Quality assessment for clinical proteomics

Multicenter Longitudinal Quality Assessment of MS-Based Proteomics in Plasma and Serum

Advancing MS-based proteomics toward clinical applications evolves around developing standardized start-to-finish and fit-for-purpose workflows for clinical specimens. Steps along the method design involve the determination and optimization of several bioanalytical parameters such as selectivity, sensitivity, accuracy, and precision. In a joint effort, eight proteomics laboratories belonging to the MSCoreSys initiative including the CLINSPECT-M, MSTARS, DIASyM, and SMART-CARE consortia performed a longitudinal round-robin study to assess the analysis performance of plasma and serum as clinically relevant samples. A variety of LC-MS/MS setups including mass spectrometer models from ThermoFisher and Bruker as well as LC systems from ThermoFisher, Evosep, and Waters Corporation were used in this study. As key performance indicators, sensitivity, precision, and reproducibility were monitored over time. Protein identifications range between 300 and 400 IDs across different state-of-the-art MS instruments, with timsTOF Pro, Orbitrap Exploris 480, and Q Exactive HF-X being among the top performers. Overall, 71 proteins are reproducibly detectable in all setups in both serum and plasma samples, and 22 of these proteins are FDA-approved biomarkers, which are reproducibly quantified (CV < 20% with label-free quantification). In total, the round-robin study highlights a promising baseline for bringing MS-based measurements of serum and plasma samples closer to clinical utility.

A Framework for Quality Control in Quantitative Proteomics

A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow, from planning to analysis. We share vignettes applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at the protein and peptide levels allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis (Skyline), longitudinal QC metrics (AutoQC), and server-based data deposition (PanoramaWeb). We propose that this integrated approach to QC is a useful starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible. Data are available on Panorama Public and ProteomeXchange under the identifier PXD051318.

A framework for quality control in quantitative proteomics

A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow from planning to analysis. We share vignettes applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at protein and peptide-level allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis (Skyline), longitudinal QC metrics (AutoQC), and server-based data deposition (PanoramaWeb). We propose that this integrated approach to QC is a useful starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible. Data are available on Panorama Public and on ProteomeXchange under the identifier PXD051318.

Evaluating cPILOT Data toward Quality Control Implementation

Multiplexing enables the monitoring of hundreds to thousands of proteins in quantitative proteomics analyses and increases sample throughput. In most mass-spectrometry-based proteomics workflows, multiplexing is achieved by labeling biological samples with heavy isotopes via precursor isotopic labeling or isobaric tagging. Enhanced multiplexing strategies, such as combined precursor isotopic labeling and isobaric tagging (cPILOT), combine multiple technologies to afford an even higher sample throughput. Critical to enhanced multiplexing analyses is ensuring that analytical performance is optimal and that missingness of sample channels is minimized. Automation of sample preparation steps and use of quality control (QC) metrics can be incorporated into multiplexing analyses and reduce the likelihood of missing information, thus maximizing the amount of usable quantitative data. Here, we implemented QC metrics previously developed in our laboratory to evaluate a 36-plex cPILOT experiment that encompassed 144 mouse samples of various tissue types, time points, genotypes, and biological replicates. The evaluation focuses on the use of a sample pool generated from all samples in the experiment to monitor the daily instrument performance and to provide a means for data normalization across sample batches. Our results show that tracking QC metrics enabled the quantification of ∼7000 proteins in each sample batch, of which ∼70% had minimal missing values across up to 36 sample channels. Implementation of QC metrics for future cPILOT studies as well as other enhanced multiplexing strategies will help yield high-quality data sets.

Establishing Quality Control Procedures for Large-Scale Plasma Proteomics Analyses

Proteomics research has been transformed due to high-throughput liquid chromatography (LC-MS/MS) tandem mass spectrometry instruments combined with highly sophisticated automated sample preparation and multiplexing workflows. However, scaling proteomics experiments to large sample cohorts (hundreds to thousands) requires thoughtful quality control (QC) protocols. Robust QC protocols can help with reproducibility, quantitative accuracy, and provide opportunities for more decisive troubleshooting. Our laboratory conducted a plasma proteomics study of a cohort of N = 335 patient samples using tandem mass tag (TMT_pro) 16-plex batches. Over the course of a 10-month data acquisition period for this cohort we collected 271 pooled QC LC-MS/MS result files obtained from MS/MS analysis of a patient-derived pooled plasma sample, representative of the entire cohort population. This sample was tagged with TMT_zero or TMT_pro reagents and used to inform the daily performance of the LC-MS/MS instruments and to allow within and across sample batch normalization. Analytical variability of a number of instrumental and data analysis metrics including protein and peptide identifications, peptide spectral matches (PSMs), number of obtained MS/MS spectra, average peptide abundance, percent of peptides with a Δ m/z between ±0.003 Da, percent of MS/MS spectra obtained at the maximum injection time, and the retention time of selected tracking peptides were evaluated to help inform the design of a robust LC-MS/MS QC workflow for use in future cohort studies. This study also led to general tips for using selected metrics to inform real-time troubleshooting of LC-MS/MS performance issues with daily QC checks.

