基于质谱技术的神经肽研究进展

神经肽是一类重要的内源活性物质,在神经系统中发挥重要的作用,并连接大脑和其他神经器官。基于质谱技术的神经肽组学研究旨在对神经肽进行大规模研究,在分子水平上得到重要信息,进一步加深对神经系统调控机制以及神经疾病致病机理的理解。文中综述了利用质谱技术进行神经肽研究的基本策略,包括样品处理、定性定量方法以及质谱成像等研究进展。     

Bioinformatics analysis of mass spectrometry-based proteomics data sets

Proteomics has made tremendous progress, attaining throughput and comprehensiveness so far only seen in genomics technologies. The consequent avalanche of proteome level data poses great analytical challenges for downstream interpretation. We review bioinformatic analysis of qualitative and quantitative proteomic data, focusing on current and emerging paradigms employed for functional analysis, data mining and knowledge discovery from high resolution quantitative mass spectrometric data. Many bioinformatics tools developed for microarrays can be reused in proteomics, however, the uniquely quantitative nature of proteomics data also offers entirely novel analysis possibilities, which directly suggest and illuminate biological mechanisms.     

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。     

基于质谱技术的蛋白质组学方法应用于 2000 年前食物残留的分析

考古样品中蛋白质残留物的保存状态能否应用质谱技术成功分析,取决于其特定埋藏环境微生物条件和埋藏年代两个主要因素。目前国际上的最新进展,是成功分析距今大约800～600年之间陶器碎片上附着的粘土状残留物,测定出其中的蛋白质成分来源于灰海狮,该样品来自邻近北冰洋的极寒地区。尝试分析距今2000年左右,出土于云南黑玛井遗址两件青铜容器内的内容物样品,其中一件的内容物外观性状为颗粒状,另外一件则为膏状。分析结果发现,颗粒状样品未能测出蛋白质残留成分,而膏状样品则保留了大量蛋白质残留的信息。这一结果表明,基于生物质谱技术的蛋白质组学方法有可能应用于温带埋藏环境以及年代更为古老的考古样品。     

Evaluation of four phosphopeptide enrichment strategies for mass spectrometry-based proteomic analysis

Information about phosphorylation status can be used to prioritize and characterize biological processes in the cell. Various analytical strategies have been proposed to address the complexity of phosphorylation status and comprehensively identify phosphopeptides. In this study, we evaluated four strategies for phosphopeptide enrichment, using titanium dioxide (TiO₂) and Phos-tag ligand particles from in-gel or in-solution digests prior to mass spectrometry-based analysis. Using TiO₂ and Phos-tag magnetic beads, it was possible to enrich phosphopeptides from in-gel digests of phosphorylated ovalbumin separated by Phos-tag SDS-PAGE or in-solution serum digests, while minimizing non-specific adsorption. The tip-column strategy with TiO₂ particles enabled enrichment of phosphopeptides from in-solution digests of whole-cell lysates with high efficiency and selectivity. However, the tip-column strategy with Phos-tag agarose beads yielded the greatest number of identified phosphopeptides. The strategies using both types of tip columns had a high degree of overlap, although there were differences in selectivity between the identified phosphopeptides. Together, our results indicate that multi-enrichment strategies using TiO₂ particles and Phos-tag agarose beads are useful for comprehensive phosphoproteomic analysis.     

An automated mass spectrometry-based screening method for analysis of sulfated glycosaminoglycans

Glycosaminoglycans (GAGs) are linear polysaccharides, consisting of repeated disaccharide units, attached to core proteins in all multicellular organisms. Chondroitin sulfate (CS) and dermatan sulfate (DS) constitute a subgroup of sulfated GAGs for which the degree of sulfation varies between species and tissues. One major goal in GAG characterization is to correlate structure to function. A common approach is to exhaustively degrade the GAG chains and thereafter determine the amount of component disaccharide units. In large-scale studies, there is a need for high-throughput screening methods since existing methods are either very time- or samples consuming. Here, we present a new strategy applying MALDI-TOF MS in positive ion mode for semi-qualitative and quantitative analysis of CS/DS derived disaccharide units. Only a few picomoles of sample are required per analysis and 10 samples can be analyzed in 25 min, which makes this approach an attractive alternative to many established assay methods. The total CS/DS concentration in 19 samples derived from Caenorhabditis elegans and mammalian tissues and cells was determined. The obtained results were well in accordance with concentrations determined by a standard liquid chromatography-based method, demonstrating the applicability of the method for samples from various biological matrices containing CS/DS of different sulfation degrees.     

