Perspectives of targeted mass spectrometry for protein biomarker verification

Targeted Analysis of Permethylated N-Glycans Using MRM/PRM Approaches

Targeted mass spectrometric analysis is widely employed across various omics fields as a validation strategy due to its high sensitivity and accuracy. The approach has been successfully employed for the structural analysis of proteins, glycans, lipids, and metabolites. Multiple reaction monitoring (MRM) and parallel reaction monitoring (PRM) have been the methods of choice for targeted structural studies of biomolecules. These target analyses simplify the analytical workflow, reduce background interference, and increase selectivity/specificity, allowing for a reliable quantification of permethylated N-glycans in complex biological matrices.

Quantification of 782 Plasma Peptides by Multiplexed Targeted Proteomics

Blood analysis is one of the foundations of clinical diagnostics. In recent years, the analysis of proteins in blood samples by mass spectrometry has taken a jump forward in terms of sensitivity and the number of identified proteins. The recent development of parallel reaction monitoring with parallel accumulation and serial fragmentation (prm-PASEF) combines ion mobility as an additional separation dimension. This increases the proteome coverage while allowing the use of shorter chromatographic gradients. To demonstrate the method's full potential, we used an isotope-labeled synthetic peptide mix of 782 peptides, derived from 579 plasma proteins, spiked into blood plasma samples with a prm-PASEF measurement allowing the quantification of 565 plasma proteins by targeted proteomics. As a less time-consuming alternative to the prm-PASEF method, we describe guided data independent acquisition (dia)-PASEF (g-dia-PASEF) and compare its application to prm-PASEF for measuring blood plasma. To demonstrate both methods' performance in clinical samples, 20 patient plasma samples from a colorectal cancer (CRC) cohort were analyzed. The analysis identified 14 differentially regulated proteins between the CRC patient and control individual plasma samples. This shows the technique's potential for the rapid and unbiased screening of blood proteins, abolishing the need for the preselection of potential biomarker proteins.

Proteomics in thrombosis research

A State of the Art lecture titled “Proteomics in Thrombosis Research” was presented at the ISTH Congress in 2021. In clinical practice, there is a need for improved plasma biomarker‐based tools for diagnosis and risk prediction of venous thromboembolism (VTE). Analysis of blood, to identify plasma proteins with potential utility for such tools, could enable an individualized approach to treatment and prevention. Technological advances to study the plasma proteome on a large scale allows broad screening for the identification of novel plasma biomarkers, both by targeted and nontargeted proteomics methods. However, assay limitations need to be considered when interpreting results, with orthogonal validation required before conclusions are drawn. Here, we review and provide perspectives on the application of affinity‐ and mass spectrometry‐based methods for the identification and analysis of plasma protein biomarkers, with potential application in the field of VTE. We also provide a future perspective on discovery strategies and emerging technologies for targeted proteomics in thrombosis research. Finally, we summarize relevant new data on this topic, presented during the 2021 ISTH Congress.

Proteomic Technologies and their Application for Ensuring Meat Quality, Safety and Authenticity

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Proteomic tools were extensively used to understand the relationship between muscle proteome and conversion of muscle to meat, post-mortem proteolysis, meat texture, and variation in meat color. Developments in proteomic tools have also resulted in their application for addressing the safety and authenticity issues including meat species identification, detection of animal byproducts, non-meat ingredients and tissues in meat products, traceability, identification of genetically modified ingredients, chemical residues and other harmful substances. Proteomic tools are also being used in some of the potential areas like understanding the effect of animal transportation, stunning, slaughter stress, halal authentication and issues related to animal welfare. Emerging advances in proteomic and peptidomic technologies and their application in traceability, meat microbiology, safety and authentication are taking a major stride as an interesting and complementary alternative to DNA-based methods currently in use. Future research in meat science need to be linked to emerging metabolomic, lipidomic and other omic technologies for ensuring integrated meat quality and safety management. In this paper, a comprehensive overview of the use of proteomics for the assessment of quality and safety in the meat value chain and their potential application is discussed.

Systematic optimization of targeted and multiplexed MS-based screening workflows for protein biomarkers

Background: The capability of targeted MS-based methods to simultaneously measure multiple analytes with high selectivity and sensitivity greatly facilitates the discovery and quantitation of novel biomarkers. However, the complexity of biological samples is a major bottleneck that requires extensive sample preparation. Results: This paper reports a generic workflow to optimize surrogate peptide-based protein biomarker screening for seven human proteins in a multiplexed manner without the need for any specific affinity reagents. Each step of the sample processing and LC-MS methods is systematically assessed and optimized for better analytical performance. Conclusion: The established method is used for the screening of multiple myeloma patient samples to determine which proteins could be robustly measured and serve as potential biomarkers of the disease.

Advancement in Analytical Strategies for Quantification of Biomarkers with a Special Emphasis on Surrogate Approaches

Accurate quantification of biomarkers has always been a challenge for many bioanalytical scientists due to their endogenous nature and low concentration in biological matrices. Different analytical approaches have been developed for quantifying biomarkers including enzyme-linked immunosorbent assay, immunohistochemistry, western blotting, and chromatographic techniques assisted with mass spectrometry. Liquid chromatography-tandem mass spectrometry-based quantification of biomarkers has gained more attention over other traditional techniques due to its higher sensitivity and selectivity. However, the primary challenge lies with this technique includes the unavailability of a blank matrix for method development. To overcome this challenge, different analytical approaches are being developed including surrogate analyte and surrogate matrix approach. Such approaches include quantification of biomarkers in a surrogate matrix or quantification of an isotopically labeled surrogate analyte in an authentic matrix. To demonstrate the authenticity of the surrogate approach, it is mandatory to establish quantitative parallelism through validation employing respective surrogate analytes and surrogate matrices. In this review, different bioanalytical approaches for biomarker quantification and recent advancements in the field aiming for improvement in the specificity of the techniques have been discussed. Liquid chromatography-tandem mass spectrometry-based surrogate approaches for biomarker quantification and significance of parallelism establishment in both surrogate matrix and surrogate analyte-based approaches have been critically discussed. In addition, different methods for demonstrating parallelism in the surrogate method have been explained.

