Predicting ion mobility collision cross sections and assessing prediction variation by combining conventional and data driven modeling.

The use of collision cross section (CCS) values derived from ion mobility studies is proving to be an increasingly important tool in the characterization and identification of molecules detected in complex mixtures. Here, a novel machine learning (ML) based method for predicting CCS integrating both molecular modeling (MM) and ML methodologies has been devised and shown to be able to accurately predict CCS values for singly charged small molecular weight molecules from a broad range of chemical classes. The model performed favorably compared to existing models, improving compound identifications for isobaric analytes in terms of ranking and assigning identification probability values to the annotation. Furthermore, charge localization was seen to be correlated with CCS prediction accuracy and with gas-phase proton affinity demonstrating the potential to provide a proxy for prediction error based on chemical structural properties. The presented approach and findings represent a further step towards accurate prediction and application of computationally generated CCS values.

Evaluation of acquisition modes for semi-quantitative analysis by targeted and untargeted mass spectrometry.

RATIONALE: Analyte quantitation by mass spectrometry underpins a diverse range of scientific endeavors. The fast-growing field of mass spectrometer development has resulted in several targeted and untargeted acquisition modes suitable for these applications. By characterizing the acquisition methods available on an ion mobility (IM)-enabled orthogonal acceleration time-of-flight (oa-ToF) instrument, the optimum modes for analyte semi-quantitation can be deduced. METHODS: Serial dilutions of commercial metabolite, peptide, or cross-linked peptide analytes were prepared in matrices of human urine or Escherichia coli digest. Each analyte dilution was introduced into an IM separation-enabled oa-ToF mass spectrometer by reversed-phase liquid chromatography and electrospray ionization. Data were acquired for each sample in duplicate using nine different acquisition modes, including four IM-enabled acquisitions modes, available on the mass spectrometer. RESULTS: Five (metabolite) or seven (peptide/cross-linked peptide) point calibration curves were prepared for analytes across each of the acquisition modes. A nonlinear response was observed at high concentrations for some modes, attributed to saturation effects. Two correction methods, one MS1 isotope-correction and one MS2 ion intensity-correction, were applied to address this observation, resulting in an up to twofold increase in dynamic range. By averaging the semi-quantitative results across analyte classes, two parameters, linear dynamic range (LDR) and lower limit of quantification (LLOQ), were determined to evaluate each mode. CONCLUSION: A comparison of the acquisition modes revealed that data-independent acquisition and parallel reaction monitoring methods are most robust for semi-quantitation when considering achievable LDR and LLOQ. IM-enabled modes exhibited sensitivity increases, but a simultaneous reduction in dynamic range required correction methods to recover. These findings will assist users in identifying the optimum acquisition mode for their analyte quantitation needs, supporting a diverse range of applications and providing guidance for future acquisition mode developments.

Segregation Lag in Polyploid Cells of the Pathogen Genus Borrelia: Implications for Antigenic Variation
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Relapsing fever agents like Borrelia hermsii undergo multiphasic antigenic variation that is attributable to spontaneous DNA non-reciprocal transpositions at a particular locus in the genome. This genetic switch results in a new protein being expressed on the cell surface, allowing cells with that phenotype to escape prevailing immunity. But the switch occurs in only one of several genomes in these spirochetes, and a newly-switched gene is effectively "recessive" until homozygosity is achieved. The longer that descendants of the switched cell expressed both old and new proteins, the longer this lineage risks neutralization by antibody to the old protein. We investigated the implications for antigenic variation of the phenotypic lag that polyploidy would confer on cells. We first experimentally determined the average genome copy number in daughter cells after division during mouse infection with B. hermsii strain HS1. We then applied discrete deterministic and stochastic simulations to predict outcomes when genomes were equably segregated either linearly, i.e. according to their position in one-dimensional arrays, or randomly partitioned, as for a sphere. Linear segregation replication provided for a lag in achievement of homozygosity that was significantly shorter than could be achieved under the random segregation condition. For cells with 16 genomes, this would be a 4-generation lag. A model incorporating the immune response and evolved matrices of switch rates indicated a greater fitness for polyploid over monoploid bacteria in terms of duration of infection.

Identification of ion series using ion mobility mass spectrometry: the example of alkyl-benzothiophene and alkyl-dibenzothiophene ions in diesel fuels.