Advances in mass spectrometry-based cancer research and analysis: from cancer proteomics to clinical diagnostics

Introduction: The last 20 years have seen significant improvements in the analytical capabilities of biological mass spectrometry (MS). Studies using advanced MS have resulted in new insights into cell biology and the etiology of diseases as well as its use in clinical applications.

Areas covered: This review discusses recent developments in MS-based technologies and their cancer-related applications with a focus on proteomics. It also discusses the issues around translating the research findings to the clinic and provides an outline of where the field is moving.

Expert commentary: Proteomics has been problematic to adapt for the clinical setting. However, MS-based techniques continue to demonstrate potential in novel clinical uses beyond classical cancer proteomics.     

Classification and identification of bacteria using mass spectrometry-based proteomics

Timely classification and identification of bacteria is of vital importance in many areas of public health. Mass spectrometry-based methods provide an attractive alternative to well-established microbiologic procedures. Mass spectrometry methods can be characterized by the relatively high speed of acquiring taxonomically relevant information. Gel-free mass spectrometry proteomics techniques allow for rapid fingerprinting of bacterial proteins using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry or, for high-throughput sequencing of peptides from protease-digested cellular proteins, using mass analysis of fragments from collision-induced dissociation of peptide ions. The latter technique uses database searching of product ion mass spectra. A database contains a comprehensive list of protein sequences translated from protein-encoding open reading frames found in bacterial genomes. The results of such searches allow the assignment of experimental peptide sequences to matching theoretical bacterial proteomes. Phylogenetic profiles of sequenced peptides are then used to create a matrix of sequence-to-bacterium assignments, which are analyzed using numerical taxonomy tools. The results thereof reveal the relatedness between bacteria, and allow the taxonomic position of an investigated strain to be inferred.     

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.     

High-field asymmetric waveform ion mobility spectrometry for mass spectrometry-based proteomics

High-field asymmetric waveform ion mobility spectrometry (FAIMS) is an atmospheric pressure ion mobility technique that separates gas-phase ions by their behavior in strong and weak electric fields. FAIMS is easily interfaced with electrospray ionization and has been implemented as an additional separation mode between liquid chromatography (LC) and mass spectrometry (MS) in proteomic studies. FAIMS separation is orthogonal to both LC and MS and is used as a means of on-line fractionation to improve the detection of peptides in complex samples. FAIMS improves dynamic range and concomitantly the detection limits of ions by filtering out chemical noise. FAIMS can also be used to remove interfering ion species and to select peptide charge states optimal for identification by tandem MS. Here, the authors review recent developments in LC-FAIMS-MS and its application to MS-based proteomics.     

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.     

Overcoming the dynamic range problem in mass spectrometry-based shotgun proteomics

Protein profiling using mass spectrometry technology has emerged as a powerful method for analyzing large-scale protein-expression patterns in cells and tissues. However, a number of challenges are present in proteomics research, one of the greatest being the high degree of protein complexity and huge dynamic range of proteins expressed in the complex biological mixtures, which exceeds six orders of magnitude in cells and ten orders of magnitude in body fluids. Since many important signaling proteins have low expression levels, methods to detect the low-abundance proteins in a complex sample are required. This review will focus on the fundamental fractionation and mass spectrometry techniques currently used for large-scale shotgun proteomics research.     