Isotopic N,N-dimethyl leucine tags for absolute quantification of clusterin and apolipoprotein E in Alzheimer's disease

Alzheimer's disease (AD) is the most common form of dementia and one of the leading causes of death in the United States. In the past decades, extensive efforts have been devoted to biomarker discovery for early diagnosis and treatment of AD. Herein, this study aims to quantify clusterin (CLU) and apolipoprotein E (APOE) in blood samples from AD patients and evaluate these two proteins as potential biomarkers in AD diagnosis. In-house synthesized 5-plex isotopic N,N-dimethyl leucine (iDiLeu) tags were used to label target peptide standards at different concentrations to construct standard curves. Our study revealed that the levels of CLU and APOE exhibited clear differences in male vs. female AD groups but not in male vs. female non-AD groups. In contrast, the levels of serum CLU and APOE did not show statistically significant differences in the AD groups and non-AD groups. Principal component analysis (PCA) with CLU and APOE showed some separation between the AD and non-AD participants. Significance: Dissecting CLU and APOE heterogeneity in AD pathogenesis may therefore facilitate delineating the pathological relevance for sex-related pathways, leading to personalized medicine in the future. Collectively, this study introduces a cost-effective absolute quantitative proteomics strategy for target protein quantitation and lays the foundation for future investigation of CLU and APOE as potential biomarkers for AD. SIGNIFICANCE STATEMENT: As blood-based biomarkers for AD diagnosis are cost-effective and introduce less invasiveness, discovery and validation of biomarkers in the blood samples of AD patients have become a hot topic in Alzheimer's and dementia research. Thus far, amyloid β (Aβ), total-tau and phosphorylated tau (p-tau) in blood show great accuracy and specificity in diagnosis of AD. However, the underlying mechanism of AD pathology remains to be elusive and complex. Besides these well studied proteins, many other proteins, such as clusterin (CLU) and apolipoprotein E (APOE) have also been found to be related to AD development. It has been implicated that these two proteins are involved in Aβ clearance and deposition. In this study, we measure the absolute concentrations of these two proteins in blood and shed some light on the potential roles of CLU and APOE in AD pathology. Dissecting CLU and APOE heterogeneity in AD pathogenesis may therefore facilitate delineating the pathological relevance for specific pathways between different genders, leading to personalized medicine in the future. Collectively, this study introduces a cost-effective absolute quantitative proteomics strategy for target protein quantitation and lays the foundation for future investigation of CLU and APOE as potential biomarkers for AD.

Mass Spectrometry-based Proteomics and Glycoproteomics in COVID-19 Biomarkers Identification: A Mini-review

The first corona-pandemic, coronavirus disease 2019 (COVID-19) caused a huge health crisis and incalculable damage worldwide. Knowledge of how to cure the disease is urgently needed. Emerging immune escaping mutants of the virus suggested that it may be potentially persistent in human society as a regular health threat as the flu virus. Therefore, it is imperative to identify appropriate biomarkers to indicate pathological and physiological states, and more importantly, clinic outcomes. Proteins are the performers of life functions, and their abundance and modification status can directly reflect the immune status. Protein glycosylation serves a great impact in modulating protein function. The use of both unmodified and glycosylated proteins as biomarkers has also been proved feasible in the studies of SARS, Zika virus, influenza, etc. In recent years, mass spectrometry-based glycoproteomics, as well as proteomics approaches, advanced significantly due to the evolution of mass spectrometry. We focus on the current development of the mass spectrometry-based strategy for COVID-19 biomarkers' investigation. Potential application of glycoproteomics approaches and challenges in biomarkers identification are also discussed.

Development and Multiple Validation of the Protein Multi-marker Panel for Diagnosis of Pancreatic Cancer

PURPOSE

To develop and validate a protein-based, multi-marker panel that provides superior pancreatic ductal adenocarcinoma (PDAC) detection abilities with sufficient diagnostic performance.

EXPERIMENTAL DESIGN

A total of 959 plasma samples from patients at multiple medical centers were used. To construct an optimal, diagnostic, multi-marker panel, we applied data preprocessing procedure to biomarker candidates. The multi-marker panel was developed using a training set comprised of 261 PDAC cases and 290 controls. Subsequent evaluations were performed in a validation set comprised of 65 PDAC cases and 72 controls. Further validation was performed in an independent set comprised of 75 PDAC cases and 47 controls.

RESULTS

A multi-marker panel containing 14 proteins was developed. The multi-marker panel achieved AUCs of 0.977 and 0.953 for the training set and validation set, respectively. In an independent validation set, the multi-marker panel yielded an AUC of 0.928. The diagnostic performance of the multi-marker panel showed significant improvements compared with carbohydrate antigen (CA) 19-9 alone (training set AUC = 0.977 vs. 0.872, P < 0.001; validation set AUC = 0.953 vs. 0.832, P < 0.01; independent validation set AUC = 0.928 vs. 0.771, P < 0.001). When the multi-marker panel and CA 19-9 were combined, the diagnostic performance of the combined panel was improved for all sets.

CONCLUSIONS

This multi-marker panel and the combined panel showed statistically significant improvements in diagnostic performance compared with CA 19-9 alone and has the potential to complement CA 19-9 as a diagnostic marker in clinical practice.

Both Targeted Mass Spectrometry and Flow Sorting Analysis Methods Detected the Decreased Serum Apolipoprotein E Level in Alzheimer's Disease Patients

Apolipoprotein E (ApoE) polymorphism has been appreciated as a valuable predictor of Alzheimer disease (AD), and the associated ε4 allele has been recognized as an indicator of susceptibility to this disease. However, serum ApoE levels have been a controversial issue in AD, due to the great variability regarding the different target detection methods, ethnicity, and the geographic variations of cohorts. The aim of this study was to validate serum ApoE levels in relation to AD, particularly using two distinct detection methods, liquid chromatography-selected reaction monitoring (SRM) mass spectrometry and microsphere-based fluorescence-activated cell sorting (FACS) analysis, to overcome experimental variations. Also, comparison of serum ApoE levels was performed between the level of protein detection by FACS and peptide level by SRM in both control and AD patients. Results from the two detection methods were cross-confirmed and validated. Both methods produced fairly consistent results, showing a significant decrease of serum ApoE levels in AD patients relative to those of a control cohort (43 control versus 45 AD, p < 0.0001). Significant correlation has been revealed between results from FACS and SRM (p < 0.0001) even though lower serum ApoE concentration values were measured in protein by FACS analysis than in peptide-level detections by SRM. Correlation study suggested that a decrease of the serum ApoE level in AD is related to the mini-mental state exam score in both results from different experimental methods, but it failed to show consistent correlation with age, gender, or clinical dementia rating.

FGF-21 biomarker detection at the sub-nanogram per mL level in human serum using normal-flow liquid chromatography/tandem mass spectrometry

RATIONALE

Quantitative detection of the FGF-21 biomarker at the sub-nanogram per mL level in human serum has generally been achieved using nanoflow liquid chromatography/tandem mass spectrometry (LC/MS/MS) due to its high sensitivity. However, a nano-LC/MS/MS-based assay can suffer from limited reproducibility and MS signal instability making it challenging to employ it as a robust analytical method for routine clinical applications.

