Guiding the choice of informatics software and tools for lipidomics research applications

Guiding the choice of informatics software and tools for lipidomics research applications

February 2023.

Zhixu Ni Michele WölkGeoff JukesKarla Mendivelso EspinosaRobert AhrendsLucila AimoJorge Alvarez-JarretaSimon AndrewsRobert AndrewsAlan BridgeGeremy C ClairMatthew J ConroyEoin FahyCaroline GaudLaura GoracciJürgen HartlerNils HoffmannDominik KopczyinkiAnsgar KorfAndrea F Lopez-ClavijoAdnan MalikJacobo Miranda AckermanMartijn R MolenaarClaire O’DonovanTomáš PluskalAndrej ShevchenkoDenise SlenterGary SiuzdakMartina KutmonHiroshi TsugawaEgon L WillighagenJianguo XiaValerie B O’DonnellMaria Fedorova

 

Abstract

Progress in mass spectrometry lipidomics has led to a rapid proliferation of studies across biology and biomedicine. These generate extremely large raw datasets requiring sophisticated solutions to support automated data processing. To address this, numerous software tools have been developed and tailored for specific tasks. However, for researchers, deciding which approach best suits their application relies on ad hoc testing, which is inefficient and time consuming. Here we first review the data processing pipeline, summarizing the scope of available tools. Next, to support researchers, LIPID MAPS provides an interactive online portal listing open-access tools with a graphical user interface. This guides users towards appropriate solutions within major areas in data processing, including (1) lipid-oriented databases, (2) mass spectrometry data repositories, (3) analysis of targeted lipidomics datasets, (4) lipid identification and (5) quantification from untargeted lipidomics datasets, (6) statistical analysis and visualization, and (7) data integration solutions. Detailed descriptions of functions and requirements are provided to guide customized data analysis workflows.

 

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A platelet lipidomics signature in patients with COVID-19

A platelet lipidomics signature in patients with COVID-19

December 2023.

Laura Goracci, Eleonora Petito, Alessandra Di Veroli, Emanuela Falcinelli, Caterina Bencivenga, Elisa Giglio, Cecilia Becattini, Edoardo De Robertis, Gaetano Vaudo, Paolo Gresele

Abstract

Ischemic cardiovascular and venous thromboembolic events are a frequent cause of death in severe COVID-19 patients. Platelet activation plays a key role in these complications, however platelet lipidomics have not been studied yet. The aim of our pilot investigation was to perform a preliminary study of platelet lipidomics in COVID-19 patients compared to healthy subjects. Lipid extraction and identification of ultrapurified platelets from eight hospitalized COVID-19 patients and eight age- and sex-matched healthy controls showed a lipidomic pattern almost completely separating COVID-19 patients from healthy controls. In particular, a significant decrease of ether phospholipids and increased levels of ganglioside GM3 were observed in platelets from COVID-19 patients. In conclusion, our study shows for the first time that platelets from COVID-19 patients display a different lipidomics signature distinguishing them from healthy controls, and suggests that altered platelet lipid metabolism may play a role in viral spreading and in the thrombotic complications of COVID-19.

Keywords: COVID-19; Platelet lipidomics.

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Analysis of Phosphatidylinositol Modifications by Reactive Nitrogen Species Using LC-MS: Coming to Grips with Their Nitroxidative Susceptibility

Analysis of Phosphatidylinositol Modifications by Reactive Nitrogen Species Using LC-MS: Coming to Grips with Their Nitroxidative Susceptibility

July 2023.

Stefano Bonciarelli, Bruna Neves, Pedro Domingues, Tânia Melo, Laura Goracci, Maria Rosário Domingues