Identification of proteins released by follicular lymphoma-derived cells using a mass spectrometry-based approach

The recent advent of mass spectrometry-based methodologies for the analysis of complex protein mixtures opens new opportunities for the discovery of biomarkers that may aid in the diagnostic work-up of cancer. Follicular lymphoma (FL) is the most common form of low-grade non-Hodgkin lymphoma in the Western Hemisphere. Identification of tumor markers that facilitate early disease detection would be a significant advance in the management of FL. We have employed a strategy that entailed propagation of a follicular-derived cell line in serum-free media, protein extraction, and reverse-phase liquid chromatography, with subsequent electrospray ionization and tandem mass spectrometry analysis for the identification of proteins that are released by FL. Using a two-peptide minimum per protein and standard criteria, 209 proteins (5.6% maximum predicted error rate) released from the FL cells were identified. The released proteins included several growth factors, cytokines, acute phase reactants and cellular components previously known to be present in FL cells. Importantly, a greater proportion of proteins previously unassociated with FL were identified with high statistical confidence. Our studies provide a list of proteins, which may be candidates for early screening, diagnosis and therapeutic monitoring of patients with a suspected or biopsy-confirmed diagnosis of FL.     

Isolation of cell surface proteins for mass spectrometry-based proteomics

Defining the cell surface proteome has profound importance for understanding cell differentiation and cell–cell interactions, as well as numerous pathogenic abnormalities. Owing to their hydrophobic nature, plasma membrane proteins that reside on the cell surface pose analytical challenges and, despite efforts to overcome difficulties, remain under-represented in proteomic studies. Limitations in the classically employed ultracentrifugation-based approaches have led to the invention of more elaborate techniques for the purification of cell surface proteins. Three of these methods – cell surface coating with cationic colloidal silica beads, biotinylation and chemical capture of surface glycoproteins – allow for marked enrichment of this subcellular proteome, with each approach offering unique advantages and characteristics for different experiments. In this article, we introduce the principles of each purification method and discuss applications from the recent literature.     

Complexity and pitfalls of mass spectrometry-based targeted metabolomics in brain research

Current quantitative metabolomic research in brain tissue is challenged by several analytical issues. To compare data of metabolite pattern, ratios of individual metabolite concentrations and composed classifiers characterizing a distinct state, standardized workup conditions, and extraction medium are crucial. Differences in physicochemical properties of individual compounds and compound classes such as polarity determine extraction yields and, thus, ratios of compounds with varying properties. Also, variations in suppressive effects related to coextracted matrix components affect standards or references and their concentration-dependent responses.The selection of a common tissue extraction protocol is an ill-posed problem because it can be regarded as a multiple objective decision depending on factors such as sample handling practicability, measurement precision, control of matrix effects, and relevance of the chemical assay. This study systematically evaluates the impact of extraction solvents and the impact of the complex brain tissue on measured metabolite levels, taking into account ionization efficiency as well as challenges encountered in the trace-level quantification of the analytes in brain matrices. In comparison with previous studies that relied on nontargeted platforms, consequently emphasizing the global behavior of the metabolomic fingerprint, here we focus on several series of metabolites spanning over extensive polarity, concentration, and molecular mass ranges.     

Imaging mass spectrometry statistical analysis

Imaging mass spectrometry is increasingly used to identify new candidate biomarkers. This clinical application of imaging mass spectrometry is highly multidisciplinary: expertise in mass spectrometry is necessary to acquire high quality data, histology is required to accurately label the origin of each pixel's mass spectrum, disease biology is necessary to understand the potential meaning of the imaging mass spectrometry results, and statistics to assess the confidence of any findings. Imaging mass spectrometry data analysis is further complicated because of the unique nature of the data (within the mass spectrometry field); several of the assumptions implicit in the analysis of LC-MS/profiling datasets are not applicable to imaging. The very large size of imaging datasets and the reporting of many data analysis routines, combined with inadequate training and accessible reviews, have exacerbated this problem. In this paper we provide an accessible review of the nature of imaging data and the different strategies by which the data may be analyzed. Particular attention is paid to the assumptions of the data analysis routines to ensure that the reader is apprised of their correct usage in imaging mass spectrometry research.     