METHODS

To tackle these limitations, parallel reaction monitoring (PRM)-based targeted protein quantification using normal-flow liquid chromatography coupled with high-resolution, accurate mass instrumentation was evaluated as a possible alternative. Different from the conventional selected reaction monitoring (SRM) using triple quadrupole MS, the proposed strategy used high-resolution orbitrap MS coupled with conventional normal-flow liquid chromatography. The primary goal of this assay development effort is to significantly improve the robustness of the LC/MS/MS-based assay while maintaining high sensitivity by the use of high-resolution MS and a large sample loading volume.

RESULTS

The performance of the normal-flow LC/MS/MS assay was evaluated by using it to quantify the FGF-21 protein, a potential biomarker for non-alcoholic fatty liver disease, in serum samples. Multiple replicated PRM sample quantification results demonstrated the excellent reproducibility and operational robustness of the assay. A limit of quantification of less than 0.4 ng/mL for FGF-21 in a complex serum matrix could be achieved by using the heavy-isotope-labeled peptide technique, a result which is comparable with the sensitivity obtained using the nano-LC/SRM MS-based assay.

CONCLUSIONS

The strategy offered an effective alternative to nano-LC/SRM MS for the quantification of protein biomarkers in a complex biomatrix with much improved reproducibility and operational robustness.

A proteomics outlook towards the elucidation of epithelial-mesenchymal transition molecular events

The main cause of death in cancer is the spread, or metastasis, of cancer cells to distant organs with consequent tumor formation. Additionally, metastasis is a process that demands special attention, as the cellular transformations make cancer at this stage very difficult or occasionally even impossible to be cured. The main process that converts epithelial tumor cells to mesenchymal-like metastatic cells is the Epithelial to Mesenchymal Transition (EMT). This process allows stationary and polarized epithelial cells, which are connected laterally to several types of junctions as well as the basement membrane, to undergo multiple biochemical changes that enable disruption of cell-cell adherence and apical-basal polarity. Moreover, the cells undergo important reprogramming to remodel the cytoskeleton and acquire mesenchymal characteristics such as enhanced migratory capacity, invasiveness, elevated resistance to apoptosis and a large increase in the production of ECM components. As expected, the alterations of the protein complement are extensive and complex, and thus exploring this by proteomic approaches is of particular interest. Here we review the overall findings of proteome modifications during EMT, mainly focusing on molecular signatures observed in multiple proteomic studies as well as coordinated pathways, cellular processes and their clinical relevance for altered proteins. As a result, an interesting set of proteins is highlighted as potential targets to be further investigated in the context of EMT, metastasis and cancer progression.

A Targeted Mass Spectrometry Strategy for Developing Proteomic Biomarkers: A Case Study of Epithelial Ovarian Cancer

Protein biomarkers for epithelial ovarian cancer are critical for the early detection of the cancer to improve patient prognosis and for the clinical management of the disease to monitor treatment response and to detect recurrences. Unfortunately, the discovery of protein biomarkers is hampered by the limited availability of reliable and sensitive assays needed for the reproducible quantification of proteins in complex biological matrices such as blood plasma. In recent years, targeted mass spectrometry, exemplified by selected reaction monitoring (SRM) has emerged as a method, capable of overcoming this limitation. Here, we present a comprehensive SRM-based strategy for developing plasma-based protein biomarkers for epithelial ovarian cancer and illustrate how the SRM platform, when combined with rigorous experimental design and statistical analysis, can result in detection of predictive analytes.Our biomarker development strategy first involved a discovery-driven proteomic effort to derive potential N-glycoprotein biomarker candidates for plasma-based detection of human ovarian cancer from a genetically engineered mouse model of endometrioid ovarian cancer, which accurately recapitulates the human disease. Next, 65 candidate markers selected from proteins of different abundance in the discovery dataset were reproducibly quantified with SRM assays across a large cohort of over 200 plasma samples from ovarian cancer patients and healthy controls. Finally, these measurements were used to derive a 5-protein signature for distinguishing individuals with epithelial ovarian cancer from healthy controls. The sensitivity of the candidate biomarker signature in combination with CA125 ELISA-based measurements currently used in clinic, exceeded that of CA125 ELISA-based measurements alone. The SRM-based strategy in this study is broadly applicable. It can be used in any study that requires accurate and reproducible quantification of selected proteins in a high-throughput and multiplexed fashion.

Selenium-isotopic signature toward mass spectrometric identification and enzyme activity assay

The unraveling of enzymatic reactions, especially identification of enzymatic substrates or products, is important to elucidate biological processes. Here a selenium-isotopic signature for mass spectrometric identification of enzymatic-related species is demonstrated by using selenium-containing peptides (SePeps) as substrates. Thus a strategy is proposed for rapid and precise assay of multiple enzyme activity. These SePeps can be synthesized by introduction of one selenomethionine residue in the sequence and simply identified in the full-scan mode with the feature of distinctive selenium-isotopic distribution without MS/MS verifications, which proposes a novel solution to the specific identification of enzyme-related species, allows to exclude the interferences of species with tiny mass differences in bio-samples, and meanwhile can offer a judgement on data accuracy for the analysis of enzyme activities. As a proof-of-concept, a method for multiple analysis of two representative enzymes in MCF-7 cell lysate has been developed with the isotopic peak areas of either SePep substrates or enzymatic products with the top intensities. These results could be the foundation to extend the method for more complicated enzyme systems. The selenium-isotopic signature provides a powerful protocol for high-throughput assays of peptide-metabolizing enzymes with enhanced confidence and can be extended to screen enzymatic reaction-related substrates.

A targeted mass spectrometry immunoassay to quantify osteopontin in fresh-frozen breast tumors and adjacent normal breast tissues

Osteopontin (OPN) is a multifunctional protein that can activate cell-signaling pathways and lead to cancer development and metastasis. Elevated OPN expression was reported in different cancer types, including breast tumors. Here, we present a new immuno-mass spectrometry method for OPN quantification in fresh-frozen malignant and adjacent normal human breast tissues. For quantification we used two proteotypic peptides: OPN-peptide-1 and OPN-peptide-2. Peptide concentrations were determined by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in multiple reaction monitoring (MRM) mode with stable isotope standards (SIS) and immuno-affinity enrichment for isolation of OPN peptides. Based on the OPN-peptide-1, the average OPN concentration in normal breast tissue was 19.42 μg/g, while the corresponding level in breast tumors was 603.9 μg/g. Based on OPN-peptide-2, the average concentration in normal breast tissue was 19.30 μg/g and in breast tumors 535.0 μg/g. In ER/PR/HER2(-) patients the OPN levels in breast tumors were significantly higher than in corresponding normal breast tissue samples, whereas in the single ER/PR/HER2(+) patient the OPN concentration in tumor samples was lower than in normal breast tissue sample. In conclusion, the current method is considered promising for the quantification of OPN in research and in clinical settings and should be further studied in breast cancer patients. SIGNIFICANCE: A new immuno-mass spectrometry method was successfully developed and applied to determine OPN concentrations in malignant tumor and normal breast tissues from six patients, and the method is promising for OPN quantification in both research and clinical settings.

