Infrared multiphoton dissociation for quantitative shotgun proteomics

We modified a dual-cell linear ion trap mass spectrometer to perform infrared multiphoton dissociation (IRMPD) in the low-pressure trap of a dual-cell quadrupole linear ion trap (dual-cell QLT) and perform large-scale IRMPD analyses of complex peptide mixtures. Upon optimization of activation parameters (precursor q-value, irradiation time, and photon flux), IRMPD subtly, but significantly, outperforms resonant-excitation collisional-activated dissociation (CAD) for peptides identified at a 1% false-discovery rate (FDR) from a yeast tryptic digest (95% confidence, p = 0.019). We further demonstrate that IRMPD is compatible with the analysis of isobaric-tagged peptides. Using fixed QLT rf amplitude allows for the consistent retention of reporter ions, but necessitates the use of variable IRMPD irradiation times, dependent upon precursor mass to charge (m/z). We show that IRMPD activation parameters can be tuned to allow for effective peptide identification and quantitation simultaneously. We thus conclude that IRMPD performed in a dual-cell ion trap is an effective option for the large-scale analysis of both unmodified and isobaric-tagged peptides.     

An automated multidimensional protein identification technology for shotgun proteomics.

We describe an automated method for shotgun proteomics named multidimensional protein identification technology (MudPIT), which combines multidimensional liquid chromatography with electrospray ionization tandem mass spectrometry. The multidimensional liquid chromatography method integrates a strong cation-exchange (SCX) resin and reversed-phase resin in a biphasic column. We detail the improvements over a system described by Link et al. (Link, A. J.; Eng, J.; Schieltz, D. M.; Carmack, E.; Mize, G. J.; Morris, D. R.; Garvik, B. M.; Yates, J. R., III. Nat. Biotechnol. 1999, 17, 676-682) that separates and acquires tandem mass spectra for thousands of peptides. Peptides elute off the SCX phase by increasing pI, and elution off the SCX material is evenly distributed across an analysis. In addition, we describe the chromatographic benchmarks of MudPIT. MudPIT was reproducible within 0.5% between two analyses. Furthermore, a dynamic range of 10000 to 1 between the most abundant and least abundant proteins/peptides in a complex peptide mixture has been demonstrated. By improving sample preparation along with separations, the method improves the overall analysis of proteomes by identifying proteins of all functional and physical classes.     

Preview: a program for surveying shotgun proteomics tandem mass spectrometry data

Database search programs for peptide identification by tandem mass spectrometry ask their users to set various parameters, including precursor and fragment mass tolerances, digestion specificity, and allowed types of modifications. Even proteomics experts with detailed knowledge of their samples may find it difficult to make these choices without significant investigation, and poor choices can lead to missed identifications and misleading results. Here we describe a program called Preview that analyzes a set of mass spectra for mass errors, digestion specificity, and known and unknown modifications, thereby facilitating parameter selection. Moreover, Preview optionally recalibrates mass over charge measurements, leading to further improvement in identification results. In a study of Bruton's tyrosine kinase, we find that the use of Preview improved the number of confidently identified mass spectra and phosphorylation sites by about 50%.     

Comparison of two-dimensional fractionation techniques for shotgun proteomics

Two-dimensional (2D) fractionation is a commonly used tool to increase dynamic range and proteome coverage for bottom-up, shotgun proteomics. However, there are few reports comparing the relative separation efficiencies of 2D methodologies using low-microgram sample quantities. In order to systematically evaluate 2D separation techniques, we fractionated microgram quantities of E. coli protein extract by seven different methods. The first dimension of separation was performed with either reversed-phase high-pressure liquid chromatography (RP-HPLC), gel electrophoresis (SDS-PAGE), or strong cation exchange (SCX-HPLC). The second dimension consisted of a standard reversed-phase capillary HPLC coupled to an electrospray ionization quadrupole time-of-flight mass spectrometer for tandem mass spectrometric analysis. The overall performance and relative fractionation efficiencies of each technique were assessed by comparing the total number of proteins identified by each method. The protein-level RP-HPLC and the high-pH RP-HPLC peptide-level separations performed the best, identifying 281 and 266 proteins, respectively. The online pH variance SCX and the SDS-PAGE returned modest performances with 178 and 139 proteins identified, respectively. The offline SCX had the worst performance with 81 proteins identified. We also examined various chromatographic factors that contribute to separation efficiency, including resolving power, orthogonality, and sample loss.     

