Contents of heavy metals in lotus seed from REEs mining area

At rare earth mining area, the pollution of heavy metals has been paid little attention. In lotus seeds and soil samples sampled from a typical rare earth elements mining area were detected the contents of eight kinds of heavy metals (Cr, Mn, Cu, Zn, As, Cd, Hg and Pb) to investigate the pollution from rare earth elements mining by ICP-MS (Inductively Coupled Plasma Mass Spectrometry). The results showed that the contents of the heavy metals Cr, Mn, Cu, Zn, As, Cd, Hg and Pb in lotus seeds were 0.25, 86.94, 8.32, 19.76, 0.13, 0.08, 0.01, and 0.20 μg/g in lotus seeds, respectively, and corresponding contents in soil were 27.68, 168.71, 20.23, 47.16, 1.83, 0.13, 0.04, and 23.15 μg/g. These data showed that the contents of heavy metals in lotus seeds from REEs mining area are no higher than reference area and meet national food safety standards of China.     

Mass spectral profiling: an effective tool for quality control of herbal medicines

Quality control of herbal medicines (HMs) is a big big headache because of the high complexity and unknown mechanism on disease treatment. In this work, mass spectral profiling, a new tool for data processing is proposed to help a lot in solving this problem as gas chromatography-mass spectroscopy (GC-MS) is used to detect both the active and non-active ingredients buried in HMs. The main idea of mass spectral profiling is employment of target m/z points of GC-MS data on the extraction of chromatographic profiles of pure and/or mixed compositions concerned. Further, the absolute or relative abundance at these m/z points can be utilized for results interpretation. With the help of this tool, the qualitative and quantitative information of chemical components within complicated HMs will be mined out effectively. It can then be recommended as reference indices to assess the importance of target compositions in HMs, such as efficacy evaluation on disease treatment of the active constituents. Mass spectral profiling with less data points significantly improves the possibility to get the rich information with no strong requirements of data preprocessing procedures, like alignment of shift of retention times among different chromatographic profiles. It is powerful for quality control of HMs coupled with pattern recognition techniques on high-throughput data sets. In this study, a commonly used herbal medicine, Houttuynia cordata Thunb and its finished injection products, were used to deliver the strategies. Absolutely, the working principles can be extended to the investigation of metabonomics with gas chromatography-time-of-flight-mass spectrometry (GC-MS-TOF). The good performance of mass spectral profiling shows that it can be a promising tool in the future studies of complex mixture systems.     

The information encrypted in accurate peptide masses-improved protein identification and assistance in glycopeptide identification and characterization

Analytically useful information from accurate mass data for peptides with an error of 相似文献   

Software Lock Mass by Two-Dimensional Minimization of Peptide Mass Errors

Mass accuracy is a key parameter in proteomic experiments, improving specificity, and success rates of peptide identification. Advances in instrumentation now make it possible to routinely obtain high resolution data in proteomic experiments. To compensate for drifts in instrument calibration, a compound of known mass is often employed. This ‘lock mass’ provides an internal mass standard in every spectrum. Here we take advantage of the complexity of typical peptide mixtures in proteomics to eliminate the requirement for a physical lock mass. We find that mass scale drift is primarily a function of the m/z and the elution time dimensions. Using a subset of high confidence peptide identifications from a first pass database search, which effectively substitute for the lock mass, we set up a global mathematical minimization problem. We perform a simultaneous fit in two dimensions using a function whose parameterization is automatically adjusted to the complexity of the analyzed peptide mixture. Mass deviation of the high confidence peptides from their calculated values is then minimized globally as a function of both m/z value and elution time. The resulting recalibration function performs equal or better than adding a lock mass from laboratory air to LTQ-Orbitrap spectra. This ‘software lock mass’ drastically improves mass accuracy compared with mass measurement without lock mass (up to 10-fold), with none of the experimental cost of a physical lock mass, and it integrated into the freely available MaxQuant analysis pipeline ().     

