Batch effects correction improves the sensitivity of significance tests in spectral counting-based comparative discovery proteomics

Shotgun proteomics has become the standard proteomics technique for the large-scale measurement of protein abundances in biological samples. Despite quantitative proteomics has been usually performed using label-based approaches, label-free quantitation offers advantages related to the avoidance of labeling steps, no limitation in the number of samples to be compared, and the gain in protein detection sensitivity. However, since samples are analyzed separately, experimental design becomes critical. The exploration of spectral counting quantitation based on LC-MS presented here gathers experimental evidence of the influence of batch effects on comparative proteomics. The batch effects shown with spiking experiments clearly interfere with the biological signal. In order to minimize the interferences from batch effects, a statistical correction is proposed and implemented. Our results show that batch effects can be attenuated statistically when proper experimental design is used. Furthermore, the batch effect correction implemented leads to a substantial increase in the sensitivity of statistical tests. Finally, the applicability of our batch effects correction is shown on two different biomarker discovery projects involving cancer secretomes. We think that our findings will allow designing and executing better comparative proteomics projects and will help to avoid reaching false conclusions in the field of proteomics biomarker discovery.     

Comprehensive comparison of iTRAQ and label-free LC-based quantitative proteomics approaches using two Chlamydomonas reinhardtii strains of interest for biofuels engineering

Comprehensive comparisons of quantitative proteomics techniques are rare in the literature, yet they are crucially important for optimal selection of approaches and methodologies that are ideal for a given proteomics initiative. In this study, two LC-based quantitative proteomics approaches--iTRAQ and label-free--were implemented using the LTQ-Orbitrap Velos platform. For this comparison, the model used was the total protein content from two Chlamydomonas reinhardtii strains in the context of alternative biofuels production. The strain comparison includes sta6 (a starch-less mutant of cw15) that produces twice as many lipid bodies (LB) containing triacylglycerols (TAGs) as its parental strain cw15 (a cell wall-deficient C. reinhardtii strain) under nitrogen starvation. Internal standard addition was used to rigorously assess the quantitation accuracy and precision of each method. Results from iTRAQ-4plex labeling using HCD (higher energy collision-induced dissociation) fragmentation were compared to those obtained using a label-free approach based on the peak area of intact peptides and collision-induced dissociation. The accuracy and precision, number of identified/quantified proteins and statistically significant protein differences detected, as well as efficiency of these two quantitative proteomics methods were evaluated and compared. Four technical and three biological replicates of each strain were performed to assess both the technical and biological variation of both approaches. A total of 896 and 639 proteins were identified with high confidence, and 329 and 124 proteins were quantified significantly with label-free and iTRAQ, respectively, using biological replicates. The results showed that both iTRAQ labeling and label-free methods provide high quality quantitative and qualitative data using nano-LC coupled with the LTQ-Orbitrap Velos mass spectrometer, but the selection of the optimal approach is dependent on experimental design and the biological question to be addressed. The functional categorization of the differential proteins between cw15 and sta6 reveals already known but also new mechanisms likely responsible for the production of lipids in sta6 and sets the baseline for future studies aimed at engineering these strains for high oil production.     

Recent progress in liquid chromatography-based separation and label-free quantitative plant proteomics

Recent innovations in liquid chromatography-mass spectrometry (LC-MS)-based methods have facilitated quantitative and functional proteomic analyses of large numbers of proteins derived from complex samples without any need for protein or peptide labelling. Regardless of its great potential, the application of these proteomics techniques to plant science started only recently. Here we present an overview of label-free quantitative proteomics features and their employment for analysing plants. Recent methods used for quantitative protein analyses by MS techniques are summarized and major challenges associated with label-free LC-MS-based approaches, including sample preparation, peptide separation, quantification and kinetic studies, are discussed. Database search algorithms and specific aspects regarding protein identification of non-sequenced organisms are also addressed. So far, label-free LC-MS in plant science has been used to establish cellular or subcellular proteome maps, characterize plant-pathogen interactions or stress defence reactions, and for profiling protein patterns during developmental processes. Improvements in both, analytical platforms (separation technology and bioinformatics/statistical analysis) and high throughput nucleotide sequencing technologies will enhance the power of this method.     

To label or not to label: applications of quantitative proteomics in neuroscience research

Proteomics has provided researchers with a sophisticated toolbox of labeling-based and label-free quantitative methods. These are now being applied in neuroscience research where they have already contributed to the elucidation of fundamental mechanisms and the discovery of candidate biomarkers. In this review, we evaluate and compare labeling-based and label-free quantitative proteomic techniques for applications in neuroscience research. We discuss the considerations required for the analysis of brain and central nervous system specimens, the experimental design of quantitative proteomic workflows as well as the feasibility, advantages, and disadvantages of the available techniques for neuroscience-oriented questions. Furthermore, we assess the use of labeled standards as internal controls for comparative studies in humans and review applications of labeling-based and label-free mass spectrometry approaches in relevant model organisms and human subjects. Providing a comprehensive guide of feasible and meaningful quantitative proteomic methodologies for neuroscience research is crucial not only for overcoming current limitations but also for gaining useful insights into brain function and translating proteomics from bench to bedside.     

