A novel informatics concept for high-throughput shotgun lipidomics based on the molecular fragmentation query language

Abstract

Shotgun lipidome profiling relies on direct mass spectrometric analysis of total lipid extracts from cells, tissues or organisms and is a powerful tool to elucidate the molecular composition of lipidomes. We present a novel informatics concept of the molecular fragmentation query language implemented within the LipidXplorer open source software kit that supports accurate quantification of individual species of any ionizable lipid class in shotgun spectra acquired on any mass spectrometry platform.

Figure 1

Making a shotgun lipidomics dataset. Experiments are repeated in several independent biological replicates for each studied phenotype. Each biological replicate is split into several samples from which lipids are extracted and extracts are independently analyzed by MS. Spectra acquired from the total lipid extract survey molecular ions of lipid precursors, which are subsequently fragmented in MS/MS experiments, yielding MS/MS spectra. Each spectrum is acquired in several scans that are subsequently averaged. A set of MS and MS/MS spectra is termed as an 'acquisition' and several acquisitions are performed continuously making a 'technical replicate'.

Figure 2

Architecture of LipidXplorer. Boxes represent functional modules and arrows represent data flow between the modules. The import module converts technical replicates (collections of MS and MS/MS spectra) into a flat file database termed the MasterScan (.sc). Then the interpretation module probes the MasterScan with interpretation queries written in molecular fragmentation query language (MFQL). Finally, the output module exports the findings in a user-defined format. All LipidXplorer settings (irrespective of what particular module they apply to) are controlled via a single graphical user interface.

Figure 3

Scan averaging algorithm. (a) Related individual scans (here as an example we only show four scans) imported as a complete *.mzXML file are recognized. (b) Peaks are combined into a single peak list and sorted. (c) The full mass range is divided into bins of [m; m+$\frac{m}{R(m)}$] size, starting from the lowest reported mass. The bold dots stand for the lowest mass of each bin, while the arrow length reflects the bin size $\frac{m}{R(m)}$. Within each bin, masses are weight averaged by peak intensities and stored. The procedure (steps (c) and (d)) is repeated two more times on the binned spectrum (not shown). (d) In this way, a single representative average spectrum (d) is produced from several individual scans (a).

Figure 4

Organization of a MasterScan file. LipidXplorer imports and aligns MS and MS/MS spectra into a flat file database MasterScan. It is shown here as a file cabinet addressed at the top-level by precursor masses in the MS spectrum, while their intensities are assigned to individual acquisitions. In this example the lipid precursor with m/z 788.55 was observed in all acquisitions with an intensity (in arbitrary units) of 203745 in Acquisition 1; 120668 in the Acquisition 2; ... till 35746 in Acquisition n. This precursor m/z 788.55 was fragmented in each acquisition. Masses of fragments were aligned and substituted by the averaged representative masses, while the intensities of corresponding peaks in each individual acquisition were stored. For example, the fragment with m/z 184.07 has an intensity of 181716 in Acquisition 1; 104364 in Acquisition 2; ..., till 27854 in Acquisition n.

Figure 5

Structural complexity of lipid species and sum composition constraints. Let us consider phosphatidylcholines (PC class lipids) as a representative example: PC molecules consist of a posphorylcholine head group attached to the glycerol backbone at the sn-3 position, while fatty acid moieties occupy sn-1 and sn-2 positions (alternatively, a fatty alcohol moiety could be attached at the sn-1 position). Fatty acid moieties differ by the number of carbon atoms and double bonds, but also by the relative location at the glycerol backbone, so that isomeric structures having exactly the same fatty acid moieties are possible. Note that isomeric structures are always isobaric, whereas isobaric molecules are not necessarily isomeric. Most generic constraints ('All lipids of PC class' or 'All PC esters') encompass sum compositions of species with all naturally occurring fatty acids. However, because of the fatty acid variability, some species of other lipid classes (such as phosphatidylethanolamines (PE class)) might meet the same constraint. Therefore, for most common glycerophospholipid classes, the characterization of individual molecular species can not rely solely on their intact masses, irrespective of how accurately they were measured. MS/MS experiments that produce structure-specific ions contribute more specific constraints, such as the number of carbons and double bonds in individual moieties, characteristic head group fragment, characteristic loss of a fatty acid moiety, among others. Within a MFQL query, these constraints can be bundled by boolean operations.

