LipidMS: An R Package for Lipid Annotation in Untargeted Liquid Chromatography-Data Independent Acquisition-Mass Spectrometry Lipidomics
- María Isabel Alcoriza-BalaguerMaría Isabel Alcoriza-BalaguerBiomarkers and Precision Medicine Unit and Analytical Unit, Instituto de Investigación Sanitaria Fundación Hospital La Fe, Valencia 46026, SpainMore by María Isabel Alcoriza-Balaguer,
- Juan Carlos García-CañaverasJuan Carlos García-CañaverasBiomarkers and Precision Medicine Unit and Analytical Unit, Instituto de Investigación Sanitaria Fundación Hospital La Fe, Valencia 46026, SpainMore by Juan Carlos García-Cañaveras,
- Adrián LópezAdrián LópezBiomarkers and Precision Medicine Unit and Analytical Unit, Instituto de Investigación Sanitaria Fundación Hospital La Fe, Valencia 46026, SpainMore by Adrián López,
- Isabel CondeIsabel CondeHepatology Unit, Department of Digestive Medicine, Hospital Universitari i Politècnic La Fe, Valencia 46026, SpainMore by Isabel Conde,
- Oscar JuanOscar JuanDepartment of Medical Oncology, Hospital Universitari i Politècnic La Fe, Valencia 46026, SpainBiomarkers and Precision Medicine Unit and Analytical Unit, Instituto de Investigación Sanitaria Fundación Hospital La Fe, Valencia 46026, SpainMore by Oscar Juan,
- Julián CarreteroJulián CarreteroDepartment of Physiology, University of Valencia, Burjassot 4100, SpainMore by Julián Carretero, and
- Agustín Lahoz*Agustín Lahoz*Mailing Address: Agustín Lahoz, Biomarkers and Precision Medicine Unit and Analytical Unit, Instituto de Investigación Sanitaria Fundación Hospital La Fe, Av. Fernando Abril Martorell 106, Valencia 46026, Spain; E-mail: [email protected]; Tel: 961246652; Fax: 961246620.Biomarkers and Precision Medicine Unit and Analytical Unit, Instituto de Investigación Sanitaria Fundación Hospital La Fe, Valencia 46026, SpainMore by Agustín Lahoz
Abstract
High resolution LC-MS untargeted lipidomics using data independent acquisition (DIA) has the potential to increase lipidome coverage, as it enables the continuous and unbiased acquisition of all eluting ions. However, the loss of the link between the precursor and the product ions combined with the high dimensionality of DIA data sets hinder accurate feature annotation. Here, we present LipidMS, an R package aimed to confidently identify lipid species in untargeted LC-DIA-MS. To this end, LipidMS combines a coelution score, which links precursor and fragment ions with fragmentation and intensity rules. Depending on the MS evidence reached by the identification function survey, LipidMS provides three levels of structural annotations: (i) “subclass level”, e.g., PG(34:1); (ii) “fatty acyl level”, e.g., PG(16:0_18:1); and (iii) “fatty acyl position level”, e.g., PG(16:0/18:1). The comparison of LipidMS with freely available data dependent acquisition (DDA) and DIA identification tools showed that LipidMS provides significantly more accurate and structural informative lipid identifications. Finally, to exemplify the utility of LipidMS, we investigated the lipidomic serum profile of patients diagnosed with nonalcoholic steatohepatitis (NASH), which is the progressive form of nonalcoholic fatty liver disease, a disorder underlying a strong lipid dysregulation. As previously published, a significant decrease in lysophosphatidylcholines, phosphatidylcholines and cholesterol esters and an increase in phosphatidylethanolamines were observed in NASH patients. Remarkably, LipidMS allowed the identification of a new set of lipids that may be used for NASH diagnosis. Altogether, LipidMS has been validated as a tool to assist lipid identification in the LC-DIA-MS untargeted analysis of complex biological samples.
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