Label-free quantitation, an extension to 2DB

Determining the differential expression of proteins under different conditions is of major importance in proteomics. Since mass spectrometry-based proteomics is often used to quantify proteins, several labelling strategies have been developed. While these are generally more precise than label-free quantitation approaches, they imply specifically designed experiments which also require knowledge about peptides that are expected to be measured and need to be modified. We recently designed the 2DB database which aids storage, analysis, and publication of data from mass spectrometric experiments to identify proteins. This database can aid identifying peptides which can be used for quantitation. Here an extension to the database application, named MSMAG, is presented which allows for more detailed analysis of the distribution of peptides and their associated proteins over the fractions of an experiment. Furthermore, given several biological samples in the database, label-free quantitation can be performed. Thus, interesting proteins, which may warrant further investigation, can be identified en passant while performing high-throughput proteomics studies.     

Quantitative plant proteomics

Quantitation is an inherent requirement in comparative proteomics and there is no exception to this for plant proteomics. Quantitative proteomics has high demands on the experimental workflow, requiring a thorough design and often a complex multi-step structure. It has to include sufficient numbers of biological and technical replicates and methods that are able to facilitate a quantitative signal read-out. Quantitative plant proteomics in particular poses many additional challenges but because of the nature of plants it also offers some potential advantages. In general, analysis of plants has been less prominent in proteomics. Low protein concentration, difficulties in protein extraction, genome multiploidy, high Rubisco abundance in green tissue, and an absence of well-annotated and completed genome sequences are some of the main challenges in plant proteomics. However, the latter is now changing with several genomes emerging for model plants and crops such as potato, tomato, soybean, rice, maize and barley. This review discusses the current status in quantitative plant proteomics (MS-based and non-MS-based) and its challenges and potentials. Both relative and absolute quantitation methods in plant proteomics from DIGE to MS-based analysis after isotope labeling and label-free quantitation are described and illustrated by published studies. In particular, we describe plant-specific quantitative methods such as metabolic labeling methods that can take full advantage of plant metabolism and culture practices, and discuss other potential advantages and challenges that may arise from the unique properties of plants.     

Computational methods for the comparative quantification of proteins in label-free LCn-MS experiments

Liquid chromatography (LC) coupled to electrospray mass spectrometry (MS) is well established in high-throughput proteomics. The technology enables rapid identification of large numbers of proteins in a relatively short time. Comparative quantification of identified proteins from different samples is often regarded as the next step in proteomics experiments enabling the comparison of protein expression in different proteomes. Differential labeling of samples using stable isotope incorporation or conjugation is commonly used to compare protein levels between samples but these procedures are difficult to carry out in the laboratory and for large numbers of samples. Recently, comparative quantification of label-free LC(n)-MS proteomics data has emerged as an alternative approach. In this review, we discuss different computational approaches for extracting comparative quantitative information from label-free LC(n)-MS proteomics data. The procedure for computationally recovering the quantitative information is described. Furthermore, statistical tests used to evaluate the relevance of results will also be discussed.     

Use of DNA ladders for reproducible protein fractionation by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) for quantitative proteomics

In proteomics, one-dimensional (1D) sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) is widely used for protein fractionation prior to mass spectrometric analysis to enhance the dynamic range of analysis and to improve the identification of low-abundance proteins. Such protein prefractionation works well for quantitation strategies if the proteins are labeled prior to separation. However, because of the poor reproducibility of cutting gel slices, especially when small amounts of samples are analyzed, its application in label-free and peptide-labeling quantitative proteomics methods has been greatly limited. To overcome this limitation, we developed a new strategy in which a DNA ladder is mixed with the protein sample before PAGE separation. After PAGE separation, the DNA ladder is stained to allow for easy, precise, and reproducible gel cutting. To this end, a novel visible DNA-staining method was developed. This staining method is fast, sensitive, and compatible with mass spectrometry. To evaluate the reproducibility of DNA-ladder-assisted gel cutting for quantitative protein fractionation, we used stable isotope labeling with amino acids in cell culture (SILAC). Our results show that the quantitative error associated with fractionation can be minimized using the DNA-assisted fractionation and multiple replicates of gel cutting. In conclusion, 1D PAGE fractionation in combination with DNA ladders can be used for label-free comparative proteomics without compromising quantitation.     

