A quantitative analysis software tool for mass spectrometry-based proteomics

We describe Census, a quantitative software tool compatible with many labeling strategies as well as with label-free analyses, single-stage mass spectrometry (MS1) and tandem mass spectrometry (MS/MS) scans, and high- and low-resolution mass spectrometry data. Census uses robust algorithms to address poor-quality measurements and improve quantitative efficiency, and it can support several input file formats. We tested Census with stable-isotope labeling analyses as well as label-free analyses.     

基于质谱技术的蛋白质组定量方法

对不同状态下的蛋白质在表达和修饰水平上进行精确定量,对于探索蛋白质的生物功能、发现疾病的生物标志物都具有重要意义,也是当前蛋白质组学的一个重要研究前沿。近年来,各种蛋白质组定量的新技术和新方法不断涌现,但仍面临着巨大挑战。本文就基于质谱技术的多种蛋白质组定量方法的基本原理、近几年的研究进展和应用进行评述。     

Mass spectrometry-based quantitative proteomics

A major aim of present-day proteomics is to study changes in protein expression levels at a global level, ideally monitoring all proteins present in cells or tissue. Mass spectrometry is a well-respected technology in proteomics that is widely used for the identification of proteins. More recently, methodologies have been introduced showing that mass spectrometry can also be used for protein quantification. This article reviews various mass spectrometry-based technologies in quantitative proteomics, highlighting several interesting applications in areas ranging from cell biology to clinical applications.     

Mass spectrometry-based quantitative proteomics

A major aim of present-day proteomics is to study changes in protein expression levels at a global level, ideally monitoring all proteins present in cells or tissue. Mass spectrometry is a well-respected technology in proteomics that is widely used for the identification of proteins. More recently, methodologies have been introduced showing that mass spectrometry can also be used for protein quantification. This article reviews various mass spectrometry-based technologies in quantitative proteomics, highlighting several interesting applications in areas ranging from cell biology to clinical applications.     

Current trends in computational inference from mass spectrometry-based proteomics

Mass spectrometry offers a high-throughput approach to quantifying the proteome associated with a biological sample and hence has become the primary approach of proteomic analyses. Computation is tightly coupled to this advanced technological platform as a required component of not only peptide and protein identification, but quantification and functional inference, such as protein modifications and interactions. Proteomics faces several key computational challenges such as identification of proteins and peptides from tandem mass spectra as well as their quantitation. In addition, the application of proteomics to systems biology requires understanding the functional proteome, including how the dynamics of the cell change in response to protein modifications and complex interactions between biomolecules. This review presents an overview of recently developed methods and their impact on these core computational challenges currently facing proteomics.     

Reference map for liquid chromatography–mass spectrometry-based quantitative proteomics

The accurate mass and time (AMT) tag strategy has been recognized as a powerful tool for high-throughput analysis in liquid chromatography–mass spectrometry (LC–MS)-based proteomics. Due to the complexity of the human proteome, this strategy requires highly accurate mass measurements for confident identifications. We have developed a method of building a reference map that allows relaxed criteria for mass errors yet delivers high confidence for peptide identifications. The samples used for generating the peptide database were produced by collecting cysteine-containing peptides from T47D cells and then fractionating the peptides using strong cationic exchange chromatography (SCX). LC–tandem mass spectrometry (MS/MS) data from the SCX fractions were combined to create a comprehensive reference map. After the reference map was built, it was possible to skip the SCX step in further proteomic analyses. We found that the reference-driven identification increases the overall throughput and proteomic coverage by identifying peptides with low intensity or complex interference. The use of the reference map also facilitates the quantitation process by allowing extraction of peptide intensities of interest and incorporating models of theoretical isotope distribution.     

Assessing bias in experiment design for large scale mass spectrometry-based quantitative proteomics

Mass spectrometry-based proteomics holds great promise as a discovery tool for biomarker candidates in the early detection of diseases. Recently much emphasis has been placed upon producing highly reliable data for quantitative profiling for which highly reproducible methodologies are indispensable. The main problems that affect experimental reproducibility stem from variations introduced by sample collection, preparation, and storage protocols and LC-MS settings and conditions. On the basis of a formally precise and quantitative definition of similarity between LC-MS experiments, we have developed Chaorder, a fully automatic software tool that can assess experimental reproducibility of sets of large scale LC-MS experiments. By visualizing the similarity relationships within a set of experiments, this tool can form the basis of systematic quality control and thus help assess the comparability of mass spectrometry data over time, across different laboratories, and between instruments. Applying Chaorder to data from multiple laboratories and a range of instruments, experimental protocols, and sample complexities revealed biases introduced by the sample processing steps, experimental protocols, and instrument choices. Moreover we show that reducing bias by correcting for just a few steps, for example randomizing the run order, does not provide much gain in statistical power for biomarker discovery.     