Capillary trap column with strong cation-exchange monolith for automated shotgun proteome analysis

A 150 microm internal diameter capillary monolithic column with a strong cation-exchange stationary phase was prepared by direct in situ polymerization of ethylene glycol methacrylate phosphate and bisacrylamide in a trinary porogenic solvent consisting dimethylsulfoxide, dodecanol, and N,N'-dimethylformamide. This phosphate monolithic column exhibits higher dynamic binding capacity, faster kinetic adsorption of peptides, and more than 10 times higher permeability than the column packed with commercially available strong cation-exchange particles. It was applied as a trap column in a nanoflow liquid chromatography-tandem mass spectrometry system for automated sample injection and online multidimensional separation. It was observed that the sample could be loaded at a flow rate as high as 40 microL/min with a back pressure of approximately 1300 psi and without compromising the separation efficiency. Because of its good orthogonality to the reversed phase separation mechanism, the phosphate monolithic trap column was coupled with a reversed-phase column for online multidimensional separation of 19 microg of the tryptic digest of yeast proteins. A total of 1522 distinct proteins were identified from 5608 unique peptides (total of 54,780 peptides) at the false positive rate only 0.46%.     

A model for random sampling and estimation of relative protein abundance in shotgun proteomics

Proteomic analysis of complex protein mixtures using proteolytic digestion and liquid chromatography in combination with tandem mass spectrometry is a standard approach in biological studies. Data-dependent acquisition is used to automatically acquire tandem mass spectra of peptides eluting into the mass spectrometer. In more complicated mixtures, for example, whole cell lysates, data-dependent acquisition incompletely samples among the peptide ions present rather than acquiring tandem mass spectra for all ions available. We analyzed the sampling process and developed a statistical model to accurately predict the level of sampling expected for mixtures of a specific complexity. The model also predicts how many analyses are required for saturated sampling of a complex protein mixture. For a yeast-soluble cell lysate 10 analyses are required to reach a 95% saturation level on protein identifications based on our model. The statistical model also suggests a relationship between the level of sampling observed for a protein and the relative abundance of the protein in the mixture. We demonstrate a linear dynamic range over 2 orders of magnitude by using the number of spectra (spectral sampling) acquired for each protein.     

A data analysis algorithm for programmed field-flow fractionation

An algorithm that employs numerical integration for analysis of field-flow fractionation (FFF) data is presented. The algorithm utilizes detector response, field strength, and channel flow rate data, monitored at discrete time intervals during sample elution to generate a distribution of sample components according to particle size or molecular weight. The field strength and channel flow rate may either be held constant or programmed as functions of time, and it is not necessary for these programs to follow specific mathematical functions. If experimental conditions are monitored during a run, the algorithm can account for any deviation from nominal set conditions. The algorithm also allows calculation of fractionating power for the actual conditions as monitored during the run. The method provides greatly increased flexibility in the application of the FFF family of techniques. It removes the limitations on experimental conditions incurred by adherence to analytically available solutions to FFF theory, allowing ad hoc variation of field strength and other experimental parameters as necessary to increase sensitivity and specificity of the method. An implementation of the algorithm is described that is independent of the FFF technique (i.e., independent of field type) and mode of operation. To reduce computation time, it uses mathematical techniques to reduce the required number of numerical integrations. This is of particular importance when the perturbations to ideal FFF theory, such as those due to the effects of hydrodynamic lift forces, particle-wall or particle-particle interactions, and secondary relaxation, necessitate relatively lengthy numerical calculations.     

Robust estimation of peptide abundance ratios and rigorous scoring of their variability and bias in quantitative shotgun proteomics

The abundance ratio between the light and heavy isotopologues of an isotopically labeled peptide can be estimated from their selected ion chromatograms. However, quantitative shotgun proteomics measurements yield selected ion chromatograms at highly variable signal-to-noise ratios for tens of thousands of peptides. This challenge calls for algorithms that not only robustly estimate the abundance ratios of different peptides but also rigorously score each abundance ratio for the expected estimation bias and variability. Scoring of the abundance ratios, much like scoring of sequence assignment for tandem mass spectra by peptide identification algorithms, enables filtering of unreliable peptide quantification and use of formal statistical inference in the subsequent protein abundance ratio estimation. In this study, a parallel paired covariance algorithm is used for robust peak detection in selected ion chromatograms. A peak profile is generated for each peptide, which is a scatterplot of ion intensities measured for the two isotopologues within their chromatographic peaks. Principal component analysis of the peak profile is proposed to estimate the peptide abundance ratio and to score the estimation with the signal-to-noise ratio of the peak profile (profile signal-to-noise ratio). We demonstrate that the profile signal-to-noise ratio is inversely correlated with the variability and bias of peptide abundance ratio estimation.     

