Trapped ion mobility spectrometry and PASEF enable in-depth lipidomics from minimal sample amounts

A comprehensive characterization of the lipidome from limited starting material remains very challenging. Here we report a high-sensitivity lipidomics workflow based on nanoflow liquid chromatography and trapped ion mobility spectrometry (TIMS). Taking advantage of parallel accumulation-serial fragmentation (PASEF), we fragment on average 15 precursors in each of 100 ms TIMS scans, while maintaining the full mobility resolution of co-eluting isomers. The acquisition speed of over 100 Hz allows us to obtain MS/MS spectra of the vast majority of isotope patterns. Analyzing 1 mu L of human plasma, PASEF increases the number of identified lipids more than three times over standard TIMS-MS/MS, achieving attomole sensitivity. Building on high intra- and inter-laboratory precision and accuracy of TIMS collisional cross sections (CCS), we compile 1856 lipid CCS values from plasma, liver and cancer cells. Our study establishes PASEF in lipid analysis and paves the way for sensitive, ion mobility-enhanced lipidomics in four dimensions

Proteomic Approach for Extracting Cytoplasmic Proteins from Streptococcus sanguinis using Mass Spectrometry

Streptococcus sanguinis is a commensal and early colonizer of oral cavity as well as an opportunistic pathogen of infectious endocarditis. Extracting the soluble proteome of this bacterium provides deep insights about the physiological dynamic changes under different growth and stress conditions, thus defining “proteomic signatures” as targets for therapeutic intervention. In this protocol, we describe an experimentally verified approach to extract maximal cytoplasmic proteins from Streptococcus sanguinis SK36 strain. A combination of procedures was adopted that broke the thick cell wall barrier and minimized denaturation of the intracellular proteome, using optimized buffers and a sonication step. Extracted proteome was quantitated using Pierce BCA Protein Quantitation assay and protein bands were macroscopically assessed by Coomassie Blue staining. Finally, a high resolution detection of the extracted proteins was conducted through Synapt G2Si mass spectrometer, followed by label-free relative quantification via Progenesis QI. In conclusion, this pipeline for proteomic extraction and analysis of soluble proteins provides a fundamental tool in deciphering the biological complexity of Streptococcus sanguinis

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics.

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included

A modular computational framework for automated peak extraction from ion mobility spectra

 There was considered the possibility of the air field lights simulation in the imitators of flight simulator. The attempt of forming the database of the air field lights with realization in the lighting program DIALux is presented in this paper. Рассмотрена возможность моделирования светосигнальных огней аэродрома в имитаторах авиационных тренажеров. Приведены попытку формирования базы данных аэродромных огней с реализацией в свитлотех-ночной программе DIALux. Розглянуто можливість моделювання світлосигнальних вогнів аеродрому в імітаторах авіаційних тренажерів. Наведено спробу формування бази даних аеродромних вогнів з реалізацією у світлотехнічній програмі DIALux

Updates in metabolomics tools and resources: 2014-2015

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources—in the form of tools, software, and databases—is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table

Intact protein bioanalysis by liquid chromatography - High-resolution mass spectrometry

This review discusses the challenges of quantitative protein bioanalysis by LC-MS at the protein level. We will notably address the possibilities and current limitations of protein sample preparation, separation by LC, the challenge of interpreting protein ESI-MS spectra and the options for protein quantification based on extracted ion chromatograms or deconvoluted spectra. The possibilities of high-resolution mass spectrometry (HRMS) with respect to improving the signal-to-noise (S/N) ratio and the challenges of analyzing complex mass spectra will be highlighted based on examples

Computational methods for metabolomic data analysis of ion mobility spectrometry data-reviewing the state of the art

Ion mobility spectrometry combined with multi-capillary columns (MCC/IMS) is a well known technology for detecting volatile organic compounds (VOCs). We may utilize MCC/IMS for scanning human exhaled air, bacterial colonies or cell lines, for example. Thereby we gain information about the human health status or infection threats. We may further study the metabolic response of living cells to external perturbations. The instrument is comparably cheap, robust and easy to use in every day practice. However, the potential of the MCC/IMS methodology depends on the successful application of computational approaches for analyzing the huge amount of emerging data sets. Here, we will review the state of the art and highlight existing challenges. First, we address methods for raw data handling, data storage and visualization. Afterwards we will introduce de-noising, peak picking and other pre-processing approaches. We will discuss statistical methods for analyzing correlations between peaks and diseases or medical treatment. Finally, we study up-to-date machine learning techniques for identifying robust biomarker molecules that allow classifying patients into healthy and diseased groups. We conclude that MCC/IMS coupled with sophisticated computational methods has the potential to successfully address a broad range of biomedical questions. While we can solve most of the data pre-processing steps satisfactorily, some computational challenges with statistical learning and model validation remain

On-tissue dataset-dependent MALDI-TIMS-MS2^{2} bioimaging

Trapped ion mobility spectrometry (TIMS) adds an additional separation dimension to mass spectrometry (MS) imaging, however, the lack of fragmentation spectra (MS$^{2}$) impedes confident compound annotation in spatial metabolomics. Here, we describe spatial ion mobility-scheduled exhaustive fragmentation (SIMSEF), a dataset-dependent acquisition strategy that augments TIMS-MS imaging datasets with MS$^{2}$ spectra. The fragmentation experiments are systematically distributed across the sample and scheduled for multiple collision energies per precursor ion. Extendable data processing and evaluation workflows are implemented into the open source software MZmine. The workflow and annotation capabilities are demonstrated on rat brain tissue thin sections, measured by matrix-assisted laser desorption/ionisation (MALDI)-TIMS-MS, where SIMSEF enables on-tissue compound annotation through spectral library matching and rule-based lipid annotation within MZmine and maps the (un)known chemical space by molecular networking. The SIMSEF algorithm and data analysis pipelines are open source and modular to provide a community resource

Chemometrics for ion mobility spectrometry data:Recent advances and future prospects

Contains fulltext : 161386.pdf (publisher's version ) (Open Access)Historically, advances in the field of ion mobility spectrometry have been hindered by the variation in measured signals between instruments developed by different research laboratories or manufacturers. This has triggered the development and application of chemometric techniques able to reveal and analyze precious information content of ion mobility spectra. Recent advances in multidimensional coupling of ion mobility spectrometry to chromatography and mass spectrometry has created new, unique challenges for data processing, yielding high-dimensional, megavariate datasets. In this paper, a complete overview of available chemometric techniques used in the analysis of ion mobility spectrometry data is given. We describe the current state-of-the-art of ion mobility spectrometry data analysis comprising datasets with different complexities and two different scopes of data analysis, i.e. targeted and non-targeted analyte analyses. Two main steps of data analysis are considered: data preprocessing and pattern recognition. A detailed description of recent advances in chemometric techniques is provided for these steps, together with a list of interesting applications. We demonstrate that chemometric techniques have a significant contribution to the recent and great expansion of ion mobility spectrometry technology into different application fields. We conclude that well-thought out, comprehensive data analysis strategies are currently emerging, including several chemometric techniques and addressing different data challenges. In our opinion, this trend will continue in the near future, stimulating developments in ion mobility spectrometry instrumentation even further