Parts per million mass accuracy on an Orbitrap mass spectrometer via lock mass injection into a C-trap

Mass accuracy is a key parameter of mass spectrometric performance. TOF instruments can reach low parts per million, and FT-ICR instruments are capable of even greater accuracy provided ion numbers are well controlled. Here we demonstrate sub-ppm mass accuracy on a linear ion trap coupled via a radio frequency-only storage trap (C-trap) to the orbitrap mass spectrometer (LTQ Orbitrap). Prior to acquisition of a spectrum, a background ion originating from ambient air is first transferred to the C-trap. Ions forming the MS or MS(n) spectrum are then added to this species, and all ions are injected into the orbitrap for analysis. Real time recalibration on the "lock mass" by corrections of mass shift removes mass error associated with calibration of the mass scale. The remaining mass error is mainly due to imperfect peaks caused by weak signals and is addressed by averaging the mass measurement over the LC peak, weighted by signal intensity. For peptide database searches in proteomics, we introduce a variable mass tolerance and achieve average absolute mass deviations of 0.48 ppm (standard deviation 0.38 ppm) and maximal deviations of less than 2 ppm. For tandem mass spectra we demonstrate similarly high mass accuracy and discuss its impact on database searching. High and routine mass accuracy in a compact instrument will dramatically improve certainty of peptide and small molecule identification.     

Highly sensitive feature detection for high resolution LC/MS

Background  

Liquid chromatography coupled to mass spectrometry (LC/MS) is an important analytical technology for e.g. metabolomics experiments. Determining the boundaries, centres and intensities of the two-dimensional signals in the LC/MS raw data is called feature detection. For the subsequent analysis of complex samples such as plant extracts, which may contain hundreds of compounds, corresponding to thousands of features – a reliable feature detection is mandatory.     

Label-free detection of differential protein expression by LC/MALDI mass spectrometry

Protein abundance changes during disease or experimental perturbation are increasingly analyzed by label-free LC/MS approaches. Here we demonstrate the use of LC/MALDI MS for label-free detection of protein expression differences using Escherichia coli cultures grown on arabinose, fructose or glucose as a carbon source. The advantages of MALDI, such as detection of only singly charged ions, and MALDI plate archiving to facilitate retrospective MS/MS data collection are illustrated. MALDI spectra from RP chromatography of tryptic digests of the E. coli lysates were aligned and quantitated using the Rosetta Elucidator system. Approximately 5000 peptide signals were detected in all LC/MALDI runs spanning over 3 orders of magnitude of signal intensity. The average coefficients of variation for all signals across the entire intensity range in all technical replicates were found to be <25%. Pearson correlation coefficients from 0.93 to 0.98 for pairwise comparisons illustrate high replicate reproducibility. Expression differences determined by Analysis of Variance highlighted over 500 isotope clusters ( p < 0.01), which represented candidates for targeted peptide identification using MS/MS. Biologically interpretable protein identifications that could be derived underpin the general utility of this label-free LC/MALDI strategy.     

Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry

Untargeted metabolomics aims to gather information on as many metabolites as possible in biological systems by taking into account all information present in the data sets. Here we describe a detailed protocol for large-scale untargeted metabolomics of plant tissues, based on reversed phase liquid chromatography coupled to high-resolution mass spectrometry (LC-QTOF MS) of aqueous methanol extracts. Dedicated software, MetAlign, is used for automated baseline correction and alignment of all extracted mass peaks across all samples, producing detailed information on the relative abundance of thousands of mass signals representing hundreds of metabolites. Subsequent statistics and bioinformatics tools can be used to provide a detailed view on the differences and similarities between (groups of) samples or to link metabolomics data to other systems biology information, genetic markers and/or specific quality parameters. The complete procedure from metabolite extraction to assembly of a data matrix with aligned mass signal intensities takes about 6 days for 50 samples.     