Stable-isotope dimethyl labeling for quantitative proteomics

In this paper, we report a novel, stable-isotope labeling strategy for quantitative proteomics that uses a simple reagent, formaldehyde, to globally label the N-terminus and epsilon-amino group of Lys through reductive amination. This labeling strategy produces peaks differing by 28 mass units for each derivatized site relative to its nonderivatized counterpart and 4 mass units for each derivatized isotopic pair. This labeling reaction is fast (less than 5 min) and complete without any detectable byproducts based on the analysis of MALDI and LC/ESI-MS/MS spectra of both derivatized and nonderivatized peptide standards and tryptic peptides of hemoglobin molecules. The intensity of the a(1) and y(n-1) ions produced, which were not detectable from most of the nonderivatized fragments, was substantially enhanced upon labeling. We further tested the method based on the analysis of an isotopic pair of peptide standards and a pair of defined protein mixtures with known H/D ratios. Using LC/MS for quantification and LC/MS/MS for peptide sequencing, the results show a negligible isotopic effect, a good mass resolution between the isotopic pair, and a good correlation between the experimental and theoretical data (errors 0-4%). The relative standard deviation of H/D values calculated from peptides deduced from the same protein are less than 13%. The applicability of the method for quantitative protein profiling was also explored by analyzing changes in nuclear protein abundance in an immortalized E7 cell with and without arsenic treatment.     

一种适用于流数据分析的快速EMD算法

采用具有局域控制特性的B样条函数直接拟合均值线,采用局域标准差终止准则判断原型模态函数（Proto-mode Function,PMF）的对称性,得到一种适用于流数据分析的快速经验模态分解（Empirical Mode Decomposition,EMD）算法。相比经典EMD算法,该快速算法不仅时间复杂度和空间复杂度均有所下降,而且分解精度基本维持不变。该快速算法适用于战场侦察传感网等流数据分析场合,实时性好,能够得到与序列等长的连贯本征模态函数（Intrinsic Mode Function,IMF）。     

Improving protein identification sensitivity by combining MS and MS/MS information for shotgun proteomics using LTQ-Orbitrap high mass accuracy data

We investigated and compared three approaches for shotgun protein identification by combining MS and MS/MS information using LTQ-Orbitrap high mass accuracy data. In the first approach, we employed a unique mass identifier method where MS peaks matched to peptides predicted from proteins identified from an MS/MS database search are first subtracted before using the MS peaks as unique mass identifiers for protein identification. In the second method, we used an accurate mass and time tag method by building a potential mass and retention time database from previous MudPIT analyses. For the third method, we used a peptide mass fingerprinting-like approach in combination with a randomized database for protein identification. We show that we can improve protein identification sensitivity for low-abundance proteins by combining MS and MS/MS information. Furthermore, "one-hit wonders" from MS/MS database searching can be further substantiated by MS information and the approach improves the identification of low-abundance proteins. The advantages and disadvantages for the three approaches are then discussed.     

High-speed data reduction, feature detection, and MS/MS spectrum quality assessment of shotgun proteomics data sets using high-resolution mass spectrometry

Advances in Fourier transform mass spectrometry have made the acquisition of high-resolution and accurate mass measurements routine on a chromatographic time scale. Here we report an algorithm, Hardkl?r, for the rapid and robust analysis of high-resolution mass spectra acquired in shotgun proteomics experiments. Our algorithm is demonstrated in the analysis of an Escherichia coli enriched membrane fraction. The mass spectrometry data of the respective peptides are acquired by microcapillary HPLC on an LTQ-orbitrap mass spectrometer with data-dependent acquisition of MS/MS spectra. Hardkl?r detects 211,272 total peptide isotope distributions over a 2-h analysis (75-min gradient) in only a small fraction of the time required to acquire the data. From these data there are 13,665 distinct, chromatographically persistent peptide isotope distributions. Hardkl?r is also used to assess the quality of the product ion spectra and finds that more than 11.2% of the MS/MS spectra are composed of fragment ions from multiple different molecular species. Additionally, a method is reported that enzymatically labels N-linked glycosylation sites on proteins, creating a unique isotope signature that can be detected with Hardkl?r. Using the protein invertase, Hardkl?r identifies 18O-labeled peptide isotope distributions of four glycosylation sites. The speed and robustness of the algorithm create a versatile tool that can be used in many different areas of mass spectrometry data analysis.     

