Multicomponent internal recalibration of an LC-FTICR-MS analysis employing a partially characterized complex peptide mixture: systematic and random errors

Mass recalibration of FT-ICR mass spectrometry imaging data using the average frequency shift of ambient ions

Achieving and maintaining high mass measurement accuracy (MMA) throughout a mass spectrometry imaging (MSI) experiment is vital to the identification of the observed ions. However, when using FTMS instruments, fluctuations in the total ion abundance at each pixel due to inherent biological variation in the tissue section can introduce space charge effects that systematically shift the observed mass. Herein we apply a recalibration based on the observed cyclotron frequency shift of ions found in the ambient laboratory environment, polydimethylcyclosiloxanes (PDMS). This calibration method is capable of achieving part per billion (ppb) mass accuracy with relatively high precision for an infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) MSI dataset. Comparisons with previously published mass calibration approaches are also presented.

Mass resolution and mass accuracy: how much is enough?

Accurate mass measurement requires the highest possible mass resolution, to ensure that only a single elemental composition contributes to the mass spectral peak in question. Although mass resolution is conventionally defined as the closest distinguishable separation between two peaks of equal height and width, the required mass resolving power can be ∼10× higher for equal width peaks whose peak height ratio is 100 : 1. Ergo, minimum resolving power requires specification of maximum dynamic range, and is thus 10-100× higher than the conventional definition. Mass resolving power also depends on mass-to-charge ratio. Mass accuracy depends on mass spectral signal-to-noise ratio and digital resolution. Finally, the reliability of elemental composition assignment can be improved by resolution of isotopic fine structure. Thus, the answer to the question of "how much is enough mass resolving power" requires that one first specify S/N ratio, dynamic range, digital resolution, mass-to-charge ratio, and (if available) isotopic fine structure. The highest available broadband mass resolving power and mass accuracy is from Fourier transform ion cyclotron resonance mass spectrometry. Over the past five years, FT-ICR MS mass accuracy has improved by about an order of magnitude, based on higher magnetic field strength, conditional averaging of time-domain transients, better mass calibration (spectral segmentation; inclusion of a space charge term); radially dispersed excitation; phase correction to yield absorption-mode display; and new ICR cell segmentation designs.

An improved calibration method for the matrix-assisted laser desorption/ionization-Fourier transform ion cyclotron resononance analysis of 15N-metabolically- labeled proteome digests using a mass difference approach

High mass measurement accuracy of peptides in enzymatic digests is critical for confident protein identification and characterization in proteomics research. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) can provide low or sub-ppm mass accuracy and ultrahigh resolving power. While for ESI-FT-ICR-MS, the mass accuracy is generally 1 ppm or better, with matrix-assisted laser desorption/ionization (MALDI)-FT-ICR-MS, the mass errors can vary from sub-ppm with internal calibration to over 100 ppm with conventional external calibration. A novel calibration method for (15)N-metabolically labeled peptides from a batch digest of a proteome is described which corrects for space charge induced frequency shifts in FT-ICR spectra without using an internal calibrant. This strategy utilizes the information from the mass difference between the (14)N/(15)N peptide peak pairs to correct for space charge induced mass shifts after data collection. A procedure for performing the mass correction has been written into a computer program and has been successfully applied to high-performance liquid chromatography-MALDI-FT- ICR-MS measurement of (15)N-metabolic labeled proteomes. We have achieved an average measured mass error of 1.0 ppm and a standard deviation of 3.5 ppm for 900 peptides from 68 MALDI-FT-ICR mass spectra of the proteolytic digest of a proteome from Methanococcus maripaludis.