Clinical potential of mass spectrometry-based proteogenomics

Cancer genomics research aims to advance personalized oncology by finding and targeting specific genetic alterations associated with cancers. In genome-driven oncology, treatments are selected for individual patients on the basis of the findings of tumour genome sequencing. This personalized approach has prolonged the survival of subsets of patients with cancer. However, many patients do not respond to the predicted therapies based on the genomic profiles of their tumours. Furthermore, studies pairing genomic and proteomic analyses of samples from the same tumours have shown that the proteome contains novel information that cannot be discerned through genomic analysis alone. This observation has led to the concept of proteogenomics, in which both types of data are leveraged for a more complete view of tumour biology that might enable patients to be more successfully matched to effective treatments than they would using genomics alone. In this Perspective, we discuss the added value of proteogenomics over the current genome-driven approach to the clinical characterization of cancers and summarize current efforts to incorporate targeted proteomic measurements based on selected/multiple reaction monitoring (SRM/MRM) mass spectrometry into the clinical laboratory to facilitate clinical proteogenomics.

Simultaneous extraction and analysis of multiple cystine-dense peptides by μSPE and microflow-MS/MS from plasma

Aim: Develop a universal extraction and liquid chromatography-mass spectrometer method to simultaneously analyze cystine-dense peptide (CDP) miniproteins from rat and human plasma. The results of the analysis will be used to assist selection of therapeutic drug candidates from the vast CDP library. Methods & results: A micro-elution solid-phase extraction method was developed for the sample preparation of the CDP peptides in rat and human plasma followed by analysis by microflow liquid chromatography MS/MS. The methods developed for drug discovery were found to be accurate (±≤15.2% from nominal concentrations) and precise (≤13.4% CV), with a dynamic range of 1.00–500 ng/ml and extraction recoveries of 47.2–99.0%. Conclusion: This bioanalytical method can be utilized to screen CDP proteins and other miniproteins for drug discovery, candidate selection and further drug development.

A Strategy for Discovery and Verification of Candidate Biomarkers in Cerebrospinal Fluid of Preclinical Alzheimer's Disease

Alzheimer's disease (AD), a progressive neurodegenerative disease, is characterized by the accumulation of senile plaques, neurofibrillary tangles, and loss of synapses and neurons in the brain. The pathophysiological process of AD begins with a long asymptomatic phase, which provides a potential opportunity for early therapeutic intervention. Therefore, it is crucial to define putative biomarkers via reliable and validated methods for early diagnosis of AD. Here, we characterized candidate biomarkers by discovery proteomics analysis of cerebrospinal fluid (CSF), revealing that 732 and 704 proteins with more than one unique peptide were identified in healthy controls and preclinical AD patients, respectively. Among them, 79 and 98 proteins were significantly altered in preclinical AD for women and men, respectively, many of which have been demonstrated with consistent regulation pattern in patients with mild cognitive impairment or AD dementia. In-house developed 5-plex isotopic N,N-dimethyl leucine (iDiLeu) tags were further utilized to verify candidate biomarkers, neurosecretory protein VGF (VGF) and apolipoprotein E (apoE). By labeling peptide standards with different iDiLeu tags, a four-point internal calibration curve was constructed to allow for determination of the absolute amount of target analytes in CSF through a single liquid chromatography-mass spectrometry run.

HST-MRM-MS: A Novel High-Sample-Throughput Multiple Reaction Monitoring Mass Spectrometric Method for Multiplex Absolute Quantitation of Hepatocellular Carcinoma Serum Biomarker

Absolute quantification of clinical biomarkers by mass spectrometry (MS) has been challenged due to low sample-throughput of current multiple reaction monitoring (MRM) methods. For this problem to be overcome, in this work, a novel high-sample-throughput multiple reaction monitoring mass spectrometric (HST-MRM-MS) quantification approach is developed to achieve simultaneous quantification of 24 samples. Briefly, triplex dimethyl reagents (L, M, and H) and eight-plex iTRAQ reagents were used to label the N- and C-termini of the Lys C-digested peptides, respectively. The triplex dimethyl labeling produces three coelute peaks in MRM traces, and the iTRAQ labeling produces eight peaks in MS2, resulting in 24 (3×8) channels in a single experiment. HST-MRM-MS has shown good accuracy ( R² > 0.98 for absolute quantification), reproducibility (RSD < 15%), and linearity (2-3 orders of magnitude). Moreover, the novel method has been successfully applied in quantifying serum biomarkers in hepatocellular carcinoma (HCC)-related serum samples. In conclusion, HST-MRM-MS is an accurate, high-sample-throughput, and broadly applicable MS-based absolute quantification method.

Bayesian Classification of Proteomics Biomarkers from Selected Reaction Monitoring Data using an Approximate Bayesian Computation-Markov Chain Monte Carlo Approach

Selected reaction monitoring (SRM) has become one of the main methods for low-mass-range–targeted proteomics by mass spectrometry (MS). However, in most SRM-MS biomarker validation studies, the sample size is very small, and in particular smaller than the number of proteins measured in the experiment. Moreover, the data can be noisy due to a low number of ions detected per peptide by the instrument. In this article, those issues are addressed by a model-based Bayesian method for classification of SRM-MS data. The methodology is likelihood-free, using approximate Bayesian computation implemented via a Markov chain Monte Carlo procedure and a kernel-based Optimal Bayesian Classifier. Extensive experimental results demonstrate that the proposed method outperforms classical methods such as linear discriminant analysis and 3NN, when sample size is small, dimensionality is large, the data are noisy, or a combination of these.

