Validation Of Protein Biomarkers Research Articles

Identification and validation of protein biomarkers for predicting gastrointestinal stromal tumor recurrence

We conducted a proteomic analysis using mass spectrometry to identify and validate protein biomarkers for accurately predicting recurrence risk in gastrointestinal stromal tumors (GIST) patients, focusing on differentially expressed proteins in metastatic versus primary GIST tissues. We selected five biomarkers—GPX4, RBM4, TPM3, PFKFB2, and PGAM5—and validated their expressions in primary tumors of recurrent and non-recurrent GIST patients via immunohistochemistry. Our analysis of the association between these biomarkers with recurrence-free survival (RFS) and overall survival (OS), along with their interrelationships, revealed that immunohistochemistry confirmed significantly higher expressions of these biomarkers in primary GIST tissues of recurrent patients. Kaplan-Meier survival analysis showed that high expressions of GPX4, RBM4, TPM3, PFKFB2, and PGAM5 correlated with lower RFS, and GPX4 and RBM4 with lower OS. All biomarker pairs showed positive associations, with high expressions correlating with increased recurrence rates, and GPX4 and RBM4 with higher mortality rates. In conclusion, the biomarkers GPX4, RBM4, TPM3, PFKFB2, and PGAM5 are clinically relevant for predicting GIST recurrence, with their high expressions in primary tumors linked to poorer RFS and OS. They serve as potential prognostic indicators, enabling early treatment and improved outcomes. The observed interrelationships among these biomarkers further validate their accuracy in predicting GIST recurrence.

Discovery and validation of protein biomarkers for monitoring the effectiveness of drug treatment for major depressive disorder

Major depressive disorder (MDD) has a high prevalence worldwide. Although the economic burden of depression increases annually, the proportion of patients with MDD receiving treatment did not increase between 2010 and 2018, suggesting an unmet treatment need. The burden of long-term treatment for depression is borne by patients. In this context, biomarkers associated with drug-treatment responses can be used as reference indicators to reduce unnecessary treatment and costs. Changes in biomolecules in response to drug treatment for depression and drug-treatment response markers have been studied extensively. The Hamilton Depression Rating Scale (HAM-D) is mainly used as an indicator of response and remission; however, it is difficult to determine whether the medication contributes to recovery when evaluating the effect of drug treatment for depression based on this assessment. Therefore, it is necessary to monitor the effect of medication compared to normal health conditions. Here, serum protein levels were compared using liquid chromatography-tandem mass spectrometry among a group of patients with depression who did not receive medication, a group of patients receiving medication, and a control group. Eight selected biomarkers, including Apolipoproteins A-I, Complement factor H, Complement C5, Complement C1q subcomponent subunit B, Alpha-2-HS-glycoprotein, Complement C1q subcomponent subunit C, Vitamin D-binding protein and Corticosteroid-binding globulin were distinguished between disease states, and protein levels in the drug-treated group were similar to those in the control group. These markers can be used to monitor the effectiveness of drug treatment.

Supramolecular Exosome Array for Efficient Capture and In Situ Detection of Protein Biomarkers

Exosomes are an emerging source for disease biomarker discovery due to the high stability of proteins protected by phospholipid bilayers. However, liquid biopsy with exosomes remains challenging due to the extreme complexity of biological samples. Herein, we introduced an amphiphile-dendrimer supramolecular probe (ADSP) for the efficient capture and high-throughput analysis of exosomes, enabling the array-based assay for marker proteins. Amphiphilic amphotericin B was functionalized onto highly branched globular dendrimers, which can then insert into the exosome membrane efficiently, forming a supramolecular complex through multivalent interactions between the probe and the bilayer of exosomes. The ADSP can be easily coated onto magnetic beads or the nitrocellulose membrane, facilitating the capture of exosomes from a minimum amount of clinical samples. The captured exosomes can be detected with target protein antibodies via Western blotting or in a high-throughput array-based dot blotting format. This new strategy exhibited excellent extraction capability from trace body fluids with superior sensitivity (less than 1 μL plasma), good quantitation ability (R2 > 0.99), and high throughput (96 samples in one batch) using clinical plasma samples. The combination of proteomics and ADSP will provide a platform for the discovery and validation of protein biomarkers for cancer diagnosis and prognosis.

