative/nitrosative stress, and immunity/inflammation-related biological functions. This proof-of-concept study introduces a potential model for distinguishing between the two disorders.

The Time Has Come for Quantitative Protein Mass Spectrometry Tests That Target Unmet Clinical Needs

Protein mass spectrometry (MS) is an enabling technology that is ideally suited for precision diagnostics. In contrast to immunoassays with indirect readouts, MS quantifications are multiplexed and include identification of proteoforms in a direct manner. Although widely used for routine measurements of drugs and metabolites, the number of clinical MS-based protein applications is limited. In this paper, we share our experience and aim to take away the concerns that have kept laboratory medicine from implementing quantitative protein MS. To ensure added value of new medical tests and guarantee accurate test results, five key elements of test evaluation have been established by a working group within the European Federation for Clinical Chemistry and Laboratory Medicine. Moreover, it is emphasized to identify clinical gaps in the contemporary clinical pathways before test development is started. We demonstrate that quantitative protein MS tests that provide an additional layer of clinical information have robust performance and meet long-term desirable analytical performance specifications as exemplified by our own experience. Yet, the adoption of quantitative protein MS tests into medical laboratories is seriously hampered due to its complexity, lack of robotization and high initial investment costs. Successful and widespread implementation in medical laboratories requires uptake and automation of this next generation protein technology by the In-Vitro Diagnostics industry. Also, training curricula of lab workers and lab specialists should include education on enabling technologies for transitioning to precision medicine by quantitative protein MS tests.

A Clinically Applicable 24-Protein Model for Classifying Risk Subgroups in Pancreatic Ductal Adenocarcinomas using Multiple Reaction Monitoring-Mass Spectrometry

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) subtypes have been identified using various methodologies. However, it is a challenge to develop classification system applicable to routine clinical evaluation. We aimed to identify risk subgroups based on molecular features and develop a classification model that was more suited for clinical applications.

EXPERIMENTAL DESIGN

We collected whole dissected specimens from 225 patients who underwent surgery at Seoul National University Hospital [Seoul, Republic of Korea (South)], between October 2009 and February 2018. Target proteins with potential relevance to tumor progression or prognosis were quantified with robust quality controls. We used hierarchical clustering analysis to identify risk subgroups. A random forest classification model was developed to predict the identified risk subgroups, and the model was validated using transcriptomic datasets from external cohorts (N = 700), with survival analysis.

RESULTS

We identified 24 protein features that could classify the four risk subgroups associated with patient outcomes: stable, exocrine-like; activated, and extracellular matrix (ECM) remodeling. The "stable" risk subgroup was characterized by proteins that were associated with differentiation and tumor suppressors. "Exocrine-like" tumors highly expressed pancreatic enzymes. Two high-risk subgroups, "activated" and "ECM remodeling," were enriched in terms such as cell cycle, angiogenesis, immunocompetence, tumor invasion metastasis, and metabolic reprogramming. The classification model that included these features made prognoses with relative accuracy and precision in multiple cohorts.

CONCLUSIONS

We proposed PDAC risk subgroups and developed a classification model that may potentially be useful for routine clinical implementations, at the individual level. This clinical system may improve the accuracy of risk prediction and treatment guidelines.See related commentary by Thakur and Singh, p. 3272.

Retention-directed and selectivity controlled chromatographic resolution: Rapid post-hoc analysis of DMPK samples to achieve high-throughput LC-MS separation

High quality chromatographic separation underpins robustness in LC-MS, frequently the analytical method of choice for pharmaceutical drug discovery work. The potential improvements in chromatographic selectivity afforded by serial column coupling (SCC), provide a useful means to enhance the resolution of complex samples. In this work, we present a revised high-throughput form of SCC, in which just two individual mixed phase columns were coupled together and combined with a gradient-optimised, retention-directed ultra-high pressure method to achieve rapid separations, with no further method optimisation necessary. The overall performance was evaluated from an open access DMPK analytical working environment perspective; where in anticipation of bioanalytical or metabolite identification chromatography challenges, or with the knowledge that stronger resolution was required for in-vitro sample analysis, the methodology could be immediately implemented by the analyst. Retention-directed selection of a shallow SCC gradient method was successful in separating peaks throughout the chromatographic window, resulting in a runtime still congruent to high-throughput analyses (3.5 min). In-vitro assay sample interferences were resolved 44-72% of the time, and the overall resolving power for isomeric separations significantly improved against single column comparisons (1.7-fold mean R_S improvement). Over a sustained period of time in our laboratory, SCC methods have been used for metabolite identification and bioanalytical samples, where both convenience and effectiveness in solving analytical challenges has been consistently demonstrated. Examples that highlight SCC chromatography, and a guided discussion of the main high-throughput considerations, are included. The technique offers wide applicability, and we would recommend it as a toolbox consideration to the laboratory analyst.