An integrated strategy for in vivo metabolite profiling using high-resolution mass spectrometry based data processing techniques

An ongoing challenge of drug metabolite profiling is to detect and identify unknown or low-level metabolites in complex biological matrices. Here we present a generic strategy for metabolite detection using multiple accurate-mass-based data processing tools via the analysis of rat samples of two model drug candidates, AZD6280 and AZ12488024. First, the function of isotopic pattern recognition was proved to be highly effective in the detection of metabolites derived from [¹⁴C]-AZD6280 that possesses a distinct isotopic pattern. The metabolites revealed using this approach were in excellent qualitative correlation to those observed in radiochromatograms. Second, the effectiveness of accurate mass based untargeted data mining tools such as background subtraction, mass defect filtering, or a data mining package (MZmine) used for metabolomic analysis in detection of metabolites of [¹⁴C]-AZ12488024 in rat urine, feces, bile and plasma samples was examined and a total of 33 metabolites of AZ12488024 were detected. Among them, at least 16 metabolites were only detected by the aid of the data mining packages and not via radiochromatograms. New metabolic pathways such as S-oxidation and thiomethylation reactions occurring on the thiazole ring were proposed based on the processed data. The results of these experiments also demonstrated that accurate mass-based mass defect filtering (MDF) and data mining techniques used in metabolomics are complementary and can be valuable tools for delineating low-level metabolites in complex matrices. Furthermore, the application of distinct multiple data-mining algorithms in parallel, or in tandem, can be effective for rapidly profiling in vivo drug metabolites.     

Towards characterization of the human urinary peptidome

Biomarker discovery in human urine has become an evolving and potentially valuable topic in relation to renal function and diseases of the urinary tract. In order to deliver on the promises and to facilitate the development of validated biomarkers or biomarker panels, protein and peptide profiling techniques need high sample throughput, speed of analysis, and reproducibility of results. Here, we outline the performance characteristics of the liquid chromatography/MALDI-TOF-MS based differential peptide display (DPD(1)) approach for separating, detecting, abundance profiling and identification of native peptides derived from human urine. The typical complexity of peptides in human urine (resolution of the technique with respect to detectable number of peptides), the reproducibility (coefficient of variation for abundance profiles of all peptides detected in biological samples) and dynamic range of the technique as well as the lower limit of detection were characterized. A substantial number of peptides present in normal human urine were identified and compared to findings in four published proteome studies. In an explorative approach, pathological urines from patients suffering from post-renal-filtration diseases were qualitatively compared to normal urine. In conclusion, the peptidomics technology as shown here has a great potential for high throughput and high resolution urine peptide profiling analyses. It is a promising tool to study not only renal physiology and pathophysiology and to determine new biomarkers of renal diseases; it also has the potential to study remotely localized or systemic aberrations within human biology.     

LC-MS/MS Peptide Mapping with Automated Data Processing for Routine Profiling of N-Glycans in Immunoglobulins

Protein N-Glycan analysis is traditionally performed by high pH anion exchange chromatography (HPAEC), reversed phase liquid chromatography (RPLC), or hydrophilic interaction liquid chromatography (HILIC) on fluorescence-labeled glycans enzymatically released from the glycoprotein. These methods require time-consuming sample preparations and do not provide site-specific glycosylation information. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) peptide mapping is frequently used for protein structural characterization and, as a bonus, can potentially provide glycan profile on each individual glycosylation site. In this work, a recently developed glycopeptide fragmentation model was used for automated identification, based on their MS/MS, of N-glycopeptides from proteolytic digestion of monoclonal antibodies (mAbs). Experimental conditions were optimized to achieve accurate profiling of glycoforms. Glycan profiles obtained from LC-MS/MS peptide mapping were compared with those obtained from HPAEC, RPLC, and HILIC analyses of released glycans for several mAb molecules. Accuracy, reproducibility, and linearity of the LC-MS/MS peptide mapping method for glycan profiling were evaluated. The LC-MS/MS peptide mapping method with fully automated data analysis requires less sample preparation, provides site-specific information, and may serve as an alternative method for routine profiling of N-glycans on immunoglobulins as well as other glycoproteins with simple N-glycans.