Progress in Targeted Mass Spectrometry (Parallel Accumulation-Serial Fragmentation) and Its Application in Plasma/Serum Proteomics.

Targeted mass spectrometry using multiple reaction monitoring (MRM) or parallel reaction monitoring (PRM) has been commonly used for protein biomarker validation in plasma, serum, or other clinically relevant specimens due to its high specificity, selectivity, and multiplexing capability compared with immunoassays. As the emerging mode termed parallel accumulation-serial fragmentation (prmPASEF) significantly improved analyte throughput (100-1000), sensitivity (attomole level), and acquisition speed, it promises to broaden the application of targeted mass spectrometry to simultaneous biomarker discovery and validation with high accuracy. Here, we summarize the general approach of the MRM and PRM techniques used for serum/plasma proteomics and describe a detailed step-by-step procedure for the development of MRM/PRM assays for secreted proteins.

UPLC-HDMSE to discover serum biomarkers in adults with type 1 diabetes

Type 1 diabetes (T1D) is a complex disease with metabolic and functional changes that can alter an individual's proteome. An LC-MS/MS analytical method, in an HDMSE system, was used to identify differentially expressed proteins in the high abundance protein-depleted serum of T1D patients and healthy controls. Samples were processed in Progenesis QI for Proteomics software. A functional enrichment of the proteins was performed with Gene Ontology and ToppGene, and the interactions were visualized by STRING 11.5. As a result, 139 proteins were identified, 14 of which were downregulated in the serum of patients with T1D compared to controls. Most of the differentially expressed proteins were shown to be involved with the immune system, inflammation, and growth hormone stimulus response, and were associated with the progression of T1D. Differential protein expression data showed for the first-time changes in CPN2 expression levels in the serum of patients with T1D. Our findings indicate that these proteins are targets of interest for future investigations and for validation of protein biomarkers in T1D.

Multiple Reaction Monitoring-Based Targeted Assays for the Validation of Protein Biomarkers in Brain Tumors.

The emergence of omics technologies over the last decade has helped in advancement of research and our understanding of complex diseases like brain cancers. However, barring genomics, no other omics technology has been able to find utility in clinical settings. The recent advancements in mass spectrometry instrumentation have resulted in proteomics technologies becoming more sensitive and reliable. Targeted proteomics, a relatively new branch of mass spectrometry-based proteomics has shown immense potential in addressing the shortcomings of the standard molecular biology-based techniques like Western blotting and Immunohistochemistry. In this study we demonstrate the utility of Multiple reaction monitoring (MRM), a targeted proteomics approach, in quantifying peptides from proteins like Apolipoprotein A1 (APOA1), Apolipoprotein E (APOE), Prostaglandin H2 D-Isomerase (PTGDS), Vitronectin (VTN) and Complement C3 (C3) in cerebrospinal fluid (CSF) collected from Glioma and Meningioma patients. Additionally, we also report transitions for peptides from proteins – Vimentin (VIM), Cystatin-C (CST3) and Clusterin (CLU) in surgically resected Meningioma tissues; Annexin A1 (ANXA1), Superoxide dismutase (SOD2) and VIM in surgically resected Glioma tissues; and Microtubule associated protein-2 (MAP-2), Splicing factor 3B subunit 2 (SF3B2) and VIM in surgically resected Medulloblastoma tissues. To our knowledge, this is the first study reporting the use of MRM to validate proteins from three types of brain malignancies and two different bio-specimens. Future studies involving a large cohort of samples aimed at accurately detecting and quantifying peptides of proteins with roles in brain malignancies could potentially result in a panel of proteins showing ability to classify and grade tumors. Successful application of these techniques could ultimately offer alternative strategies with increased accuracy, sensitivity and lower turnaround time making them translatable to the clinics.