Pathway attenuation of fatty acid beta-oxidation in the skeletal muscle of a type 2 diabetic mouse model

RATIONALE

Whether catabolic abnormalities of fatty acids exist in the skeletal muscle of type 2 diabetes mellitus (T2DM) has not been determined. In this study, we postulated that a systematic evaluation of the protein abundance and metabolic activity related to fatty acids in the skeletal muscle tissues of a T2DM mouse model was feasible to address this question.

METHODS

Mitochondria were extracted from wild-type (WT) and db/db mice followed by quantitative analysis of the proteins involved in mitochondrial fatty acid oxidation (mFAO). The pathway activity of mFAO in skeletal muscle tissues was monitored in vitro using mass spectrometry, and tissue lipidomic analysis was conducted in profiling and target mode to distinguish the levels of long-chain acylcarnitines between WT and db/db mice.

RESULTS

Two proteins related to the mFAO pathway were significantly downregulated in the skeletal muscle mitochondria of db/db mice. The measurement of mFAO pathway activity in vitro revealed that the abundance of long-chain acylcarnitines (C14 to C18) in db/db mice was lower than that in WT mice, and the determination of acylcarnitines in skeletal muscle tissues in vivo revealed that most long-chain acylcarnitines were decreased in db/db mice.

CONCLUSIONS

The findings of lower abundance of ACAD9 and CPT1B, reduced activity of the mFAO pathway in vitro and decreased acylcarnitines in vivo firmly support that the mFAO pathway in the skeletal muscle of diabetic mice is attenuated, possibly resulting in cell/tissue dysfunction in diabetes.

Clinical Application of Multiple Reaction Monitoring-Mass Spectrometry to Human Epidermal Growth Factor Receptor 2 Measurements as a Potential Diagnostic Tool for Breast Cancer Therapy

Background

Human epidermal growth factor receptor 2 (HER2) is often overexpressed in breast cancer and correlates with a worse prognosis. Thus, the accurate detection of HER2 is crucial for providing the appropriate measures for patients. However, the current techniques used to detect HER2 status, immunohistochemistry and fluorescence in situ hybridization (FISH), have limitations. Specifically, FISH, which is mandatory for arbitrating 2+ cases, is time-consuming and costly. To address this shortcoming, we established a multiple reaction monitoring-mass spectrometry (MRM-MS) assay that improves on existing methods for differentiating HER2 status.

Methods

We quantified HER2 expression levels in 210 breast cancer formalin-fixed paraffin-embedded (FFPE) tissue samples by MRM-MS. We aimed to improve the accuracy and precision of HER2 quantification by simplifying the sample preparation through predicting the number of FFPE slides required to ensure an adequate amount of protein and using the expression levels of an epithelial cell-specific protein as a normalization factor when measuring HER2 expression levels.

Results

To assess the correlation between MRM-MS and IHC/FISH data, HER2 quantitative data from MRM-MS were divided by the expression levels of junctional adhesion molecule A, an epithelial cell-specific protein, prior to statistical analysis. The normalized HER2 amounts distinguished between HER2 2+/FISH-negative and 2+/FISH-positive groups (AUROC = 0.908), which could not be differentiated by IHC. In addition, all HER2 status were discriminated by MRM-MS.

Conclusions

This MRM-MS assay yields more accurate HER2 expression levels relative to immunohistochemistry and should help to guide clinicians toward the proper treatment for breast cancer patients, based on their HER2 expression.

Parallel Reaction Monitoring Mass Spectrometry Method for Detection of Both Casein and Whey Milk Allergens from a Baked Food Matrix

Milk allergy is among the most common food allergies present in early childhood, which in some cases may persist into adulthood as well. Proteins belonging to both casein and whey fractions of milk can trigger an allergic response in susceptible individuals. Milk is present as an ingredient in many foods, and it can also be present as casein- or whey-enriched milk-derived ingredients. As whey proteins are more susceptible to thermal processing than caseins, conventional methods often posed a challenge in accurate detection of whey allergens, particularly from a processed complex food matrix. In this study, a targeted mass spectrometry method has been developed to detect the presence of both casein and whey allergens from thermally processed foods. A pool of 19 candidate peptides representing four casein proteins and two whey proteins was identified using a discovery-driven target selection approach from various milk-derived ingredients. These target peptides were evaluated by parallel reaction monitoring of baked cookie samples containing known amounts of nonfat dry milk (NFDM). The presence of milk could be detected from baked cookies incurred with NFDM at levels as low as 1 ppm using seven peptides representing α-, β-, and κ-casein proteins and three peptides representing a whey protein, β-lactoglobulin, by this consensus PRM method.