Automated data reduction for hydrogen/deuterium exchange experiments, enabled by high-resolution Fourier transform ion cyclotron resonance mass spectrometry

Mass analysis of proteolytic fragment peptides following hydrogen/deuterium exchange offers a general measure of solvent accessibility/hydrogen bonding (and thus conformation) of solution-phase proteins and their complexes. The primary problem in such mass analyses is reliable and rapid assignment of mass spectral peaks to the correct charge state and degree of deuteration of each fragment peptide, in the presence of substantial overlap between isotopic distributions of target peptides, autolysis products, and other interferant species. Here, we show that at sufficiently high mass resolving power (m/Delta m(50%) > or = 100,000), it becomes possible to resolve enough of those overlaps so that automated data reduction becomes possible, based on the actual elemental composition of each peptide without the need to deconvolve isotopic distributions. We demonstrate automated, rapid, reliable assignment of peptide masses from H/D exchange experiments, based on electrospray ionization FT-ICR mass spectra from H/D exchange of solution-phase myoglobin. Combined with previously demonstrated automated data acquisition for such experiments, the present data reduction algorithm enhances automation (and thus expands generality and applicability) for high-resolution mass spectrometry-based analysis of H/D exchange of solution-phase proteins.

Petroleomics: a test bed for ultra-high-resolution Fourier transform ion cyclotron resonance mass spectrometry

Within a relative abundance dynamic range of approximately 10,000:1, the world's most compositionally complex organic mixture is petroleum crude oil. As such, it provides the most challenging target for mass spectral resolution and identification of molecules below m/z 2000. The mass "splits" in petroleum include most of those that also appear in proteomics, metabolomics and other complex organic mixture analysis. Therefore, petroleum provides an excellent test bed for optimizing mass spectrometer performance in general. The presence of multiple elemental compositions spanning less than 1 Da in mass facilitates mapping and correction of rf phase variation across a Fourier transform ion cyclotron resonance mass spectrum, as well as exposing otherwise inaccessible systematic mass deviations, for additional improvement in mass resolving power and mass accuracy by a factor of up to 5. Internal mass calibration, combined with systematic peak assignment for successive homologous series, enables automated elemental composition assignment of tens of thousands of peaks in a single mass spectrum.

DtaRefinery, a software tool for elimination of systematic errors from parent ion mass measurements in tandem mass spectra data sets

Hybrid two-stage mass spectrometers capable of both highly accurate mass measurement and high throughput MS/MS fragmentation have become widely available in recent years, allowing for significantly better discrimination between true and false MS/MS peptide identifications by the application of a relatively narrow window for maximum allowable deviations of measured parent ion masses. To fully gain the advantage of highly accurate parent ion mass measurements, it is important to limit systematic mass measurement errors. Based on our previous studies of systematic biases in mass measurement errors, here, we have designed an algorithm and software tool that eliminates the systematic errors from the peptide ion masses in MS/MS data. We demonstrate that the elimination of the systematic mass measurement errors allows for the use of tighter criteria on the deviation of measured mass from theoretical monoisotopic peptide mass, resulting in a reduction of both false discovery and false negative rates of peptide identification. A software implementation of this algorithm called DtaRefinery reads a set of fragmentation spectra, searches for MS/MS peptide identifications using a FASTA file containing expected protein sequences, fits a regression model that can estimate systematic errors, and then corrects the parent ion mass entries by removing the estimated systematic error components. The output is a new file with fragmentation spectra with updated parent ion masses. The software is freely available.

Generating peptide titration-type curves using polymeric reverse micelles as selective extraction agents along with matrix-assisted laser desorption ionization-mass spectrometry detection

Amphiphilic homopolymers that self-assemble into reverse micelles in nonpolar solvents have been used by us in the context of a two-phase liquid-liquid extraction protocol to selectively extract peptides from aqueous solution for MALDI-MS detection. In this manuscript, we investigate the scope of these materials in terms of its extraction capabilities, using compounds with varying isoelectric points (pI) and pK(a) values over a range of aqueous solution pHs. We find that the aqueous solution pH and analyte pK(a) values are the major factors controlling extraction selectivity. We also find that the experimental extraction efficiencies correspond very well with the fractional compositions of species calculated using analyte pK(a) values, indicating that these extraction materials can be used to simultaneously generate titration-type curves for each individual peptide in a mixture. We predict that such titration curves, along with accurate mass measurements, could represent a new way of improving protein identification procedures.

