Expanding Lipidome Coverage Using LC-MS/MS Data-Dependent Acquisition with Automated Exclusion List Generation
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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 that 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.
KeywordsLipids Lipidomics Data-dependent Analysis Metabolomics Mass spectrometry Tandem mass spectrometry Diglyceride
Lipids serve numerous functions in organisms, and the understanding of these roles has been shown to be important in the characterization of human disease, health, and nutrition. Lipids serve as membrane structure and functional components, modulators of immune system function , energy storage molecules, signaling molecules [2, 3, 4], and targets of oxidation . Perturbations in these biological functions frequently correlate with shifts in lipid composition. Therefore, by examining the lipidome, scientists can identify new diagnostic markers that could increase our understanding of disease etiology, detection, and progression. Recently, biomedical-related international initiatives have inrcreased the awareness and methods for lipidomics (i.e., the measurement of all lipids within a biological substrate of interest using mass spectrometry) including Japan’s LipidBank founded in 1989 , Lipid Metabolites and Pathways Strategy (LIPID MAPS) in the USA founded in 2003 [7, 8], and the European Lipidomics Initiative: shaping the life sciences (ELIFE) founded in 2004 .
Comprehensive characterization of the lipidome in biological systems is challenging due to the diversity and quantity of lipid structures, wide range of biological concentrations, and subtle, but biologically important, differences in isomeric lipid structures. Lipid diversity stems from the numerous possible combinations of “backbones,” such as glycerol or sphingosine, with various fatty acids differing in carbon length, number, and positions of unsaturation. The number of lipids present in LIPID MAPS structural databases is over 40,000 , while over 300,000 possible lipid structures have been simulated in our in-house lipid identification workflow, LipidMatch, which takes into account oxidized species. Lipids have been shown to differ in concentrations of over seven orders of magnitude between classes; even within a lipid class, lipid concentrations can differ over three orders of magnitude [11, 12, 13]. Electrospray ionization mass spectrometry (ESI-MS) is the most widely employed ionization strategy for lipidomics  because its ability to ionize the diverse range of structures and concentrations.
However, isomeric species, lipid species containing different fatty acid constituents with the same total number of carbons and degrees of unsaturation [e.g., PC(16:0_20:1) and PC(18:0_18:1)], cannot be separated using ESI-MS alone. If this structural detail is desired, one solution is to employ liquid chromatography to separate the isomers for quantification based on polarity using reverse phase chromatography, in combination with tandem mass spectrometry (MS/MS) to identify the fatty acid constituents based on fragmentation patterns. However, this strategy often leads to a conundrum, as increasing the peak capacity in liquid chromatography to deconvolute more lipid features for quantification requires narrower chromatographic peaks, thus limiting the number of MS/MS scans that can be obtained across the chromatographic peaks . Therefore, within lipid-rich retention time and m/z regimes, numerous ions of different mass-to-charge ratios will be ionized at the same elution time, but only a few can be selected for fragmentation in a single injection. For lipidomic experiments where the lipids of interest are unknown, heuristic rules have been developed to fragment ions. One approach is to select ions with the highest intensity for fragmentation, commonly referred to as data-dependent topN (ddMS2-topN). Owing to concentration bias, this strategy could miss important less abundant lipid species, such as diacylglycerides and phosphatidylinositols in plasma, which are both important signal molecule classes [16, 17].
Although numerous applications have shown the benefit of using IE across platforms for proteomics, most omics analyses do not take full advantage of IE. In part, this may be due to a lack of a simple software program capable of generating exclusion lists automatically as traditionally this is achieved manually. Furthermore, using available software, iterative acquisitions cost time and money, thus putting heavy emphasis on determining whether additional sample injections for IE are worth added instrument time. To this end, we have developed an R-script named “IE-omics” for generating exclusion lists from open-source formatted data easily converted from various vendor formats; as a demonstration, we have applied it to lipidomics. IE-Omics is advantageous over the IE-MS script in providing multiple user parameters in a relatively simple interface and providing the ability to directly import multiple vendor formats.
Recently, IE has been adapted to other omics fields, such as in lipidomics and metabolomics, as noted in Sandra et al.  and Edmands et al. , respectively. For lipidomics, IE type analyses have been used in direct infusion approaches by increasing the duration of dynamic exclusion to the length of the analysis. For example, Nazari and Muddiman  used gas-phase fractionation and dynamic exclusion to increase coverage of the lipidome, especially of low abundance species. Schwudke et al.  emulated precursor and neutral loss scanning using data-dependent analysis with a dynamic exclusion and inclusion list-based workflow for increasing the lipidome coverage. The success of IE type approaches for direct infusion supports the utility of IE for LC-MS/MS. While direct infusion allows rapid biomarker discovery, LC-MS/MS has been found to be more comprehensive .