Assessment of Blood Biomarker Profile After Acute Concussion During Combative Training Among US Military Cadets

Validation of protein biomarkers for concussion diagnosis and management in military combative training is important, as these injuries occur outside of traditional health care settings and are generally difficult to diagnose. To investigate acute blood protein levels in military cadets after combative training-associated concussions. This multicenter prospective case-control study was part of a larger cohort study conducted by the National Collegiate Athletic Association and the US Department of Defense Concussion Assessment Research and Education (CARE) Consortium from February 20, 2015, to May 31, 2018. The study was performed among cadets from 2 CARE Consortium Advanced Research Core sites: the US Military Academy at West Point and the US Air Force Academy. Cadets who incurred concussions during combative training (concussion group) were compared with cadets who participated in the same combative training exercises but did not incur concussions (contact-control group). Clinical measures and blood sample collection occurred at baseline, the acute postinjury point (<6 hours), the 24- to 48-hour postinjury point, the asymptomatic postinjury point (defined as the point at which the cadet reported being asymptomatic and began the return-to-activity protocol), and 7 days after return to activity. Biomarker levels and estimated mean differences in biomarker levels were natural log (ln) transformed to decrease the skewness of their distributions. Data were collected from August 1, 2016, to May 31, 2018, and analyses were conducted from March 1, 2019, to January 14, 2020. Concussion incurred during combative training. Proteins examined included glial fibrillary acidic protein, ubiquitin C-terminal hydrolase-L1, neurofilament light chain, and tau. Quantification was conducted using a multiplex assay (Simoa; Quanterix Corp). Clinical measures included the Sport Concussion Assessment Tool-Third Edition symptom severity evaluation, the Standardized Assessment of Concussion, the Balance Error Scoring System, and the 18-item Brief Symptom Inventory. Among 103 military service academy cadets, 67 cadets incurred concussions during combative training, and 36 matched cadets who engaged in the same training exercises did not incur concussions. The mean (SD) age of cadets in the concussion group was 18.6 (1.3) years, and 40 cadets (59.7%) were male. The mean (SD) age of matched cadets in the contact-control group was 19.5 (1.3) years, and 25 cadets (69.4%) were male. Compared with cadets in the contact-control group, those in the concussion group had significant increases in glial fibrillary acidic protein (mean difference in ln values, 0.34; 95% CI, 0.18-0.50; P < .001) and ubiquitin C-terminal hydrolase-L1 (mean difference in ln values, 0.97; 95% CI, 0.44-1.50; P < .001) levels at the acute postinjury point. The glial fibrillary acidic protein level remained high in the concussion group compared with the contact-control group at the 24- to 48-hour postinjury point (mean difference in ln values, 0.22; 95% CI, 0.06-0.38; P = .007) and the asymptomatic postinjury point (mean difference in ln values, 0.21; 95% CI, 0.05-0.36; P = .01). The area under the curve for all biomarkers combined, which was used to differentiate cadets in the concussion and contact-control groups, was 0.80 (95% CI, 0.68-0.93; P < .001) at the acute postinjury point. This study's findings indicate that blood biomarkers have potential for use as research tools to better understand the pathobiological changes associated with concussion and to assist with injury identification and recovery from combative training-associated concussions among military service academy cadets. These results extend the previous findings of studies of collegiate athletes with sport-associated concussions.

Development and validation of protein biomarkers of health in grizzly bears.

Large carnivores play critical roles in the maintenance and function of natural ecosystems; however, the populations of many of these species are in decline across the globe. Therefore, there is an urgent need to develop novel techniques that can be used as sensitive conservation tools to detect new threats to the health of individual animals well in advance of population-level effects. Our study aimed to determine the expression of proteins related to energetics, reproduction and stress in the skin of grizzly bears (Ursus arctos) using a liquid chromatography and multiple reaction monitoring mass spectrometry assay. We hypothesized that a suite of target proteins could be measured using this technique and that the expression of these proteins would be associated with biological (sex, age, sample location on body) and environmental (geographic area, season, sample year) variables. Small skin biopsies were collected from free-ranging grizzly bears in Alberta, Canada, from 2013 to 2019 (n = 136 samples from 111 individuals). Over 700 proteins were detected in the skin of grizzly bears, 19 of which were chosen as targets because of their established roles in physiological function. Generalized linear mixed model analysis was used for each target protein. Results indicate that sample year influenced the majority of proteins, suggesting that physiological changes may be driven in part by responses to changes in the environment. Season influenced the expression of proteins related to energetics, reproduction and stress, all of which were lower during fall compared to early spring. The expression of proteins related to energetics and stress varied by geographic area, while the majority of proteins that were affected by biological attributes (age class, sex and age class by sex interaction) were related to reproduction and stress. This study provides a novel method by which scientists and managers can further assess and monitor physiological function in wildlife.