Extending fluorescent microsphere methods for regional organ blood flow to 13 simultaneous colors

Seven fluorescent microsphere colors can be used in a single experiment to estimate regional blood flow without correcting for spillover of emitted fluorescence. To extend the method to 13 colors, we compared the accuracy of three methods for spillover correction. Fixed wavelength intensities were corrected by matrix inversion, and synchronous scan spectra were corrected by least squares fit of an overdetermined system of linear equations and by least squares fit of a sum of Gaussian and Lorentzian functions. Correction methods were validated in pigs and sheep by simultaneous injections of radioactive microspheres and fluorescent microspheres of 7, 10, and 13 different colors. We induced extreme changes in flow to create regions with low fluorescent signals bound on either side by high fluorescent signals. Blood flow was determined by radioactivity and by fluorescence using both fixed excitation and emission wavelength pairs and synchronous scanning and then corrected for spillover. Correlation between fluorescent intensity and radioactivity were excellent for all three correction methods [R2 = 0.98 +/- 0.02 (mean +/- SD)]. Low-flow regions requiring large spillover correction had systematic errors for some color combinations in all methods. We conclude that for 13 fluorescent colors spillover error can be minimized so that all three correction methods provide accurate estimates of regional blood flow.     

Microarray optimizations: increasing spot accuracy and automated identification of true microarray signals

In this paper, fluorescent microarray images and various analysis techniques are described to improve the microarray data acquisition processes. Signal intensities produced by rarely expressed genes are initially correctly detected, but they are often lost in corrections for background, log or ratio. Our analyses indicate that a simple correlation between the mean and median signal intensities may be the best way to eliminate inaccurate microarray signals. Unlike traditional quality control methods, the low intensity signals are retained and inaccurate signals are eliminated in this mean and median correlation. With larger amounts of microarray data being generated, it becomes increasingly more difficult to analyze data on a visual basis. Our method allows for the automatic quantitative determination of accurate and reliable signals, which can then be used for normalization. We found that a mean to median correlation of 85% or higher not only retains more data than current methods, but the retained data is more accurate than traditional thresholds or common spot flagging algorithms. We have also found that by using pin microtapping and microvibrations, we can control spot quality independent from initial PCR volume.     

Nearline acquisition and processing of liquid chromatography-tandem mass spectrometry data

Liquid chromatography–mass spectrometry (LC–MS) is a commonly used analytical platform for non-targeted metabolite profiling experiments. Although data acquisition, processing and statistical analyses are almost routine in such experiments, further annotation and subsequent identification of chemical compounds are not. For identification, tandem mass spectra provide valuable information towards the structure of chemical compounds. These are typically acquired online, in data-dependent mode, or offline, using handcrafted acquisition methods and manually extracted from raw data. Here, we present several methods to fast-track and improve both the acquisition and processing of LC–MS/MS data. Our nearly online (nearline) data-dependent tandem MS strategy creates a minimal set of LC–MS/MS acquisition methods for relevant features revealed by a preceding non-targeted profiling experiment. Using different filtering criteria, such as intensity or ion type, the acquisition of irrelevant spectra is minimized. Afterwards, LC–MS/MS raw data are processed with feature detection and grouping algorithms. The extracted tandem mass spectra can be used for both library search and de-novo identification methods. The algorithms are implemented in the R package MetShot and support the export to Bruker, Agilent or Waters QTOF instruments and the vendor-independent TraML standard. We evaluate the performance of our workflow on a Bruker micrOTOF-Q by comparison of automatically acquired and extracted tandem mass spectra obtained from a mixture of natural product standards against manually extracted reference spectra. Using Arabidopsis thaliana wild-type and biosynthetic gene knockout plants, we characterize the metabolic products of a biosynthetic pathway and demonstrate the integration of our approach into a typical non-targeted metabolite profiling workflow.

     

Nearline acquisition and processing of liquid chromatography-tandem mass spectrometry data