N-terminal isotope tagging strategy for quantitative proteomics: results-driven analysis of protein abundance changes

Comparing the relative abundance of each protein present in two or more complex samples can be accomplished using isotope-coded tags incorporated at the peptide level. Here we describe a chemical labeling strategy for the incorporation of a single isotope label per peptide, which is completely sequence-independent so that it potentially labels every peptide from a protein including those containing posttranslational modifications. It is based on a gentle chemical labeling strategy that specifically labels the N-terminus of all peptides in a digested sample with either a d5- or d0-propionyl group. Lysine side chains are blocked by guanidination prior to N-terminal labeling to prevent the incorporation of multiple labels. In this paper, we describe the optimization of this N-terminal isotopic tagging strategy and validate its use for peptide-based protein abundance measurements with a 10-protein standard mixture. Using a results-driven strategy, which targets proteins for identification based on MALDI TOF-MS analysis of isotopically labeled peptide pairs, we also show that this labeling strategy can detect a small number of differentially expressed proteins in a mixture as complex as a yeast cell lysate. Only peptides that show a difference in relative abundance are targeted for identification by tandem MS. Despite the fact that many peptides are quantitated, only those few showing a difference in abundance are targeted for protein identification. Proteins are identified by either targeted LC-ES MS/MS or MALDI TOF/TOF. Identifications can be accomplished equally well by either technique on the basis of multiple peptides. This increases the confidence level for both identification and quantitation. The merits of ES MS/MS or MALDI MS/MS for protein identification in a results-driven strategy are discussed.     

Two-dimensional correlation optimized warping algorithm for aligning GC x GC-MS data

A two-dimensional (2-D) correlation optimized warping (COW) algorithm has been developed to align 2-D gas chromatography coupled with time-of-flight mass spectrometry (GC x GC/TOF-MS) data. By partitioning raw chromatographic profiles and warping the grid points simultaneously along the first and second dimensions on the basis of applying a one-dimensional COW algorithm to characteristic vectors, nongrid points can be interpolatively warped. This 2-D algorithm was directly applied to total ion counts (TIC) chromatographic profiles of homogeneous chemical samples, i.e., samples including mostly identical compounds. For heterogeneous chemical samples, the 2-D algorithm is first applied to certain selected ion counts chromatographic profiles, and the resultant warping parameters are then used to warp the corresponding TIC chromatographic profiles. The developed 2-D COW algorithm can also be applied to align other 2-D separation images, e.g., LC x LC data, LC x GC data, GC x GC data, LC x CE data, and CE x CE data.     

Pseudo internal standard approach for label-free quantitative proteomics

Quantitative proteome analysis has become a versatile tool to understand biological functions. Although stable isotope labeling is the most reliable method for quantitative mass spectrometry, preparation of isotope-labeled compounds is time-consuming and expensive. Simple label-free approaches have been introduced, but intensity-based quantitation without standards is not generally accepted as reliable, especially for small molecules. We have developed a novel label-free quantitative proteome analysis using pseudo internal standards (PISs). This idea was derived from northern blotting analysis, in which housekeeping genes are used as standards to normalize and compare target gene expression levels in different samples. In many proteomics studies, most proteins do not change their expression levels under different conditions, and therefore, these proteins can be employed as pseudo internal standards. This new approach is simple and does not require additional standards or labeling reagents. The PIS method represents a novel approach for mass spectrometry-based comprehensive quantitatitation and may also be applicable to quantitative metabolome analysis.     