Integrated SWATH-based and targeted-based proteomics provide insights into the retinal emmetropization process in guinea pig

Myopia is generally regarded as a failure of normal emmetropization process, however, its underlying molecular mechanisms are unclear. To investigate the retinal protein profile changes during emmetropization, we studied differential protein expressions of ocular growth in young guinea pigs at 3 and 21 days old respectively, when significant axial elongation was detected (P < 0.001, n = 10). Independent pooled retinal samples of both eyes were subjected to SWATH mass spectrometry (MS) followed by bioinformatics analysis using cloud-based platforms. A comprehensive retina SWATH ion-library consisting of 3138 (22,871) unique proteins (peptides) at 1% FDR was constructed. 40 proteins were found to be significantly up-regulated and 8 proteins down-regulated during emmetropization (≥log2 of 0.43 with ≥2 peptides matched per protein; P < 0.05). Using pathway analysis, the most significant pathway identifiable was 'phototransduction' (P = 1.412e-4). Expression patterns of 7 proteins identified in this pathway were further validated and confirmed (P < 0.05) with high-resolution Multiple Reaction Monitoring (MRM-HR) MS. Combining discovery and targeted proteomics approaches, this study for the first time comprehensively profiled protein changes in the guinea pig retina during normal emmetropization-associated eye growth. The findings of this study are also relevant to the myopia development, which is the result of failed emmetropization.

SIGNIFICANCE

Myopia is considered as a failure of emmetropization. However, the underlying biochemical mechanism of emmetropization, a visually guided process in which eye grows towards the optimal optical state of clear vision during early development, is not well understood. Retina is known as the key tissue to regulate this active eye growth. we studied eye growth of young guinea pigs and harvested their retinal tissues. A comprehensive SWATH ion library with identification of a total 3138 unique proteins were established, in which 48 proteins exhibited significant differential expressions between 3 and 21 days old. After MRM-HR confirmation, 'phototransduction' were found as the most active pathway during emmetropic eye growth. This study is the first in discovering key retinal protein players and pathways which are presumably orchestrated by biological mechanism(s) underlying emmetropization.

Advancing the immunoaffinity platform AFFIRM to targeted measurements of proteins in serum in the pg/ml range

There is a great need for targeted protein assays with the capacity of sensitive measurements in complex samples such as plasma or serum, not the least for clinical purposes. Proteomics keeps generating hundreds of biomarker candidates that need to be transferred towards true clinical application through targeted verification studies and towards clinically applicable analysis formats. The immunoaffinity assay AFFIRM (AFFInity sRM) combines the sensitivity of recombinant single chain antibodies (scFv) for targeted protein enrichment with a specific mass spectrometry readout through selected reaction monitoring (SRM) in an automated workflow. Here we demonstrate a 100 times improved detection capacity of the assay down to pg/ml range through the use of oriented antibody immobilization to magnetic beads. This was achieved using biotin-tagged scFv coupled to streptavidin coated magnetic beads, or utilizing the FLAG tag for coupling to anti-FLAG antibody coated magnetic beads. An improved multiplexing capacity with an 11-plex setup was also demonstrated compared to a previous 3-plex setup, which is of great importance for the analysis of panels of biomarker targets.

Mass spectrometry for the evaluation of cardiovascular diseases based on proteomics and lipidomics

The identification and quantification of proteins and lipids is of major importance for the diagnosis, prognosis and understanding of the molecular mechanisms involved in disease development. Owing to its selectivity and sensitivity, mass spectrometry has become a key technique in analytical platforms for proteomic and lipidomic investigations. Using this technique, many strategies have been developed based on unbiased or targeted approaches to highlight or monitor molecules of interest from biomatrices. Although these approaches have largely been employed in cancer research, this type of investigation has been met by a growing interest in the field of cardiovascular disorders, potentially leading to the discovery of novel biomarkers and the development of new therapies. In this paper, we will review the different mass spectrometry- based proteomic and lipidomic strategies applied in cardiovascular diseases, especially atherosclerosis. Particular attention will be given to recent developments and the role of bioinformatics in data treatment. This review will be of broad interest to the medical community by providing a tutorial of how mass spectrometric strategies can support clinical trials.

Shotgun and Targeted Plasma Proteomics to Predict Prognosis of Non-Small Cell Lung Cancer

Lung cancer is the leading cause of cancer deaths worldwide. Clinically, the treatment of non-small cell lung cancer (NSCLC) can be improved by the early detection and risk screening among population. To meet this need, here we describe in detail a shotgun following the targeted proteomics workflow that we previously applied for human plasma analysis, which involves (1) the application of extensive peptide-level fractionation coupled with label-free quantitative proteomics for the discovery of plasma biomarker candidates for lung cancer and (2) the usage of the multiple reaction monitoring (MRM) assays for the follow-up validations in the verification phase. The workflow features simplicity, low cost, high transferability, high robustness, and flexibility with specific instrumental settings.

Translational Rodent Models for Research on Parasitic Protozoa-A Review of Confounders and Possibilities

Rodents, in particular Mus musculus, have a long and invaluable history as models for human diseases in biomedical research, although their translational value has been challenged in a number of cases. We provide some examples in which rodents have been suboptimal as models for human biology and discuss confounders which influence experiments and may explain some of the misleading results. Infections of rodents with protozoan parasites are no exception in requiring close consideration upon model choice. We focus on the significant differences between inbred, outbred and wild animals, and the importance of factors such as microbiota, which are gaining attention as crucial variables in infection experiments. Frequently, mouse or rat models are chosen for convenience, e.g., availability in the institution rather than on an unbiased evaluation of whether they provide the answer to a given question. Apart from a general discussion on translational success or failure, we provide examples where infections with single-celled parasites in a chosen lab rodent gave contradictory or misleading results, and when possible discuss the reason for this. We present emerging alternatives to traditional rodent models, such as humanized mice and organoid primary cell cultures. So-called recombinant inbred strains such as the Collaborative Cross collection are also a potential solution for certain challenges. In addition, we emphasize the advantages of using wild rodents for certain immunological, ecological, and/or behavioral questions. The experimental challenges (e.g., availability of species-specific reagents) that come with the use of such non-model systems are also discussed. Our intention is to foster critical judgment of both traditional and newly available translational rodent models for research on parasitic protozoa that can complement the existing mouse and rat models.

Current trends in quantitative proteomics - an update

Proteins can provide insights into biological processes at the functional level, so they are very promising biomarker candidates. The quantification of proteins in biological samples has been routinely used for the diagnosis of diseases and monitoring the treatment. Although large-scale protein quantification in complex samples is still a challenging task, a great amount of effort has been made to advance the technologies that enable quantitative proteomics. Seven years ago, in 2009, we wrote an article about the current trends in quantitative proteomics. In writing this current paper, we realized that, today, we have an even wider selection of potential tools for quantitative proteomics. These tools include new derivatization reagents, novel sampling formats, new types of analyzers and scanning techniques, and recently developed software to assist in assay development and data analysis. In this review article, we will discuss these innovative methods, and their current and potential applications in proteomics. Copyright © 2017 John Wiley & Sons, Ltd.