Development of mass spectrometry-based relatively quantitative targeted method for amino acids and neurotransmitters: Applications in the diagnosis of major depression

Targeted metabolomics analysis based on triple quadrupole (QQQ) MS coupled with multiple reaction monitoring mode (MRM) is the gold standard for metabolite quantification and it is widely applied in metabolomics. However, standard compounds for each metabolite and the corresponding analogs are necessary for quantitative measurements. To identify the differentially present metabolites in various groups, determining the relative concentration of metabolites would be more efficient than accurate quantification. In this study, a relatively quantitative targeted method was established for metabonomics research, on the basis of hydrophilic interaction liquid chromatography (HILIC)/QQQ MS operated in MRM mode. The quality control-base random forest signal correction algorithm (QC-RFSC algorithm) was applied for quality control instead of the internal standard method. High quality relative quantification was achieved without internal standards, and integrated peak areas were successfully used for statistical and pathway analyses. Amino acids and neurotransmitters (dopamine, kynurenic acid, urocanic acid, tryptophan, kynurenine, tyrosine, valine, threonine, serine, alanine, glycine, glutamine, citrulline, GABA, glutamate, aspartate, arginine, ornithine and histidine) in serum samples were simultaneously determined with the newly developed method. To demonstrate the applicability of this method in large-scale analyses, we analyzed the above metabolites in serum from patients with major depression. The serum levels of glutamate, aspartate, threonine, glycine and alanine were significantly higher, and those of citrulline, kynurenic acid and urocanic acid were significantly lower, in patients with major depression than in controls. This is the first report of the difference in urocanic acid, a compound reported to improve glutamate biosynthesis and release in the central nervous system, between healthy controls and patients with major depression.

A Review of the Scientific Rigor, Reproducibility, and Transparency Studies Conducted by the ABRF Research Groups

Shared research resource facilities, also known as core laboratories (Cores), are responsible for generating a significant and growing portion of the research data in academic biomedical research institutions. Cores represent a central repository for institutional knowledge management, with deep expertise in the strengths and limitations of technology and its applications. They inherently support transparency and scientific reproducibility by protecting against cognitive bias in research design and data analysis, and they have institutional responsibility for the conduct of research (research ethics, regulatory compliance, and financial accountability) performed in their Cores. The Association of Biomolecular Resource Facilities (ABRF) is a FASEB-member scientific society whose members are scientists and administrators that manage or support Cores. The ABRF Research Groups (RGs), representing expertise for an array of cutting-edge and established technology platforms, perform multicenter research studies to determine and communicate best practices and community-based standards. This review provides a summary of the contributions of the ABRF RGs to promote scientific rigor and reproducibility in Cores from the published literature, ABRF meetings, and ABRF RGs communications.

Simple Peptide Quantification Approach for MS-Based Proteomics Quality Control

Despite its growing popularity and use, bottom-up proteomics remains a complex analytical methodology. Its general workflow consists of three main steps: sample preparation, liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS), and computational data analysis. Quality assessment of the different steps and components of this workflow is instrumental to identify technical flaws and avoid loss of precious measurement time and sample material. However, assessment of the extent of sample losses along with the sample preparation protocol, in particular, after proteolytic digestion, is not yet routinely implemented because of the lack of an accurate and straightforward method to quantify peptides. Here, we report on the use of a microfluidic UV/visible spectrophotometer to quantify MS-ready peptides directly in the MS-loading solvent, consuming only 2 μL of sample. We compared the performance of the microfluidic spectrophotometer with a standard device and determined the optimal sample amount for LC-MS/MS analysis on a Q Exactive HF mass spectrometer using a dilution series of a commercial K562 cell digest. A careful evaluation of selected LC and MS parameters allowed us to define 3 μg as an optimal peptide amount to be injected into this particular LC-MS/MS system. Finally, using tryptic digests from human HEK293T cells and showing that injecting equal peptide amounts, rather than approximate ones, result in less variable LC-MS/MS and protein quantification data. The obtained quality improvement together with easy implementation of the approach makes it possible to routinely quantify MS-ready peptides as a next step in daily proteomics quality control.

In Silico Tools and Phosphoproteomic Software Exclusives

Proteomics and phosphoproteomics have been emerging as new dimensions of omics. Phosphorylation has a profound impact on the biological functions and applications of proteins. It influences everything from intrinsic activity and extrinsic executions to cellular localization. This post-translational modification has been subjected to detailed study and has been an object of analytical curiosity with the advent of faster instrumentation. The major strength of phosphoproteomic research lies in the fact that it gives an overall picture of the workforce of the cell. Phosphoproteomics gives deeper insights into understanding the mechanism behind development and progression of a disease. This review for the first time consolidates the list of existing bioinformatics tools developed for phosphoproteomics. The gap between development of bioinformatics tools and their implementation in clinical research is highlighted. The challenge facing progress is ideally believed to be the interdisciplinary arena this field of research is associated with. For meaningful solutions and deliverables, these tools need to be implemented in clinical studies for obtaining answers to pharmacodynamic questions, saving time, costs and energy. This review hopes to invoke some thought in this direction.

