Orbitrap Exploris 480质谱在定量蛋白质组学应用中的优化和评测

基于数据依赖的扫描模式(data-dependent acquisition, DDA)和数据非依赖的扫描模式(data-independent acquisition,DIA)的非标记定量(label-free quantitative,LFQ)和同位素标记TMT (tandem mass tag)定量是蛋白质组学定量中较常见的技术.本文利用最新的Orbitrap Exploris 480质谱,优化了DDA、FAIMS DDA、FAIMS DIA的非标记定量方法以及TMT定量策略的关键质谱参数,并将其应用在人细胞蛋白质组、单细胞蛋白质组、血浆蛋白质组和酵母蛋白质组分析.结果表明,在DDA实验中,设置碰撞能量为27、二级谱图的分辨率为15 K、最大离子注入时间为22 ms是最佳的参数组合.针对极微量样品200 pg～5 ng,可以根据样品量相应设置最佳的质谱参数.使用200 pg和500 pg的HeLa细胞样品,分别鉴定到1 259和1 725个蛋白质,从而实现了单细胞蛋白质组学的深度覆盖.在FAIMS DDA实验中,60 min或90 min梯度时选择CV-45V的补偿电压,120 ...     

Sensitive and Specific Peak Detection for SELDI-TOF Mass Spectrometry Using a Wavelet/Neural-Network Based Approach

SELDI-TOF mass spectrometer''s compact size and automated, high throughput design have been attractive to clinical researchers, and the platform has seen steady-use in biomarker studies. Despite new algorithms and preprocessing pipelines that have been developed to address reproducibility issues, visual inspection of the results of SELDI spectra preprocessing by the best algorithms still shows miscalled peaks and systematic sources of error. This suggests that there continues to be problems with SELDI preprocessing. In this work, we study the preprocessing of SELDI in detail and introduce improvements. While many algorithms, including the vendor supplied software, can identify peak clusters of specific mass (or m/z) in groups of spectra with high specificity and low false discover rate (FDR), the algorithms tend to underperform estimating the exact prevalence and intensity of peaks in those clusters. Thus group differences that at first appear very strong are shown, after careful and laborious hand inspection of the spectra, to be less than significant. Here we introduce a wavelet/neural network based algorithm which mimics what a team of expert, human users would call for peaks in each of several hundred spectra in a typical SELDI clinical study. The wavelet denoising part of the algorithm optimally smoothes the signal in each spectrum according to an improved suite of signal processing algorithms previously reported (the LibSELDI toolbox under development). The neural network part of the algorithm combines those results with the raw signal and a training dataset of expertly called peaks, to call peaks in a test set of spectra with approximately 95% accuracy. The new method was applied to data collected from a study of cervical mucus for the early detection of cervical cancer in HPV infected women. The method shows promise in addressing the ongoing SELDI reproducibility issues.     

Optimization of Whole-Body Zebrafish Sectioning Methods for Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) methods and protocols have become widely adapted to a variety of tissues and species. However, the MSI literature contains minimal information on whole-body cryosection preparation for the zebrafish (ZF; Danio rerio), a model organism routinely used in developmental, toxicity, and carcinogenicity studies. The optimal medium for embedding and cryosectioning a whole organism or soft-tissue specimen for histological examination is a synthetic polymer mixture that is incompatible with MSI as a result of ion suppression. We describe the optimal methods and results for embedding and cryosectioning whole-body ZF for MALDI-MSI. We evaluated 13 distinct embedding media formulations and found a supportive hydrogel with the consistency of cartilage to be the optimal embedding medium. The hydrogel medium does not interfere with MSI data collection, aids in tissue stability, is readily available for purchase, and is easy to prepare and handle during cryosectioning. Additionally, we decreased the matrix cluster interference commonly caused by α-cyano-4-hydroxycinnamic acid by adding ammonium phosphate to the solvent spray solution. The optimized methods developed in our laboratory produced high-quality cryosections, as well as high-quality mass spectral images of sectioned ZF.     

Metabolomic Responses of Guard Cells and Mesophyll Cells to Bicarbonate

Anthropogenic CO₂ presently at 400 ppm is expected to reach 550 ppm in 2050, an increment expected to affect plant growth and productivity. Paired stomatal guard cells (GCs) are the gate-way for water, CO₂, and pathogen, while mesophyll cells (MCs) represent the bulk cell-type of green leaves mainly for photosynthesis. We used the two different cell types, i.e., GCs and MCs from canola (Brassica napus) to profile metabolomic changes upon increased CO₂ through supplementation with bicarbonate (HCO₃ ^-). Two metabolomics platforms enabled quantification of 268 metabolites in a time-course study to reveal short-term responses. The HCO₃ ^- responsive metabolomes of the cell types differed in their responsiveness. The MCs demonstrated increased amino acids, phenylpropanoids, redox metabolites, auxins and cytokinins, all of which were decreased in GCs in response to HCO₃ ^-. In addition, the GCs showed differential increases of primary C-metabolites, N-metabolites (e.g., purines and amino acids), and defense-responsive pathways (e.g., alkaloids, phenolics, and flavonoids) as compared to the MCs, indicating differential C/N homeostasis in the cell-types. The metabolomics results provide insights into plant responses and crop productivity under future climatic changes where elevated CO₂ conditions are to take center-stage.     