Probing genetic algorithms for feature selection in comprehensive metabolic profiling approach

Six different clones of 1-year-old loblolly pine (Pinus taeda L.) seedlings grown under standardized conditions in a green house were used for sample preparation and further analysis. Three independent and complementary analytical techniques for metabolic profiling were applied in the present study: hydrophilic interaction chromatography (HILIC-LC/ESI-MS), reversed-phase liquid chromatography (RP-LC/ESI-MS), and gas chromatography all coupled to mass spectrometry (GC/TOF-MS). Unsupervised methods, such as principle component analysis (PCA) and clustering, and supervised methods, such as classification, were used for data mining. Genetic algorithms (GA), a multivariate approach, was probed for selection of the smallest subsets of potentially discriminative classifiers. From more than 2000 peaks found in total, small subsets were selected by GA as highly potential classifiers allowing discrimination among six investigated genotypes. Annotated GC/TOF-MS data allowed the generation of a small subset of identified metabolites. LC/ESI-MS data and small subsets require further annotation. The present study demonstrated that combination of comprehensive metabolic profiling and advanced data mining techniques provides a powerful metabolomic approach for biomarker discovery among small molecules. Utilizing GA for feature selection allowed the generation of small subsets of potent classifiers.     

From primary structure to function: biological insights from large-molecule mass spectra

As increasing information is available from genomic databases, mass spectrometry has begun to be used to identify and/or assess regions of predicted DNA or protein sequence. Mass spectrometry performance limits, together with experiments designed for genomic interplay, are being extended to allow accurate genotyping and protein profiling of cells at rates commensurate with the data-intensive future of biology.     

Protein identification by in-gel digestion, high-performance liquid chromatography, and mass spectrometry: Peptide analysis by complementary ionization techniques

A biologically active protein fraction was isolated from rabbit intestine, purified by one-dimensional SDS-PAGE and stained with Coomassie Brilliant Blue. A predominant band of approximately 110-130 kDa was excised and digested in-gel with trypsin. The resulting peptides were extracted then separated by microbore reversed-phase high-performance liquid chromatography (HPLC). Mass spectrometric data from one HPLC fraction obtained by two different ionization techniques proved to be complementary. Matrix-assisted laser desorption/ionization (MALDI) showed nine peptide masses, which by post source decay analysis and database searching were attributed to two proteins. Nanoflow electrospray analysis performed on a hybrid tandem mass spectrometer of quadrupole-quadrupole-orthogonal acceleration time-of-flight (QqTOF) geometry detected six additional peptide components. On the basis of the additional peptides and superior quality collision-induced dissociation spectra typical of this instrument type, two further proteins were identified. The resolution afforded by the QqTOF instrument permitted charge state determination for the fragment ions while preserving the high detection sensitivity that was essential in obtaining the composition of this mixture of proteins.     

Liquid chromatography–mass spectrometry based global metabolite profiling: A review

Untargeted, global metabolite profiling (often described as metabonomics or metabolomics) represents an expanding research topic and is, potentially, a major pillar for systems biology studies. To obtain holistic metabolic profiles from complex samples, such as biological fluids or tissue extracts, requires powerful, high resolution and information-rich analytical methods and for this spectroscopic technologies are generally used. Mass spectrometry, coupled to liquid chromatography (LC–MS), is increasingly being used for such investigations as a result of the significant advances in both technologies over the past decade. Here we try to critically review the topic of LC–MS-based global metabolic profiling and describe and compare the results offered by different analytical strategies and technologies. This review highlights the current challenges, limitations and opportunities of the current methodology.     

Mass‐spectrometry‐based lipidomics

Lipids, which have a core function in energy storage, signalling and biofilm structures, play important roles in a variety of cellular processes because of the great diversity of their structural and physiochemical properties. Lipidomics is the large‐scale profiling and quantification of biogenic lipid molecules, the comprehensive study of their pathways and the interpretation of their physiological significance based on analytical chemistry and statistical analysis. Lipidomics will not only provide insight into the physiological functions of lipid molecules but will also provide an approach to discovering important biomarkers for diagnosis or treatment of human diseases. Mass‐spectrometry‐based analytical techniques are currently the most widely used and most effective tools for lipid profiling and quantification. In this review, the field of mass‐spectrometry‐based lipidomics was discussed. Recent progress in all essential steps in lipidomics was carefully discussed in this review, including lipid extraction strategies, separation techniques and mass‐spectrometry‐based analytical and quantitative methods in lipidomics. We also focused on novel resolution strategies for difficult problems in determining C=C bond positions in lipidomics. Finally, new technologies that were developed in recent years including single‐cell lipidomics, flux‐based lipidomics and multiomics technologies were also reviewed.     