Selection of Features with Consistent Profiles Improves Relative Protein Quantification in Mass Spectrometry Experiments

In bottom-up mass spectrometry-based proteomics, relative protein quantification is often achieved with data-dependent acquisition (DDA), data-independent acquisition (DIA), or selected reaction monitoring (SRM). These workflows quantify proteins by summarizing the abundances of all the spectral features of the protein (e.g. precursor ions, transitions or fragments) in a single value per protein per run. When abundances of some features are inconsistent with the overall protein profile (for technological reasons such as interferences, or for biological reasons such as post-translational modifications), the protein-level summaries and the downstream conclusions are undermined. We propose a statistical approach that automatically detects spectral features with such inconsistent patterns. The detected features can be separately investigated, and if necessary, removed from the data set. We evaluated the proposed approach on a series of benchmark-controlled mixtures and biological investigations with DDA, DIA and SRM data acquisitions. The results demonstrated that it could facilitate and complement manual curation of the data. Moreover, it can improve the estimation accuracy, sensitivity and specificity of detecting differentially abundant proteins, and reproducibility of conclusions across different data processing tools. The approach is implemented as an option in the open-source R-based software MSstats.

The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics

Skyline is a freely available, open-source Windows client application for accelerating targeted proteomics experimentation, with an emphasis on the proteomics and mass spectrometry community as users and as contributors. This review covers the informatics encompassed by the Skyline ecosystem, from computationally assisted targeted mass spectrometry method development, to raw acquisition file data processing, and quantitative analysis and results sharing.

Protein Biomarker Quantification by Immunoaffinity Liquid Chromatography–Tandem Mass Spectrometry: Current State and Future Vision

Immunoaffinity–mass spectrometry (IA-MS) is an emerging analytical genre with several advantages for profiling and determination of protein biomarkers. Because IA-MS combines affinity capture, analogous to ligand binding assays (LBAs), with mass spectrometry (MS) detection, this platform is often described using the term hybrid methods. The purpose of this report is to provide an overview of the principles of IA-MS and to demonstrate, through application, the unique power and potential of this technology. By combining target immunoaffinity enrichment with the use of stable isotope-labeled internal standards and MS detection, IA-MS achieves high sensitivity while providing unparalleled specificity for the quantification of protein biomarkers in fluids and tissues. In recent years, significant uptake of IA-MS has occurred in the pharmaceutical industry, particularly in the early stages of clinical development, enabling biomarker measurement previously considered unattainable. By comparison, IA-MS adoption by CLIA laboratories has occurred more slowly. Current barriers to IA-MS use and opportunities for expanded adoption are discussed. The path forward involves identifying applications for which IA-MS is the best option compared with LBA or MS technologies alone. IA-MS will continue to benefit from advances in reagent generation, more sensitive and higher throughput MS technologies, and continued growth in use by the broader analytical community. Collectively, the pursuit of these opportunities will secure expanded long-term use of IA-MS for clinical applications.

Development of a novel nanoflow liquid chromatography-parallel reaction monitoring mass spectrometry-based method for quantification of angiotensin peptides in HUVEC cultures

BACKGROUND

This study aimed to develop an analytical method using liquid chromatography tandem mass spectrometry (LC-MS/MS) for the determination of angiotensin (Ang) I, Ang (1-9), Ang II, Ang (1-7), Ang (1-5), Ang III, Ang IV in human umbilical vein endothelial cell (HUVEC) culture supernatant.

METHODS

HUVEC culture supernatant was added with gradient concentrations (0.05-1,000 ng/ml) of standard solutions of the Ang peptides. These samples underwent C18 solid-phase extraction and separation using a preconcentration nano-liquid chromatography mass spectrometry system. The target peptides were detected by a Q Exactive quadrupole orbitrap high-resolution mass spectrometer in the parallel reaction monitoring mode. Ang converting enzyme (ACE) in HUVECs was silenced to examine Ang I metabolism.

RESULTS

The limit of detection was 0.1 pg for Ang II and Ang III, and 0.5 pg for Ang (1-9), Ang (1-7), and Ang (1-5). The linear detection range was 0.1-2,000 pg (0.05-1,000 ng/ml) for Ang II and Ang III, and 0.5-2,000 pg (0.25-1,000 ng/ml) for Ang (1-9) and Ang (1-5). Intra-day and inter-day precisions (relative standard deviation) were <10%. Ang II, Ang III, Ang IV, and Ang (1-5) were positively correlated with ACE expression by HUVECs, while Ang I, Ang (1-7), and Ang (1-9) were negatively correlated.

CONCLUSION

The nanoflow liquid chromatography-parallel reaction monitoring mass spectrometry-based methodology established in this study can evaluate the Ang peptides simultaneously in HUVEC culture supernatant.

Precision Profiling of the Cardiovascular Post-Translationally Modified Proteome: Where There Is a Will, There Is a Way

There is an exponential increase in biological complexity as initial gene transcripts are spliced, translated into amino acid sequence, and post-translationally modified. Each protein can exist as multiple chemical or sequence-specific proteoforms, and each has the potential to be a critical mediator of a physiological or pathophysiological signaling cascade. Here, we provide an overview of how different proteoforms come about in biological systems and how they are most commonly measured using mass spectrometry-based proteomics and bioinformatics. Our goal is to present this information at a level accessible to every scientist interested in mass spectrometry and its application to proteome profiling. We will specifically discuss recent data linking various protein post-translational modifications to cardiovascular disease and conclude with a discussion for enablement and democratization of proteomics across the cardiovascular and scientific community. The aim is to inform and inspire the readership to explore a larger breadth of proteoform, particularity post-translational modifications, related to their particular areas of expertise in cardiovascular physiology.

