As the lipidomics field continues to advance, self-evaluation within the community is critical. Here, we performed an interlaboratory comparison exercise for lipidomics using Standard Reference Material (SRM) 1950-Metabolites in Frozen Human Plasma, a commercially available reference material. The interlaboratory study comprised 31 diverse laboratories, with each laboratory using a different lipidomics workflow. A total of 1,527 unique lipids were measured across all laboratories and consensus location estimates and associated uncertainties were determined for 339 of these lipids measured at the sum composition level by five or more participating laboratories. These evaluated lipids detected in SRM 1950 serve as community-wide benchmarks for intra- and interlaboratory quality control and method validation. These analyses were performed using nonstandardized laboratory-independent workflows. The consensus locations were also compared with a previous examination of SRM 1950 by the LIPID MAPS consortium. While the central theme of the interlaboratory study was to provide values to help harmonize lipids, lipid mediators, and precursor measurements across the community, it was also initiated to stimulate a discussion regarding areas in need of improvement.

BackgroundLipids are ubiquitous and serve numerous biological functions; thus lipids have been shown to have great potential as candidates for elucidating biomarkers and pathway perturbations associated with disease. Methods expanding coverage of the lipidome increase the likelihood of biomarker discovery and could lead to more comprehensive understanding of disease etiology.ResultsWe introduce LipidMatch, an R-based tool for lipid identification for liquid chromatography tandem mass spectrometry workflows. LipidMatch currently has over 250,000 lipid species spanning 56 lipid types contained in in silico fragmentation libraries. Unique fragmentation libraries, compared to other open source software, include oxidized lipids, bile acids, sphingosines, and previously uncharacterized adducts, including ammoniated cardiolipins. LipidMatch uses rule-based identification. For each lipid type, the user can select which fragments must be observed for identification. Rule-based identification allows for correct annotation of lipids based on the fragments observed, unlike typical identification based solely on spectral similarity scores, where over-reporting structural details that are not conferred by fragmentation data is common. Another unique feature of LipidMatch is ranking lipid identifications for a given feature by the sum of fragment intensities. For each lipid candidate, the intensities of experimental fragments with exact mass matches to expected in silico fragments are summed. The lipid identifications with the greatest summed intensity using this ranking algorithm were comparable to other lipid identification software annotations, MS-DIAL and Greazy. For example, for features with identifications from all 3 software, 92% of LipidMatch identifications by fatty acyl constituents were corroborated by at least one other software in positive mode and 98% in negative ion mode.ConclusionsLipidMatch allows users to annotate lipids across a wide range of high resolution tandem mass spectrometry experiments, including imaging experiments, direct infusion experiments, and experiments employing liquid chromatography. LipidMatch leverages the most extensive in silico fragmentation libraries of freely available software. When integrated into a larger lipidomics workflow, LipidMatch may increase the probability of finding lipid-based biomarkers and determining etiology of disease by covering a greater portion of the lipidome and using annotation which does not over-report biologically relevant structural details of identified lipid molecules.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1744-3) contains supplementary material, which is available to authorized users.

Metabolic reprogramming has been described in rapidly growing tumors, which are thought to mostly contain fast‐cycling cells (FCCs) that have impaired mitochondrial function and rely on aerobic glycolysis. Here, we characterize the metabolic landscape of glioblastoma (GBM) and explore metabolic specificities as targetable vulnerabilities. Our studies highlight the metabolic heterogeneity in GBM, in which FCCs harness aerobic glycolysis, and slow‐cycling cells (SCCs) preferentially utilize mitochondrial oxidative phosphorylation for their functions. SCCs display enhanced invasion and chemoresistance, suggesting their important role in tumor recurrence. SCCs also demonstrate increased lipid contents that are specifically metabolized under glucose‐deprived conditions. Fatty acid transport in SCCs is targetable by pharmacological inhibition or genomic deletion of FABP7, both of which sensitize SCCs to metabolic stress. Furthermore, FABP7 inhibition, whether alone or in combination with glycolysis inhibition, leads to overall increased survival. Our studies reveal the existence of GBM cell subpopulations with distinct metabolic requirements and suggest that FABP7 is central to lipid metabolism in SCCs and that targeting FABP7‐related metabolic pathways is a viable therapeutic strategy.

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The autoimmune disease systemic lupus erythematosus (SLE) is characterized by the production of pathogenic autoantibodies. It has been postulated that gut microbial dysbiosis may be one of the mechanisms involved in SLE pathogenesis. Here, we demonstrate that the dysbiotic gut microbiota of triple congenic (TC) lupus-prone mice (B6.Sle1.Sle2.Sle3) stimulated the production of autoantibodies and activated immune cells when transferred into germfree congenic C57BL/6 (B6) mice. Fecal transfer to B6 mice induced autoimmune phenotypes only when the TC donor mice exhibited autoimmunity. Autoimmune pathogenesis was mitigated by horizontal transfer of the gut microbiota between co-housed lupus-prone TC mice and control congenic B6 mice. Metabolomic screening identified an altered distribution of tryptophan metabolites in the feces of TC mice including an increase in kynurenine, which was alleviated after antibiotic treatment. Low dietary tryptophan prevented autoimmune pathology in TC mice, whereas high dietary tryptophan exacerbated disease. Reducing dietary tryptophan altered gut microbial taxa in both lupus-prone TC mice and control B6 mice. Consequently, fecal transfer from TC mice fed a high tryptophan diet, but not a low tryptophan diet, induced autoimmune phenotypes in germfree B6 mice. The interplay of gut microbial dysbiosis, tryptophan metabolism and host genetic susceptibility in lupus-prone mice suggest that aberrant tryptophan metabolism may contribute to autoimmune activation in this disease.