lipID—a software tool for automated assignment of lipids in mass spectra

Hübner, Göran; Crone, Catharina; Lindner, Buko

doi:10.1002/jms.1673

Cited by 32 publications

(22 citation statements)

References 40 publications

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“…Various software have been developed to identify and quantify lipids in global lipidomics analysis, such as LipidSearch [22], LipID [36] and Progenesis QI [37]. The LC–MS/MS data acquired from the rat liver and plasma samples were searched using Lipid-Search, which has a database containing more than 1.5 million lipid ions and their predicted fragment ions.…”

Section: Resultsmentioning

confidence: 99%

Comprehensive untargeted lipidomic analysis using core–shell C30 particle column and high field orbitrap mass spectrometer

Narváez-Rivas

Zhang

2016

Journal of Chromatography A

142

102

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The goal of untargeted lipidomics is to have high throughput, yet comprehensive and unambiguous identification and quantification of lipids. Novel stationary phases in LC separation and new mass spectrometric instruments capable of high mass resolving power and faster scanning rate are essential to achieving this goal. In this work, 4 reversed phase LC columns coupled with a high field quadrupole orbitrap mass spectrometer (Q Exactive HF) were thoroughly compared using complex lipid standard mixture and rat plasma and liver samples. A good separation of all lipids was achieved in 24 min of gradient. The columns compared include C30 and C18 functionalization on either core–shell or totally porous silica particles, with size ranging from 1.7 to 2.6 μm. Accucore C30 column showed the narrowest peaks and highest theoretical plate number, and excellent peak capacity and retention time reproducibility (<1% standard deviation). As a result, it resulted in 430 lipid species identified from rat plasma and rat liver samples with highest confidence. The high resolution offered by the up-front RPLC allowed discrimination of cis/trans isomeric lipid species, and the high field orbitrap mass spectrometer afforded the clear distinction of isobaric lipid species in full scan MS and the unambiguous assignment of sn-positional isomers for lysophospholipids in MS/MS. Taken together, the high efficiency LC separation and high mass resolving MS analysis are very promising tools for untargeted lipidomics analysis.

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Section: Resultsmentioning

confidence: 99%

Comprehensive untargeted lipidomic analysis using core–shell C30 particle column and high field orbitrap mass spectrometer

Narváez-Rivas

Zhang

2016

Journal of Chromatography A

142

102

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“…Some are limited to the lipid classes covered by their fragment ion databases, such as LipidQA [17]. Another software tool lipID supported more comprehensive lipid classes, but unfortunately lacked the ability to analyze MS n data [18]. In 2009, AMDMS-SL was developed to automate the analyses of multidimensional mass spectrometry (MDMS)-based shotgun lipidomics [19,20].…”

Section: Introductionmentioning

confidence: 99%

Automated Lipid A Structure Assignment from Hierarchical Tandem Mass Spectrometry Data

Ting

Shaffer

Jones

et al. 2011

J. Am. Soc. Mass Spectrom.

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Infusion-based electrospray ionization (ESI) coupled to multiple-stage tandem mass spectrometry (MS n ) is a standard methodology for investigating lipid A structural diversity (Shaffer et al. J. Am. Soc. Mass. Spectrom. 18(6), [1080][1081][1082][1083][1084][1085][1086][1087][1088][1089][1090][1091][1092] 2007). Annotation of these MS n spectra, however, has remained a manual, expert-driven process. In order to keep up with the data acquisition rates of modern instruments, we devised a computational method to annotate lipid A MS n spectra rapidly and automatically, which we refer to as hierarchical tandem mass spectrometry (HiTMS) algorithm. As a first-pass tool, HiTMS aids expert interpretation of lipid A MS n data by providing the analyst with a set of candidate structures that may then be confirmed or rejected. HiTMS deciphers the signature ions (e.g., A-, Y-, and Z-type ions) and neutral losses of MS n spectra using a species-specific library based on general prior structural knowledge of the given lipid A species under investigation. Candidates are selected by calculating the correlation between theoretical and acquired MS n spectra. At a false discovery rate of less than 0.01, HiTMS correctly assigned 85% of the structures in a library of 133 manually annotated Francisella tularensis subspecies novicida lipid A structures. Additionally, HiTMS correctly assigned 85% of the structures in a smaller library of lipid A species from Yersinia pestis demonstrating that it may be used across species.

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“…Several software tools have been developed for the identification of lipids in shotgun lipidomics, including LipidQA [76], LIMSA [77], FAAT [78], lipID [79], LipidSearch [80], LipidView [81], LipidInspector [82], LipidXplorer [70], LipidBlast [83], and ALEX [69], both for specific applications/instrumentation and for cross-platform software featuring user-specified commands querying spectral data in an open-source format. Typically, in the top-down approach, the data are searched for specific molecular fragmentation.…”

Section: Data Processing and Identificationmentioning

confidence: 99%

Optimizing the lipidomics workflow for clinical studies—practical considerations

Hyötyläinen

Orešič

2015

Anal Bioanal Chem

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Lipidomics is increasingly being used in clinical research, offering new opportunities for disease prediction and detection. One of the key challenges of clinical applications of lipidomics is the high sensitivity of measured lipid levels to many analytical, physiological, and environmental factors, which therefore must be taken into account when designing the studies. Here we critically discuss the complete clinical lipidomics workflow, including selection of the subjects, the sample type, the sample preprocessing conditions, and the analytical method and methods for data processing. We also review the lipidomics applications which investigate the confounding factors such as age, gender, fasting time, and handling procedures for measuring blood lipid metabolites.

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lipID—a software tool for automated assignment of lipids in mass spectra

Cited by 32 publications

References 40 publications

Comprehensive untargeted lipidomic analysis using core–shell C30 particle column and high field orbitrap mass spectrometer

Comprehensive untargeted lipidomic analysis using core–shell C30 particle column and high field orbitrap mass spectrometer

Automated Lipid A Structure Assignment from Hierarchical Tandem Mass Spectrometry Data

Optimizing the lipidomics workflow for clinical studies—practical considerations

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