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.

Phospholipidosis and steatosis are two toxic effects, which course with overaccumulation of different classes of lipids in the liver. MS-based lipidomics has become a powerful tool for the comprehensive determination of lipids. LC-MS lipid profiling of HepG2 cells is proposed as an in vitro assay to study and anticipate phospholipidosis and steatosis. Cells with and without preincubation with a mixture of free fatty acids (FFA; i.e. oleic and palmitic) were exposed to a set of well-known steatogenic and phospholipidogenic compounds. The use of FFA preloading accelerated the accumulation of phospholipids, thus leading to a better discrimination of phospholipidosis, and magnified the lipidomic alterations induced by steatogenic drugs. Phospholipidosis was characterized by increased levels of phosphatidylcholines, phosphatidylethanolamines, phosphatidylserines, and phosphatidylinositols, while steatosis induced alterations in FA oxidation and triacylglyceride (TG) synthesis pathways (with changes in the levels of FFA, acylcarnitines, monoacylglycerides, diacylglycerides, and TG). Interestingly, palmitic and oleic acids incorporation into lipids differed. A characteristic pattern was observed in the fold of change of particular TG species in the case of steatosis (TG(54:3) > TG(52:2) > TG(50:1) > TG(48:0)). Based on the levels of those lipids containing only palmitic and/or oleic acid moieties a partial least squares-discriminant analysis model was built, which showed good discrimination among nontoxic, phospholipidogenic and steatogenic compounds. In conclusion, it has been shown that the use of FFA preincubation together with intracellular LC-MS based lipid profiling could be a useful approach to identify the potential of drug candidates to induce phospholipidosis and/or steatosis.

Human dermal fibroblasts can be reprogrammed into hepatocyte-like (HEP-L) cells by the expression of a set of transcription factors. Yet, the metabolic rewiring suffered by reprogrammed fibroblasts remains largely unknown. Here we report, using stable isotope-resolved metabolic analysis in combination with metabolomic-lipidomic approaches that HEP-L cells mirrors glutamine/glutamate metabolism in primary cultured human hepatocytes that is very different from parental human fibroblasts. HEP-L cells diverge glutamine from multiple metabolic pathways into deamidation and glutamate secretion, just like periportal hepatocytes do. Exceptionally, glutamine contribution to lipogenic acetyl-CoA through reductive carboxylation is increased in HEP-L cells, recapitulating that of primary cultured human hepatocytes. These changes can be explained by transcriptomic rearrangements of genes involved in glutamine/glutamate metabolism. Although metabolic changes in HEP-L cells are in line with reprogramming towards the hepatocyte lineage, our conclusions are limited by the fact that HEP-L cells generated do not display a complete mature phenotype. Nevertheless, our findings are the first to characterize metabolic adaptation in HEP-L cells that could ultimately be targeted to improve fibroblasts direct reprogramming to HEP-L cells.

Motivation LipidMS was initially envisioned to use fragmentation rules and data-independent acquisition (DIA) for lipid annotation. However, data-dependent acquisition (DDA) remains the most widespread acquisition mode for untargeted LC-MS/MS-based lipidomics. Here we present LipidMS 3.0, an R package that not only adds DDA and new lipid classes to its pipeline, but also the required functionalities to cover the whole data analysis workflow from pre-processing (i.e., peak-peaking, alignment and grouping) to lipid annotation. Results We applied the new workflow in the data analysis of a commercial human serum pool spiked with 68 representative lipid standards acquired in full scan, DDA and DIA modes. When focusing on the detected lipid standard features and total identified lipids, LipidMS 3.0 data pre-processing performance is similar to XCMS, whereas it complements the annotations returned by MS-DIAL, providing a higher level of structural information and a lower number of incorrect annotations. To extend and facilitate LipidMS 3.0 usage among less experienced R-programming users, the workflow was also implemented as a web-based application. Availability The LipidMS R-package is freely available at https://CRAN.R-project.org/package=LipidMS and as a website at http://www.lipidms.com. Supplementary information Supplementary data are available at Bioinformatics online.