敌百虫、多菌灵对蚯蚓的自然土壤法毒性实验

在采用滤纸接触法研究的基础上,本试验采用自然土壤法研究了敌百虫、多菌灵对蚯蚓的毒性效应,结果显示,敌百虫对蚯蚓的LC50为4.332 g·L-1,多菌灵对蚯蚓的LC50为5.3645 g·L-1.自然土壤法中敌百虫对蚯蚓的毒性比多菌灵对蚯蚓的毒性大.     

Evaluation of HCH,DDT and Endosulfan Levels in Soil by Gas Chromatography/Tandem Mass Spectrometry

Persistent organic pollutants (Σ DDX, Σ HCH, and Σ Endosulfan) were quantified in top soil and deep soil of a pesticide manufacturing industry. It was also possible to identify the presence of some other organochlorinated compounds (OCs) in the soil. A suitable multiresidue analysis of persistent organic pollutants in soil samples was developed based on soxhlet extraction and gas chromatography–mass spectrometry for quantifying parent compounds and degradation products, namely OCs and other miscellaneous pesticides. The quantification protocol was developed using Programmed Temperature Vaporization (PTV) and GC/MS/MS as identification tools. Extraction, PTV and MS/MS conditions were optimized for 11 pesticides with unambiguous spectral confirmation. The protocol has been applied to a large number of environmental samples and has proved to be reliable. The degradation ratios between the parent substances and their metabolites (DDX and HCH isomers) were calculated to determine whether there were any fresh inputs of parent pesticide at the site. Pesticide concentrations in the low to high concentration range (159 μ g/kg to 133 mg/kg) have been measured. The investigations clearly indicate pesticide contamination in the soil.     

Combinatorial Approach for Large-scale Identification of Linked Peptides from Tandem Mass Spectrometry Spectra