Challenges in mass spectrometry-based proteomics

During the last decade, protein analysis and proteomics have been established as new tools for understanding various biological problems. As the identification of proteins after classical separation techniques, such as two-dimensional gel electrophoresis, have become standard methods, new challenges arise in the field of proteomics. The development of "functional proteomics" combines functional characterization, like regulation, localization and modification, with the identification of proteins for deeper insight into cellular functions. Therefore, different mass spectrometric techniques for the analysis of post-translational modifications, such as phosphorylation and glycosylation, have been established as well as isolation and separation methods for the analysis of highly complex samples, e.g. protein complexes or cell organelles. Furthermore, quantification of protein levels within cells is becoming a focus of interest as mass spectrometric methods for relative or even absolute quantification have currently not been available. Protein or genome databases have been an essential part of protein identification up to now. Thus, de novo sequencing offers new possibilities in protein analytical studies of organisms not yet completely sequenced. The intention of this review is to provide a short overview about the current capabilities of protein analysis when addressing various biological problems.     

Mass spectrometry-based proteomics turns quantitative

The field of proteomics is built on technologies to analyze large numbers of proteins--ideally the entire proteome--in the same experiment. Mass spectrometry (MS) has been successfully used to characterize proteins in complex mixtures, but results so far have largely been qualitative. Two recently developed methodologies offer the opportunity to obtain quantitative proteomic information. Comparing the signals from the same peptide under different conditions yields a rough estimate of relative protein abundance between two proteomes. Alternatively, and more accurately, peptides are labeled with stable isotopes, introducing a predictable mass difference between peptides from two experimental conditions. Stable isotope labels can be incorporated 'post-harvest', by chemical approaches or in live cells through metabolic incorporation. This isotopic handle facilitates direct quantification from the mass spectra. Using these quantitative approaches, precise functional information as well as temporal changes in the proteome can be captured by MS.     

Liquid chromatography-mass spectrometry-based quantitative proteomics

LC-MS-based quantitative proteomics has become increasingly applied to a wide range of biological applications due to growing capabilities for broad proteome coverage and good accuracy and precision in quantification. Herein, we review the current LC-MS-based quantification methods with respect to their advantages and limitations and highlight their potential applications.     

Signal maps for mass spectrometry-based comparative proteomics

Mass spectrometry-based proteomic experiments, in combination with liquid chromatography-based separation, can be used to compare complex biological samples across multiple conditions. These comparisons are usually performed on the level of protein lists generated from individual experiments. Unfortunately given the current technologies, these lists typically cover only a small fraction of the total protein content, making global comparisons extremely limited. Recently approaches have been suggested that are built on the comparison of computationally built feature lists instead of protein identifications. Although these approaches promise to capture a bigger spectrum of the proteins present in a complex mixture, their success is strongly dependent on the correctness of the identified features and the aligned retention times of these features across multiple experiments. In this experimental-computational study, we went one step further and performed the comparisons directly on the signal level. First signal maps were constructed that associate the experimental signals across multiple experiments. Then a feature detection algorithm used this integrated information to identify those features that are discriminating or common across multiple experiments. At the core of our approach is a score function that faithfully recognizes mass spectra from similar peptide mixtures and an algorithm that produces an optimal alignment (time warping) of the liquid chromatography experiments on the basis of raw MS signal, making minimal assumptions on the underlying data. We provide experimental evidence that suggests uniqueness and correctness of the resulting signal maps even on low accuracy mass spectrometers. These maps can be used for a variety of proteomic analyses. Here we illustrate the use of signal maps for the discovery of diagnostic biomarkers. An imple-mentation of our algorithm is available on our Web server.     

An assessment of software solutions for the analysis of mass spectrometry based quantitative proteomics data

Over the past decade, a series of experimental strategies for mass spectrometry based quantitative proteomics and corresponding computational methodology for the processing of the resulting data have been generated. We provide here an overview of the main quantification principles and available software solutions for the analysis of data generated by liquid chromatography coupled to mass spectrometry (LC-MS). Three conceptually different methods to perform quantitative LC-MS experiments have been introduced. In the first, quantification is achieved by spectral counting, in the second via differential stable isotopic labeling, and in the third by using the ion current in label-free LC-MS measurements. We discuss here advantages and challenges of each quantification approach and assess available software solutions with respect to their instrument compatibility and processing functionality. This review therefore serves as a starting point for researchers to choose an appropriate software solution for quantitative proteomic experiments based on their experimental and analytical requirements.     