Addressing Complex Matrix Interference Improves Multiplex Food Allergen Detection by Targeted LC-MS/MS

The frequent use of precautionary food allergen labeling (PAL) such as "may contain" frustrates allergic individuals who rely on such labeling to determine whether a food is safe to consume. One technique to study whether foods contain allergens is targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) employing scheduled multiple reaction monitoring (MRM). However, the applicability of a single MRM method to many commercial foods is unknown as complex and heterogeneous interferences derived from the unique composition of each food matrix can hinder quantification of trace amounts of allergen contamination. We developed a freely available, open source software package MAtrix-Dependent Interference Correction (MADIC) to identify interference and applied it with a method targeting 14 allergens. Among 84 unique food products, we found patterns of allergen contamination such as wheat in grains, milk in chocolate-containing products, and soy in breads and corn flours. We also found additional instances of contamination in products with and without PAL as well as highly variable soy content in foods containing only soybean oil and/or soy lecithin. These results demonstrate the feasibility of applying LC-MS/MS to a variety of food products with sensitive detection of multiple allergens in spite of variable matrix interference.

Cost-Effective Automated Preparation of Serum Samples for Reproducible Quantitative Clinical Proteomics

Reproducible sample preparation remains a significant challenge in large-scale clinical research using selected reaction monitoring-mass spectrometry (SRM-MS), which enables a highly sensitive multiplexed assay. Although automated liquid-handling platforms have tremendous potential for addressing this issue, the high cost of their consumables is a drawback that renders routine operation expensive. Here we evaluated the performance of a liquid-handling platform in preparing serum samples compared with a standard experiment while reducing the outlay for consumables, such as tips, wasted reagents, and reagent stock plates. A total of 26 multiplex assays were quantified by SRM-MS using four sets of 24 pooled human serum aliquots; the four sets used a fixed number (1, 4, 8, or 24) of tips to dispense digestion reagents. This study demonstrated that the use of 4 or 8 tips is comparable to 24 tips (standard experiment), as evidenced by their coefficients of variation: 13.5% (for 4 and 8 tips) versus 12.0% (24 tips). Thus we can save 37% of the total experimental cost compared with the standard experiment, maintaining nearly equivalent reproducibility. The routine operation of cost-effective liquid-handling platforms can enable researchers to process large-scale samples with high throughput, adding credibility to their findings by minimizing human error.

Protein markers and risk of type 2 diabetes and prediabetes: a targeted proteomics approach in the KORA F4/FF4 study

The objective of the present study was to identify proteins that contribute to pathophysiology and allow prediction of incident type 2 diabetes or incident prediabetes. We quantified 14 candidate proteins using targeted mass spectrometry in plasma samples of the prospective, population-based German KORA F4/FF4 study (6.5-year follow-up). 892 participants aged 42–81 years were selected using a case-cohort design, including 123 persons with incident type 2 diabetes and 255 persons with incident WHO-defined prediabetes. Prospective associations between protein levels and diabetes, prediabetes as well as continuous fasting and 2 h glucose, fasting insulin and insulin resistance were investigated using regression models adjusted for established risk factors. The best predictive panel of proteins on top of a non-invasive risk factor model or on top of HbA1c, age, and sex was selected. Mannan-binding lectin serine peptidase (MASP) levels were positively associated with both incident type 2 diabetes and prediabetes. Adiponectin was inversely associated with incident type 2 diabetes. MASP, adiponectin, apolipoprotein A-IV, apolipoprotein C-II, C-reactive protein, and glycosylphosphatidylinositol specific phospholipase D1 were associated with individual continuous outcomes. The combination of MASP, apolipoprotein E (apoE) and adiponectin improved diabetes prediction on top of both reference models, while prediabetes prediction was improved by MASP plus CRP on top of the HbA1c model. In conclusion, our mass spectrometric approach revealed a novel association of MASP with incident type 2 diabetes and incident prediabetes. In combination, MASP, adiponectin and apoE improved type 2 diabetes prediction beyond non-invasive risk factors or HbA1c, age and sex.

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.