The combination of chemical cross-linking and mass spectrometry has recently been shown to constitute a powerful tool for studying protein–protein interactions and elucidating the structure of large protein complexes. However, computational methods for interpreting the complex MS/MS spectra from linked peptides are still in their infancy, making the high-throughput application of this approach largely impractical. Because of the lack of large annotated datasets, most current approaches do not capture the specific fragmentation patterns of linked peptides and therefore are not optimal for the identification of cross-linked peptides. Here we propose a generic approach to address this problem and demonstrate it using disulfide-bridged peptide libraries to (i) efficiently generate large mass spectral reference data for linked peptides at a low cost and (ii) automatically train an algorithm that can efficiently and accurately identify linked peptides from MS/MS spectra. We show that using this approach we were able to identify thousands of MS/MS spectra from disulfide-bridged peptides through comparison with proteome-scale sequence databases and significantly improve the sensitivity of cross-linked peptide identification. This allowed us to identify 60% more direct pairwise interactions between the protein subunits in the 20S proteasome complex than existing tools on cross-linking studies of the proteasome complexes. The basic framework of this approach and the MS/MS reference dataset generated should be valuable resources for the future development of new tools for the identification of linked peptides.The study of protein–protein interactions is crucial to understanding how cellular systems function because proteins act in concert through a highly organized set of interactions. Most cellular processes are carried out by large macromolecular assemblies and regulated through complex cascades of transient protein–protein interactions (1). In the past several years numerous high-throughput studies have pioneered the systematic characterization of protein–protein interactions in model organisms (2–4). Such studies mainly utilize two techniques: the yeast two-hybrid system, which aims at identifying binary interactions (5), and affinity purification combined with tandem mass spectrometry analysis for the identification of multi-protein assemblies (6–8). Together these led to a rapid expansion of known protein–protein interactions in human and other model organisms. Patche and Aloy recently estimated that there are more than one million interactions catalogued to date (9).But despite rapid progress, most current techniques allow one to determine only whether proteins interact, which is only the first step toward understanding how proteins interact. A more complete picture comes from characterizing the three-dimensional structures of protein complexes, which provide mechanistic insights that govern how interactions occur and the high specificity observed inside the cell. Traditionally the gold-standard methods used to solve protein structures are x-ray crystallography and NMR, and there have been several efforts similar to structural genomics (10) aiming to comprehensively solve the structures of protein complexes (11, 12). Although there has been accelerated growth of structures for protein monomers in the Protein Data Bank in recent years (11), the growth of structures for protein complexes has remained relatively small (9). Many factors, including their large size, transient nature, and dynamics of interactions, have prevented many complexes from being solved via traditional approaches in structural biology. Thus, the development of complementary analytical techniques with which to probe the structure of large protein complexes continues to evolve (13–18).Recent developments have advanced the analysis of protein structures and interaction by combining cross-linking and tandem mass spectrometry (17, 19–24). The basic idea behind this technique is to capture and identify pairs of amino acid residues that are spatially close to each other. When these linked pairs of residues are from the same protein (intraprotein cross-links), they provide distance constraints that help one infer the possible conformations of protein structures. Conversely, when pairs of residues come from different proteins (interprotein cross-links), they provide information about how proteins interact with one another. Although cross-linking strategies date back almost a decade (25, 26), difficulty in analyzing the complex MS/MS spectrum generated from linked peptides made this approach challenging, and therefore it was not widely used. With recent advances in mass spectrometry instrumentation, there has been renewed interest in employing this strategy to determine protein structures and identify protein–protein interactions. However, most studies thus far have been focused on purified protein complexes. With today''s mass spectrometers being capable of analyzing tens of thousands of spectra in a single experiment, it is now potentially feasible to extend this approach to the analysis of complex biological samples. Researchers have tried to realize this goal using both experimental and computational approaches. Indeed, a plethora of chemical cross-linking reagents are now available for stabilizing these complexes, and some are designed to allow for easier peptide identification when employed in concert with MS analysis (20, 27, 28). There have also been several recent efforts to develop computational methods for the automatic identification of linked peptides from MS/MS spectra (29–36). However, because of the lack of large annotated training data, most approaches to date either borrow fragmentation models learned from unlinked, linear peptides or learn the fragmentation statistics from training data of limited size (30, 37), which might not generalize well across different samples. In some cases it is possible to generate relatively large training data, but it is often very labor intensive and involves hundreds of separate LC-MS/MS runs (36). Here, employing disulfide-bridged peptides as an example, we propose a novel method that uses a combinatorial peptide library to (a) efficiently generate a large mass spectral reference dataset for linked peptides and (b) use these data to automatically train our new algorithm, MXDB, which can efficiently and accurately identify linked peptides from MS/MS spectra.     

Gas Chromatography/Mass Spectrometry-Based Metabolomic Profiling Reveals Alterations in Mouse Plasma and Liver in Response to Fava Beans

Favism is a life-threatening hemolytic anemia resulting from the intake of fava beans by susceptible individuals with low erythrocytic glucose 6-phosphate dehydrogenase (G6PD) activity. However, little is known about the metabolomic changes in plasma and liver after the intake of fava beans in G6PD normal and deficient states. In this study, gas chromatography/mass spectrometry was used to analyze the plasma and liver metabolic alterations underlying the effects of fava beans in C3H- and G6PD-deficient (G6PDx) mice, and to find potential biomarkers and metabolic changes associated with favism. Our results showed that fava beans induced oxidative stress in both C3H and G6PDx mice. Significantly, metabolomic differences were observed in plasma and liver between the control and fava bean treated groups of both C3H and G6PDx mice. The levels of 7 and 21 metabolites in plasma showed significant differences between C3H-control (C3H-C)- and C3H fava beans-treated (C3H-FB) mice, and G6PDx-control (G6PDx-C)- and G6PDx fava beans-treated (G6PDx-FB) mice, respectively. Similarly, the levels of 7 and 25 metabolites in the liver showed significant differences between C3H and C3H-FB, and G6PDx and G6PDx-FB, respectively. The levels of oleic acid, linoleic acid, and creatinine were significantly increased in the plasma of both C3H-FB and G6PDx-FB mice. In the liver, more metabolic alterations were observed in G6PDx-FB mice than in C3H-FB mice, and were involved in a sugar, fatty acids, amino acids, cholesterol biosynthesis, the urea cycle, and the nucleotide metabolic pathway. These findings suggest that oleic acid, linoleic acid, and creatinine may be potential biomarkers of the response to fava beans in C3H and G6PDx mice and therefore that oleic acid and linoleic acid may be involved in oxidative stress induced by fava beans. This study demonstrates that G6PD activity in mice can affect their metabolic pathways in response to fava beans.     