Stoichiometry of chromatin-associated protein complexes revealed by label-free quantitative mass spectrometry-based proteomics

Many cellular proteins assemble into macromolecular protein complexes. The identification of protein–protein interactions and quantification of their stoichiometry is therefore crucial to understand the molecular function of protein complexes. Determining the stoichiometry of protein complexes is usually achieved by mass spectrometry-based methods that rely on introducing stable isotope-labeled reference peptides into the sample of interest. However, these approaches are laborious and not suitable for high-throughput screenings. Here, we describe a robust and easy to implement label-free relative quantification approach that combines the detection of high-confidence protein–protein interactions with an accurate determination of the stoichiometry of the identified protein–protein interactions in a single experiment. We applied this method to two chromatin-associated protein complexes for which the stoichiometry thus far remained elusive: the MBD3/NuRD and PRC2 complex. For each of these complexes, we accurately determined the stoichiometry of the core subunits while at the same time identifying novel interactors and their stoichiometry.     

Target class strategies in mass spectrometry-based proteomics

The cornerstone of proteomics resides in using traditional methods of protein chemistry, to extract and resolve complex mixtures, in concert with the powerful engines of mass spectrometry to decipher peptide and protein identities. The broad utility of proteomics technologies to map protein interactions, understand regulatory mechanisms and identify biomarkers associated with disease states and drug treatments necessitates a targeted biochemical approach tailored to the characteristics of the tissue, fluid or cellular extract being studied. The application of affinity methods in proteomic studies to focus on particular classes of molecules is being used with increasing frequency and comprises the subject of this review. An overview of successfully applied affinity methods is provided, along with speculation on the use of innovative approaches. Sample preparation and processing are critical for proteomics with affinity reagents, as only functional and active proteins can be isolated in most cases. Considerations for methods of sample preparation to optimize affinity capture and release are also discussed.     

Modified cysteine S-phosphopeptide standards for mass spectrometry-based proteomics

The regulatory role of protein cysteine phosphorylation is an under-researched area. The difficulty of accessing reference S-phosphorylated peptides (pCys-peptides) hampers progress in MS-driven cysteine phosphoproteomics, which requires targeted analytical procedures. This work describes an uncomplicated process for the conversion of disulfide-bridged protein into a complex model mixture of combinatorially modified peptides. Hen egg-white lysozyme was reduced with tris(2-carboxyethyl)phosphine (TCEP) followed by alkylation of cysteine with (3-acrylamidopropyl)trimethyl-ammonium chloride (APTA) and subsequent beta-elimination in aqueous Ba(OH)₂ to yield modified polypeptides containing multiple dehydroalanine (Dha) residues. The conjugate addition of thiophosphoric acid to Dha residues followed by trypsinolysis led to numerous D/L phosphocysteine-containing peptides, which were identified by higher-energy collisional-dissociation tandem mass spectrometry (HCD-MS/MS). Our results show that some pCys-peptides produce prominent neutral losses of 80 Da, 98 Da and a weak 116 Da loss. These are similar to the neutral-loss triplets generated by phosphohistidine peptides.

     

Target class strategies in mass spectrometry-based proteomics

The cornerstone of proteomics resides in using traditional methods of protein chemistry, to extract and resolve complex mixtures, in concert with the powerful engines of mass spectrometry to decipher peptide and protein identities. The broad utility of proteomics technologies to map protein interactions, understand regulatory mechanisms and identify biomarkers associated with disease states and drug treatments necessitates a targeted biochemical approach tailored to the characteristics of the tissue, fluid or cellular extract being studied. The application of affinity methods in proteomic studies to focus on particular classes of molecules is being used with increasing frequency and comprises the subject of this review. An overview of successfully applied affinity methods is provided, along with speculation on the use of innovative approaches. Sample preparation and processing are critical for proteomics with affinity reagents, as only functional and active proteins can be isolated in most cases. Considerations for methods of sample preparation to optimize affinity capture and release are also discussed.     

The mzIdentML data standard for mass spectrometry-based proteomics results

We report the release of mzIdentML, an exchange standard for peptide and protein identification data, designed by the Proteomics Standards Initiative. The format was developed by the Proteomics Standards Initiative in collaboration with instrument and software vendors, and the developers of the major open-source projects in proteomics. Software implementations have been developed to enable conversion from most popular proprietary and open-source formats, and mzIdentML will soon be supported by the major public repositories. These developments enable proteomics scientists to start working with the standard for exchanging and publishing data sets in support of publications and they provide a stable platform for bioinformatics groups and commercial software vendors to work with a single file format for identification data.