Liquid chromatography–mass spectrometry (LC–MS) is a commonly used analytical platform for non-targeted metabolite profiling experiments. Although data acquisition, processing and statistical analyses are almost routine in such experiments, further annotation and subsequent identification of chemical compounds are not. For identification, tandem mass spectra provide valuable information towards the structure of chemical compounds. These are typically acquired online, in data-dependent mode, or offline, using handcrafted acquisition methods and manually extracted from raw data. Here, we present several methods to fast-track and improve both the acquisition and processing of LC–MS/MS data. Our nearly online (nearline) data-dependent tandem MS strategy creates a minimal set of LC–MS/MS acquisition methods for relevant features revealed by a preceding non-targeted profiling experiment. Using different filtering criteria, such as intensity or ion type, the acquisition of irrelevant spectra is minimized. Afterwards, LC–MS/MS raw data are processed with feature detection and grouping algorithms. The extracted tandem mass spectra can be used for both library search and de-novo identification methods. The algorithms are implemented in the R package MetShot and support the export to Bruker, Agilent or Waters QTOF instruments and the vendor-independent TraML standard. We evaluate the performance of our workflow on a Bruker micrOTOF-Q by comparison of automatically acquired and extracted tandem mass spectra obtained from a mixture of natural product standards against manually extracted reference spectra. Using Arabidopsis thaliana wild-type and biosynthetic gene knockout plants, we characterize the metabolic products of a biosynthetic pathway and demonstrate the integration of our approach into a typical non-targeted metabolite profiling workflow.     

A model-free method for mass spectrometer response correction

A new method for correction of mass spectrometer output signals is described. Response-time distortion is reduced independently of any model of mass spectrometer behavior. The delay of the system is found first from the cross-correlation function of a step change and its response. A two-sided time-domain digital correction filter (deconvolution filter) is generated next from the same step response data using a regression procedure. Other data are corrected using the filter and delay. The mean squared error between a step response and a step is reduced considerably more after the use of a deconvolution filter than after the application of a second-order model correction. O2 consumption and CO2 production values calculated from data corrupted by a simulated dynamic process return to near the uncorrupted values after correction. Although a clean step response or the ensemble average of several responses contaminated with noise is needed for the generation of the filter, random noise of magnitude less than or equal to 0.5% added to the response to be corrected does not impair the correction severely.     

Modeling nonlinear errors in surface electromyography due to baseline noise: A new methodology

The surface electromyographic (EMG) signal is often contaminated by some degree of baseline noise. It is customary for scientists to subtract baseline noise from the measured EMG signal prior to further analyses based on the assumption that baseline noise adds linearly to the observed EMG signal. The stochastic nature of both the baseline and EMG signal, however, may invalidate this assumption. Alternately, “true” EMG signals may be either minimally or nonlinearly affected by baseline noise. This information is particularly relevant at low contraction intensities when signal-to-noise ratios (SNR) may be lowest. Thus, the purpose of this simulation study was to investigate the influence of varying levels of baseline noise (approximately 2–40% maximum EMG amplitude) on mean EMG burst amplitude and to assess the best means to account for signal noise. The simulations indicated baseline noise had minimal effects on mean EMG activity for maximum contractions, but increased nonlinearly with increasing noise levels and decreasing signal amplitudes. Thus, the simple baseline noise subtraction resulted in substantial error when estimating mean activity during low intensity EMG bursts. Conversely, correcting EMG signal as a nonlinear function of both baseline and measured signal amplitude provided highly accurate estimates of EMG amplitude. This novel nonlinear error modeling approach has potential implications for EMG signal processing, particularly when assessing co-activation of antagonist muscles or small amplitude contractions where the SNR can be low.     

Intensity-based protein identification by machine learning from a library of tandem mass spectra

Tandem mass spectrometry (MS/MS) has emerged as a cornerstone of proteomics owing in part to robust spectral interpretation algorithms. Widely used algorithms do not fully exploit the intensity patterns present in mass spectra. Here, we demonstrate that intensity pattern modeling improves peptide and protein identification from MS/MS spectra. We modeled fragment ion intensities using a machine-learning approach that estimates the likelihood of observed intensities given peptide and fragment attributes. From 1,000,000 spectra, we chose 27,000 with high-quality, nonredundant matches as training data. Using the same 27,000 spectra, intensity was similarly modeled with mismatched peptides. We used these two probabilistic models to compute the relative likelihood of an observed spectrum given that a candidate peptide is matched or mismatched. We used a 'decoy' proteome approach to estimate incorrect match frequency, and demonstrated that an intensity-based method reduces peptide identification error by 50-96% without any loss in sensitivity.     