A Bayesian approach to peptide identification using accurate mass and time tags from LC-FTICR-MS proteomics experiments

In high-throughput proteomics, one promising approach presently being explored is the Accurate Mass and Time (AMT) tag approach, in which reversed-phase liquid chromatography coupled to high accuracy mass spectrometry provide measurements of both the masses and chromatographic retention times of tryptic peptides in complex mixtures. These measurements are matched to the mass and predicted retention times of peptides in library. There are two varieties of peptides in the library: peptides whose retention time predictions are derived from previous peptide identifications and therefore are of high precision, and peptides whose retention time predictions are derived from a sequence-based model and therefore have lower precision. We present a Bayesian statistical model that provides probability estimates for the correctness of each match by separately modeling the data distributions of correct matches and incorrect matches. For matches to peptides with high-precision retention time predictions, the model distinguishes correct matches from incorrect matches with high confidence. For matches to peptides having low-precision retention time predictions, match probabilities do not approach certainty; however, even moderate probability matches may provide biologically interesting findings, motivating further investigations.

Characterization of strategies for obtaining confident identifications in bottom-up proteomics measurements using hybrid FTMS instruments

Hybrid FTMS instruments, such as the LTQ-FT and LTQ-Orbitrap, are capable of generating high duty cycle linear ion trap MS/MS data along with high resolution information without compromising the overall throughput of measurements. Combined with online LC separations, these instruments provide powerful capabilities for proteomics research. In the present work, we explore three alternative strategies for high throughput proteomics measurements using hybrid FTMS instruments. Our accurate mass and time tag (AMT tag) strategy enables identification of thousands of peptides in a single LC-FTMS analysis by comparing accurate molecular mass and LC elution time information from the analysis to a reference database. An alternative strategy considered here, termed accurate precursor mass filter (APMF), employs linear ion trap (low resolution) MS/MS identifications generated by an appropriate search engine, such as SEQUEST, refined with high resolution precursor ion data obtained from FTMS mass spectra. The APMF results can be additionally filtered using the LC elution time information from the AMT tag database, which constitutes a precursor mass and time filter (PMTF), the third approach implemented in this study. Both the APMF and the PMTF approaches are evaluated for coverage and confidence of peptide identifications and contrasted with the AMT tag strategy. The commonly used decoy database method and an alternative method based on mass accuracy histograms were used to reliably quantify identification confidence, revealing that both methods yielded similar results. Comparison of the AMT, APMF and PMTF approaches indicates that the AMT tag approach is preferential for studies desiring a highest achievable number of identified peptides. In contrast, the APMF approach does not require an AMT tag database and provides a moderate level of peptide coverage combined with acceptable confidence values of approximately 99%. The PMTF approach yielded a significantly better peptide identification confidence, >99.9%, that essentially excluded any false peptide identifications. Since AMT tag databases that exclude incorrect identifications are desirable, this study points to the value of a multipass APMF approach to generate AMT tag databases, which are then validated using the PMTF approach. The resulting compact, high quality databases can then be used for subsequent high-throughput, high peptide coverage AMT tag studies.

