Recommendations for reporting ion mobility mass spectrometry measurements

© 2019 The Authors. Mass Spectrometry Reviews Published by Wiley Periodicals, Inc. Here we present a guide to ion mobility mass spectrometry experiments, which covers both linear and nonlinear methods: what is measured, how the measurements are done, and how to report the results, including the uncertainties of mobility and collision cross section values. The guide aims to clarify some possibly confusing concepts, and the reporting recommendations should help researchers, authors and reviewers to contribute comprehensive reports, so that the ion mobility data can be reused more confidently. Starting from the concept of the definition of the measurand, we emphasize that (i) mobility values (K0) depend intrinsically on ion structure, the nature of the bath gas, temperature, and E/N; (ii) ion mobility does not measure molecular surfaces directly, but collision cross section (CCS) values are derived from mobility values using a physical model; (iii) methods relying on calibration are empirical (and thus may provide method-dependent results) only if the gas nature, temperature or E/N cannot match those of the primary method. Our analysis highlights the urgency of a community effort toward establishing primary standards and reference materials for ion mobility, and provides recommendations to do so. © 2019 The Authors. Mass Spectrometry Reviews Published by Wiley Periodicals, Inc

Enhancing Computational Tools for Ion Mobility-Mass Spectrometry-Based Untargeted Workflows (Cascadia Proteomics Symposium 17)

<div>Ion mobility spectrometry (IMS) is a rapid and highly reproducible molecular-shape separation technique. While IMS has shown great utility when coupled with MS for analysis of complex samples, methods for processing the complex data generated have lagged behind. In fact, the incorporation of this extra IMS separation dimension requires upgrades and optimization of existing computational pipelines and the development of new algorithmic strategies to fully exploit the advantages of the technology. Here, we investigate MS pre-processing algorithms to extend feature detection and quantification performance, as well as integration of IMS collisional cross section (CCS) libraries into data analysis tools for molecular characterization. These strategies were applied for untargeted analyzes of biofluid samples to evaluate changes in endogenous metabolites and xenobiotics. IMS-MS data files were acquired by an Agilent 6560 Ion Mobility Q-TOF MS system. A software tool for IMS data analysis and processing was developed (C#) to apply multidimensional smoothing, saturation correction and generate new raw data files. Intensity values in each frame (an IMS cycle) were smoothed first in the drift dimension followed by smoothing of the chromatographic dimension considering neighboring frames. High signal intensity values that reached beyond detector capacity were identified by the characteristic flat profile at the apex of saturated peaks. Agilent Mass Profiler was used for feature extraction and sample alignment. A CCS library was created from small molecule standards. Features were annotated using Agilent ID Browser. Statistical analyses of results were performed in R. The developed software tools were integrated to process IMS-MS data from urine samples previously analyzed using a solid-phase extraction method. To minimize the effects of low ion statistics (e.g., jagged profiles), several smoothing kernels were evaluated: Gaussian, Savitzky-Golay, moving average and weighted moving average. Among those, moving average smoothing provided the best results for retrieving low-abundance features and merging at least 40% more of the features with split profiles. Smoothing increased by a factor of 2.5 the number of high quality features (quality score ≥ 80; a 0−100 scale considering signal-to-noise, number of isotopic ions and m/z stability). More specifically, 1332 features were found in at least 20 of the 96 analyzed samples, compared to 360 features found without smoothing. These improvements were also reflected by a 3-fold increase of the number of features having abundances with less than 20% coefficient of variation. A filtering strategy was incorporated in the smoothing heuristic to reduce background noise, consequently decreasing (by half, on average) file size, memory usage and processing time for feature detection and alignment. Furthermore, saturation correction improved quantification and mass accuracy of analytes with high intensity signals. We therefore observed that the implemented strategies enhanced the dynamic range of measurement: smoothing towards the lower end and saturation correction towards the higher end of abundances.</div><div>These data quality improvements also allowed us to compute more accurate CCS values from the IM-MS measurements, which in combination with CCS libraries can help to discriminate the feature of interest. For instance, our CCS library increased the identification confidence of creatinine for a detected feature (114.0656 m/z, 122.69 CCS), distinguishing among 6 hits (0.01 mass tolerance) from the METLIN database. Work is in progress to compare and examine in detail the benefits of incorporating predicted and experimental CCS libraries in the metabolite identification workflow. </div><p></p