LC-MS/MS-based quantification of efflux transporter proteins at the BBB

Targeted protein quantification using tandem mass spectrometry coupled to high performance chromatography (LC-MS/MS) has been used to quantify proteins involved in the absorption, distribution, metabolism and excretion (ADME) of xenobiotics to better understand these processes. At the blood-brain barrier (BBB), these proteins are particularly important for the maintenance of brain homeostasis, but also regulate the distribution of therapeutic drugs. Absolute quantification (AQUA) is achieved by using stable isotope labeled surrogate peptides specific to the target protein and analyzing the digested proteins in a triple-quadrupole mass spectrometer in multiple reaction monitoring (MRM) mode to achieve a high specificity, sensitivity, accuracy and reproducibility. The main objective in this work was to develop and validate an UHPLC-MS/MS method for quantification of the ATP-binding cassette (ABC) transporter proteins Bcrp and P-gp and Na+/K + ATPase pump at the BBB. Three isoforms of the α-subunit from this pump (Atp1a 1, 2 and 3) were quantified to evaluate the presence of non-endothelial cells in the BBB using one common and three isoform-specific peptides; while Bcrp ad P-gp were quantified using 2 and 3 peptides, respectively, to improve the confidence on their quantification. The protein digestion was optimized, and the analytical method was comprehensively validated according to the American Food and Drug Administration Bioanalytical Method Validation Guidance published in 2018. Linearity across four magnitude orders (0.125 to 510 pmol·mL^-1) sub-pmol·mL^-1 LOD and LOQ, accuracy and precision (deviation < 15% and CV < 15%) were proven for most of the peptides by analyzing calibration curves and four levels of quality controls in both a pure solution and a complex matrix of digested yeast proteins, to mimic the matrix effect. In addition, digestion performance and stability of the peptides was shown using standard peptides spiked in a yeast digest or mouse kidney plasma membrane proteins as a study case. The validated method was used to characterize mouse kidney plasma membrane proteins, mouse brain cortical vessels and rat brain cortical microvessels. Most of the results agree with previously reported values, although some differences are seen due to different sample treatment, heterogeneity of the sample or peptide used. Importantly, the use of three peptides allowed the quantification of P-gp in mouse kidney plasma membrane proteins which was below the limit of quantification of the previously NTTGALTTR peptide. The different levels obtained for each peptide highlight the importance and difficulty of choosing surrogate peptides for protein quantification. In addition, using isoform-specific peptides for the quantification of the Na+/K + ATPase pump, we evaluated the presence of neuronal and glial cells on rat and mouse brain cortical vessels in addition to endothelial cells. In mouse liver and kidney, only the alpha-1 isoform was detected.

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.

An HS-MRM Assay for the Quantification of Host-cell Proteins in Protein Biopharmaceuticals by Liquid Chromatography Ion Mobility QTOF Mass Spectrometry

The analysis of low-level (1-100 ppm) protein impurities (e.g., host-cell proteins (HCPs)) in protein biotherapeutics is a challenging assay requiring high sensitivity and a wide dynamic range. Mass spectrometry-based quantification assays for proteins typically involve protein digestion followed by the selective reaction monitoring/multiple reaction monitoring (SRM/MRM) quantification of peptides using a low-resolution (Rs ~1,000) tandem quadrupole mass spectrometer. One of the limitations of this approach is the interference phenomenon observed when the peptide of interest has the "same" precursor and fragment mass (in terms of m/z values) as other co-eluting peptides present in the sample (within a 1-Da window). To avoid this phenomenon, we propose an alternative mass spectrometric approach, a high selectivity (HS) MRM assay that combines the ion mobility separation of peptide precursors with the high-resolution (Rs ~30,000) MS detection of peptide fragments. We explored the capabilities of this approach to quantify low-abundance peptide standards spiked in a monoclonal antibody (mAb) digest and demonstrated that it has the sensitivity and dynamic range (at least 3 orders of magnitude) typically achieved in HCP analysis. All six peptide standards were detected at concentrations as low as 0.1 nM (1 femtomole loaded on a 2.1-mm ID chromatographic column) in the presence of a high-abundance peptide background (2 µg of a mAb digest loaded on-column). When considering the MW of rabbit phosphorylase (97.2 kDa), from which the spiked peptides were derived, the LOQ of this assay is lower than 50 ppm. Relative standard deviations (RSD) of peak areas (n = 4 replicates) were less than 15% across the entire concentration range investigated (0.1-100 nM or 1-1,000 ppm) in this study.

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.

Application of targeted mass spectrometry in bottom-up proteomics for systems biology research

The enormous diversity of proteoforms produces tremendous complexity within cellular proteomes, facilitates intricate networks of molecular interactions, and constitutes a formidable analytical challenge for biomedical researchers. Currently, quantitative whole-proteome profiling often relies on non-targeted liquid chromatography-mass spectrometry (LC-MS), which samples proteoforms broadly, but can suffer from lower accuracy, sensitivity, and reproducibility compared with targeted LC-MS. Recent advances in bottom-up proteomics using targeted LC-MS have enabled previously unachievable identification and quantification of target proteins and posttranslational modifications within complex samples. Consequently, targeted LC-MS is rapidly advancing biomedical research, especially systems biology research in diverse areas that include proteogenomics, interactomics, kinomics, and biological pathway modeling. With the recent development of targeted LC-MS assays for nearly the entire human proteome, targeted LC-MS is positioned to enable quantitative proteomic profiling of unprecedented quality and accessibility to support fundamental and clinical research. Here we review recent applications of bottom-up proteomics using targeted LC-MS for systems biology research. SIGNIFICANCE: Advances in targeted proteomics are rapidly advancing systems biology research. Recent applications include systems-level investigations focused on posttranslational modifications (such as phosphoproteomics), protein conformation, protein-protein interaction, kinomics, proteogenomics, and metabolic and signaling pathways. Notably, absolute quantification of metabolic and signaling pathway proteins has enabled accurate pathway modeling and engineering. Integration of targeted proteomics with other technologies, such as RNA-seq, has facilitated diverse research such as the identification of hundreds of "missing" human proteins (genes and transcripts that appear to encode proteins but direct experimental evidence was lacking).