Review of Issues and Solutions to Data Analysis Reproducibility and Data Quality in Clinical Proteomics

In any analytical discipline, data analysis reproducibility is closely interlinked with data quality. In this book chapter focused on mass spectrometry-based proteomics approaches, we introduce how both data analysis reproducibility and data quality can influence each other and how data quality and data analysis designs can be used to increase robustness and improve reproducibility. We first introduce methods and concepts to design and maintain robust data analysis pipelines such that reproducibility can be increased in parallel. The technical aspects related to data analysis reproducibility are challenging, and current ways to increase the overall robustness are multifaceted. Software containerization and cloud infrastructures play an important part.We will also show how quality control (QC) and quality assessment (QA) approaches can be used to spot analytical issues, reduce the experimental variability, and increase confidence in the analytical results of (clinical) proteomics studies, since experimental variability plays a substantial role in analysis reproducibility. Therefore, we give an overview on existing solutions for QC/QA, including different quality metrics, and methods for longitudinal monitoring. The efficient use of both types of approaches undoubtedly provides a way to improve the experimental reliability, reproducibility, and level of consistency in proteomics analytical measurements.

The Human Ovary and Future of Fertility Assessment in the Post-Genome Era

Proteomics has opened up new avenues in the field of gynecology in the post-genome era, making it possible to meet patient needs more effectively and improve their care. This mini-review aims to reveal the scope of proteomic applications through an overview of the technique and its applications in assisted procreation. Some of the latest technologies in this field are described in order to better understand the perspectives of its clinical applications. Proteomics seems destined for a promising future in gynecology, more particularly in relation to the ovary. Nevertheless, we know that reproductive biology proteomics is still in its infancy and major technical and ethical challenges must first be overcome.

Proteomics and phosphoproteomics in precision medicine: applications and challenges

Recent advances in proteomics allow the accurate measurement of abundances for thousands of proteins and phosphoproteins from multiple samples in parallel. Therefore, for the first time, we have the opportunity to measure the proteomic profiles of thousands of patient samples or disease model cell lines in a systematic way, to identify the precise underlying molecular mechanism and discover personalized biomarkers, networks and treatments. Here, we review examples of successful use of proteomics and phosphoproteomics data sets in as well as their integration other omics data sets with the aim of precision medicine. We will discuss the bioinformatics challenges posed by the generation, analysis and integration of such large data sets and present potential reasons why proteomics profiling and biomarkers are not currently widely used in the clinical setting. We will finally discuss ways to contribute to the better use of proteomics data in precision medicine and the clinical setting.

Mass spectrometry-based proteomics techniques and their application in ovarian cancer research

Ovarian cancer has emerged as one of the leading cause of gynecological malignancies. So far, the measurement of CA125 and HE4 concentrations in blood and transvaginal ultrasound examination are essential ovarian cancer diagnostic methods. However, their sensitivity and specificity are still not sufficient to detect disease at the early stage. Moreover, applied treatment may appear to be ineffective due to drug-resistance. Because of a high mortality rate of ovarian cancer, there is a pressing need to develop innovative strategies leading to a full understanding of complicated molecular pathways related to cancerogenesis. Recent studies have shown the great potential of clinical proteomics in the characterization of many diseases, including ovarian cancer. Therefore, in this review, we summarized achievements of proteomics in ovarian cancer management. Since the development of mass spectrometry has caused a breakthrough in systems biology, we decided to focus on studies based on this technique. According to PubMed engine, in the years 2008-2010 the number of studies concerning OC proteomics was increasing, and since 2010 it has reached a plateau. Proteomics as a rapidly evolving branch of science may be essential in novel biomarkers discovery, therapy decisions, progression predication, monitoring of drug response or resistance. Despite the fact that proteomics has many to offer, we also discussed some limitations occur in ovarian cancer studies. Main difficulties concern both complexity and heterogeneity of ovarian cancer and drawbacks of the mass spectrometry strategies. This review summarizes challenges, capabilities, and promises of the mass spectrometry-based proteomics techniques in ovarian cancer management.

Quality control in mass spectrometry-based proteomics

Mass spectrometry is a highly complex analytical technique and mass spectrometry-based proteomics experiments can be subject to a large variability, which forms an obstacle to obtaining accurate and reproducible results. Therefore, a comprehensive and systematic approach to quality control is an essential requirement to inspire confidence in the generated results. A typical mass spectrometry experiment consists of multiple different phases including the sample preparation, liquid chromatography, mass spectrometry, and bioinformatics stages. We review potential sources of variability that can impact the results of a mass spectrometry experiment occurring in all of these steps, and we discuss how to monitor and remedy the negative influences on the experimental results. Furthermore, we describe how specialized quality control samples of varying sample complexity can be incorporated into the experimental workflow and how they can be used to rigorously assess detailed aspects of the instrument performance.

