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.

Untargeted omics analyses aim to comprehensively characterize biomolecules within a biological system. Changes in the presence or quantity of these biomolecules can indicate important biological perturbations, such as those caused by disease. With current technological advancements, the entire genome can now be sequenced; however, in the burgeoning fields of lipidomics, only a subset of lipids can be identified. The recent emergence of high resolution tandem mass spectrometry (HR-MS/MS), in combination with ultra-high performance liquid chromatography, has resulted in an increased coverage of the lipidome. Nevertheless, identifications from MS/MS are generally limited by the number of precursors which can be selected for fragmentation during chromatographic elution. Therefore, we developed the software IE-Omics to automate iterative exclusion (IE), where selected precursors using data-dependent topN analyses are excluded in sequential injections. In each sequential injection, unique precursors are fragmented until HR-MS/MS spectra of all ions above a user-defined intensity threshold are acquired. IE-Omics was applied to lipidomic analyses in Red Cross plasma and substantia nigra tissue. Coverage of the lipidome was drastically improved using IE. When applying IE-Omics to Red Cross plasma and substantia nigra lipid extracts in positive ion mode, 69 % and 40 % more molecular identifications were obtained, respectively. In addition, applying IE-Omics to a lipidomics workflow increased the coverage of trace species, including odd-chained and short-chained diacylglycerides and oxidized lipid species. By increasing the coverage of the lipidome, applying IE to a lipidomics workflow increases the probability of finding biomarkers and provides additional information for determining etiology of disease.

In order to profile the lipidome for untargeted lipidomics applications, analysis by ultra-high performance liquid chromatography – high resolution mass spectrometry (UHPLC-HRMS) typically requires the extraction of lipid content from sample matrices using matrix-specific conditions. The Folch, Bligh-Dyer, and Matyash extraction methods, while promising approaches, were originally tailored to specific matrices (brain tissue, fish muscle, and E. coli, respectively). Each of these methods have specific solvent ratios that must be adhered to achieve optimal extraction. Thus, the sample-to-solvent ratios for these methods should be optimized for the sample matrix of interest prior to employment. This study evaluated the appropriate sample-to-extraction solvent ratios for human plasma-based lipidomics studies. An advantage of employing biphasic lipid extractions is the ability to investigate both the aqueous and organic layers for increased analyte coverage in untargeted studies. Therefore, this work also evaluated the multi-omic capability of each lipid extraction method for plasma in an effort to provide a workflow capable of increasing analyte coverage in a single extraction, thus providing a more complete understanding of complex biological systems. In plasma, a decrease in sample-to-solvent ratios from 1:4, 1:10, 1:20, to 1:100 (v/v) resulted in a gradual increase in the peak area of a diverse range of metabolite (aqueous layer) and lipid (organic layer) species for each extraction method. The Bligh-Dyer and Folch methods yielded the highest peak areas at every plasma sample-to-solvent ratios for both metabolite and lipid species. Depending on the lipid class of interest, the Folch or Bligh-Dyer method is best suited for analysis of human plasma at a 1:20 (v/v) sample to total solvent ratio.

2017-08-25T16:27:50

No abstract

Background The precise concentrations of full-length parathyroid hormone (PTH1-84) and the identity and concentrations of PTH fragments in patients with various stages of chronic renal failure are unknown. Methods We developed a liquid chromatography-high resolution mass spectrometry (LC-HRMS) method to characterize and quantify PTH1-84 and PTH fragments in serum of 221 patients with progressive renal dysfunction. Following capture by matrix-bound amino-terminal or carboxyl-terminal region-specific antibodies and elution from matrix, PTH1-84 and PTH fragments were identified and quantitated using LC-HRMS. PTH was simultaneously measured using an intact PTH (iPTH) immunoassay. Results Full-length PTH1-84 and 8 PTH fragments (PTH28-84, 34-77, 34-84, 37-77, 37-84, 38-77, 38-84, and 45-84) were unequivocally identified and were shown to increase significantly when an eGFR declined to ≤17-23 mL/min/1.73m2. Serum concentrations of PTH1-84 were similar when measured by LC-HRMS following capture by amino-terminal or carboxyl-terminal immunocapture methods. In patients with an eGFR of <30 mL/min/1.73 m2, serum PTH concentrations measured using LC-HRMS were significantly lower than PTH measured using an iPTH immunoassay. PTH7-84 and oxidized forms of PTH1-84 were below the limit of detection (30 and 50 pg/mL, respectively). Conclusions LC-HRMS identifies circulating PTH1-84, carboxyl-terminal PTH fragments, and mid-region PTH fragments, in patients with progressive renal failure. Serum PTH1-84 and its fragments markedly rise when an eGFR decreases to ≤17-23 mL/min/1.73 m2. PTH concentrations measured using LC-HRMS tend to be lower than those measured using an iPTH immunoassay, particularly in severe chronic renal failure. Our data do not support the existence of circulating PTH7-84 and oxidized PTH1-84.

The continued growth of the lipidomics research community, combined with a concomitant increase in the number of lipidomic applications, has culminated in an emerging need for the harmonization and standardization of lipidomics measurement. Harmonization and standardization of lipidomic measurement is a considerable undertaking, owing to the vast structural diversity and complexity of lipids, which also subsequently coincides with the use of a broad range of qualitative and quantitative measurement strategies employed by the lipidomics community. The lipidomics community needs to address the variability present in current lipidomics measurement before harmonization and standardization can begin to occur. Accordingly, this work encompasses the first community-supported harmonization effort via an interlaboratory comparison exercise, focused on ascertaining sources of lipidomic measurement variability and/or agreement, while also highlighting measurement challenges in regards to lipid quantitation.The main objectives of the interlaboratory comparison exercise were to 1) generate consensus estimates in nmol/mL for those lipids routinely measured by participants, 2) determine the extent of agreement present within the community using current quantitation lipidomics workflows, 3) and identify those lipids or lipid classes that require more attention. The basic framework of the National Institute of Standards and Technology (NIST) interlaboratory comparison for lipidomics was to distribute one vial of Standard Reference Material (SRM) 1950 -Metabolites in Frozen Human Plasma to each participating laboratory, and to encourage each participant to employ the analytical methodologies that they typically use to quantify lipids in their laboratory. SRM 1950 was chosen as the vehicle for the comparison exercise as it has been previously recognized and promoted as an appropriate reference material for metabolomics (1)(2)(3)(4)(5). In addition, SRM 1950 was constructed to approximate "normal" blood plasma indicative of the United States population (see http://srm1950.nist.gov/). Invitations were sent to a cohort of laboratories that were representative of the diverse cross-section of lipid measurement methodologies present within the lipidomics community. Consensus estimates (at sum composition level), with corresponding uncertainties, were generated for those lipids measured by at least five laboratories. Additional analyses were performed to further assess the collective submitted data, including coefficient of dispersion (COD) for each consensus estimate and zetascores (ζ-scores). COD values were used to evaluate the quality or "usefulness" of the consensus estimates. ζ-scores were used to determine the relative measurement agreement amongst the consensus estimates by lipid species and lipid class.The final consensus estimates and associated uncertainties generated from this exercise hold considerable potential for the lipidomics community, both to serve as inter-and intralaboratory benchmarks but also to initiate follow-up...