To our knowledge, no research has shown the benefit of applying IE versus traditional approaches for lipidomics using LC-MS/MS. In addition, no omics studies have compared results across different matrices with varying amounts of features. Herein, we report the use of our user customizable R-script for IE to lipid extracts of both Red Cross plasma and substantia nigra brain tissue in both positive and negative polarity. The results show that due to the spectral density of numerous lipid species in a chromatographic run, especially in positive ion mode, a substantial benefit is obtained using IE for LC-MS/MS based lipidomics.
Chemicals and Materials
Ammonium acetate and all analytical grade solvents (formic acid, chloroform, and methanol) were purchased from Fisher Scientific (Waltham, MA, USA). All mobile-phase solvents were Fisher Optima LC/MS grade (acetonitrile, isopropanol, and water). For Red Cross plasma, the following lipid standards were used: triglyceride [TG(15:0/15:0/15:0) and TG(17:0/17:0/17:0)] purchased from Sigma-Aldrich (St. Louis, MO, USA), and lysophosphatidylcholine [LPC(17:0) and LPC(19:0)], phosphatidylcholine [PC(17:0/17:0) and PC(19:0/19:0)], phosphatidylethanolamine [PE(15:0/15:0) and PE(17:0/17:0)], phosphatidylserine [PS(14:0/14:0) and PS(17:0/17:0)], and phosphatidylglycerol [PG(14:0/14:0) and PG(17:0/17:0)] purchased from Avanti Polar Lipids (Alabaster, AL, USA). For substantia nigra samples, the following standards were used: TG(15:0/15:0/15:0) from Sigma-Aldrich, and PC(19:0/19:0), DG(14:0/14:0), SM(d18:1/17:0), Cer(d18:1/17:0), 13C2-cholesterol, PE(15:0/15:0), LPC(19:0), PG(14:0/14:0), and PS(14:0/14:0) purchased from Avanti Polar Lipids. All lipid standards were diluted prior to analysis in 1:2 (v/v) chloroform:methanol and a working 100 mg/L standard mix was then prepared by diluting the stock solution with the same solvent mixture.
Pooled Red Cross human EDTA plasma was purchased from the American Red Cross National Testing Laboratories (Detroit, MI, USA) and samples were stored at –80 °C. All plasma aliquots (40 μL) were thawed on ice prior to extraction.
Substantia nigra samples were obtained from C57BL/6 mice. The Institutional Animal Care and Use Committee (IACUC # 20148382) at the University of Florida approved the use of all mice and procedures. The mice were housed with a 12 h light-12 h dark schedule and were provided food and water ad libitum. Five-month-old mice were anesthetized using isoflurane vapors. The mice were sacrificed and whole brains were harvested immediately from the skull and placed on a glass petri dish. A scalpel was used to carefully remove the substantia nigra region of the brain. Upon receipt, the tissue was placed in a freezer maintained at –80 °C for storage. A Bel-Art mortar (Bel-Art Scienceware, Wayne, NJ, USA) was used to pulverize the frozen tissue samples under liquid nitrogen condition. The frozen tissue powder (10–20 mg, in triplicate) was weighed in homogenization tubes containing zirconium beads (0.7 mm diameter; BioSpec Products, Bartlesville, OK, USA).
Both Red Cross plasma and frozen substantia nigra tissue powder were extracted using the Folch method (2:1, v/v, chloroform:methanol) . Briefly, 5 μL of internal standard (IS) mix (100 mg/L) was spiked into the Red Cross blood plasma (40 μL) on ice (IS info in Chemicals and Materials). For Red Cross plasma, 160 μL of methanol and 320 μL of chloroform were added to all samples. Samples were incubated on ice for 30 min and centrifuged at 4 °C for 5 min at 15,000 rpm. To induce phase separation, 150 μL of water was added and samples were incubated on ice for an additional 10 min. The organic layer was removed and the aqueous layer was re-extracted with 250 μL of chloroform:methanol (2:1, v/v). The organic layers were combined, evaporated under nitrogen, and reconstituted in 100 μL of isopropanol (for lipidomics). For substantia nigra tissue, the ground tissue was homogenized for 120 s, with 100 μL of methanol and 200 μL of chloroform for every 15 mg of tissue. Five μL of IS mix (100 mg/L) was spiked into the chloroform:methanol (2:1, v/v) mixture before homogenization. Samples were incubated as for plasma, and water was added at a volume of one-fourth of the Folch solvent. Substantia nigra was re-extracted with 50 μL of methanol and 100 μL of chloroform for every 15 mg of tissue and dried down. Substantia nigra samples were reconstituted in 200 μL of isopropanol.