Promise and Implementation of Proteomic Prostate Cancer Biomarkers

Prostate cancer is one of the most commonly diagnosed malignancy and the fifth leading cause of cancer mortality in men. Despite the broad use of prostate-specific antigen test that resulted in an increase in number of diagnosed cases, disease management needs to be improved. Proteomic biomarkers alone and or in combination with clinical and pathological risk calculators are expected to improve on decreasing the unnecessary biopsies, stratify low risk patients, and predict response to treatment. To this end, significant efforts have been undertaken to identify novel biomarkers that can accurately discriminate between indolent and aggressive cancer forms and indicate those men at high risk for developing prostate cancer that require immediate treatment. In the era of “big data” and “personalized medicine” proteomics-based biomarkers hold great promise to provide clinically applicable tools, as proteins regulate all biological functions, and integrate genomic information with the environmental impact. In this review article, we aim to provide a critical assessment of the current proteomics-based biomarkers for prostate cancer and their actual clinical applicability. For that purpose, a systematic review of the literature published within the last 10 years was performed using the Web of Science Database. We specifically discuss the potential and prospects of use for diagnostic, prognostic and predictive proteomics-based biomarkers, including both body fluid- and tissue-based markers.

Differential Integration of Transcriptome and Proteome Identifies Pan-Cancer Prognostic Biomarkers

High-throughput analysis of the transcriptome and proteome individually are used to interrogate complex oncogenic processes in cancer. However, an outstanding challenge is how to combine these complementary, yet partially disparate data sources to accurately identify tumor-specific gene products and clinical biomarkers. Here, we introduce inteGREAT for robust and scalable differential integration of high-throughput measurements. With inteGREAT, each data source is represented as a co-expression network, which is analyzed to characterize the local and global structure of each node across networks. inteGREAT scores the degree by which the topology of each gene in both transcriptome and proteome networks are conserved within a tumor type, yet different from other normal or malignant cells. We demonstrated the high performance of inteGREAT based on several analyses: deconvolving synthetic networks, rediscovering known diagnostic biomarkers, establishing relationships between tumor lineages, and elucidating putative prognostic biomarkers which we experimentally validated. Furthermore, we introduce the application of a clumpiness measure to quantitatively describe tumor lineage similarity. Together, inteGREAT not only infers functional and clinical insights from the integration of transcriptomic and proteomic data sources in cancer, but also can be readily applied to other heterogeneous high-throughput data sources. inteGREAT is open source and available to download from https://github.com/faryabib/inteGREAT.

QCloud: A cloud-based quality control system for mass spectrometry-based proteomics laboratories

The increasing number of biomedical and translational applications in mass spectrometry-based proteomics poses new analytical challenges and raises the need for automated quality control systems. Despite previous efforts to set standard file formats, data processing workflows and key evaluation parameters for quality control, automated quality control systems are not yet widespread among proteomics laboratories, which limits the acquisition of high-quality results, inter-laboratory comparisons and the assessment of variability of instrumental platforms. Here we present QCloud, a cloud-based system to support proteomics laboratories in daily quality assessment using a user-friendly interface, easy setup, automated data processing and archiving, and unbiased instrument evaluation. QCloud supports the most common targeted and untargeted proteomics workflows, it accepts data formats from different vendors and it enables the annotation of acquired data and reporting incidences. A complete version of the QCloud system has successfully been developed and it is now open to the proteomics community (http://qcloud.crg.eu). QCloud system is an open source project, publicly available under a Creative Commons License Attribution-ShareAlike 4.0.

Optimizing High-Resolution Mass Spectrometry for the Identification of Low-Abundance Post-Translational Modifications of Intact Proteins

Intact protein analysis by liquid chromatography-mass spectrometry (LC-MS) is now possible due to the improved capabilities of mass spectrometers yielding greater resolution, mass accuracy, and extended mass ranges. Concurrent measurement of post-translational modifications (PTMs) during LC-MS of intact proteins is advantageous while monitoring critical proteoform status, such as for clinical samples or during production of reference materials. However, difficulties exist for PTM identification when the protein is large or contains multiple modification sites. In this work, analyses of low-abundance proteoforms of proteins of clinical or therapeutic interest, including C-reactive protein, vitamin D-binding protein, transferrin, and immunoglobulin G (NISTmAb), were performed on an Orbitrap Elite mass spectrometer. This work investigated the effect of various instrument parameters including source temperatures, in-source CID, microscan type and quantity, resolution, and automatic gain control on spectral quality. The signal-to-noise ratio was found to be a suitable spectral attribute which facilitated identification of low abundance PTMs. Source temperature and CID voltage were found to require specific optimization for each protein. This study identifies key instrumental parameters requiring optimization for improved detection of a variety of PTMs by LC-MS and establishes a methodological framework to ensure robust proteoform identifications, the first step in their ultimate quantification.