Improving the reliability and throughput of mass spectrometry-based proteomics by spectrum quality filtering

In contemporary peptide-centric or non-gel proteome studies, vast amounts of peptide fragmentation data are generated of which only a small part leads to peptide or protein identification. This motivates the development and use of a filtering algorithm that removes spectra that contribute little to protein identification. Removal of unidentifiable spectra reduced both the amount of computational and human time spent on analyzing spectra as well as the chances of obtaining false identifications. Thorough testing on various proteome datasets from different instruments showed that the best suggested machine-learning classifier is, on average, able to recognize half of the unidentified spectra as bad spectra. Further analyses showed that several unidentified spectra classified as good were derived from peptides carrying unanticipated amino acid modifications or contained sequence tags that allowed peptide identification using homology searches. The implementation of the classifiers is available under the GNU General Public License at http://www.bioinfo.no/software/spectrumquality.     

A mass spectrometry-based high-throughput screening method for engineering fatty acid synthases with improved production of medium-chain fatty acids

Microbial cell factories have been extensively engineered to produce free fatty acids (FFAs) as key components of crucial nutrients, soaps, industrial chemicals, and fuels. However, our ability to control the composition of microbially synthesized FFAs is still limited, particularly, for producing medium-chain fatty acids (MCFAs). This is mainly due to the lack of high-throughput approaches for FFA analysis to engineer enzymes with desirable product specificity. Here we report a mass spectrometry (MS)-based method for rapid profiling of MCFAs in Saccharomyces cerevisiae by using membrane lipids as a proxy. In particular, matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) MS was used to detect shorter acyl chain phosphatidylcholines from membrane lipids and a higher m/z peak ratio at 730 and 758 was used as an indication for improved MCFA production. This colony-based method can be performed at a rate of ~2 s per sample, representing a substantial improvement over gas chromatography-MS (typically >30 min per sample) as the gold standard method for FFA detection. To demonstrate the power of this method, we performed site-saturation mutagenesis of the yeast fatty acid synthase and identified nine missense mutations that resulted in improved MCFA production relative to the wild-type strain. Colony-based MALDI-ToF MS screening provides an effective approach for engineering microbial fatty acid compositions in a high-throughput manner.     

Integrating a 250 mL-spinner flask with other stirred bench-scale cell culture devices: a mass transfer perspective

The bioprocess development cycle is a complex task that requires a complete understanding of the engineering of the process (e.g., mass transfer, mixing, CO(2) removal, process monitoring, and control) and its affect on cell biology and product quality. Despite their widespread use in bioprocess development, spinner flasks generally lack engineering characterization of critical physical parameters such as k(L)a, P/V, or mixing time. In this study, mass transfer characterization of a 250-mL spinner flask using optical patch-based sensors is presented. The results quantitatively show the effect of the impeller type, liquid filling volume, and agitation speed on the volumetric mass transfer coefficient (k(L)a) in a 250-mL spinner flask, and how they can be manipulated to match mass transfer capability at large culture devices. Thus, process understanding in spinner flasks can be improved, and these devices can be seamlessly integrated in a rational scale-up strategy from cell thawing to bench-scale bioreactors (and beyond) in biomanufacturing.     

基于质谱的蛋白质生物标志物发现中的特征选择与机器学习方法研究进展

随着质谱技术的进步以及生物信息学与统计学算法的发展,以疾病研究为主要目的之一的人类蛋白质组计划正快速推进。蛋白质生物标志物在疾病早期诊断和临床治疗等方面有着非常重要的意义,其发现策略和方法的研究已成为一个重要的热点领域。特征选择与机器学习对于解决蛋白质组数据"高维度"及"稀疏性"问题有较好的效果,因而逐渐被广泛地应用于发现蛋白质生物标志物的研究中。文中主要阐述蛋白质生物标志物的发现策略以及其中特征选择与机器学习方法的原理、应用实例和适用范围,并讨论深度学习方法在本领域的应用前景及局限性,以期为相关研究提供参考。