Comparative LC-MS: a landscape of peaks and valleys

Quantitative proteomics approaches using stable isotopes are well-known and used in many labs nowadays. More recently, high resolution quantitative approaches are reported that rely on LC-MS quantitation of peptide concentrations by comparing peak intensities between multiple runs obtained by continuous detection in MS mode. Characteristic of these comparative LC-MS procedures is that they do not rely on the use of stable isotopes; therefore the procedure is often referred to as label-free LC-MS. In order to compare at comprehensive scale peak intensity data in multiple LC-MS datasets, dedicated software is required for detection, matching and alignment of peaks. The high accuracy in quantitative determination of peptide abundance provides an impressive level of detail. This approach also requires an experimental set-up where quantitative aspects of protein extraction and reproducible separation conditions need to be well controlled. In this paper we will provide insight in the critical parameters that affect the quality of the results and list an overview of the most recent software packages that are available for this procedure.     

A review on recent developments in mass spectrometry instrumentation and quantitative tools advancing bacterial proteomics

Proteomics has evolved substantially since its early days, some 20 years ago. In this mini-review, we aim to provide an overview of general methodologies and more recent developments in mass spectrometric approaches used for relative and absolute quantitation of proteins. Enhancement of sensitivity of the mass spectrometers as well as improved sample preparation and protein fractionation methods are resulting in a more comprehensive analysis of proteomes. We also document some upcoming trends for quantitative proteomics such as the use of label-free quantification methods. Hopefully, microbiologists will continue to explore proteomics as a tool in their research to understand the adaptation of microorganisms to their ever changing environment. We encourage them to incorporate some of the described new developments in mass spectrometry to facilitate their analyses and improve the general knowledge of the fascinating world of microorganisms.     

Label-free quantification in clinical proteomics

Nowadays, proteomic studies no longer focus only on identifying as many proteins as possible in a given sample, but aiming for an accurate quantification of them. Especially in clinical proteomics, the investigation of variable protein expression profiles can yield useful information on pathological pathways or biomarkers and drug targets related to a particular disease. Over the time, many quantitative proteomic approaches have been established allowing researchers in the field of proteomics to refer to a comprehensive toolbox of different methodologies. In this review we will give an overview of different methods of quantitative proteomics with focus on label-free proteomics and its use in clinical proteomics.     

A comparison of MS2-based label-free quantitative proteomic techniques with regards to accuracy and precision

The advent of algorithms for fragmentation spectrum-based label-free quantitative proteomics has enabled straightforward quantification of shotgun proteomic experiments. Despite the popularity of these approaches, few studies have been performed to assess their performance. We have therefore profiled the precision and the accuracy of three distinct relative label-free methods on both the protein and the proteome level. We derived our test data from two well-characterized publicly available quantitative data sets.     

Redox proteomics: from residue modifications to putative biomarker identification by gel- and LC-MS-based approaches

Quantitative determination of reactive oxygen species and reactive nitrogen species in body fluids, tissues or cells has always been problematic due to their high chemical reactivity and the resulting short half-life. This high reactivity may involve reversible and/or irreversible protein modifications, in particular the covalent oxidative modification of specific amino acid residues. Thus, the occurrence of reactive oxygen species and reactive nitrogen species can be monitored indirectly from the identification of specific protein-chemical footprints. In combination with classical gel-based proteomics or liquid chromatography labeling or label-free techniques, mass spectrometry has emerged as a powerful tool to identify these protein modifications in biological samples. In this review, we present the main methodological approaches for gel-based proteomics and quantitative mass spectrometry applied to oxidative protein modifications, mainly Cys. Representative examples from their application in identifying respective biomarkers in diseases related to oxidative stress are also presented.     

Current challenges in software solutions for mass spectrometry-based quantitative proteomics

Mass spectrometry-based proteomics has evolved as a high-throughput research field over the past decade. Significant advances in instrumentation, and the ability to produce huge volumes of data, have emphasized the need for adequate data analysis tools, which are nowadays often considered the main bottleneck for proteomics development. This review highlights important issues that directly impact the effectiveness of proteomic quantitation and educates software developers and end-users on available computational solutions to correct for the occurrence of these factors. Potential sources of errors specific for stable isotope-based methods or label-free approaches are explicitly outlined. The overall aim focuses on a generic proteomic workflow.     

Quantitative proteomic analyses of crop seedlings subjected to stress conditions; a commentary

Quantitative proteomics is one of the analytical approaches used to clarify crop responses to stress conditions. Recent remarkable advances in proteomics technologies allow for the identification of a wider range of proteins than was previously possible. Current proteomic methods fall into roughly two categories: gel-based quantification methods, including conventional two-dimensional gel electrophoresis and two-dimensional fluorescence difference gel electrophoresis, and MS-based quantification methods consists of label-based and label-free protein quantification approaches. Although MS-based quantification methods have become mainstream in recent years, gel-based quantification methods are still useful for proteomic analyses. Previous studies examining crop responses to stress conditions reveal that each method has both advantages and disadvantages in regard to protein quantification in comparative proteomic analyses. Furthermore, one proteomics approach cannot be fully substituted by another technique. In this review, we discuss and highlight the basis and applications of quantitative proteomic analysis approaches in crop seedlings in response to flooding and osmotic stress as two environmental stresses.     