Heterologous fibrin sealant derived from snake venom: from bench to bedside - an overview

Hemostatic and adhesive agents date back to World War II, when homologous fibrin sealant came onto scene. Considering that infectious diseases can be transmitted via human blood, a new heterologous fibrin sealant was standardized in the 1990s. Its components were a serine protease (a thrombin-like enzyme) extracted from the venom of Crotalus durissus terrificus snakes and a fibrinogen-rich cryoprecipitate extracted from the blood of Bubalus bubalis buffaloes. This new bioproduct has been used as a coagulant, sealant, adhesive and recently as a candidate scaffold for mesenchymal stem cells and bone and cartilage repair. This review discusses the composition of a new heterologous fibrin sealant, and cites published articles related to its preclinical applications aiming at repairing nervous system traumas and regenerating bone marrow. Finally, we present an innovative safety trial I/II that found the product to be a safe and clinically promising candidate for treating chronic venous ulcers. A multicenter clinical trial, phase II/III, with a larger number of participants will be performed to prove the efficacy of an innovative biopharmaceutical product derived from animal venom.

The Human Proteome Organization-Proteomics Standards Initiative Quality Control Working Group: Making Quality Control More Accessible for Biological Mass Spectrometry

To have confidence in results acquired during biological mass spectrometry experiments, a systematic approach to quality control is of vital importance. Nonetheless, until now, only scattered initiatives have been undertaken to this end, and these individual efforts have often not been complementary. To address this issue, the Human Proteome Organization-Proteomics Standards Initiative has established a new working group on quality control at its meeting in the spring of 2016. The goal of this working group is to provide a unifying framework for quality control data. The initial focus will be on providing a community-driven standardized file format for quality control. For this purpose, the previously proposed qcML format will be adapted to support a variety of use cases for both proteomics and metabolomics applications, and it will be established as an official PSI format. An important consideration is to avoid enforcing restrictive requirements on quality control but instead provide the basic technical necessities required to support extensive quality control for any type of mass spectrometry-based workflow. We want to emphasize that this is an open community effort, and we seek participation from all scientists with an interest in this field.

A Golden Age for Working with Public Proteomics Data

Data sharing in mass spectrometry (MS)-based proteomics is becoming a common scientific practice, as is now common in the case of other, more mature ‘omics’ disciplines like genomics and transcriptomics. We want to highlight that this situation, unprecedented in the field, opens a plethora of opportunities for data scientists. First, we explain in some detail some of the work already achieved, such as systematic reanalysis efforts. We also explain existing applications of public proteomics data, such as proteogenomics and the creation of spectral libraries and spectral archives. Finally, we discuss the main existing challenges and mention the first attempts to combine public proteomics data with other types of omics data sets.

The field of proteomics has matured and diversified substantially over the past 10 years.

Proteomics data are increasingly shared through centralized, public repositories.

Standardization efforts have ensured that a large proportion of these public data can be read and processed by any interested researcher.

Because any proteomics data set is only partially understood, there is great opportunity for (orthogonal) reuse of public data.

While public proteomics data has so far remained outside ethics and privacy discussions, recent work indicates that there is an inherent risk.

The diverse and expanding role of mass spectrometry in structural and molecular biology

The emergence of proteomics has led to major technological advances in mass spectrometry (MS). These advancements not only benefitted MS-based high-throughput proteomics but also increased the impact of mass spectrometry on the field of structural and molecular biology. Here, we review how state-of-the-art MS methods, including native MS, top-down protein sequencing, cross-linking-MS, and hydrogen-deuterium exchange-MS, nowadays enable the characterization of biomolecular structures, functions, and interactions. In particular, we focus on the role of mass spectrometry in integrated structural and molecular biology investigations of biological macromolecular complexes and cellular machineries, highlighting work on CRISPR-Cas systems and eukaryotic transcription complexes.

An Automated Pipeline to Monitor System Performance in Liquid Chromatography-Tandem Mass Spectrometry Proteomic Experiments

We report the development of a completely automated pipeline to monitor system suitability in bottom-up proteomic experiments. LC-MS/MS runs are automatically imported into Skyline and multiple identification-free metrics are extracted from targeted peptides. These data are then uploaded to the Panorama Skyline document repository where metrics can be viewed in a web-based interface using powerful process control techniques, including Levey-Jennings and Pareto plots. The interface is versatile and takes user input, which allows the user significant control over the visualization of the data. The pipeline is vendor and instrument-type neutral, supports multiple acquisition techniques (e.g., MS 1 filtering, data-independent acquisition, parallel reaction monitoring, and selected reaction monitoring), can track performance of multiple instruments, and requires no manual intervention aside from initial setup. Data can be viewed from any computer with Internet access and a web browser, facilitating sharing of QC data between researchers. Herein, we describe the use of this pipeline, termed Panorama AutoQC, to evaluate LC-MS/MS performance in a range of scenarios including identification of suboptimal instrument performance, evaluation of ultrahigh pressure chromatography, and identification of the major sources of variation throughout years of peptide data collection.