Reverse Phase Protein array for the quantification and validation of protein biomarkers of beef qualities: The case of meat color from Charolais breed

Reverse Phase Protein Arrays (RPPA) were applied for the quantification and validation of protein biomarkers of beef qualities on M. longissimus thoracis sampled early post-mortem from young Charolais bulls. pHu was related to six proteins, three of which are glycolytic enzymes (ENO1, ENO3 and TPI1), while others belong to structural (TTN and α-actin) and proteolytic (μ-calpain) pathways. For color traits, several correlations were found, interestingly with structural proteins. The relationships were in some cases trait-dependent. To understand the mechanisms and explore animal variability, color data were categorized into three classes. α-actin and TTN allowed efficient separation of the classes and were strongly related with all color traits. Biomarkers belonging to heat stress and metabolism pathways were also involved. Two identified proteins, namely Four and a half LIM domains 1 (FHL1) and Tripartite motif-containing 72 (TRIM72), were for the first time related to beef color. Overall, these relationships could be used to develop muscle-specific processing strategies to improve beef color stability.

Current trends in quantitative proteomics – an update

In the Biomarker era, quantitative proteomics has become one of the most important tools for the discovery and validation of protein biomarkers in complex biological samples. The challenge has become how to reproducibly identify and accurately quantify the protein/peptide targets at low concentrations, and distinguish real signals from noise. The past 7 years have witnessed the development of new instrumentation and quantitation technologies such as Parallel Reaction Monitoring (PRM) and Sequential Window Acquisition of all THeoretical fragment ion mass spectra (SWATH). New sampling formats such as dried blood spot (DBS) and MALDI imaging, have evolved, as well as new sample derivatization techniques (the TMT 10-plex). In this perspective special feature, Christoph Borchers and colleagues discus these innovations and give critical insights on the future of the field. Dr. Borchers is the director of the University of Victoria-Genome British Columbia Proteomics Centre (Victoria, BC, Canada). His prime research interests are centered on technology development for protein identification and characterization, quantitative proteomics for biomarker discovery and validation, and bioinformatics.

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.

The Role of Proteomics in Biomarker Development for Improved Patient Diagnosis and Clinical Decision Making in Prostate Cancer.

Prostate Cancer (PCa) is the second most commonly diagnosed cancer in men worldwide. Although increased expression of prostate-specific antigen (PSA) is an effective indicator for the recurrence of PCa, its intended use as a screening marker for PCa is of considerable controversy. Recent research efforts in the field of PCa biomarkers have focused on the identification of tissue and fluid-based biomarkers that would be better able to stratify those individuals diagnosed with PCa who (i) might best receive no treatment (active surveillance of the disease); (ii) would benefit from existing treatments; or (iii) those who are likely to succumb to disease recurrence and/or have aggressive disease. The growing demand for better prostate cancer biomarkers has coincided with the development of improved discovery and evaluation technologies for multiplexed measurement of proteins in bio-fluids and tissues. This review aims to (i) provide an overview of these technologies as well as describe some of the candidate PCa protein biomarkers that have been discovered using them; (ii) address some of the general limitations in the clinical evaluation and validation of protein biomarkers; and (iii) make recommendations for strategies that could be adopted to improve the successful development of protein biomarkers to deliver improvements in personalized PCa patient decision making.