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.

Diagnostic performance enhancement of pancreatic cancer using proteomic multimarker panel

Due to its high mortality rate and asymptomatic nature, early detection rates of pancreatic ductal adenocarcinoma (PDAC) remain poor. We measured 1000 biomarker candidates in 134 clinical plasma samples by multiple reaction monitoring-mass spectrometry (MRM-MS). Differentially abundant proteins were assembled into a multimarker panel from a training set (n=684) and validated in independent set (n=318) from five centers. The level of panel proteins was also confirmed by immunoassays. The panel including leucine-rich alpha-2 glycoprotein (LRG1), transthyretin (TTR), and CA19-9 had a sensitivity of 82.5% and a specificity of 92.1%. The triple-marker panel exceeded the diagnostic performance of CA19-9 by more than 10% (AUC_CA19-9 = 0.826, AUC_panel= 0.931, P < 0.01) in all PDAC samples and by more than 30% (AUC_CA19-9 = 0.520, AUC_panel = 0.830, P < 0.001) in patients with normal range of CA19-9 (<37U/mL). Further, it differentiated PDAC from benign pancreatic disease (AUC_CA19-9 = 0.812, AUC_panel = 0.892, P < 0.01) and other cancers (AUC_CA19-9 = 0.796, AUC_panel = 0.899, P < 0.001). Overall, the multimarker panel that we have developed and validated in large-scale samples by MRM-MS and immunoassay has clinical applicability in the early detection of PDAC.

Serum fibronectin distinguishes the early stages of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death, necessitating the discovery of serum markers for its early detection. In this study, a total of 180 serum samples from liver cirrhosis (LC), hepatocellular carcinoma (HCC) patients and paired samples of HCC patients who recovered (Recovery) were analyzed by multiple reaction monitoring-mass spectrometry (MRM-MS) to verify biomarkers. The three-fold crossvalidation was repeated 100 times in the training and test sets to evaluate statistical significance of 124 candidate proteins. This step resulted in 2 proteins that had an area under the receiver operating curve (AUROC) values ≥0.800 in the training (n = 90) and test sets (n = 90). Specifically, fibronectin (FN1, WCGTTQNYDADQK), distinguished HCC from LC patients, with an AUROC value of 0.926 by logistic regression. A FN1 protein was selected for validation in an independent sample (n = 60) using enzyme-linked immunosorbent assay (ELISA). The combination of alpha-fetoprotein (AFP) and FN1 improved the diagnostic performance and differentiated HCC patients with normal AFP levels. Our study has examined candidate markers for the benign disease state and malignancy and has followed up on the consequent recovery. Thus, improvement in the early detection of HCC by a 2-marker panel (AFP + FN1) might benefit HCC patients.

High Sensitivity Quantitative Proteomics Using Automated Multidimensional Nano-flow Chromatography and Accumulated Ion Monitoring on Quadrupole-Orbitrap-Linear Ion Trap Mass Spectrometer

Quantitative proteomics using high-resolution and accuracy mass spectrometry promises to transform our understanding of biological systems and disease. Recent development of parallel reaction monitoring (PRM) using hybrid instruments substantially improved the specificity of targeted mass spectrometry. Combined with high-efficiency ion trapping, this approach also provided significant improvements in sensitivity. Here, we investigated the effects of ion isolation and accumulation on the sensitivity and quantitative accuracy of targeted proteomics using the recently developed hybrid quadrupole-Orbitrap-linear ion trap mass spectrometer. We leveraged ultrahigh efficiency nano-electrospray ionization under optimized conditions to achieve yoctomolar sensitivity with more than seven orders of linear quantitative accuracy. To enable sensitive and specific targeted mass spectrometry, we implemented an automated, two-dimensional (2D) ion exchange-reversed phase nanoscale chromatography system. We found that automated 2D chromatography improved the sensitivity and accuracy of both PRM and an intact precursor scanning mass spectrometry method, termed accumulated ion monitoring (AIM), by more than 100-fold. Combined with automated 2D nano-scale chromatography, AIM achieved subattomolar limits of detection of endogenous proteins in complex biological proteomes. This allowed quantitation of absolute abundance of the human transcription factor MEF2C at ∼100 molecules/cell, and determination of its phosphorylation stoichiometry from as little as 1 μg of extracts isolated from 10,000 human cells. The combination of automated multidimensional nano-scale chromatography and targeted mass spectrometry should enable ultrasensitive high-accuracy quantitative proteomics of complex biological systems and diseases.

Development of a UHPLC-MS/MS (SRM) method for the quantitation of endogenous glucagon and dosed GLP-1 from human plasma

AIM

The performance of glucagon and GLP-1 immunoassays is often poor, but few sensitive LC-MS/MS methods exist as alternatives.