Design and analysis issues in quantitative proteomics studies

Quantitative proteomics is the comparison of distinct proteomes which enables the identification of protein species which exhibit changes in expression or post-translational state in response to a given stimulus. Many different quantitative techniques are being utilized and generate large datasets. Independent of the technique used, these large datasets need robust data analysis to ensure valid conclusions are drawn from such studies. Approaches to address the problems that arise with large datasets are discussed to give insight into the types of statistical analyses of data appropriate for the various experimental strategies that can be employed by quantitative proteomic studies. This review also highlights the importance of employing a robust experimental design and highlights various issues surrounding the design of experiments. The concepts and examples discussed within will show how robust design and analysis will lead to confident results that will ensure quantitative proteomics delivers.     

Combination of peptide OFFGEL fractionation and label-free quantitation facilitated proteomics profiling of extraocular muscle

Several label-free quantitation strategies have been introduced that obliterate the need for expensive isotopically labeled molecules. However label-free approaches have considerably higher demands in respect of repeatability of sample preparation and fractionation than multiplexing isotope labeling-based strategies. OFFGEL fractionation promises the necessary separation efficiency and repeatability. To test this platform, 12-fraction peptide OFFGEL electrophoresis and online reversed-phase LC connected to a quadrupole TOF mass spectrometer were used to determine differences of the physiological, pathological and biochemical distinct extraocular muscle allotype in comparison to hind-limb muscle. Close to 70% of the peptides separated by OFFGEL electrophoresis were detected only in a single fraction. To determine the separation repeatability of four samples, we compared the ion volumes of multiple peptides deriving from the thick filament-associated protein titin over several fractions and determined a coefficient of variation below 20%. Of the 474 proteins identified, 61 proteins were differently expressed between the two muscle allotypes and were involved in metabolism, muscle contraction, stress response, or gene expression. Several expression differences were validated using immunohistochemistry and Western blot analysis. We therefore consider peptide OFFGEL fractionation an effective and efficient addition to our label-free quantitative proteomics workflow.     

定量蛋白质组学无标记定量方法的研究进展

依靠质谱技术的蛋白质组学快速发展,寻求速度快、重复性好以及准确度高的定量方法是该领域的一项艰巨任务,定量蛋白质组学分支领域应运而生．其中,无标记定量方法以其样品制备简单、耗材费用低廉以及结果数据分析便捷等优点渐露锋芒．无标记定量方法通常分为信号强度法和谱图计数法两大类．本文在这两种无标记定量方法计算原理的基础上,针对各种常用的无标记定量方法及最新进展做一个较为全面的介绍,并将详细讨论两类方法的异同点,以及目前蛋白质组学中无标记定量方法所面临的主要挑战,希望能为这一领域的研究人员在选择无标记定量方法时提供一个合理的参考．     

Advances in quantitative proteomics

Large-scale protein quantification has become a major proteomics application in many areas of biological and medical research. During the past years, different techniques have been developed, including gel-based such as differential in-gel electrophoresis (DIGE) and liquid chromatography-based such as isotope labeling and label-free quantification. These quantitative proteomics tools hold significant promise for biomarker discovery, diagnostic and therapeutic applications. They are also important for research in functional genomics and systems biology towards basic understanding of molecular networks and pathway interactions. In this review, we summarize current technologies in quantitative proteomics and discuss recent applications of the technologies.     

基于质谱的定量蛋白质组学策略和方法研究进展

定量蛋白质组学已经成为组学领域研究的热点之一.相关实验技术和计算方法的不断创新极大地促进了定量蛋白质组学的飞速发展.常用的定量蛋白质组学策略按照是否需要稳定同位素标记可以分为无标定量和有标定量两大类.每类策略又产生了众多定量方法和工具,它们一方面推动了定量蛋白质组学的深入发展;另一方面,也在实验策略与技术的发展过程中不断更新.因此对这些定量实验策略和方法进行系统总结和归纳将有助于定量蛋白质组学的研究.本文主要从方法学角度全面归纳了目前定量蛋白质组学研究的相关策略和算法,详述了无标定量和有标定量的具体算法流程并比较了各自特点,还对以研究蛋白质绝对丰度为目标的绝对定量算法进行了总结,列举了常用的定量软件和工具,最后概述了定量结果的质量控制方法,对定量蛋白质组学方法发展的前景进行了展望.