Quantitative proteomic analysis of anticancer drug RH1 resistance in liver carcinoma

Acquired resistance of tumor cells to the therapeutic treatment is a major challenge in virtually any chemotherapy. A novel anticancer agent 2,5-diaziridinyl-3-(hydroxymethyl)-6-methyl-1,4-benzoquinone (RH1) is designed to be activated by NAD(P)H:quinone oxidoreductase, an enzyme expressed at high levels in many types of tumors. Here we investigated the potential mechanisms of acquired RH1 drug resistance in cancer cells by applying high-throughput differential quantitative proteomic analysis of the newly established RH1-resistant hepatoma cell lines. Over 400 proteins display significantly altered levels between drug-sensitive and drug-resistant cell lines. Differentially expressed proteins were clustered into more than 14 groups according to their functional annotation and protein–protein interactions. Bioinformatic analysis highlights the biological processes that might be responsible for acquired resistance to RH1. The level of several xenobiotic metabolism enzymes (total n=17) involved in RH1 activation and detoxification is decreased (Nqo1, catalase, Gst, Gsr), corresponding with the decrease in their catalytic activity. The altered biological processes also include the decrease of cell cycle positive regulators (n=15) and the increase of DNA repair proteins (n=5) as well as annexin family members (n=5) in the RH1-resistant cells. Drug-resistant hepatoma cell proteomes are also distinguished by the altered level of proteins involved in energy production and metabolism (n=55). Our data provide the basis for in-depth study of molecular mechanisms of tumor cell resistance to the promising anticancer drug RH1 enabling the further validation of protein biomarkers for the drug insusceptibility and of potential secondary pharmacological targets of RH1 resistant cells.

Plasma preparation to measure FDA‐approved protein markers by selected reaction monitoring

BackgroundThe development of commercially available panels for human blood plasma screening via selected reaction monitoring (SRM) offers reliable, cost-efficient and highly-standardized discovery and validation of protein biomarkers. However, protein detection by SRM can be hampered by interfering peptide fragment ions. To estimate the influence of interference on protein detection, we performed different types of sample preparation and implemented SRM measurements for well-characterized protein targets approved by the US Food and Drug Administration.MethodsWe used the PlasmaDeepDive™ SRM assay from BiognoSYS AG for absolute quantification of 18 proteins in 19 samples of human plasma using three different protocols for sample preparation. SRM measurements were performed using iRT standards for retention time normalization and isotopically-labeled reference peptides for absolute quantification. SpectroDive™ software was used for automated detection of reliable peak groups.ResultsFourteen targeted proteins were quantitatively measured in more than half of the samples. Depletion of highly-abundant plasma proteins and peptide fraction clean-up on centrifuge plates resulted in detection of all 18 targeted proteins in femtomolar to picomolar concentrations.ConclusionsIt was shown that commercially designed SRM kits are suitable for SRM detection of well-established plasma/serum biomarkers.Electronic supplementary materialThe online version of this article (doi:10.1186/s40169-015-0071-4) contains supplementary material, which is available to authorized users.

Identification and Validation of Protein Biomarkers of Response to Neoadjuvant Platinum Chemotherapy in Muscle Invasive Urothelial Carcinoma.