A Flexible Learning Infrastructure for Proteomics (ASMS 2017)

Developing a scoring model for MS/MS sequence-spectrum matches has been typically considered as part of the software development process and is rarely a user-serviceable component. Models are usually created from scratch and hard-coded for each project or fragmentation, which hinders adaptation and requires significant source code modifications to process new data types. In addition, advances such as increased resolving power and new dissociation methods result in new types of spectra where different fragment ions (e.g. due to neutral and side chain losses) can be considered for scoring enhancements. Here we introduce the Flexible Learning Infrastructure for Proteomics (FLIP). As in machine learning, FLIP conceives training/learning as an ongoing process, allowing rapid customizations over time.FLIP accepts as input raw MS/MS data, true-positive sequence matches, and true-negative sequence matches in community standard data formats for both spectra and identifications. A classifier is used to weight fragment ion features, such as mass error, isotopic fit, and intensity that best separate the true-positive and true-negative training data. By default, it supports both logistic regression and support vector machine models through the Accord.NET machine learning framework. Cross-validation feature reduction is used to select relevant fragment ions. The trained model is written to a tab-separated file, which serves as input for MS/MS scoring in a search engine. It also provides figures such as score histograms and ROC curves for performance evaluation of the trained model.The FLIP framework utilizes a modular design and the dependency injection software design pattern. It has been divided into five customizable core modules: parsing, for reading raw spectra and identifications; pre-processing, which performs deconvolution and spectrum filtering; modelling, which selects features from data for training; learning, which runs a machine learning classifier; and cross-validation. This allows users to substitute any of these modules with their own code without recompilation of the FLIP framework code and provides flexibility for new implementations such as support for their own data formats, custom features to train on, learning models, or cross validation metrics.<div><br></div><div>FLIP does not require the user to manually determine which type of fragment ions to train on. It starts with a very large set of possible fragment ions and performs multiple rounds of 10-fold cross-validation, each round reducing the number of fragment ions used for training. Area under the ROC curve is calculated each round to determine the optimal set of fragment ions.</div><div><br></div><div>Because our goal is to make this framework flexible to various types of data, we have used it to train scoring models on samples from peptides and intact proteins from both HCD and ultraviolet photodissociation (UVPD) dissociation methods. We created bottom up and top down MS/MS spectra on a Thermo QExactive for 6 bacterial organisms, which resulted in over 100,000 unique peptides for training and testing the scoring algorithms. The scoring models were evaluated with a Hela Lysate sample using our database search tool, MSPathFinder. We observed that FLIP was able to define effective models with significant differences in the numbers and types of fragment ions found for each of these data types, increasing the number of confident identifications (<1% false discovery rate) by at least 14% when compared to a peak counting scoring model. </div

Enhancing Computational Tools for LC-IM-MS-Based Small Molecule Workflows (ASMS 2017)

Ion mobility spectrometry (IMS) is a rapid and highly reproducible molecular-shape separation technique. While IMS has shown great utility when coupled with MS for analysis of complex samples, methods for processing the complex data generated have lagged behind. In fact, the incorporation of this extra IMS separation dimension requires upgrades and optimization of existing computational pipelines and the development of new algorithmic strategies to fully exploit the advantages of the technology. Here, we investigate MS pre-processing algorithms to extend feature detection and quantification performance, as well as integration of IMS collisional cross section (CCS) libraries into data analysis tools for molecular characterization. These strategies were applied for untargeted analyzes of biofluid samples to evaluate changes in endogenous metabolites and xenobiotics