Elimination of systematic mass measurement errors in liquid chromatography-mass spectrometry based proteomics using regression models and a priori partial knowledge of the sample content

The high mass measurement accuracy and precision available with recently developed mass spectrometers is increasingly used in proteomics analyses to confidently identify tryptic peptides from complex mixtures of proteins, as well as post-translational modifications and peptides from nonannotated proteins. To take full advantage of high mass measurement accuracy instruments, it is necessary to limit systematic mass measurement errors. It is well known that errors in m/z measurements can be affected by experimental parameters that include, for example, outdated calibration coefficients, ion intensity, and temperature changes during the measurement. Traditionally, these variations have been corrected through the use of internal calibrants (well-characterized standards introduced with the sample being analyzed). In this paper, we describe an alternative approach where the calibration is provided through the use of a priori knowledge of the sample being analyzed. Such an approach has previously been demonstrated based on the dependence of systematic error on m/z alone. To incorporate additional explanatory variables, we employed multidimensional, nonparametric regression models, which were evaluated using several commercially available instruments. The applied approach is shown to remove any noticeable biases from the overall mass measurement errors and decreases the overall standard deviation of the mass measurement error distribution by 1.2-2-fold, depending on instrument type. Subsequent reduction of the random errors based on multiple measurements over consecutive spectra further improves accuracy and results in an overall decrease of the standard deviation by 1.8-3.7-fold. This new procedure will decrease the false discovery rates for peptide identifications using high-accuracy mass measurements.

Dynamic range and mass accuracy of wide-scan direct infusion nanoelectrospray fourier transform ion cyclotron resonance mass spectrometry-based metabolomics increased by the spectral stitching method

Direct infusion nanoelectrospray Fourier transform ion cyclotron resonance mass spectrometry (DI nESI FT-ICR MS) offers high mass accuracy and resolution for analyzing complex metabolite mixtures. High dynamic range across a wide mass range, however, can only be achieved at the expense of mass accuracy, since the large numbers of ions entering the ICR detector induce adverse space-charge effects. Here we report an optimized strategy for wide-scan DI nESI FT-ICR MS that increases dynamic range but maintains high mass accuracy. It comprises the collection of multiple adjacent selected ion monitoring (SIM) windows that are stitched together using novel algorithms. The final SIM-stitching method, derived from several optimization experiments, comprises 21 adjoining SIM windows each of width m/z 30 (from m/z 70 to 500; adjacent windows overlap by m/z 10) with an automated gain control (AGC) target of 1 x 10(5) charges. SIM-stitching and wide-scan range (WSR; Thermo Electron) were compared using a defined standard to assess mass accuracy and a liver extract to assess peak count and dynamic range. SIM-stitching decreased the maximum mass error by 1.3- and 4.3-fold, and increased the peak count by 5.3- and 1.8-fold, versus WSR (AGC targets of 1 x 10(5) and 5 x 10(5), respectively). SIM-stitching achieved an rms mass error of 0.18 ppm and detected over 3000 peaks in liver extract. This novel approach increases metabolome coverage, has very high mass accuracy, and at 5.5 min/sample is conducive for high-throughput metabolomics.

Nonparametric Mass Calibration Using Hundreds of Internal Calibrants

In situations where many molecular ions (>100) can be identified to the level of their elemental composition, such as in proteomics, metabolomics, and glycomics, a final mass calibration is possible for every sample without reliance on any analytical description of instrument behavior. This is achieved by applying a nonparametric calibration curve determined from the difference in observed, centroided m/z values of the known, internal calibrant molecular ions versus that calculated from their elemental compositions, over the m/z range. In examples here, proteomic data are examined for two sets of samples of complex mixtures composed of tryptic peptides from human and mouse blood proteins using high-resolution time-of-flight mass spectra from on-line liquid chromatography-mass spectrometry experiments. Resultant, postcalibration median absolute value mass errors and root-mean-square errors for peptides between 300 and 1100 m/z for many samples ranged from 3.1 to 4.4 and 5.2 to 6.9 ppm, respectively. The method may be applied to other types of mass spectrometers.

Fourier transform ion cyclotron resonance: state of the art

This short review summarizes recent and projected advances in Fourier transform ion cyclotron resonance mass spectrometry instrumentation and applications, ranging from petroleomics to proteomics. More details are available from the cited primary literature and topical reviews.