A New Approach for the Comparative Analysis of Multiprotein Complexes Based on 15N Metabolic Labeling and Quantitative Mass Spectrometry

The introduced protocol provides a tool for the analysis of multiprotein complexes in the thylakoid membrane, by revealing insights into complex composition under different conditions. In this protocol the approach is demonstrated by comparing the composition of the protein complex responsible for cyclic electron flow (CEF) in Chlamydomonas reinhardtii, isolated from genetically different strains. The procedure comprises the isolation of thylakoid membranes, followed by their separation into multiprotein complexes by sucrose density gradient centrifugation, SDS-PAGE, immunodetection and comparative, quantitative mass spectrometry (MS) based on differential metabolic labeling (¹⁴N/¹⁵N) of the analyzed strains. Detergent solubilized thylakoid membranes are loaded on sucrose density gradients at equal chlorophyll concentration. After ultracentrifugation, the gradients are separated into fractions, which are analyzed by mass-spectrometry based on equal volume. This approach allows the investigation of the composition within the gradient fractions and moreover to analyze the migration behavior of different proteins, especially focusing on ANR1, CAS, and PGRL1. Furthermore, this method is demonstrated by confirming the results with immunoblotting and additionally by supporting the findings from previous studies (the identification and PSI-dependent migration of proteins that were previously described to be part of the CEF-supercomplex such as PGRL1, FNR, and cyt f). Notably, this approach is applicable to address a broad range of questions for which this protocol can be adopted and e.g. used for comparative analyses of multiprotein complex composition isolated from distinct environmental conditions.     

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.     

植物地上部对铝毒的生理响应及其耐性

全世界50％以上潜在的可耕地属于酸性土壤,铝毒害是酸性土壤上植物生长最有害因素之一。近年来,为了阐明植物铝毒害及其耐性,前人已进行了大量的研究,并有一些综述性文章发表。然而,大多数文章主要综述铝对植物根系的影响及其耐性,因为根生长受抑是最早的铝毒害症状之一和溶液培养时最容易辨认的铝毒害症状。为此,本文综述了铝对植物地上部光合作用、光保护系统、水分利用效率、含水量、碳水化合物含量、矿质营养、有机酸和氮代谢的影响,并对富铝植物的解铝毒机制（铝与小分子有机酸螯合和把铝隔离在对铝不敏感的表皮细胞和液泡内）进行了综述。本文还对植物耐铝遗传学和分子生物学及今后需要研究的问题进行了讨论。     

蛋白质质谱数据缺失值填补方法的比较与评估

基于质谱数据的蛋白质定量分析一直是目前高通量蛋白质组学的重要研究手段.但是基于现有质谱技术的限制,大规模蛋白质定量过程中往往会产生大量的缺失值,这在一定程度上影响了下游分析的准确性.尽管很多缺失值填补方法被不断提出,但是蛋白质组学领域对于不同情况下缺失值填补方法效力的综合评估仍然缺乏.本研究基于真实数据的分布特征,构建...     

基于质谱技术筛选差异表达蛋白的统计学策略研究进展

随着质谱技术的快速发展,蛋白质组学已成为继基因组学、转录组学之后的又一研究热点,寻找可靠的差异表达蛋白对于生物标记物的发现至关重要.因此,如何准确、灵敏地筛选出差异蛋白已成为基于质谱的定量蛋白质组学的主要研究内容之一.目前,针对该问题的研究方法众多,但这些方法策略的适用范围不尽相同.总体来说,基于质谱技术筛选差异蛋白的统计学策略可以分为3类:基于经典统计学派的策略、基于贝叶斯学派的统计检验策略和其他策略,这3类方法有各自的应用范围、特点及不足.此外,筛选过程还将产生部分假阳性结果,可以采用其他方法对差异表达蛋白的质量进行控制,以提高统计检验结果的可靠性.     

Mass Spectrometry Based Immunopeptidomics Leads to Robust Predictions of Phosphorylated HLA Class I Ligands

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Highlights

• •Curation of 2066 phosphorylated HLA class I peptides from immunopeptidomics data.
• •Determination of 22 HLA class I binding motifs for phosphorylated peptides.
• •Observation of a higher frequency of phosphorylated ligands binding HLA-C molecules.
• •Development of a predictor of phosphorylated peptide interactions with HLA class I.

     

N-Dimethylaminomethylene-O-trialkylsilyl Derivatives of Nucleosides for Chromatography and Mass Spectrometry

Abstract

Dimethylformamide dimethyl acetal (DMF-DMA) reacts with nucleosides under mild conditions to give N-dimethylaminomethylene (N-DMAM) derivatives. Silylation provides the DMAM-O-trialkylsilyl mixed derivatives which have good chromatographic and mass spectral properties.