Pyrolysis mass spectra,sulfhydryl and tryptophan content of the embryonic nuclei from adult human normal and nuclear-cataractous lenses

Cataractous state-related alterations were studied by Curie-point pyrolysis low voltage mass spectrometry performed on the embryonic nuclei from adult normal and nuclear-cataractous human lenses. It was shown that the relative intensities of ion signals assigned to sulfur dioxide and, tentatively, to other sulfur oxidation products correlate with the increase in lens nuclear color. Since these ion signals may represent pyrolysis fragments from methionine sulfoxide, methionine sulfone, cysteic acid and disulfide compounds present in the parent material, it is concluded that progressive oxidation of sulfur compounds appears to take place during nuclear cataractogenesis. Automated pyrolysis mass spectrometry coupled with multivariate analysis of the spectral data by computer turned out to be a rapid method of characterizing submilligram samples of lens material.The results were supported by data obtained by conventional determination of sulfhydryl and tryptophan groups.     

Peptide mass fingerprinting peak intensity prediction: extracting knowledge from spectra

Matrix-assisted laser desorption/ionization-time of flight mass spectrometry has become a valuable tool in proteomics. With the increasing acquisition rate of mass spectrometers, one of the major issues is the development of accurate, efficient and automatic peptide mass fingerprinting (PMF) identification tools. Current tools are mostly based on counting the number of experimental peptide masses matching with theoretical masses. Almost all of them use additional criteria such as isoelectric point, molecular weight, PTMs, taxonomy or enzymatic cleavage rules to enhance prediction performance. However, these identification tools seldom use peak intensities as parameter as there is currently no model predicting the intensities based on the physicochemical properties of peptides. In this work, we used standard datamining methods such as classification and regression methods to find correlations between peak intensities and the properties of the peptides composing a PMF spectrum. These methods were applied on a dataset comprising a series of PMF experiments involving 157 proteins. We found that the C4.5 method gave the more informative results for the classification task (prediction of the presence or absence of a peptide in a spectra) and M5' for the regression methods (prediction of the normalized intensity of a peptide peak). The C4.5 result correctly classified 88% of the theoretical peaks; whereas the M5' peak intensities had a correlation coefficient of 0.6743 with the experimental peak intensities. These methods enabled us to obtain decision and model trees that can be directly used for prediction and identification of PMF results. The work performed permitted to lay the foundations of a method to analyze factors influencing the peak intensity of PMF spectra. A simple extension of this analysis could lead to improve the accuracy of the results by using a larger dataset. Additional peptide characteristics or even PMF experimental parameters can also be taken into account in the datamining process to analyze their influence on the peak intensity. Furthermore, this datamining approach can certainly be extended to the tandem mass spectrometry domain or other mass spectrometry derived methods.     

Local mean normalization of microarray element signal intensities across an array surface: quality control and correction of spatially systematic artifacts

Here we present a methodology for the normalization of element signal intensities to a mean intensity calculated locally across the surface of a DNA microarray. These methods allow the detection and/or correction of spatially systematic artifacts in microarray data. These include artifacts that can be introduced during the robotic printing, hybridization, washing, or imaging of microarrays. Using array element signal intensities alone, this local mean normalization process can correct for such artifacts because they vary across the surface of the array. The local mean normalization can be usedfor quality control and data correction purposes in the analysis of microarray data. These algorithms assume that array elements are not spatially ordered with regard to sequence or biological function and require that this spatial mapping is identical between the two sets of intensities to be compared. The tool described in this report was developed in the R statistical language and is freely available on the Internet as part of a larger gene expression analysis package. This Web implementation is interactive and user-friendly and allows the easy use of the local mean normalization tool described here, without programming expertise or downloading of additional software.     

A new quantitative LC tandem mass spectrometry assay for serum 25-hydroxy vitamin D