Fast quantitative correlation analysis and information deviation analysis for evaluating the performances of image fusion techniques

Image fusion is becoming one of the hottest techniques in image processing. Its performance evaluation method that can compare and analyze different fusion techniques is an essential part of image fusion techniques. In this paper, we proposed two intuitive schemes - correlation and information deviation schemes - for evaluating the performances of image fusion techniques. The former scheme is a fast and compact version of the quantitative correlation analysis (QCA). The latter scheme is an information deviation analysis. Using the two schemes, the performances of different fusion techniques can be compared directly and quantitatively based on the source images and the fused images with a faster speed than the QCA method. Two multiresolution analysis-based image fusion methods, wavelet transform-based fusion and pyramid transform-based fusion, which operate on typical hyperspectral image sets [airborne visible/infrared imaging spectrometer (AVIRIS)], are evaluated by the proposed schemes. The simulation results show the correctness and effectiveness of the proposed two schemes.     

A correlation of thermal conductivity data for helium

A correlation for the thermal conductivity of helium has been developed which covers the temperature range from temperatures just above the lambda line to 830 K, and densities up to about 160 kg m⁻³. The data used incorporate some recent experimental results which cover the temperature range from 4 K to 20 K including the critical region. The correlation gives an equation which generally fits the experimental data within ± 5%. However, at low temperature, the experimental data deviate up to about 10% from predictions based upon viscosity measurements or molecular dynamics calculations.     

In vivo termini amino acid labeling for quantitative proteomics

Quantitative proteomics is one of the research hotspots in the proteomics field and presently maturing rapidly into an important branch. The two most typical quantitative methods, stable isotope labeling with amino acids in cell culture (SILAC) and isobaric tags for relative and absolute quantification (iTRAQ), have been widely and effectively applied in solving various biological and medical problems. Here, we describe a novel quantitative strategy, termed "IVTAL", for in vivo termini amino acid labeling, which combines some advantages of the two methods above. The core of this strategy is a set of heavy amino acid (13)C(6)-arginine and (13)C(6)-lysine and specific endoproteinase Lys-N and Arg-C that yield some labeled isobaric peptides by cell culture and enzymatic digestion, which are indistinguishable in the MS scan but exhibit multiple MS/MS reporter b, y ion pairs in a full mass range that support quantitation. Relative quantification of cell states can be achieved by calculating the intensity ratio of the corresponding reporter b, y ions in the MS/MS scan. The experimental analysis for various proportions of mixed HeLa cell samples indicated that the novel strategy showed an abundance of reliable quantitative information, a high sensitivity, and a good dynamic range of nearly 2 orders of magnitude. IVTAL, as a highly accurate and reliable quantitative proteomic approach, is expected to be compatible with any cell culture system and to be especially effective for the analysis of multiple post-translational modificational sites in one peptide.     

A quantitative analysis of determinants of non-citation using a panel data model

The term “non-citation factor” refers to the percentage of never-cited papers in a citation time window, a common phenomenon in the science world. Some scholars have qualitatively explored the reasons for not citing a publication, and quantitatively analyzed the mathematical functional relations between the “non-citation factor” and “impact factor of a journal.” This study simultaneously considers the mutual relations and closeness degree between the “non-citation factor” and different influencing factors from a novel perspective—that of using a more structuralized panel data model. The analysis revealed that the determinants, including “impact factor of journal,” “age of journal,” “average number of references per paper in journal,” and “issues of journal,” exerted an extremely small but positive influence (<?0.025) on the decline of “percentage of never-cited papers in the citation time window of publication year or 3 years.” That means the improvement of these determinants can decrease the percentage of never-cited papers. The “impact factor of the journal” always had the biggest positive influence, while the “average number of references per paper in journal” always had the smallest positive influence. In wider citation time windows of 3 or 6 years, two determinants—“number of publications in journal” and “amount of interdisciplinarity in journal”—began to exert a negative effect with a positive correlation coefficient on the decline of the “non-citation factor.” That means the improvement of these two determinants cannot decrease the value of the “non-citation factor,” even though they can increase its value. It is worth noting that the “impact factor of the journal” had a positive influence on the decline of the percentages of never-cited papers in the citation time window of publication year or 3 years, and began to play a negative role in the decline of percentage of never-cited papers in the citation time window of 6 years. Finally, three variables—“average number of authors per paper in journal,” “average number of references per paper in journal,” and “issues of journal”—no longer exerted an influence on the decline of percentages of never-cited papers in the citation time window of 6 years, while “age of journal” and “average number of pages per paper in journal” still made a positive contribution. Our findings could help research institutions, researchers, editors, and publishers understand the positively or negatively influential factors that lead to non-citation, thus improving the chance of papers being cited and having some academic influence.