Mass Measurement Accuracy in Analyses of Highly Complex Mixtures Based Upon Multidimensional Recalibration

Mass spectrometry combined with a range of on-line separation techniques has become a powerful tool for characterization of complex mixtures, including protein digests in proteomics studies. Accurate mass measurements can be compromised due to variations that occur in the course of an on-line separation, e.g., due to excessive space charge in an ion trap, temperature changes, or other sources of instrument "drift". We have developed a multidimensional recalibration approach that utilizes existing information on the likely mixture composition, taking into account variable conditions of mass measurements, and that corrects the mass calibration for sets of individual peaks binned by, for example, the total ion count for the mass spectrum, the individual peak abundance, m/z value, and liquid chromatography separation time. The multidimensional recalibration approach uses a statistical matching of measured masses in such measurements, often exceeding 105, to a significant number of putative known species likely to be present in the mixture (i.e., having known accurate masses), to identify a subset of the detected species that serve as effective calibrants. The recalibration procedure involves optimization of the mass accuracy distribution (histogram), to provide a more confident distinction between true and false identifications. We report the mass accuracy improvement obtained for data acquired using a TOF and several FTICR mass spectrometers. We show that the multidimensional recalibration better compensates for systematic mass measurement errors and also significantly reduces the mass error spread: i.e., both the accuracy and precision of mass measurements are improved. The mass measurement improvement is found to be virtually independent of the initial instrument calibration, allowing, for example, less frequent calibration. We show that this recalibration can provide sub-ppm mass measurement accuracy for measurements of a complex fungal proteome tryptic digest and provide improved confidence or numbers of peptide identifications.

Robust Algorithm for Alignment of Liquid Chromatography−Mass Spectrometry Analyses in an Accurate Mass and Time Tag Data Analysis Pipeline

Liquid chromatography coupled to mass spectrometry (LC-MS) and tandem mass spectrometry (LC-MS/MS) has become a standard technique for analyzing complex peptide mixtures to determine composition and relative abundance. Several high-throughput proteomics techniques attempt to combine complementary results from multiple LC-MS and LC-MS/MS analyses to provide more comprehensive and accurate results. To effectively collate and use results from these techniques, variations in mass and elution time measurements between related analyses need to be corrected using algorithms designed to align the various types of data: LC-MS/MS versus LC-MS/MS, LC-MS versus LC-MS/MS, and LC-MS versus LC-MS. Described herein are new algorithms referred to collectively as liquid chromatography-based mass spectrometric warping and alignment of retention times of peptides (LCMSWARP), which use a dynamic elution time warping approach similar to traditional algorithms that correct for variations in LC elution times using piecewise linear functions. LCMSWARP is compared to the equivalent approach based upon linear transformation of elution times. LCMSWARP additionally corrects for temporal drift in mass measurement accuracies. We also describe the alignment of LC-MS results and demonstrate their application to the alignment of analyses from different chromatographic systems, showing the suitability of the present approach for more complex transformations.

Automated Analysis of Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectra of Natural Organic Matter

The advent of ultra-high-resolution mass spectrometry has revolutionized the ability of aquatic biogeochemists to examine molecular-level components of complex mixtures of organic matter. The ability to accurately assess the chemical composition, elemental formulas, or both of detected compounds is critical to these studies. Here we build on previous work that uses functional group relationships between compounds to extend elemental formulas of low molecular weight compounds to those of higher molecular weight. We propose an automated compound identification algorithm (CIA) for the analysis of ultra-high-resolution mass spectra of natural organic matter acquired by electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry. This approach is benchmarked with synthetic data sets of compounds cited in the literature. The sensitivity of our results is examined for different sources of error, and CIA is applied to two previously published data sets. We find that CIA works well for data sets with high mass accuracy (<1 ppm) and can accurately determine the elemental formulas for >95% of all compounds composed of C, H, O, and N. Data with lower mass accuracy must be accompanied with additional knowledge of chemical structure, composition, or both in order to yield accurate elemental formulas.