BackgroundThe accurate measurement of 25-hydoxy vitamin D (25OH-D) in serum has been a challenge for many years. We developed a liquid chromatography tandem mass spectrometry (LC Tandem MS) assay for the quantitative determination of 25OH-D₂ and 25OH-D₃ in serum. The new method was compared with two widely used commercially available immunoassays.MethodsSample preparation involved protein precipitation with acetonitrile containing deuterated forms of the target species as internal standards. An API 5000 mass spectrometer coupled with a photoionization source was used for quantitation. The performance of the new LC Tandem MS assay was compared with a radioimmunoassay (RIA, Diasorin) and a chemiluminescence immunoassay (ECLIA, Roche Diagnostics), analysing serum obtained from 152 individuals.ResultsUsing 100 μl of serum, the LC Tandem MS assay had a limit of quantitation of 1.3 nmol/L for both 25OH-D₂ and 25OH-D₃ with a linear response between 1.3 and 625 nmol/L and accuracy of between 95 and 124%. Intra- and inter-assay precision were ≤7% and ≤4%, respectively. Measurement of 25OH-D levels in 152 serum samples gave run averages of 71, 56 and 62 nmol/L for LC Tandem MS, ECLIA and RIA, respectively. Correlations between the various methods were: LC Tandem MS vs. RIA: r = 0.931; LC Tandem MS vs. ECLIA: r = 0.784; RIA vs. ECLIA: r = 0.787. The LC Tandem MS method had a positive proportional bias of 26% over the RIA, whereas the ECLIA showed variable differences.ConclusionThe new LC Tandem MS assay is accurate and precise at physiologically relevant 25OH-D concentrations, and compares favourably with the RIA. In contrast, the ECLIA shows variable bias with the other assays tested.     

Ultra-high intra-spectrum mass accuracy enables unambiguous identification of fragment reporter ions in isobaric multiplexed quantitative proteomics

Isobaric tagging using reagents such as tandem mass tags (TMT) and isobaric tags for relative and absolute quantification (iTRAQ) have become popular tools for mass spectrometry based quantitative proteomics. Because the peptide quantification information is collected in tandem mass spectra, the accuracy and precision of this method largely depend on the resolution with which precursor ions can be selected for the fragmentation and the specificity of the generated reporter ion. The latter can constitute an issue if near isobaric ion signals are present in such spectra because they may distort quantification results. We propose a simple remedy for this problem by identifying reporter ions via the accurate mass differences within a single tandem mass spectrum instead of applying fixed mass error tolerances for all tandem mass spectra. Our results show that this leads to unambiguous reporter ion identification and complete removal of interfering signals. This mode of data processing is easily implemented in software and offers advantages for protein quantification based on few peptides.     

Detection of peptides covalently modified with multiple fatty acids by MALDI-TOF mass spectrometry.

Analysis and characterization of membrane proteins and hydrophobic peptides by matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) is a considerable challenge because of their lower ionization efficiency. Detergents are used to solubilize hydrophobic peptides and proteins. However, in MALDI-MS, the presence of detergents can cause considerable loss of signal intensity. The extent of interference depends on the matrix/sample preparation method and experimental conditions. In the present study, we have analyzed the MALDI response of multiple fatty acylated peptides in the presence of the matrices alpha-cyano-4-hydroxy cinnamic acid (HCCA) and 2,5-dihydroxy benzoic acid (DHB). The effect of adding the nonionic detergent n-octylglucoside (OG) was also examined. The presence of OG facilitated detection of tetrapalmitoylated peptide, particularly when HCCA was used as the matrix. When DHB was used as the matrix, good signal intensity was observed in the absence of OG. Lower laser pulse rate in the linear mode of analysis resulted in good signal intensity for the tetrapalmitoylated peptide. Conditions for obtaining good signal intensities for dipalmitoylated and N-myristoyl peptides with both HCCA and DHB as matrices were also investigated.     

Reference map for liquid chromatography–mass spectrometry-based quantitative proteomics

The accurate mass and time (AMT) tag strategy has been recognized as a powerful tool for high-throughput analysis in liquid chromatography–mass spectrometry (LC–MS)-based proteomics. Due to the complexity of the human proteome, this strategy requires highly accurate mass measurements for confident identifications. We have developed a method of building a reference map that allows relaxed criteria for mass errors yet delivers high confidence for peptide identifications. The samples used for generating the peptide database were produced by collecting cysteine-containing peptides from T47D cells and then fractionating the peptides using strong cationic exchange chromatography (SCX). LC–tandem mass spectrometry (MS/MS) data from the SCX fractions were combined to create a comprehensive reference map. After the reference map was built, it was possible to skip the SCX step in further proteomic analyses. We found that the reference-driven identification increases the overall throughput and proteomic coverage by identifying peptides with low intensity or complex interference. The use of the reference map also facilitates the quantitation process by allowing extraction of peptide intensities of interest and incorporating models of theoretical isotope distribution.