EXPERIMENTAL

A multiplexed LC-MS/MS method using a 2D extraction technique was developed.

RESULTS

The method was established for the quantitation of endogenous glucagon (LLOQ: 15 pg/ml) and dosed GLP-1 (LLOQ: 25 pg/ml) in human plasma, and is the first such method avoiding immunoenrichment. Specificity of endogenous glucagon quantitation was assured using a novel approach with a supercharging mobile phase additive to access a sensitive qualifier SRM. Endogenous glucagon concentrations were within the expected range, and showed good reproducibility after extended sample storage. A cross-validation against established immunoassays using physiological study samples demonstrated some similarities between methods.

CONCLUSION

The LC-MS/MS method offers a viable alternative to immunoassays for quantitation of endogenous glucagon, dosed glucagon and/or dosed GLP-1.

A selected reaction monitoring mass spectrometric assessment of biomarker candidates diagnosing large-cell neuroendocrine lung carcinoma by the scaling method using endogenous references

Selected reaction monitoring mass spectrometry (SRM-MS) -based semi-quantitation was performed to assess the validity of 46 selected candidate proteins for specifically diagnosing large-cell neuroendocrine lung carcinoma (LCNEC) and differentiating it from other lung cancer subtypes. The scaling method was applied in this study using specific SRM peak areas (AUCs) derived from the endogenous reference protein that normalizes all SRM AUCs obtained for the candidate proteins. In a screening verification study, we found that seven out of the 46 candidate proteins were statistically significant for the LCNEC phenotype, including 4F2hc cell surface antigen heavy chain (4F2hc/CD98) (p-ANOVA ≤ 0.0012), retinal dehydrogenase 1 (p-ANOVA ≤ 0.0029), apolipoprotein A-I (p-ANOVA ≤ 0.0004), β-enolase (p-ANOVA ≤ 0.0043), creatine kinase B-type (p-ANOVA ≤ 0.0070), and galectin-3-binding protein (p-ANOVA = 0.0080), and phosphatidylethanolamine-binding protein 1 (p-ANOVA ≤ 0.0012). In addition, we also identified candidate proteins specific to the small-cell lung carcinoma (SCLC) subtype. These candidates include brain acid soluble protein 1 (p-ANOVA < 0.0001) and γ-enolase (p-ANOVA ≤ 0.0013). This new relative quantitation-based approach utilizing the scaling method can be applied to assess hundreds of protein candidates obtained from discovery proteomic studies as a first step of the verification phase in biomarker development processes.

Towards reproducible MRM based biomarker discovery using dried blood spots

There is an increasing interest in the use of dried blood spot (DBS) sampling and multiple reaction monitoring in proteomics. Although several groups have explored the utility of DBS by focusing on protein detection, the reproducibility of the approach and whether it can be used for biomarker discovery in high throughput studies is yet to be determined. We assessed the reproducibility of multiplexed targeted protein measurements in DBS compared to serum. Eighty-two medium to high abundance proteins were monitored in a number of technical and biological replicates. Importantly, as part of the data analysis, several statistical quality control approaches were evaluated to detect inaccurate transitions. After implementing statistical quality control measures, the median CV on the original scale for all detected peptides in DBS was 13.2% and in Serum 8.8%. We also found a strong correlation (r = 0.72) between relative peptide abundance measured in DBS and serum. The combination of minimally invasive sample collection with a highly specific and sensitive mass spectrometry (MS) technique allows for targeted quantification of multiple proteins in a single MS run. This approach has the potential to fundamentally change clinical proteomics and personalized medicine by facilitating large-scale studies.

Large-Scale SRM Screen of Urothelial Bladder Cancer Candidate Biomarkers in Urine

Urothelial bladder cancer is a condition associated with high recurrence and substantial morbidity and mortality. Noninvasive urinary tests that would detect bladder cancer and tumor recurrence are required to significantly improve patient care. Over the past decade, numerous bladder cancer candidate biomarkers have been identified in the context of extensive proteomics or transcriptomics studies. To translate these findings in clinically useful biomarkers, the systematic evaluation of these candidates remains the bottleneck. Such evaluation involves large-scale quantitative LC-SRM (liquid chromatography-selected reaction monitoring) measurements, targeting hundreds of signature peptides by monitoring thousands of transitions in a single analysis. The design of highly multiplexed SRM analyses is driven by several factors: throughput, robustness, selectivity and sensitivity. Because of the complexity of the samples to be analyzed, some measurements (transitions) can be interfered by coeluting isobaric species resulting in biased or inconsistent estimated peptide/protein levels. Thus the assessment of the quality of SRM data is critical to allow flagging these inconsistent data. We describe an efficient and robust method to process large SRM data sets, including the processing of the raw data, the detection of low-quality measurements, the normalization of the signals for each protein, and the estimation of protein levels. Using this methodology, a variety of proteins previously associated with bladder cancer have been assessed through the analysis of urine samples from a large cohort of cancer patients and corresponding controls in an effort to establish a priority list of most promising candidates to guide subsequent clinical validation studies.