Bayesian ABC-MCMC Classification of Liquid Chromatography-Mass Spectrometry Data

Proteomics promises to revolutionize cancer treatment and prevention by facilitating the discovery of molecular biomarkers. Progress has been impeded, however, by the small-sample, high-dimensional nature of proteomic data. We propose the application of a Bayesian approach to address this issue in classification of proteomic profiles generated by liquid chromatography–mass spectrometry (LC-MS). Our approach relies on a previously proposed model of the LC-MS experiment, as well as on the theory of the optimal Bayesian classifier (OBC). Computation of the OBC requires the combination of a likelihood-free methodology called approximate Bayesian computation (ABC) as well as Markov chain Monte Carlo (MCMC) sampling. Numerical experiments using synthetic LC-MS data based on an actual human proteome indicate that the proposed ABC-MCMC classification rule outperforms classical methods such as support vector machines, linear discriminant analysis, and 3-nearest neighbor classification rules in the case when sample size is small or the number of selected proteins used to classify is large.

Targeted Proteomics for Multiplexed Verification of Markers of Colorectal Tumorigenesis

Targeted proteomic methods can accelerate the verification of multiple tumor marker candidates in large series of patient samples. We utilized the targeted approach known as selected/multiple reaction monitoring (S/MRM) to verify potential protein markers of colorectal adenoma identified by our group in previous transcriptomic and quantitative shotgun proteomic studies of a large cohort of precancerous colorectal lesions. We developed SRM assays to reproducibly detect and quantify 25 (62.5%) of the 40 selected proteins in an independent series of precancerous and cancerous tissue samples (19 adenoma/normal mucosa pairs; 17 adenocarcinoma/normal mucosa pairs). Twenty-three proteins were significantly up-regulated (n = 17) or downregulated (n = 6) in adenomas and/or adenocarcinomas, as compared with normal mucosa (linear fold changes ≥ ±1.3, adjusted p value <0.05). Most changes were observed in both tumor types (up-regulation of ANP32A, ANXA3, SORD, LDHA, LCN2, NCL, S100A11, SERPINB5, CDV3, OLFM4, and REG4; downregulation of ARF6 and PGM5), and a five-protein biomarker signature distinguished neoplastic tissue from normal mucosa with a maximum area under the receiver operating curve greater than 0.83. Other changes were specific for adenomas (PPA1 and PPA2 up-regulation; KCTD12 downregulation) or adenocarcinoma (ANP32B, G6PD, RCN1, and SET up-regulation; downregulated AKR1B1, APEX1, and PPA1). Some changes significantly correlated with a few patient- or tumor-related phenotypes. Twenty-two (96%) of the 23 proteins have a potential to be released from the tumors into the bloodstream, and their detectability in plasma has been previously reported. The proteins identified in this study expand the pool of biomarker candidates that can be used to develop a standardized precolonoscopy blood test for the early detection of colorectal tumors.

Analytical validation of protein biomarkers for risk of spontaneous preterm birth

Presented are the validation results of a second-generation assay for determining the relative abundances of two protein biomarkers found in maternal serum that predict an individual's risk of spontaneous preterm birth. The sample preparation workflow is complex, consisting of immuno-depletion of high-abundance serum proteins, tryptic digestion of the immuno-depleted fraction to generate surrogate peptide analytes, and detection by tandem mass spectrometry. The method was determined to be robust on observation of the following characteristics: classifier peptide detection precision was excellent; results were accurate when compared to a reference method; results were linear over a clinically relevant range; the limits of quantitation encompassed the range of expected results; and the method demonstrated analytical specificity and resilience to differences in patient serum and common endogenous interferents.

Proteotypic Peptides and Their Applications

Recent advances in mass spectrometry based proteomic techniques and publicly available large proteomic repositories are being exploited to characterize the proteome of multiple organisms. While humongous amount of proteomic data is being acquired and analyzed, many biological questions still remain unanswered. Proteotypic peptides which uniquely represent target proteins or a protein isoform are used as an alternative strategy for protein identification in the field of immunological methods and targeted proteomic techniques. Using different computational approaches, resources and techniques used in the identification of proteotypic peptides of target proteins is discussed here.

Chemical Glycoproteomics

Chemical tools have accelerated progress in glycoscience, reducing experimental barriers to studying protein glycosylation, the most widespread and complex form of posttranslational modification. For example, chemical glycoproteomics technologies have enabled the identification of specific glycosylation sites and glycan structures that modulate protein function in a number of biological processes. This field is now entering a stage of logarithmic growth, during which chemical innovations combined with mass spectrometry advances could make it possible to fully characterize the human glycoproteome. In this review, we describe the important role that chemical glycoproteomics methods are playing in such efforts. We summarize developments in four key areas: enrichment of glycoproteins and glycopeptides from complex mixtures, emphasizing methods that exploit unique chemical properties of glycans or introduce unnatural functional groups through metabolic labeling and chemoenzymatic tagging; identification of sites of protein glycosylation; targeted glycoproteomics; and functional glycoproteomics, with a focus on probing interactions between glycoproteins and glycan-binding proteins. Our goal with this survey is to provide a foundation on which continued technological advancements can be made to promote further explorations of protein glycosylation.

Analytical performance of nano-LC-SRM using nondepleted human plasma over an 18-month period

A standardized procedure for label-free nano-LC-SRM analysis of 32 high-medium abundance proteins from nondepleted human plasma was established and SRM data were acquired on 45 separate days for a control sample that was independently prepared on 39 distinct dates over an 18-month period (542 days). This case study enabled us to assess quantitative variance associated with nano-LC-SRM plasma analysis, mimicking experimental conditions that would be experienced with clinical trial biomarker studies. We assessed sample preparation variability attributed to different technicians and sample storage stability. Instrument performance varied over the 18-month period requiring ion path cleaning, so we assessed the impact of declining performance on specific peptide ion sensitivity and evaluated how various data normalization strategies could compensate for these changes. Our analysis demonstrated that while sample preparation was the main contributor for data variances when MS data were acquired within days, variability in SRM sensitivity was a far greater source of variance when data were acquired over a long period. The overall median multiplexed assay CV was 13% over the 18-month period. This case study is illustrative of large-scale plasma biomarker studies using nano-LC-SRM over extended periods and highlights aspects of bioanalysis that require careful attention to ensure reliable quantitation.

