生物质谱技术与蛋白质组学

生物质谱技术是蛋白质组学的支撑技术.详细论述了质谱技术的分类与基本分析原理,重点论述了质谱技术的发展变化,包括基质辅助激光解吸飞行时间质谱技术,电喷雾质谱技术,MALDI-Q-TOF和MAL-DI-TOF-TOF等质谱技术,以及质谱技术在蛋白质组学研究中的应用与未来的发展和挑战.     

基于质谱的蛋白质组学信号网络研究进展

细胞信号转导网络调控着所有细胞和器官的生物学过程。以往信号转导网络的研究主要采用一些生物化学方法开展,如抗体技术。目前,基于质谱的大规模蛋白质组学研究可以在翻译后修饰、蛋白质互作及蛋白质表达水平上,系统地研究信号转导事件。基于蛋白质组学的大规模信号转导的研究将改变我们对信号转导网络的理解。从蛋白质组翻译后修饰、蛋白质互作及蛋白质表达3个方面综述了质谱在信号转导方面的研究。     

生物质谱技术在蛋白质组学研究中的应用

随着技术的进步,蛋白质组学的研究重心由最初旨在鉴定细胞或组织内基因组所表达的全部蛋白质转移到从整个蛋白质组水平上阐述包括蛋白翻译后修饰、生物大分子相互作用等反映蛋白质功能的层次。多种质谱离子化技术的突破使质谱技术成为蛋白质组学研究必不可少的手段。质谱技术联合蛋白质组学多角度、深层次探索生命系统分子本质成为现阶段生命科学研究领域的主旋律之一。本文简要综述了肽和蛋白质等生物大分子质谱分析的原理、方式和应用,并对其发展前景做出展望。     

现代质谱技术在蛋白质组学中的应用及其最新进展

简述了蛋白质组学的概念、内容和意义,重点综述了现代质谱技术在蛋白质组学中的应用,主要包括蛋白质和肽段的鉴定和定量、蛋白质翻译后修饰的鉴定和蛋白质间相互作用的检测等。随着新的高质量精确度、分辨率、灵敏度和通量质谱仪的出现,现代质谱技术在蛋白质组学中的应用将越来越广泛,并给蛋白质组学研究带来新的机遇。     

化学交联质谱技术的研究进展

化学交联质谱技术是解析蛋白质结构和研究蛋白质相互作用的重要工具。近5年以来,该技术在方法和应用上都取得了很大的进步。方法上,一方面可断裂交联剂与新型分离富集方法展现了较好的应用前景,另一方面更加高效的交联肽段搜索引擎和质量控制方法为交联质谱数据分析提供了有力的工具。应用上,一方面与冷冻电镜技术结合解析了大量蛋白质的结构,另一方面从研究蛋白质复合物的相互作用发展到研究全蛋白质组水平的相互作用网络。化学交联质谱技术在方法和应用上的蓬勃发展,体现了这一技术的重要作用。本文对化学交联质谱技术的各个环节进行了详细的综述,包括交联剂选择、交联反应、酶切、交联肽段富集、液质联用、交联肽段鉴定、质量控制和生物学应用,重点介绍了最近5年的研究进展。最后,讨论了化学交联质谱技术面临的挑战及未来的发展方向。     

基于质谱的植物蛋白质组学研究方法

基于质谱的植物蛋白质组学研究方法,从定性和定量蛋白质组学两个方向进行了归纳总结,并对近年来出现的靶向蛋白质组学、DIA/SWATH技术、化学蛋白质组学,以及多组学联合分析等蛋白质组学研究的新技术、新方法和新应用进行了综述。     

生物质谱及其在蛋白质组学研究中的应用

生物质谱是蛋白质组学研究必不可少的关键技术。近年来,生物质谱在鉴定通量、分辨率和灵敏度等方面均有质的飞跃,从而促进了蛋白质组研究各个领域的飞速发展。本文就生物质谱技术的原理、技术和仪器发展现状,及其在蛋白质组学研究中的应用进展作一简要的综述。     

用蛋白质组学方法解析磷酸化蛋白质

蛋白质磷酸化和去磷酸化这一可逆过程参与了高等真核生物细胞信号转导、细胞分化和细胞生长等重要过程,并与许多疾病、肿瘤的发生密切相关。蛋白质组学技术的不断发展和完善,可以更好、更多地识别和鉴定磷酸化蛋白质,为解析磷酸化蛋白质提供了可能。章综述了用于分离和鉴定磷酸化蛋白质的蛋白质组学方法。     

生物质谱技术及其应用

质谱是带电粒子按质荷比大小顺序排列的图谱,最初主要用来测定元素或同位素的原子量,随着科学的发展及高性能质谱仪器的出现,质谱被越来越多地应用生命科学研究的许多领域,以其质辅助激光解吸附飞行时间质谱和电喷雾质谱为代表的现代生物质谱技术,为蛋白质等生物大分子的研究提供了必要的技术手段。本文在简介近年来比较常用的几种生物质谱技术的基础上,概述了生物质谱技术在蛋白质,核酸研究及检测分析等几个方面的初步应用。     

蛋白质结构与相互作用研究新方法——交联质谱技术

蛋白质的空间结构信息以及蛋白质间的相互作用信息对于研究蛋白质的功能有重要意义.研究蛋白质结构与相互作用的传统技术,如核磁共振技术、X射线晶体衍射技术等,对于蛋白质的纯度、结晶性和绝对量均有比较高的要求,限制了其广泛应用.交联质谱技术是近十多年来发展起来的新技术,它将质谱技术与交联技术相结合,在研究蛋白质结构与相互作用方面具有速度快、成本小、蛋白质各方面性状要求低等优势.本文就交联质谱技术各个环节的技术方法加以综述,包括交联质谱实验分离富集技术、常见交联剂特性、交联质谱数据库搜索算法、结果验证研究和交联质谱技术的应用等方面,并展望了该研究方向未来的发展.     

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.     

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。     

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.     

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.     

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.     

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.     

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.     

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.     

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.