BatMass: a Java Software Platform for LC-MS Data Visualization in Proteomics and Metabolomics

Mass spectrometry (MS) coupled to liquid chromatography (LC) is a commonly used technique in metabolomic and proteomic research. As the size and complexity of LC-MS-based experiments grow, it becomes increasingly more difficult to perform quality control of both raw data and processing results. In a practical setting, quality control steps for raw LC-MS data are often overlooked, and assessment of an experiment's success is based on some derived metrics such as "the number of identified compounds". The human brain interprets visual data much better than plain text, hence the saying "a picture is worth a thousand words". Here, we present the BatMass software package, which allows for performing quick quality control of raw LC-MS data through its fast visualization capabilities. It also serves as a testbed for developers of LC-MS data processing algorithms by providing a data access library for open mass spectrometry file formats and a means of visually mapping processing results back to the original data. We illustrate the utility of BatMass with several use cases of quality control and data exploration.

Designing biomedical proteomics experiments: state-of-the-art and future perspectives

With the current expanded technical capabilities to perform mass spectrometry-based biomedical proteomics experiments, an improved focus on the design of experiments is crucial. As it is clear that ignoring the importance of a good design leads to an unprecedented rate of false discoveries which would poison our results, more and more tools are developed to help researchers designing proteomic experiments. In this review, we apply statistical thinking to go through the entire proteomics workflow for biomarker discovery and validation and relate the considerations that should be made at the level of hypothesis building, technology selection, experimental design and the optimization of the experimental parameters.

Public proteomics data: How the field has evolved from sceptical inquiry to the promise of in silico proteomics

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Proteomics data sharing moved from validation to re-use.

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New tools and services make data very easily accessible.

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Metadata provision can still benefit from improvements.

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Quality control metrics will soon be reported along with submitted data.

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Data re-use will enable the advent of actual in silico proteomics.

Unsupervised Quality Assessment of Mass Spectrometry Proteomics Experiments by Multivariate Quality Control Metrics

Despite many technological and computational advances, the results of a mass spectrometry proteomics experiment are still subject to a large variability. For the understanding and evaluation of how technical variability affects the results of an experiment, several computationally derived quality control metrics have been introduced. However, despite the availability of these metrics, a systematic approach to quality control is often still lacking because the metrics are not fully understood and are hard to interpret. Here, we present a toolkit of powerful techniques to analyze and interpret multivariate quality control metrics to assess the quality of mass spectrometry proteomics experiments. We show how unsupervised techniques applied to these quality control metrics can provide an initial discrimination between low-quality experiments and high-quality experiments prior to manual investigation. Furthermore, we provide a technique to obtain detailed information on the quality control metrics that are related to the decreased performance, which can be used as actionable information to improve the experimental setup. Our toolkit is released as open-source and can be downloaded from https://bitbucket.org/proteinspector/qc_analysis/ .

Proteomics Quality Control: Quality Control Software for MaxQuant Results

Mass spectrometry-based proteomics coupled to liquid chromatography has matured into an automatized, high-throughput technology, producing data on the scale of multiple gigabytes per instrument per day. Consequently, an automated quality control (QC) and quality analysis (QA) capable of detecting measurement bias, verifying consistency, and avoiding propagation of error is paramount for instrument operators and scientists in charge of downstream analysis. We have developed an R-based QC pipeline called Proteomics Quality Control (PTXQC) for bottom-up LC-MS data generated by the MaxQuant software pipeline. PTXQC creates a QC report containing a comprehensive and powerful set of QC metrics, augmented with automated scoring functions. The automated scores are collated to create an overview heatmap at the beginning of the report, giving valuable guidance also to nonspecialists. Our software supports a wide range of experimental designs, including stable isotope labeling by amino acids in cell culture (SILAC), tandem mass tags (TMT), and label-free data. Furthermore, we introduce new metrics to score MaxQuant's Match-between-runs (MBR) functionality by which peptide identifications can be transferred across Raw files based on accurate retention time and m/z. Last but not least, PTXQC is easy to install and use and represents the first QC software capable of processing MaxQuant result tables. PTXQC is freely available at https://github.com/cbielow/PTXQC .

Recent progress in urinary proteome analysis for prostate cancer diagnosis and management

Prostate cancer (PCa) is fifth leading cause of cancer-associated deaths in men worldwide. Although the application of the serum prostate-specific antigen (PSA) screening test resulted in an increase in the PCa diagnosed cases, it demonstrated a negligible benefit regarding the associated mortality. Treatment options vary, with active surveillance to be preferable for patients with low-risk PCa and therapy of advanced castration-resistant PCa to rely on α-emitters and cytotoxic chemotherapy. Although recent developments have led to the approval of novel drugs for the treatment of castration-resistant PCa, the optimal sequence and timing of medication have not been yet determined. New screening modalities could improve the discriminatory accuracy between tumors with favorable clinical prognosis. Implementation of proteomic-based biomarkers appears to be a promising improvement, which could enable a more accurate diagnosis, guide treatment and improve patient outcome. Reviewed here are urinary proteome-based approaches for detection of PCa and patient management.