Figure 6

MFQL identification of phosphatidylcholines (PC). The chemical structure of PC is shown in Figure 5. Upon their collisional fragmentation, molecular cations of PC species produce the specific head group fragment with m/z 184.07 and sum composition 'C5 H15 O4 P1 N1'. (a) MS spectrum acquired by direct infusion of a total lipid extract into a QSTAR mass spectrometer (inset). All detectable peaks were subjected to MS/MS. The spectrum acquired from the precursor m/z 788.55 (designated by arrow) is presented at the lower panel. The precursor ion was isolated within 1 Da mass range and therefore several isobaric lipid precursors were co-isolated for MS/MS and produced abundant fragment ions unrelated to PC. These ions were disregarded by this MFQL query and did not affect PC identification. (b) MFQL query identifying PC species, details are provided in the text. (c) Screenshot of the output spreadsheet file; column annotation and content is determined by the REPORT section of the above MFQL (see also text for details).

Figure 7

Validation of the isotopic correction algorithm using a PA mixture. Molar ratios of PA standards were determined in four replicates with and without isotopic correction of abundances of peaks within partially overlapping isotopic clusters. Molar ratios in MS spectra were determined from the abundances of precursor peaks and in MS/MS spectra as the sum of the abundances of acyl anions of the fatty acids moieties. Error bars stand for standard deviations from the average molar ratios.

Figure 8

Pearson correlation factors of peak abundances in the MasterScan and individual spectra. In total, the dataset consisted of 128 high resolution MS spectra of total lipid extracts in which 219 peaks of individual lipid species were recognized. The exact number of peaks assigned to lipid species is provided for each PCF bin. The average PCF calculated for the entire dataset had a value of 0.94

Figure 9

LipidXplorer accurately interprets both high and low resolution mass spectra. The number of PE-O species falsely assigned by LipidXplorer software in shotgun analysis of a total E. coli lipid extract under different target mass resolutions.

Figure 10

LipidXplorer supports the interpretation of spectra acquired using different mass spectrometers. (a) Comparison of the relative abundances of 24 major PE and PG lipid species identified in a total E. coli extract in MS and data-dependent MS/MS modes on the LTQ Orbitrap Velos (red bars) and QSTAR Pulsar i (blue bars) mass spectrometers, while spectra were interpreted by LipidXplorer. The same extract was analyzed by MPIS on the QSTAR Pulsar i and LipidProfiler software (green bars). Species abundances were normalized to the total abundance of the lipid class; error bars (standard deviation) were calculated on the basis of six experiments. Correlation coefficients and slopes of scatter plots for each pair-wise comparison are presented in Table 5.

Figure 11

Identification of PC and PC-O species by MFQL queries relying on complementary signature ions. (a) MS/MS spectrum of the precursor ion of the acetate adduct of PC 36:1 (m/z 846.6224), in which four signature ions are recognized: molecular ion (MS); fragment of neutral loss of acetate and methyl group (Δm/z = 74.0 (NL)); acyl anions of the two fatty acid moieties (FA 281.3 and FA 283.2; both boxed in the chemical structure at the top). (b) MS/MS spectrum of the acetate adduct of PC-O 34:3 (m/z 800.5808). Signature ions are the same as in (a), except m/z 464.4 representing the fragment produced by neutral loss of the sn-2 fatty acid moiety. (c) Quantitative profiles of PC and PC-O species reported from abundances of different signature ions. MS, precursor ions in MS spectra; NL 74, neutral loss Δm/z 74 in MS/MS spectra; FA/FAO, acyl anions of fatty acid moieties and (for PC-O) neutral loss of sn-2 fatty acid moiety. The relative abundance of species was normalized to the total abundance of species within each (PC or PC-O) class.