For both lipidomics analyses, a Dionex Ultimate 3000 RS UHLPC system (Thermo Scientific, San Jose, CA, USA) was employed. Ionization was performed with heated electrospray ionization probe (HESI II) and mass spectra acquired using a Q-Exactive Orbitrap (Thermo Scientific). Source parameters for lipidomics, in positive and negative polarity, are provided in Supplementary Table S-3. Samples were maintained at 4 °C in the autosampler. Two μL of sample was injected onto a Waters Acquity BEH C18 column (50 mm × 2.1 mm, 1.7 μm, Waters, Milford, MA, USA) maintained at 30 °C. For negative ion mode, 5 μL of sample was injected onto the column and analyzed with the same mass spectral parameters (Supplementary Table S-2). A gradient ramp (Supplementary Table S-1) was employed consisting of mobile phase C (60:40 acetonitrile:water, volume fraction) and mobile phase D (90:8:2 isopropanol:acetonitrile:water, volume fraction), both with 10 mmol/L ammonium formate and 0.1% formic acid.
Mass spectra were acquired in full scan mode using data-dependent top5 analysis (ddMS2-top5) in both positive and negative polarity with a mass resolution of 70,000. Full scan and ddMS2-top5 scan parameters are shown in Supplementary Table S-2. Before each analysis, the instrument was externally calibrated and at least three blanks were analyzed. Internal mass calibrants (lock masses) were used in positive ion mode and consisted of diisooctyl phthalate (m/z 391.2842) and polysiloxanes (m/z 371.1012 and 445.1200). No stable lock mass was observed to be used in negative ion mode. To compare IE with traditional ddMS2-top5 for lipidomics, a minimum of four sequential injections were analyzed by ddMS2-top5 with IE and four without IE, for both negative and positive polarity analysis of Red Cross and substantia nigra lipid extracts. For excluding ions previously selected for fragmentation and placed on an exclusion list, a 10 ppm exclusion tolerance was used.
Software Platform for IE
A software program “IE-Omics” was written using R  to directly intake a .ms2 file converted using MSConvert  and output an exclusion list (.csv) in a format that can be directly imported by Q-Exactive instruments. User-defined parameters include the retention time and m/z window for combining selected precursors to reduce the size of the exclusion list. A 0.02 m/z window and 0.3 min retention time window was used in this experiment. In this case, ions selected at m/z values of 400.02, 400.01, and 400.01, and respective retention times of 5.10, 5.15, and 5.30 min, would be combined in one row as m/z 400.02 excluded between 4.95 and 5.45 min. In addition, users can denote the number of times ions with the same m/z are selected before being considered background ions and excluded for the entire duration of the chromatographic run. In this experiment, a minimum of 15 instances of ions selected for fragmentation with the same m/z was used, excluding these ions from 0 to 18 min. The IE-Omics script can be found in the Supplementary Information and the most up-to-date version on the Southeast Center for Integrated Metabolomics (SECIM) webpage (http://secim.ufl.edu/secim-tools/).
Feature Detection and Identification
Lipids were identified using both an in-house workflow, LipidMatch, and LipidSearch (Thermo Scientific, San Jose, CA, USA) . LipidMatch consists of R-scripts which identify lipids by matching MS/MS fragments indicative of class and fatty-acid constituents from experimental fragmentation to in-silico fragmentation libraries (covering over 250,000 lipid species across 56 lipid types). Only exact mass of the MS/MS fragments (not intensity) is used for matching. Fragment criteria used for identification of lipids by LipidMatch can be found in Table S-6. Before LipidMatch was applied, features were determined using MZmine 2.0 , with the batch mode file containing all the parameters. Both the MZmine batch file and LipidMatch software can be found at <http://secim.ufl.edu/secim-tools/> in the LipidMatch zip file. LipidMatch used the features exact mass determined by MZmine with a 10 ppm m/z window for precursor ion matching. Both for LipidMatch and LipidSearch, a 10 ppm m/z window was used for fragment matching. In LipidMatch, fragments were only considered confirmed if they were above 1000 intensity units and found in at least one scan within a 0.3 min window of the feature being identified; in LipidSearch, only lipids classified with grade A were kept. The 0.3-min retention time used for finding MS/MS scans and excluding precursors in sequential injections employing the IE-Omics script was close to the median of the full width at half maximum (FWHM) for all features (Supplementary Figure S-1). After lipid ions were annotated, redundant annotations, for example, different lipid ion adducts of the same molecular species, were combined separately for positive and negative analysis.
