We previously demonstrated that, because of the two conceptually novel solutions – MasterScan and MFQL, LipidXplorer enabled accurate interpretation of shotgun lipidomics datasets acquired on different tandem mass spectrometers 
. One factor contributing to the interpretation consistency was that, irrespective of the employed instrument, we processed similarly structured datasets obtained by data-dependent acquisition. They consisted of survey MS spectra and full MS/MS spectra acquired either from peaks detected in survey spectra, or from peaks whose masses matched the masses from a pre-compiled inclusion list. Although data-dependent acquisition is a powerful approach 
it is only applied on rapid scanning high mass resolution tandem mass spectrometers, such as hybrid quadrupole time-of-flight (reviewed in 
) or LTQ Orbitrap 
. However, currently the largest body of shotgun lipidomics work is performed using triple quadrupole or triple quadrupole - linear ion trap (QTRAP) mass spectrometers (reviewed in 
) by precursor- or neutral loss scanning 
. In these analyses no full MS/MS spectra are acquired: the instrument is set to detect one particular fragment (precursor ion scanning), or fragments with pre-defined mass difference to the fragmented precursor (neutral loss scanning) that are originating from all precursors within certain m/z
range. In each analysis, only one fragment mass (in case of precursor ion scanning) or mass difference (in case of neutral loss scanning) is monitored and then the analysis is repeated for the next chosen fragment mass/mass difference. These analyses produce differently structured datasets to which generic “full MS/MS spectra” interpretations are not applicable directly.
Here we demonstrate how low mass resolution precursor ion spectra and neutral loss spectra can be interpreted by LipidXplorer and provide evidence that these interpretations are consistent with alternative analyses by DDA-driven acquisition of full MS/MS spectra. Let us explain the interpretation algorithm using a precursor ion scan spectrum as an example. A precursor ion scanning spectrum plots the abundance of one specific fragment ion produced from a precursor isolated within the resolution-dependent mass window that is moving with a certain small increment, usually 0.05 to 0.2 Th, along m/z
range (). If several precursor ion spectra are acquired, they can be aligned by precursor masses and then transposed to the format: [precursor mass]: [frag1, abundance], [frag2, abundance], …, [fragn
, abundance], where the precursor masses are all masses within the considered mass range. This operation corresponds to the transformation of spectra in panel B to spectra in panel A, however the latter only comprises a restricted set of fragment masses. The spectra alignment algorithm employed by LipidXplorer 
effectively creates a “virtual” MS/MS spectrum for each precursor mass observed in individual precursor ion spectra: the only difference with shotgun datasets acquired by DDA is that they comprise no survey MS spectra. The algorithm is also adjustable to the actual mass resolution and accuracy. Therefore, upon building a MasterScan from transposed spectra, lipid identification could proceed with the same MFQL queries in the usual way.
DDA-driven MS/MS and Precursor Ion Scanning (PIS) Spectra.
To validate cross-platform interpretation capabilities of LipidXplorer, we analyzed a commercial sample of the total lipid extract from E.coli
(Avanti Polar Lipids, Alabaster, AL) by shotgun experiments performed in different analytical modes and on different mass spectrometers. Upon collision induced fragmentation, molecular anions of phosphatidylethanolamines (PE) and phosphatidylglycerols (PG) – the major constituents of E.coli
lipidome produce abundant acyl anion fragments of their fatty acid moieties that, together with precursor masses, unequivocally identify their molecular species 
(). We first acquired MS/MS spectra from all PE and PG precursors on a quadrupole time-of-flight instrument QSTAR Pulsar i
and on LTQ Orbitrap Velos using data-dependent acquisition in negative ion mode. Molecular anions were selected with the unit mass resolution to prevent co-fragmenting neighboring precursors. Collision energies were optimized as described in 
and, in the course of analyses, either ramped with precursor masses within the range of 44 to 56.5 eV (m/z
600 to 850) (QSTAR) or applied as a normalized collision energy nCE
(LTQ Orbitrap Velos). For better consistency, spectra were acquired with approximately the same mass resolution of 7
500 (full width at half maximum, FWHM) for both LTQ Orbitrap Velos and QSTAR; the impact of mass resolution on lipid identification accuracy was examined in 
. Experiments were performed in 4 replicas; a shotgun dataset comprising 31 MS and 321 MS/MS spectra was processed and individual species were quantified (). Note that E.coli
does not produce ether glycerophospholipids. If analyzed by precursor scanning for acyl anions of fatty acid moieties, identification of ether lipids would rely on matching a single acyl anion to the precursor mass since complementary alkoxide fragment is usually ca
20-fold less abundant 
. In positive mode plasmenyl species of PE could be distinguished from plasmanyl species by specific fragments 
accountable via boolean
Comparison of the lipid profiles obtained by three independent analytical methods.
In parallel, the same extract was infused into a triple quadrupole mass spectrometer TSQ Vantage (Thermo Fisher Scientific) and 72 precursor ion scan spectra were successively acquired for masses of acyl anion fragments of common fatty acids, including all fatty acids recognized in the experiment above. Spectra were also acquired under unit mass resolution consistently with the experiment settings applied on the QSTAR and LTQ Orbitrap; collision energy (CE) was 50 eV; collision gas pressure 1.5 mTorr. The MasterScan was composed from the aligned and transformed spectra and interpreted by the same MFQL queries identifying species of PE and PG lipid classes 
. To quantify the species, isotopic correction of precursors and fragment intensities was applied 
. We found that quantitative profiles obtained in three independent experiments on hybrid tandem machines QSTAR and LTQ Orbitrap and on a triple quadrupole Vantage instrument, were consistent ().
Using the same E.coli
lipid extract, we further tested if LipidXplorer could consistently interpret neutral loss scanning spectra. Upon collisional fragmentation in positive ion mode, molecular cations of PE and ammonium adducts of PG undergo facile neutral losses of their head groups (Δ m/z
141.02 and Δ m/z
189.04, respectively), which are conventionally used for their shotgun profiling 
. We performed shotgun neutral loss experiments on the TSQ Vantage under basic instrument settings similar to described above; however spectra were acquired in positive mode under CE
22 eV. Spectra were acquired in three replicas, processed using MFQL queries accounting for the head group neutral losses (available at the LipidXplorer wiki site) and normalized abundances of species compared (). Note that in neutral loss scanning spectra identified lipids may only be annotated by the total number of carbons and double bonds in both fatty acid moieties. For consistent comparison with profiles deduced from negative mode precursor ion spectra (), the abundances of isobaric species of the same lipid class were combined. For each lipid class, we observed good correlation between the profiles, suggesting that LipidXplorer consistently interpreted both precursor ion scanning and neutral loss scanning spectra.
Comparison of the lipid profiles obtained by precursor ion scanning and neutral loss scanning on a triple quadrupole mass spectrometer.
These experiments demonstrated that LipidXplorer informatics concept is generic and offers consistent interpretation of shotgun datasets irrespectively of the instrument platform and acquisition mode. Hence, it enables direct quantitative comparison of lipid species profiles acquired from complex lipid extracts in different laboratories by any shotgun methodology and constitutes an important step towards consensual instrument platform-independent lipidomics.