Cancer associated proteins in blood plasma: Determining normal variation

Protein biomarkers have the potential to improve diagnosis, stratification of patients into treatment cohorts, follow disease progression and treatment response. One distinct group of potential biomarkers comprises proteins which have been linked to cancer, known as cancer associated proteins (CAPs). We determined the normal variation of 86 CAPs in 72 individual plasma samples collected from ten individuals using SRM mass spectrometry. Samples were collected weekly during 5 weeks from ten volunteers and over one day at nine fixed time points from three volunteers. We determined the degree of the normal variation depending on interpersonal variation, variation due to time of day, and variation over weeks and observed that the variation dependent on the time of day appeared to be the most important. Subdivision of the proteins resulted in two predominant protein groups containing 21 proteins with relatively high variation in all three factors (day, week and individual), and 22 proteins with relatively low variation in all factors. We present a strategy for prioritizing biomarker candidates for future studies based on stratification over their normal variation and have made all data publicly available. Our findings can be used to improve selection of biomarker candidates in future studies and to determine which proteins are most suitable depending on study design.

Analytical Performance of ELISA Assays in Urine: One More Bottleneck towards Biomarker Validation and Clinical Implementation

ELISA is the main approach for the sensitive quantification of protein biomarkers in body fluids and is currently employed in clinical laboratories for the measurement of clinical markers. As such, it also constitutes the main methodological approach for biomarker validation and further qualification. For the latter, specific assay performance requirements have to be met, as described in respective guidelines of regulatory agencies. Even though many clinical ELISA assays in serum are regularly used, ELISA clinical applications in urine are significantly less. The scope of our study was to evaluate ELISA assay analytical performance in urine for a series of potential biomarkers for bladder cancer, as a first step towards their large scale clinical validation. Seven biomarkers (Secreted protein acidic and rich in cysteine, Survivin, Slit homolog 2 protein, NRC-Interacting Factor 1, Histone 2B, Proteinase-3 and Profilin-1) previously described in the literature as having differential expression in bladder cancer were included in the study. A total of 11 commercially available ELISA tests for these markers were tested by standard curve analysis, assay reproducibility, linearity and spiking experiments. The results show disappointing performance with coefficients of variation>20% for the vast majority of the tests performed. Only 3 assays (for Secreted protein acidic and rich in cysteine, Survivin and Slit homolog 2 protein) passed the accuracy thresholds and were found suitable for further application in marker quantification. These results collectively reflect the difficulties in developing urine-based ELISA assays of sufficient analytical performance for clinical application, presumably attributed to the urine matrix itself and/or presence of markers in various isoforms.

Biomarker discovery in mass spectrometry-based urinary proteomics

Urinary proteomics has become one of the most attractive topics in disease biomarker discovery. MS-based proteomic analysis has advanced continuously and emerged as a prominent tool in the field of clinical bioanalysis. However, only few protein biomarkers have made their way to validation and clinical practice. Biomarker discovery is challenged by many clinical and analytical factors including, but not limited to, the complexity of urine and the wide dynamic range of endogenous proteins in the sample. This article highlights promising technologies and strategies in the MS-based biomarker discovery process, including study design, sample preparation, protein quantification, instrumental platforms, and bioinformatics. Different proteomics approaches are discussed, and progresses in maximizing urinary proteome coverage and standardization are emphasized in this review. MS-based urinary proteomics has great potential in the development of noninvasive diagnostic assays in the future, which will require collaborative efforts between analytical scientists, systems biologists, and clinicians.

Complexity reduction of clinical samples for routine mass spectrometric analysis

The precise measurement of protein abundance levels in highly complex biological samples such as plasma remains challenging. The wide range of protein concentrations impairs the detection of low-abundant species and the high number of peptide components to analyze results in interferences leading to erroneous quantitative results. The advances in MS instrumentation, with improved selectivity and sensitivity, partially address these issues, but sample preparation techniques remain the pivotal element to obtain robust routine mass spectrometric assays with a low LOD. A number of methodologies have been proposed and refined over the past two decades to reduce the range of protein concentrations and the number of peptide components. Whereas most of the methods have proven their utility for discovery studies, only a few are actually applicable to routine quantitative studies. In this account, common protein- and peptide-based fractionation methods are discussed, and illustrated with practical examples, with a focus on methods suited for clinical samples scheduled for biomarker validation assays and subsequent routine clinical mass spectrometric analyses.

SWATH enables precise label-free quantification on proteome scale

MS-based proteomics has emerged as a powerful tool in biological studies. The shotgun proteomics strategy, in which proteolytic peptides are analyzed in data-dependent mode, enables a detection of the most comprehensive proteome (>10 000 proteins from whole-cell lysate). The quantitative proteomics uses stable isotopes or label-free method to measure relative protein abundance. The isotope labeling strategies are more precise and accurate compared to label-free methods, but labeling procedures are complicated and expensive, and the sample number and types are also limited. Sequential window acquisition of all theoretical mass spectra (SWATH) is a recently developed technique, in which data-independent acquisition is coupled with peptide spectral library match. In principle SWATH method is able to do label-free quantification in an MRM-like manner, which has higher quantification accuracy and precision. Previous data have demonstrated that SWATH can be used to quantify less complex systems, such as spiked-in peptide mixture or protein complex. Our study first time assessed the quantification performance of SWATH method on proteome scale using a complex mouse-cell lysate sample. In total 3600 proteins got identified and quantified without sample prefractionation. The SWATH method shows outstanding quantification precision, whereas the quantification accuracy becomes less perfect when protein abundances differ greatly. However, this inaccuracy does not prevent discovering biological correlates, because the measured signal intensities had linear relationship to the sample loading amounts; thus the SWATH method can predict precisely the significance of a protein. Our results prove that SWATH can provide precise label-free quantification on proteome scale.

A clinically based protein discovery strategy to identify potential biomarkers of response to anti-TNF-α treatment of psoriatic arthritis

PURPOSE

Psoriatic arthritis (PsA) can be treated using biologic therapies targeting biomolecules such as tumor necrosis factor alpha, interleukins (IL)-17 and IL-23. Although 70% PsA patients respond well to therapy, 30% patients show no or limited clinical improvement. Biomarkers that predict response to therapy would help to avoid unnecessary use of expensive biologics in nonresponding patients and enable alternative treatments to be explored.

EXPERIMENTAL DESIGN

Patient synovial tissue samples from two clinical studies were analysed using difference in-gel electrophoresis-based proteomics to identify protein expression differences in response to anti-TNF-α treatment. Subsequent multiplexed MRM measurements were used to verify potential biomarkers.

RESULTS

A total of 119 proteins were differentially expressed (p<0.05) in response to anti-TNF-α treatment and 25 proteins were differentially expressed (p<0.05) between "good responders" and "poor responders". From these differentially expressed proteins, MRM assays were developed for four proteins to explore their potential as treatment predictive biomarkers.