Investigation of heroin profiling using trace organic impurities

The acidic and neutral impurities in heroin samples (46 Purified South-East Asian (PSEA) and 8 non-PSEA samples) were analysed using gas chromatography. Mass spectral detection allowed the construction of a comprehensive library of over 649 impurities. A variety of statistical procedures were used to select 70 viable profiling parameters. Cluster analyses were used to investigate the similarities between samples using organic parameters. A blended PSEA profile was constructed for comparison with samples of unknown origins. Resolution between samples of PSEA/non-PSEA origins was demonstrated with a selection of 25 (18 continuous and 7 dichotomised) discriminating factors. Several predictive logistic models were created using up to 18 parameters, explaining 85-100% of the variation in the experimental data.     

Oblique orientated alpha-helices and their prediction

Oblique orientated alpha-helices possess hydrophobicity gradients, which allow the parent alpha-helices to penetrate the membrane at a shallow angle, thereby destabilising membrane lipid organisation and promoting a range of biological processes. These alpha-helices occur in a variety of membrane interactive proteins and a number of techniques have been developed to guide their identification using sequence data alone. Hydrophobicity profiling, which provides a one-dimensional analysis of sequence data, identified only 30% of known tilted peptides in a control dataset and was thus of limited predictive use. In contrast, extended hydrophobic moment plot methodology and amphipilic profiling which take residue distribution into account and provide two-dimensional analysis of primary structural data, were found to be good indicators of tilted peptide structure. Amphiphilic profiling identified 67% of tilted peptides in the control dataset and showed that potentially, approximately 40% of transmembrane alpha-helices possess tilted peptide structure. However, it has been shown that extending these simple methods to take into account the three-dimensional spatial distribution of residues gives no clear additional benefit to identifying tilted peptides.     

An introduction to hybrid ion trap/time‐of‐flight mass spectrometry coupled with liquid chromatography applied to drug metabolism studies

Metabolism studies play an important role at various stages of drug discovery and development. Liquid chromatography combined with mass spectrometry (LC/MS) has become a most powerful and widely used analytical tool for identifying drug metabolites. The suitability of different types of mass spectrometers for metabolite profiling differs widely, and therefore, the data quality and reliability of the results also depend on which instrumentation is used. As one of the latest LC/MS instrumentation designs, hybrid ion trap/time‐of‐flight MS coupled with LC (LC‐IT‐TOF‐MS) has successfully integrated ease of operation, compatibility with LC flow rates and data‐dependent MSⁿ with high mass accuracy and mass resolving power. The MSⁿ and accurate mass capabilities are routinely utilized to rapidly confirm the identification of expected metabolites or to elucidate the structures of uncommon or unexpected metabolites. These features make the LC‐IT‐TOF‐MS a very powerful analytical tool for metabolite identification. This paper begins with a brief introduction to some basic principles and main properties of a hybrid IT‐TOF instrument. Then, a general workflow for metabolite profiling using LC‐IT‐TOF‐MS, starting from sample collection and preparation to final identification of the metabolite structures, is discussed in detail. The data extraction and mining techniques to find and confirm metabolites are discussed and illustrated with some examples. This paper is directed to readers with no prior experience with LC‐IT‐TOF‐MS and will provide a broad understanding of the development and utility of this instrument for drug metabolism studies. Copyright © 2012 John Wiley & Sons, Ltd.     

Comparison of methods for simultaneous identification of bacterial species and determination of metabolic activity by protein‐based stable isotope probing (Protein‐SIP) experiments

We developed a concept for analysing carbon and nitrogen fluxes in microbial communities by employing protein‐based stable isotope probing (Protein‐SIP) in metabolic labelling experiments with stable isotope labelled substrates. For identification of microbial species intact protein profiling (IPP) can be used, whereas the assessment of their metabolic activity is achieved by shotgun mass mapping (SMM). Microbial cultures were grown on substrates containing ¹³C or ¹⁵N. For identification of species we tested both the IPP and the SMM approaches. Mass spectra (MALDI‐MS) were taken from mixtures of either intact proteins or peptides from tryptic digestion for generating species‐specific peak patterns. In the case of SMM, the fragmentation of peptides was additionally used to obtain sequence information for species identification. Mass spectra of peptide sequences allow calculation of the amount of ¹³C or ¹⁵N incorporation within peptides for determining metabolic activity of the specific species. The comparison of IPP and SMM revealed a higher robustness of species identification by SMM. In addition, the assessment of incorporation levels of ¹³C and ¹⁵N into peptides by SMM revealed a lower uncertainty (0.5–0.8 atom %) compared to IPP (6.4–8.9 atom %). The determination of metabolic activity and function of individual species by Protein‐SIP can help to analyse carbon and nitrogen fluxes within microbial communities. Copyright © 2009 John Wiley & Sons, Ltd.     