Ion mobility-mass spectrometry (IMMS) has been presented as a promising method for analysis of highly complex mixtures. This coupling adds an additional postionization separation dimension to MS. The IM separation of ions is obtained in the millisecond time scale and can be particularly helpful when chromatographic separation is not possible. For obtaining relevant information about the samples, data processing is usually the bottleneck because of the high amount of data generated with IMMS. In the current work, we present a new workflow using specific comparison software dedicated to IMMS data, which allows one to compare m/z-drift time plots to highlight differences between samples. Two diesel fuels have been compared, i.e., the feed and the product of hydrodesulfurization (HDS) process, and this approach allowed us to clearly highlight the variation of intensity of several ions distributed along the plots of both samples. Accurate mass measurements and post IM collision induced dissociation experiments allowed us to identify two series of polycyclic aromatic sulfur-containing heterocycle (PASH) compounds among the matrix ions.

A probabilistic framework for peptide and protein quantification from data-dependent and data-independent LC-MS proteomics experiments.

A probability-based quantification framework is presented for the calculation of relative peptide and protein abundance in label-free and label-dependent LC-MS proteomics data. The results are accompanied by credible intervals and regulation probabilities. The algorithm takes into account data uncertainties via Poisson statistics modified by a noise contribution that is determined automatically during an initial normalization stage. Protein quantification relies on assignments of component peptides to the acquired data. These assignments are generally of variable reliability and may not be present across all of the experiments comprising an analysis. It is also possible for a peptide to be identified to more than one protein in a given mixture. For these reasons the algorithm accepts a prior probability of peptide assignment for each intensity measurement. The model is constructed in such a way that outliers of any type can be automatically reweighted. Two discrete normalization methods can be employed. The first method is based on a user-defined subset of peptides, while the second method relies on the presence of a dominant background of endogenous peptides for which the concentration is assumed to be unaffected. Normalization is performed using the same computational and statistical procedures employed by the main quantification algorithm. The performance of the algorithm will be illustrated on example data sets, and its utility demonstrated for typical proteomics applications. The quantification algorithm supports relative protein quantification based on precursor and product ion intensities acquired by means of data-dependent methods, originating from all common isotopically-labeled approaches, as well as label-free ion intensity-based data-independent methods.

Simultaneous qualitative and quantitative analysis of the Escherichia coli proteome: a sweet tale.

We describe a novel LCMS approach to the relative quantitation and simultaneous identification of proteins within the complex milieu of unfractionated Escherichia coli. This label-free, LCMS acquisition method observes all detectable, eluting peptides and their corresponding fragment ions. Postacquisition data analysis methods extract both the chromatographic and the mass spectrometric information on the tryptic peptides to provide time-resolved, accurate mass measurements, which are subsequently used for quantitation and identification of constituent proteins. The response of E. coli to carbon source variation is well understood, and it is thus commonly used as a model biological system when validating an analytical method. Using this LCMS approach, we characterized proteins isolated from E. coli grown in glucose, lactose, and acetate. The change in relative abundance of the corresponding proteins was measured from peptides common to both conditions. Protein identities were also determined for those peptides that were unique to each condition, and these identities were found to be consistent with the underlying biochemical restrictions imposed by the growth conditions. The relative change in abundance of the characterized proteins ranged from 0.1- to 90-fold among the three binary comparisons. The overall coverage of the characterized proteins ranged from 10 to 80%, consisting of one to 34 peptides per protein. The quantitative results obtained from our study were comparable to other existing proteomic and transcriptional profiling approaches. This study illustrates the robustness of this novel LCMS approach for the simultaneous quantitative and comprehensive qualitative analysis of proteins in complex mixtures.

Quantitative proteomic analysis by accurate mass retention time pairs.

Current methodologies for protein quantitation include 2-dimensional gel electrophoresis techniques, metabolic labeling, and stable isotope labeling methods to name only a few. The current literature illustrates both pros and cons for each of the previously mentioned methodologies. Keeping with the teachings of William of Ockham, "with all things being equal the simplest solution tends to be correct", a simple LC/MS based methodology is presented that allows relative changes in abundance of proteins in highly complex mixtures to be determined. Utilizing a reproducible chromatographic separations system along with the high mass resolution and mass accuracy of an orthogonal time-of-flight mass spectrometer, the quantitative comparison of tens of thousands of ions emanating from identically prepared control and experimental samples can be made. Using this configuration, we can determine the change in relative abundance of a small number of ions between the two conditions solely by accurate mass and retention time. Employing standard operating procedures for both sample preparation and ESI-mass spectrometry, one typically obtains under 5 ppm mass precision and quantitative variations between 10 and 15%. The principal focus of this paper will demonstrate the quantitative aspects of the methodology and continue with a discussion of the associated, complementary qualitative capabilities.

ProbSeq--a fragmentation model for interpretation of electrospray tandem mass spectrometry data.

We describe a probabilistic peptide fragmentation model for use in protein databank searching and de novo sequencing of electrospray tandem mass spectrometry data. A probabilistic framework for tuning of the model using a range of well-characterized samples are introduced. We present preliminary results of our tuning efforts.