BackgroundThe 5-year cancer specific survival (CSS) for patients with muscle invasive urothelial carcinoma of the bladder (MIBC) treated with cystectomy alone is approximately 50%. Platinum based neoadjuvant chemotherapy (NAC) plus cystectomy results in a marginal 5-10% increase in 5-year CSS in MIBC. Interestingly, responders to NAC (<ypT2) have a 5-year CSS of 90% which is in stark contrast to the 30-40% CSS for those whose MIBC is resistance to NAC. While the implementation of NAC for MIBC is increasing, it is still not widely utilized due to concerns related to delay of cystectomy, potential side-effects, and inability to predict effectiveness. Recently suggested molecular signatures of chemoresponsiveness, which could prove useful in this setting, would be of considerable utility but are yet to be translated into clinical practice.MethodsmRNA expression data from a prior report on a NAC-treated MIBC cohort were re-analyzed in conjunction with the antibody database of the Human Protein Atlas (HPA) to identify candidate protein based biomarkers detectable by immunohistochemistry (IHC). These candidate biomarkers were subsequently tested in tissue microarrays derived from an independent cohort of NAC naive MIBC biopsy specimens from whom the patients were treated with neoadjuvant gemcitabine cisplatin NAC and subsequent cystectomy. The clinical parameters that have been previously associated with NAC response were also examined in our cohort.ResultsOur analyses of the available mRNA gene expression data in a discovery cohort (n = 33) and the HPA resulted in 8 candidate protein biomarkers. The combination of GDPD3 and SPRED1 resulted in a multivariate classification tree that was significantly associated with NAC response status (Goodman-Kruskal γ = 0.85 p<0.0001) in our independent NAC treated MIBC cohort. This model was independent of the clinical factors of age and clinical tumor stage, which have been previously associated with NAC response by our group. The combination of both these protein biomarkers detected by IHC in biopsy specimens along with the relevant clinical parameters resulted in a prediction model able to significantly stratify the likelihood of NAC resistance in our cohort (n = 37) into two well separated halves: low-26% n = 19 and high-89% n = 18, Fisher’s exact p = 0.0002).ConclusionWe illustrate the feasibility of translating a gene expression signature of NAC response from a discovery cohort into immunohistochemical markers readily applicable to MIBC biopsy specimens in our independent cohort. The results from this study are being characterized in additional validation cohorts. Additionally, we anticipate that emerging somatic mutations in MIBC will also be important for NAC response prediction. The relationship of the findings in this study to the current understanding of variant histologic subtypes of MIBC along with the evolving molecular subtypes of MIBC as it relates to NAC response remains to be fully characterized.

A Combined Shotgun and Targeted Mass Spectrometry Strategy for Breast Cancer Biomarker Discovery.

It is of highest importance to find proteins responsible for breast cancer dissemination, for use as biomarkers or treatment targets. We established and performed a combined nontargeted LC-MS/MS and a targeted LC-SRM workflow for discovery and validation of protein biomarkers. Eighty breast tumors, stratified for estrogen receptor status and development of distant recurrence (DR ± ), were collected. After enrichment of N-glycosylated peptides, label-free LC-MS/MS was performed on each individual tumor in triplicate. In total, 1515 glycopeptides from 778 proteins were identified and used to create a map of the breast cancer N-glycosylated proteome. Based on this specific proteome map, we constructed a 92-plex targeted label-free LC-SRM panel. These proteins were quantified across samples by LC-SRM, resulting in 10 proteins consistently differentially regulated between DR+/DR- tumors. Five proteins were further validated in a separate cohort as prognostic biomarkers at the gene expression level. We also compared the LC-SRM results to clinically reported HER2 status, demonstrating its clinical accuracy. In conclusion, we demonstrate a combined mass spectrometry strategy, at large scale on clinical samples, leading to the identification and validation of five proteins as potential biomarkers for breast cancer recurrence. All MS data are available via ProteomeXchange and PASSEL with identifiers PXD001685 and PASS00643.

Automated Extraction of Proteotypic Peptides by Shotgun Proteomic Experiments: A New Computational Tool and Two Actual Cases

The rapid development of mass spectrometry-based proteomic technologies has allowed the quantification and validation of protein biomarkers toward detection of signature molecules, called proteotypic peptides. To facilitate their extraction from experimental protein and peptide lists, we present here a friendly computational tool called Experimental Proteotypic Peptides Investigator (EPPI). In this study, it was used for extracting proteotypic peptides from two collections of experimental data obtained by MudPIT analysis of adipose and gut human tissue. In particular, EPPI allows the selection of peptides presenting higher occurrence, evaluates their uniqueness by molecular weight and amino-acid sequence, and takes into consideration combinations of multiple proteotypic peptides (proteotypic peptide sets) for evaluating their capacity to target a single protein. In fact, in combination with high-resolution MS instruments, it could be a starting point for targeting proteins by following only precursor ions in full MS scan mode. The software is available under the permissive Apache 2.0 open-source license, and the code can be accessed from https://github.com/ITB-ProtMet/eppi.git. Keywords: Computational tool, MudPIT, python, proteotypic peptide, targeted proteomics.

Using data-independent, high-resolution mass spectrometry in protein biomarker research: perspectives and clinical applications.