Results and Discussion
It is well-known that background ions compete with analytes of interest for selection and fragmentation. Therefore, by discerning the background ion patterns and automatically placing those ions on an exclusion list, analyte coverage can be increased. A background ion from the mobile phase, ESI source, or from column bleed (to name a few sources), can be discerned by a single m/z covering a large portion of the retention time region, as in m/z 300.2253 discussed above. An exclusion list for background ions can often be generated by running several blank injections, but this can be an inefficient process; when the column or mobile phase changes, a new list would need to be created. From this IE type of run, it can be seen that a large portion of ions selected and fragmented are background ions as depicted by a horizontal pattern across the chromatographic run (Figure 2). Therefore, this pattern can be readily used to exclude background ions in the IE-Omics software.
Using the default IE-Omics parameters, if the same m/z is selected more than 15 times, with each instance being at least 0.15 min apart, that m/z is annotated as background and placed on an exclusion list across the entire analysis time. For example, m/z 391.28 was excluded across the entire retention time in the second injection, after being selected in the first injection of Red Cross plasma 134 times (Figure 2). Annotated background ions can also be excluded in future experiments. After the first injection, 17 ions were automatically annotated as background ions by IE-Omics software in positive ion mode of Red Cross plasma. After six injections, 54 m/z values were annotated as background ions according to this algorithm.
In addition to generation of exclusion lists of background ions, IE also enhanced coverage of the lipidome. When comparing the second to first injection after applying IE, it can be seen that many additional unique precursors are selected in both the glycerophospholipid (GP) (about m/z 700–900 at 5–10 min) and triglyceride (TG) regions (about m/z 700–1100 at 11–16 min) (Figure 2). Figure 3 compares unique ions selected for fragmentation in positive mode analysis of Red Cross plasma lipid extracts in six sequential injections without IE applied (Figure 3a) and with IE applied (Figure 3b). The IE-omics approach shows that after six sequential injections, the number of unique ions fragments is substantially higher (Figure 3b). As discussed previously, both new background ion signatures and lipid ions (as can be seen in the GP and TG region) are selected using IE (Figure 3). An analogous figure for substantia nigra is shown in the Supplementary Information (Supplementary Figure S-2), with a zoom in of the GP region overlaid with unique molecular species annotated by LipidMatch (Supplementary Figure S-3).
Based on these results, it is clear that the number of additional identifications obtained when applying IE depends on sample type and the polarity measured by the mass spectrometer. It is expected that if the mass spectrum is sparse, a traditional ddMS2 approach will likely select the majority of ions above an MS2 threshold limit. In the lipidomic analyses, where applying IE was less advantageous in negative ion mode, negative ion spectra showed fewer ions than positive ion spectra. For example, the number of features (which is inversely related to spectral sparseness), was drastically lower in negative mode than positive mode, with only 4258 features in Red Cross negative mode data versus 19,231 features in Red Cross positive mode data. Therefore, after applying exclusion lists generated by IE, fewer precursors remain above the threshold to be selected for fragmentation in negative ion mode. For example, in negative polarity analysis of plasma, MS/MS scans drastically declined from the first to the fifth sequential injection (from 2491 to 414 scans), showing depletion of precursors for selection, whereas in positive polarity there was less of a decline in MS/MS scans (from 2746 to 2581 scans) (Supplementary Figure S-5). Therefore, experimental design affecting spectral density will be a major factor in determining the additional benefit of IE. For example, increasing the chromatographic gradient time would increase separation of lipids while decreasing spectral density at a given time point, and, hence, potentially decrease the advantage of applying IE versus traditional ddMS2-topN approaches.
The reduced intensity of precursor ions selected after applying IE suggests lower MS/MS spectral quality. This is especially true for positive ion mode fragmentation of most glycerophospholipids, where fatty acyl indicative fragments are of low abundance. To determine the quality of MS/MS spectra across sequential injections, the percentage of lipids identified with grade A, calculated by (A/(A + B + C)), was determined using LipidSearch. These grades are based on the number of fragments identified that contain species-specific structural information, with lipid identifications graded A having the most structural information in MS/MS spectra. Following a similar trend to the selected ion signal, the sequential injections after applying IE had a general drop in percent A and, hence, decrease in MS/MS spectral quality (Supplementary Figure S-7). Injections in which IE was applied had significantly lower average percentages of A compared with sequential injections without IE, for negative and positive polarity analysis of substantia nigra tissue lipid extracts (P-value < 0.05) and for positive polarity analysis of Red Cross plasma (P-value < 0.005). No significant difference was observed for negative polarity analysis of Red Cross plasma. Therefore, unique identifications provided by IE of low abundance species often provide less structural information and are more tentative.