Peptide serum markers in islet autoantibody-positive children

AIMS/HYPOTHESIS

We sought to identify minimal sets of serum peptide signatures as markers for islet autoimmunity and predictors of progression rates to clinical type 1 diabetes in a case-control study.

METHODS

A double cross-validation approach was applied to first prioritise peptides from a shotgun proteomic approach in 45 islet autoantibody-positive and -negative children from the BABYDIAB/BABYDIET birth cohorts. Targeted proteomics for 82 discriminating peptides were then applied to samples from another 140 children from these cohorts.

RESULTS

A total of 41 peptides (26 proteins) enriched for the functional category lipid metabolism were significantly different between islet autoantibody-positive and autoantibody-negative children. Two peptides (from apolipoprotein M and apolipoprotein C-IV) were sufficient to discriminate autoantibody-positive from autoantibody-negative children. Hepatocyte growth factor activator, complement factor H, ceruloplasmin and age predicted progression time to type 1 diabetes with a significant improvement compared with age alone.

CONCLUSION/INTERPRETATION

Distinct peptide signatures indicate islet autoimmunity prior to the clinical manifestation of type 1 diabetes and enable refined staging of the presymptomatic disease period.

Towards comprehensive and quantitative proteomics for diagnosis and therapy of human disease

Given superior analytical features, MS proteomics is well suited for the basic investigation and clinical diagnosis of human disease. Modern MS enables detailed functional characterization of the pathogenic biochemical processes, as achieved by accurate and comprehensive quantification of proteins and their regulatory chemical modifications. Here, we describe how high-accuracy MS in combination with high-resolution chromatographic separations can be leveraged to meet these analytical requirements in a mechanism-focused manner. We review the quantification methods capable of producing accurate measurements of protein abundance and posttranslational modification stoichiometries. We then discuss how experimental design and chromatographic resolution can be leveraged to achieve comprehensive functional characterization of biochemical processes in complex biological proteomes. Finally, we describe current approaches for quantitative analysis of a common functional protein modification: reversible phosphorylation. In all, current instrumentation and methods of high-resolution chromatography and MS proteomics are poised for immediate translation into improved diagnostic strategies for pediatric and adult diseases.

Transitioning from Targeted to Comprehensive Mass Spectrometry Using Genetic Algorithms

Targeted proteomic assays are becoming increasingly popular because of their robust quantitative applications enabled by internal standardization, and they can be routinely executed on high performance mass spectrometry instrumentation. However, these assays are typically limited to 100s of analytes per experiment. Considerable time and effort are often expended in obtaining and preparing samples prior to targeted analyses. It would be highly desirable to detect and quantify 1000s of analytes in such samples using comprehensive mass spectrometry techniques (e.g., SWATH and DIA) while retaining a high degree of quantitative rigor for analytes with matched internal standards. Experimentally, it is facile to port a targeted assay to a comprehensive data acquisition technique. However, data analysis challenges arise from this strategy concerning agreement of results from the targeted and comprehensive approaches. Here, we present the use of genetic algorithms to overcome these challenges in order to configure hybrid targeted/comprehensive MS assays. The genetic algorithms are used to select precursor-to-fragment transitions that maximize the agreement in quantification between the targeted and the comprehensive methods. We find that the algorithm we used provided across-the-board improvement in the quantitative agreement between the targeted assay data and the hybrid comprehensive/targeted assay that we developed, as measured by parameters of linear models fitted to the results. We also found that the algorithm could perform at least as well as an independently-trained mass spectrometrist in accomplishing this task. We hope that this approach will be a useful tool in the development of quantitative approaches for comprehensive proteomics techniques. Graphical Abstract ᅟ.

Simultaneous Proteomic Discovery and Targeted Monitoring using Liquid Chromatography, Ion Mobility Spectrometry, and Mass Spectrometry

Current proteomic approaches include both broad discovery measurements and quantitative targeted analyses. In many cases, discovery measurements are initially used to identify potentially important proteins (e.g. candidate biomarkers) and then targeted studies are employed to quantify a limited number of selected proteins. Both approaches, however, suffer from limitations. Discovery measurements aim to sample the whole proteome but have lower sensitivity, accuracy, and quantitation precision than targeted approaches, whereas targeted measurements are significantly more sensitive but only sample a limited portion of the proteome. Herein, we describe a new approach that performs both discovery and targeted monitoring (DTM) in a single analysis by combining liquid chromatography, ion mobility spectrometry and mass spectrometry (LC-IMS-MS). In DTM, heavy labeled target peptides are spiked into tryptic digests and both the labeled and unlabeled peptides are detected using LC-IMS-MS instrumentation. Compared with the broad LC-MS discovery measurements, DTM yields greater peptide/protein coverage and detects lower abundance species. DTM also achieved detection limits similar to selected reaction monitoring (SRM) indicating its potential for combined high quality discovery and targeted analyses, which is a significant step toward the convergence of discovery and targeted approaches.