ExMS: Data Analysis for HX-MS Experiments

A previous paper considered the problems that presently limit the hydrogen exchange - mass spectrometry (HX-MS) method for studying the biophysical and functional properties of proteins. Many of these problems can be overcome by obtaining and analyzing hundreds of sequentially overlapping peptide fragments that cover the protein many times over (Mayne et al. J. Am. Soc. Mass Spectrom. 2011: ). This paper describes a computer program called ExMS that furthers this advance by making it possible to efficiently process crowded mass spectra and definitively identify and characterize these many peptide fragments. ExMS automatically scans through high resolution MS data to find the individual isotopic peaks and isotopic envelopes of a list of peptides previously identified by MS/MS. It performs a number of tests to ensure correct identification in spite of peptide overlap in both chromatographic and mass spectrometric dimensions and possible multi-modal envelopes due to static or dynamic structural heterogeneity or HX EX1 behavior. The program can automatically process data from many sequential HX time points with no operator intervention at the rate of ~2 sec per peptide per HX time point using desktop computer equipment, but it also provides for rapid manual checking and decision when ambiguity exists. Additional subroutines can provide a step by step report of performance at each test along the way and parameter adjustment, deconvolute isotopic envelopes, and plot the time course of single and multi-modal H-D exchange. The program will be available on an open source basis at:     

Diagnostic protein discovery using liquid chromatography/mass spectrometry for proteolytic peptide targeting

A peptide targeting method has been developed for diagnostic protein discovery, which combines proteolytic digestion of fractionated plasma proteins and liquid chromatography coupled to electrospray time-of-flight mass spectrometry (LC/ESI-TOFMS) profiling. Proteolysis prior to profiling overcomes molecular weight limitations and compensates for the poor sensitivity of matrix-assisted laser desorption/ionization (MALDI) protein profiling. LC/MS increases the peak capacity compared to crude fractionation techniques or single sample MALDI analysis. Differentially expressed peptides are targeted in the mass chromatograms using bioinformatic techniques and subsequently sequenced with MALDI tandem MS. In a model study comparing pancreatic cancer patients to controls, 74% of the peptide targets were successfully sequenced. This profiling method was superior to previous experiments using single sample MALDI analysis for protein profiling or proteolytic peptide profiling, because more potential protein markers were identified.     

Determination of depth profiling of metal trace impurities on Si surface using total reflection X-ray fluorescence

Summary Total reflection X-ray fluorescence (TXRF) is used for non-destructive determination of depth profiling. A numerical processing is presented as impurity quantification in the continuum excitation TXRF without using standards. Dependences of concentration of impurities on depths ranging from a few tens to thousands Angströms are given for Fe and Cu on Si-wafer. The detection limits are in the range of 10¹⁰ atoms/cm². The method was checked with Secondary Ion Mass Spectrometry (SIMS) and the agreement is reasonably good.     

高通量蛋白质组学分析研究进展

基于质谱的蛋白质组学技术已经日趋成熟,可以对细胞和组织中的成千上万种蛋白质进行全面的定性和定量分析,逐步实现“深度覆盖”。随着生物医学日益增长的大队列蛋白质组学分析需求,如何在保持较为理想的覆盖深度下实现短时间、快速的“高通量”蛋白质组学分析已成为当前亟需解决的关键问题之一。常规的蛋白质组学分析流程通常包括样品前处理、色谱分离、质谱检测和数据分析。该文从以上4个方面展开介绍近10年以来高通量蛋白质组学分析技术取得的一系列研究进展,主要包括:(1)基于高通量、自动化移液工作站的蛋白质组样品前处理方法;(2)基于微升流速液相色谱与质谱联用的高通量蛋白质组检测方法;(3)利用灵敏度高、扫描速度快的质谱仪实现短色谱梯度分离下蛋白质组深度覆盖的分析方法;(4)基于人工智能、深度神经网络、机器学习等的蛋白质组学大数据分析方法。此外,对高通量蛋白质组学面临的挑战及其发展进行展望。总而言之,预期在不久的将来高通量蛋白质组学技术将会逐步“落地转化”,成为大队列蛋白质组学分析的利器。