The 2012/2013 ABRF Proteomic Research Group Study: Assessing Longitudinal Intralaboratory Variability in Routine Peptide Liquid Chromatography Tandem Mass Spectrometry Analyses

Questions concerning longitudinal data quality and reproducibility of proteomic laboratories spurred the Protein Research Group of the Association of Biomolecular Resource Facilities (ABRF-PRG) to design a study to systematically assess the reproducibility of proteomic laboratories over an extended period of time. Developed as an open study, initially 64 participants were recruited from the broader mass spectrometry community to analyze provided aliquots of a six bovine protein tryptic digest mixture every month for a period of nine months. Data were uploaded to a central repository, and the operators answered an accompanying survey. Ultimately, 45 laboratories submitted a minimum of eight LC-MSMS raw data files collected in data-dependent acquisition (DDA) mode. No standard operating procedures were enforced; rather the participants were encouraged to analyze the samples according to usual practices in the laboratory. Unlike previous studies, this investigation was not designed to compare laboratories or instrument configuration, but rather to assess the temporal intralaboratory reproducibility. The outcome of the study was reassuring with 80% of the participating laboratories performing analyses at a medium to high level of reproducibility and quality over the 9-month period. For the groups that had one or more outlying experiments, the major contributing factor that correlated to the survey data was the performance of preventative maintenance prior to the LC-MSMS analyses. Thus, the Protein Research Group of the Association of Biomolecular Resource Facilities recommends that laboratories closely scrutinize the quality control data following such events. Additionally, improved quality control recording is imperative. This longitudinal study provides evidence that mass spectrometry-based proteomics is reproducible. When quality control measures are strictly adhered to, such reproducibility is comparable among many disparate groups. Data from the study are available via ProteomeXchange under the accession code PXD002114.

Evaluation of protein extraction methods for enhanced proteomic analysis of tomato leaves and roots

Proteomics is an outstanding area in science whose increasing application has advanced to distinct purposes. A crucial aspect to achieve a good proteome resolution is the establishment of a methodology that results in the best quality and wide range representation of total proteins. Another important aspect is that in many studies, limited amounts of tissue and total protein in the tissue to be studied are found, making difficult the analysis. In order to test different parameters, combinations using minimum amount of tissue with 4 protocols for protein extraction from tomato (Solanum lycopersicum L.) leaves and roots were evaluated with special attention to their capacity for removing interferents and achieving suitable resolution in bidimensional gel electrophoresis, as well as satisfactory protein yield. Evaluation of the extraction protocols revealed large protein yield differences obtained for each one. TCA/acetone was shown to be the most efficient protocol, which allowed detection of 211 spots for leaves and 336 for roots using 500 µg of leaf protein and 800 µg of root protein per gel.

Proteomics in cancer research: Are we ready for clinical practice?

Although genomics has delivered major advances in cancer prognostics, treatment and diagnostics, it still only provides a static image of the situation. To study more dynamic molecular entities, proteomics has been introduced into the cancer research field more than a decade ago. Currently, however, the impact of clinical proteomics on patient management and clinical decision-making is low and the implementations of scientific results in the clinic appear to be scarce. The search for cancer-related biomarkers with proteomics however, has major potential to improve risk assessment, early detection, diagnosis, prognosis, treatment selection and monitoring. In this review, we provide an overview of the transition of oncoproteomics towards translational oncology. We describe which lessons are learned from currently approved protein biomarkers and previous proteomic studies, what the pitfalls and challenges are in clinical proteomics applications, and how proteomic research can be successfully translated into medical practice.

Translating clinical proteomics: the importance of study design

Mass spectrometry-based clinical proteomics approaches were introduced into the biomedical field more than two decades ago. Despite recent developments both in the field of mass spectrometry and bioinformatics, the gap between proteomics results and their translation into clinical practice still needs to be closed, as implementation of proteomics results in the clinic appears to be scarce. An extra focus on the importance of the experimental design is therefore of crucial importance.

iMonDB: Mass Spectrometry Quality Control through Instrument Monitoring

Over the past few years, awareness has risen that for mass-spectrometry-based proteomics methods to mature into everyday analytical and clinical practices, extensive quality assessment is mandatory. A currently overlooked source of qualitative information originates from the mass spectrometer itself. Apart from the actual mass spectral data, raw-data objects also contain parameter settings and sensory information about the mass instrument. This information gives a detailed account of the operation of the instrument, which eventually can be related to observations in mass spectral data. The advantage of instrument information at the lowest level is the high sensitivity to detect emerging defects in a timely fashion. To this end, we introduce the Instrument MONitoring DataBase (iMonDB), which allows us to automatically extract, store, and manage the instrument parameters from raw-data objects into a highly efficient database structure. This enables us to monitor the instrument parameters over a considerable time period. Time course information about the instrument performance is necessary to define the normal range of operation and to detect anomalies that may correlate with instrument failure. The proposed tools foster an additional handle on quality control and are released as open source under the permissive Apache 2.0 license. The tools can be downloaded from https://bitbucket.org/proteinspector/imondb.