CONCLUSION AND CLINICAL RELEVANCE

Gel-based proteomics strategy has demonstrated differential protein expression in synovial tissue of PsA patients, in response to anti-TNF-α treatment. Development of multiplex MRM assays to these differentially expressed proteins has the potential to predict response to therapy and allow alternative, more effective treatments to be explored sooner.

Serum apolipoprotein A-1 quantification by LC-MS with a SILAC internal standard reveals reduced levels in smokers

Background:

Absolute quantification of protein biomarkers such as serum apolipoprotein A1 by both immunoassays and LC–MS can provide misleading results.

Results:

Recombinant ApoA-1 internal standard was prepared using stable isotope labeling by amino acids in cell culture with [¹³C₆¹⁵N₂]-lysine and [¹³C₉¹⁵N₁]-tyrosine in human cells. A stable isotope dilution LC–MS method for serum ApoA-1 was validated and levels analyzed for 50 nonsmokers and 50 smokers.

Conclusion:

The concentration of ApoA-1 in nonsmokers was 169.4 mg/dl with an 18.4% reduction to 138.2 mg/dl in smokers. The validated assay will have clinical utility for assessing effects of smoking cessation and therapeutic or dietary interventions in high-risk populations.

[The possibility of using PlasmaDeepDive™ MRM panel in clinical diagnostics]

Concentrations of 46 proteins have been determined in human blood plasma using PlasmaDeepDive™ MRM Panel ("Biognosys AG", Switzerland). 18 of them were included into the group of proteins with higher concentrations, also identified by the shotgun proteomic analysis. Based on literature data it is concluded that the PlasmaDeepDive™ MRM Panel is applicable for studies of human plasma samples for potential biomarkers of various nervous system disorders.

Proteomics analysis of bodily fluids in pancreatic cancer

Proteomics study of pancreatic cancer using bodily fluids emphasizes biomarker discovery and clinical application, presenting unique prospect and challenges. Depending on the physiological nature of the bodily fluid and its proximity to pancreatic cancer, the proteomes of bodily fluids, such as pancreatic juice, pancreatic cyst fluid, blood, bile, and urine, can be substantially different in terms of protein constitution and the dynamic range of protein concentration. Thus, a comprehensive discovery and specific detection of cancer-associated proteins within these varied fluids is a complex task, requiring rigorous experiment design and a concerted approach. While major challenges still remain, fluid proteomics studies in pancreatic cancer to date have provided a wealth of information in revealing proteome alterations associated with pancreatic cancer in various bodily fluids.

Quantitative mass spectrometric profiling of cancer-cell proteomes derived from liquid and solid tumors

In-depth analyses of cancer cell proteomes are needed to elucidate oncogenic pathomechanisms, as well as to identify potential drug targets and diagnostic biomarkers. However, methods for quantitative proteomic characterization of patient-derived tumors and in particular their cellular subpopulations are largely lacking. Here we describe an experimental set-up that allows quantitative analysis of proteomes of cancer cell subpopulations derived from either liquid or solid tumors. This is achieved by combining cellular enrichment strategies with quantitative Super-SILAC-based mass spectrometry followed by bioinformatic data analysis. To enrich specific cellular subsets, liquid tumors are first immunophenotyped by flow cytometry followed by FACS-sorting; for solid tumors, laser-capture microdissection is used to purify specific cellular subpopulations. In a second step, proteins are extracted from the purified cells and subsequently combined with a tumor-specific, SILAC-labeled spike-in standard that enables protein quantification. The resulting protein mixture is subjected to either gel electrophoresis or Filter Aided Sample Preparation (FASP) followed by tryptic digestion. Finally, tryptic peptides are analyzed using a hybrid quadrupole-orbitrap mass spectrometer, and the data obtained are processed with bioinformatic software suites including MaxQuant. By means of the workflow presented here, up to 8,000 proteins can be identified and quantified in patient-derived samples, and the resulting protein expression profiles can be compared among patients to identify diagnostic proteomic signatures or potential drug targets.

YPED: an integrated bioinformatics suite and database for mass spectrometry-based proteomics research

We report a significantly-enhanced bioinformatics suite and database for proteomics research called Yale Protein Expression Database (YPED) that is used by investigators at more than 300 institutions worldwide. YPED meets the data management, archival, and analysis needs of a high-throughput mass spectrometry-based proteomics research ranging from a single laboratory, group of laboratories within and beyond an institution, to the entire proteomics community. The current version is a significant improvement over the first version in that it contains new modules for liquid chromatography-tandem mass spectrometry (LC-MS/MS) database search results, label and label-free quantitative proteomic analysis, and several scoring outputs for phosphopeptide site localization. In addition, we have added both peptide and protein comparative analysis tools to enable pairwise analysis of distinct peptides/proteins in each sample and of overlapping peptides/proteins between all samples in multiple datasets. We have also implemented a targeted proteomics module for automated multiple reaction monitoring (MRM)/selective reaction monitoring (SRM) assay development. We have linked YPED's database search results and both label-based and label-free fold-change analysis to the Skyline Panorama repository for online spectra visualization. In addition, we have built enhanced functionality to curate peptide identifications into an MS/MS peptide spectral library for all of our protein database search identification results.

An integrated quantification method to increase the precision, robustness, and resolution of protein measurement in human plasma samples

Background

Current quantification methods for mass spectrometry (MS)-based proteomics either do not provide sufficient control of variability or are difficult to implement for routine clinical testing.

Results

We present here an integrated quantification (InteQuan) method that better controls pre-analytical and analytical variability than the popular quantification method using stable isotope-labeled standard peptides (SISQuan). We quantified 16 lung cancer biomarker candidates in human plasma samples in three assessment studies, using immunoaffinity depletion coupled with multiple reaction monitoring (MRM) MS. InteQuan outperformed SISQuan in precision in all three studies and tolerated a two-fold difference in sample loading. The three studies lasted over six months and encountered major changes in experimental settings. Nevertheless, plasma proteins in low ng/ml to low μg/ml concentrations were measured with a median technical coefficient of variation (CV) of 11.9% using InteQuan. The corresponding median CV using SISQuan was 15.3% after linear fitting. Furthermore, InteQuan surpassed SISQuan in measuring biological difference among clinical samples and in distinguishing benign versus cancer plasma samples.

Conclusions

We demonstrated that InteQuan is a simple yet robust quantification method for MS-based quantitative proteomics, especially for applications in biomarker research and in routine clinical testing.

Electronic supplementary material

The online version of this article (doi:10.1186/1559-0275-12-3) contains supplementary material, which is available to authorized users.