In medicine, there is an urgent need for protein biomarkers in a range of applications that includes diagnostics, disease stratification, and therapeutic decisions. One of the main technologies to address this need is MS, used for protein biomarker discovery and, increasingly, also for protein biomarker validation. Currently, data-dependent analysis (also referred to as shotgun proteomics) and targeted MS, exemplified by SRM, are the most frequently used mass spectrometric methods. Recently developed data-independent acquisition techniques combine the strength of shotgun and targeted proteomics, while avoiding some of the limitations of the respective methods. They provide high-throughput, accurate quantification, and reproducible measurements within a single experimental setup. Here, we describe and review data-independent acquisition strategies and their recent use in clinically oriented studies. In addition, we also provide a detailed guide for the implementation of SWATH-MS (where SWATH is sequential window acquisition of all theoretical mass spectra)-one of the data-independent strategies that have gained wide application of late.

Modeling and systematic analysis of biomarker validation using selected reaction monitoring.

BackgroundDiscovery and validation of protein biomarkers with high specificity is the main challenge of current proteomics studies. Different mass spectrometry models are used as shotgun tools for the discovery of biomarkers. Validation of a set of selected biomarkers from a list of candidates is an important stage in the biomarker identification pipeline. Validation is typically done by triple quadrupole (QQQ) mass spectrometry (MS) running in selected reaction monitoring (SRM) mode. Although the individual modules of this pipeline have been studied, there is little work on integrating the components from a systematic point of view.ResultsThis paper analyzes the SRM experiment pipeline in a systematic fashion, by modeling the main stages of the biomarker validation process. The proposed models for SRM and protein mixture are then used to study the effect of different parameters on the final performance of biomarker validation. Sample complexity, purification, peptide ionization, and peptide specificity are among the parameters of the SRM experiment that are studied. We focus on the sensitivity of the SRM pipeline to the working parameters, in order to identify the bottlenecks where time and energy should be spent in designing the experiment.ConclusionsThe model presented in this paper can be utilized to observe the effect of different instrument and experimental settings on biomarker validation by SRM. On the other hand, the model would be beneficial for optimization of the work flow as well as identification of the bottlenecks of the pipeline. Also, it creates the required infrastructure for predicting the performance of the SRM pipeline for a specific setting of the parameters.Electronic supplementary materialThe online version of this article (doi:10.1186/s13637-014-0017-y) contains supplementary material, which is available to authorized users.

Urine sample preparation in 96-well filter plates to characterize inflammatory and infectious diseases of the urinary tract.

Urine has been an important body fluid source for diagnostic and prognostic biomarkers of diseases for a long time. Technological advances during the last two decades have enabled a fundamental shift from the discovery of candidate protein biomarkers using single-assay platforms to highly parallel liquid chromatography tandem mass spectrometry (LC-MS/MS)-based proteomic analysis platforms. MS/MS-based approaches such as multiple reaction monitoring (MRM) are also being used increasingly for targeted protein biomarker validation. In large part due to the fact that the majority of protein in voided urine is soluble, such studies have focused on the analysis of urine supernatants, whereas the pellets were discarded after centrifugal sedimentation. Urine sediments are of particular value in the analysis of urinary tract infections (UTI). The LC-MS/MS methods now have sufficient resolving power and sensitivity to survey metaproteomes--the entirety of proteins derived from multiple organisms that interact with each other in mutualistic or antagonistic fashion. Challenges of proteomic analysis of urine include the high dynamic range of protein abundance, high levels of protein post-translational modifications, and high quantities of natural protease inhibitors. Recently, a robust and scalable workflow that can parallelize the processing of multiple urinary supernatant and sediment samples was developed and validated in our lab. This method utilizes 96-well format filter-aided sample preparation (96FASP) strategy and was shown to successfully identify large numbers of proteins from urine samples. Processing 10-50 µg total protein in single experiment, LC-MS/MS with a Q-Exactive mass spectrometer resulted in more than 1,100 distinct human protein identifications from urine supernatants, and around 400 microbial and 1,400 human protein identifications from urine sediments. The surveys are a rich data resource not only for biomarker discovery but also to interrogate mechanisms of pathogenesis in the urinary system.