Streaming visualisation of quantitative mass spectrometry data based on a novel raw signal decomposition method

As data rates rise, there is a danger that informatics for high-throughput LC-MS becomes more opaque and inaccessible to practitioners. It is therefore critical that efficient visualisation tools are available to facilitate quality control, verification, validation, interpretation, and sharing of raw MS data and the results of MS analyses. Currently, MS data is stored as contiguous spectra. Recall of individual spectra is quick but panoramas, zooming and panning across whole datasets necessitates processing/memory overheads impractical for interactive use. Moreover, visualisation is challenging if significant quantification data is missing due to data-dependent acquisition of MS/MS spectra. In order to tackle these issues, we leverage our seaMass technique for novel signal decomposition. LC-MS data is modelled as a 2D surface through selection of a sparse set of weighted B-spline basis functions from an over-complete dictionary. By ordering and spatially partitioning the weights with an R-tree data model, efficient streaming visualisations are achieved. In this paper, we describe the core MS1 visualisation engine and overlay of MS/MS annotations. This enables the mass spectrometrist to quickly inspect whole runs for ionisation/chromatographic issues, MS/MS precursors for coverage problems, or putative biomarkers for interferences, for example. The open-source software is available from http://seamass.net/viz/.

Viewing the proteome: how to visualize proteomics data?

Proteomics has become one of the main approaches for analyzing and understanding biological systems. Yet similar to other high-throughput analysis methods, the presentation of the large amounts of obtained data in easily interpretable ways remains challenging. In this review, we present an overview of the different ways in which proteomics software supports the visualization and interpretation of proteomics data. The unique challenges and current solutions for visualizing the different aspects of proteomics data, from acquired spectra via protein identification and quantification to pathway analysis, are discussed, and examples of the most useful visualization approaches are highlighted. Finally, we offer our ideas about future directions for proteomics data visualization.

qcML: an exchange format for quality control metrics from mass spectrometry experiments

Quality control is increasingly recognized as a crucial aspect of mass spectrometry based proteomics. Several recent papers discuss relevant parameters for quality control and present applications to extract these from the instrumental raw data. What has been missing, however, is a standard data exchange format for reporting these performance metrics. We therefore developed the qcML format, an XML-based standard that follows the design principles of the related mzML, mzIdentML, mzQuantML, and TraML standards from the HUPO-PSI (Proteomics Standards Initiative). In addition to the XML format, we also provide tools for the calculation of a wide range of quality metrics as well as a database format and interconversion tools, so that existing LIMS systems can easily add relational storage of the quality control data to their existing schema. We here describe the qcML specification, along with possible use cases and an illustrative example of the subsequent analysis possibilities. All information about qcML is available at http://code.google.com/p/qcml.

Bioinformatics for proteomics: opportunities at the interface between the scientists, their experiments, and the community

Within the last decade, bioinformatics has moved from command line scripts dedicated to single experiments towards production grade software integrated in experimental workflows providing a rich environment for biological investigation. Located at the interface between the scientists, their experiments, and the community, bioinformatics acts as a gateway to a wide source of information. This chapter does not list tools and methods, but rather hints at how bioinformatics can help in improving biological projects, all the way from their initial design to the dissemination of the results.

Combining bioinformatics and MS-based proteomics: clinical implications

Clinical proteomics research aims at i) discovery of protein biomarkers for screening, diagnosis and prognosis of disease, ii) discovery of protein therapeutic targets for improvement of disease prevention, treatment and follow-up, and iii) development of mass spectrometry (MS)-based assays that could be implemented in clinical chemistry, microbiology or hematology laboratories. MS has been increasingly applied in clinical proteomics studies for the identification and quantification of proteins. Bioinformatics plays a key role in the exploitation of MS data in several aspects such as the generation and curation of protein sequence databases, the development of appropriate software for MS data treatment and integration with other omics data and the establishment of adequate standard files for data sharing. In this article, we discuss the main MS approaches and bioinformatics solutions that are currently applied to accomplish the objectives of clinical proteomic research.

Clinical proteomic biomarkers: relevant issues on study design & technical considerations in biomarker development

Biomarker research is continuously expanding in the field of clinical proteomics. A combination of different proteomic-based methodologies can be applied depending on the specific clinical context of use. Moreover, current advancements in proteomic analytical platforms are leading to an expansion of biomarker candidates that can be identified. Specifically, mass spectrometric techniques could provide highly valuable tools for biomarker research. Ideally, these advances could provide with biomarkers that are clinically applicable for disease diagnosis and/ or prognosis. Unfortunately, in general the biomarker candidates fail to be implemented in clinical decision making. To improve on this current situation, a well-defined study design has to be established driven by a clear clinical need, while several checkpoints between the different phases of discovery, verification and validation have to be passed in order to increase the probability of establishing valid biomarkers. In this review, we summarize the technical proteomic platforms that are available along the different stages in the biomarker discovery pipeline, exemplified by clinical applications in the field of bladder cancer biomarker research.