Comparison of Diglyceride (DG) Peak Heights and Fatty Acid Compositions Between the First ddMS2-top5 Acquisition Using LipidMatch (IE 1, a) and After Applying an Exclusion List Using the Algorithm Described in this Paper Using Six Iterations (IE 6, b)
DG fatty acid chains
(a) IE 1 (plasma positive)
1.7 × 107
2.0 × 107
2.4 × 106
1.9 × 107
5.1 × 106
3.5 × 107
1.0 × 107
4.7 × 104
(b) IE 6 (plasma positive)
4.2 × 105
3.5 × 105
2.6 × 105
2.2 × 105
3.6 × 105
3.3 × 105
3.9 × 105
3.0 × 105
3.4 × 105
5.8 × 105
6.9 × 105
9.1 × 105
4.1 × 105
In substantia nigra positive mode analysis (Figure 6b and d), IE improved the coverage of phosphatidylserine (PS), oxidized phosphatidylcholine (OxPC), phosphatidylglycerol (PG), and sulfatide species, which were minimally covered by the traditional approach. In negative ion mode analysis of substantia nigra tissue, using the traditional ddMS2-top5 approach, there was no coverage of sulfatides and minimal coverage of phosphatidic acid (PA). Applying IE significantly increased coverage of both of these species (Supplementary Figure S-9). These findings highlight that IE not only increases the total number of lipid identifications but increases identifications of trace lipid species of potential interest, which are minimally covered by traditional approaches.
Future developments will continue to increase the advantages of applying IE-Omics. Currently, the script is not integrated in Xcalibur software, and therefore exclusion lists are not generated in real time and must be uploaded into new method files before each iterative injection. In our lipidomics workflow, we suggest using three to four iterative injections on pooled samples, which can be used to identify features of a given sample group. Therefore, this method is sufficient for lipid identification in large quantitative studies to determine biomarkers where thousands of samples are required, as only a few additional injections are used for IE and, hence, there is minimal addition of acquisition time. Fewer injections may be required if the exclusion window is increased from 10 ppm, for example, to 100 ppm. By increasing the exclusion window, isobaric ions will only be selected in one injection, reducing the number of injections needed to select all ions above a certain threshold. In the future, fully automated exclusion list generation may be developed.
We have semi-automated the IE approach using a simple open source R script. The script uses open source formatted files that can be converted from various vendor formats and produces an exclusion list in the format required for importing into Thermo Scientific instruments (.csv). Features include smart exclusion list generation, which combines ions selected in similar m/z and retention time windows to generate a shorter exclusion list, and automatic annotation of background ions. After applying the software, IE-Omics, to lipidomic datasets in Red Cross plasma and substantia nigra brain tissue lipid extracts, IE was shown to be most advantageous in complex matrices with a high number of analyte species. Applying IE to lipidomics analyses in certain cases increased identifications by over 50%. The greatest advantage using IE was shown in positive ion mode and in Red Cross plasma versus substantia nigra lipid extracts, where spectra were most dense. In lipidomics, trace species, such as odd-chained and short-chained DGs, were identified only after applying the IE technique.
Future data acquisition strategies, for example only including precursor ions for fragmentation that match lipid masses and identifying polymer patterns for exclusion, could prove advantageous. In most cases, however, such as in negative mode, after only using a few sequential injections, all ions above the threshold limit for fragmentation were selected, and therefore new data-acquisition methods would not provide additional advantage in terms of MS/MS spectral coverage. New data-acquisition methods might be able to reduce the number of injections needed for coverage of the majority of lipid ions and notify the user when additional injections are no longer required. Applying IE expands the scope of the lipidome covered, both increasing the total number and diversity of lipids identified.
This research was done in collaboration between Core 1 and Core 3 of the Southeast Center for Metabolomics (SECIM) <http://secim.ufl.edu/> (NIH grant #U24 DK097209). The authors thank Jason Cochran for writing R code to compile the fragments required for identification in Supplementary Table S-6, and for helping to expand the in-silico libraries used for lipid identification.
Compliance with Ethical Standards
Certain commercial equipment, instruments, or materials are identified in this paper to specify adequately the experimental procedure. Such identification does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the materials or equipment identified are necessarily the best for the purpose.
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