Abstract

Phosphatidylinositols (PIs) are complex lipids that play a key role in cell signaling. Like other phospholipids, they are esterified with unsaturated fatty acyl residues (FAs), making them susceptible to modification by reactive oxygen and nitrogen species (RNS). Recent studies using mass spectrometry (MS)-based lipidomics approaches have revealed that lipid nitration results in a plethora of structurally and chemically modified lipids (epilipids), including nitrated and nitroxidized derivatives of phosphatidylcholines, phosphatidylethanolamines, phosphatidylserines, and cardiolipins. However, there is a notable lack of knowledge regarding the characterization of RNS-modified PI derivatives. In this study, we used C18 high-resolution liquid chromatography-tandem MS approaches to describe the fragmentation signature of nitrated and nitroxidized PIs, bearing different fatty acyl chains. Using this approach and accurate mass measurements, we were able to identify nitro- PI derivatives, dinitro- and nitrohydroxy- derivatives for a few PI species. The data showed the typical neutral loss of nitrous acid (HNO2) as well as the fragmentation patterns corresponding to modified fatty acyl chains (such as NOx-RCOO, [M – NOx-RCOOH – H] and [M – NOx-RCOOH – C6H10O5 – H]), making it possible to identify these epilipids. The susceptibility of PIs to nitration was also investigated, revealing that it depends exclusively on the chains of unsaturated FAs esterified in PI, showing a higher conversion rate for those with C18:1. Overall, the knowledge gathered in this study will contribute to the precise characterization of these epilipids in complex biological samples, offering new opportunities to unveil the pathophysiological roles of nitrated and nitroxidized PI derivatives at the cellular and tissue levels.

Keywords: LC-MS; lipidomics; nitration; nitrative stress; nitroxidative stress.

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Automated identification of potential pesticides residues in fruit samples using HRMS data

Automated identification of potential pesticides residues in fruit samples using HRMS data

70th ASMS Conference on Mass Spectrometry. June 2022

Elisabeth Ortega1; Ismael Zamora1; Pol Giménez1; Luca Morettoni1; Roberto Romero-gonzalez2; Rosalía Lopez-Ruiz2; Antonia Garrido Frenich2

1Lead Molecular Design, S.L., Sant Cugat del Valles, Spain; 2Research Group ‘Analytical Chemistry of Contaminants’, Department of Chemistry and Physics, Research Centre for Agricultural and Food Biotechnology (BITAL), Agrifood Campus of International Excellence, University of Almeria, Almeria, Spain

Abstract

Introduction

In food safety and related fields, High Resolution Mass Spectrometry techniques applied for multiresidue analysis had become an alternative to the historical routine procedures involving triple quadrupole instruments. This evolution was mainly driven by the possibility to interrogate hundreds or thousands of compounds without a prior individual study of all of them. However, due to the big amount of information that can be generated during the data acquisition, the later data processing and data analysis steps can be quite time demanding. In this presentation we will show how this late step could be automized using Chemical Monitoring workflow included in MassChemSite 3.1.

Methods

For chromatographic analysis, Thermo Fisher Scientific Vanquish Flex Quaternary LC (Thermo Scientific Transcend™, Thermo Fisher Scientific, San Jose, CA, USA) was used. The chromatographic system is coupled to a hybrid mass spectrometer Q-Exactive Orbitrap Thermo Fisher Scientific (ExactiveTM, Thermo Fisher Scientific, Bremen, Germany) using an electrospray interface (ESI) (HESI-II, Thermo Fisher Scientific, San Jose, CA, USA) in positive-negative mode. ESI parameters were as follows: spray voltage, 4 kV; sheath gas (N2, 95%), 35 (adimensional); auxiliary gas (N2, 95%), 10 (adimensional); S-lens RF level, 50 (adimensional); heater temperature, 305 °C; and capillary temperature, 300 °C.

Data processing has been done using MassChemSite 3.1 (Molecular Discovery, Ltd. Borehamwood, UK). Data analysis was performed in ONIRO server (Molecular Discovery, Ltd. Borehamwood, UK).

Preliminary data

Strawberry, white grape and orange samples providing from Almeria (Spain) greenhouses were acquired in the University of Almería and processed using the Chemical Monitoring data workflow included in MassChemSite 3.1. Data was interrogated against an in-house pesticide database generated by literature search including up to 1500 different pesticides. From the total, up to 10 different pesticides were detected in all the samples in less than five minutes of data processing.

The identification step was performed using the MS and MSMS information: MS was used to detect the pesticide in the sample, while fragmentation information was used to finally elucidate the structure of the detected pesticide, by means of a computational fragmentation of the detected pesticide and a later assignation to the MSMS data provided by the instrument. The fitting among computed and experimental fragments is reported as “score” which can be used to discriminate among other structural isobaric compounds associated to the same chromatographic peak.

Data analysis and reporting were done in ONIRO server after an automatic uploading of the raw data. Later filtering steps were applied and tracked by the application for further inspection. Additionally, a final report was generated automatically once the experiment was reviewed. Data generated during the acquisition remained on the server for later use or further re-analysis.

 

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