The relevance of preanalytical factors in metabolomics and lipidomics research
Gil Quintero, Jorge Andres
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Gil Quintero, J. A. (2018). The relevance of preanalytical factors in metabolomics and lipidomics research. Rijksuniversiteit Groningen.
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5
One- vs Two-phase
Extraction: Re-evaluation
of Sample Preparation
Procedures for
Untargeted Lipidomics in
plasma samples
Accepted for publication as: A. Gil, W. Zhang, J.C. Wolters, H. Permentier, T. Boer, P. Horvatovich, M.R. Heiner-Fokkema, D.J. Reijngoud, R. Bischoff. One- vs Two-phase Extraction: Re-evaluation of Sample Preparation Procedures for Untargeted Lipidomics in plasma samples. Analytical and Bioanalytical Chemistry.
Lipidomics is a rapidly developing field in modern biomedical research. While LC/MS systems are able to detect most of the known lipid classes in a biological matrix, there is no single technique able to extract all of them simultaneously. In comparison to two-phase extractions, one-phase extraction systems are of particular interest, since they decrease the complexity of the experimental procedure. By using an untargeted lipidomics approach, we explored the differences/similarities between the most commonly used two-phase extraction systems (Folch, Bligh & Dyer, and MTBE) and one of the more recently introduced one-phase extraction systems for lipid analysis based on the MMC solvent mixture (MeOH/MTBE/ CHCl3). The four extraction methods were evaluated and thoroughly compared against a pooled extract that qualitatively and quantitatively represents the average of the combined extractions. Our results show that the lipid profile obtained with the MMC system displayed the highest similarity to the pooled extract indicating that it was most representative of the lipidome in the original sample. Furthermore, it showed better extraction efficiencies for moderate and highly apolar lipid species in comparison with the Folch, Bligh & Dyer, and MTBE extraction systems. Finally, the technical simplicity of the MMC procedure makes this solvent system highly suitable for automated, untargeted lipidomics analysis.
5.1 Introduction
Several studies have shown that in addition to roles in cellular membranes and the provision of energy, lipids also have important bioactivities and signaling functions that may be altered in widespread human diseases, including cardiovascular disease, diabetes type 2, Alzheimer’s disease, and cancer [1, 2]. Consequently, lipidomics is a rapidly developing area of research mainly focused on searching biomarkers for diagnostic purposes [3, 4]. Irrespective of this rapid growth of the field and the technological advances in chromatography and mass spectrometry that resulted in the development of more sensitive, selective and high-throughput methods over the last decade [5–8], extraction of all lipid species in a comprehensive manner remains an active area of research in the lipidomics field.
Currently, there is no single extraction technique able to extract all lipid classes from a biological matrix (tissue, biological fluid, cell) in a quantitative manner [3]. The most commonly used methods for lipid extraction were introduced by Folch et al [9] and by Bligh & Dyer [10]. Over time, these methods have been modified according to particular needs [11, 12], while maintaining the basic principle of using different proportions of chloroform/ methanol that separate into an upper methanol-rich layer, containing hydrophilic compounds, and a lower chloroform-rich layer mainly containing lipids.
Despite the popularity of two-phase extractions, an important drawback is the need of retrieving lipids from the lower chloroform-rich layer, which may lead to contamination and possibly compromise the analysis due to the presence of insoluble material accumulating at the interphase [2]. To avoid this issue, the methyl tert-butyl ether (MTBE) extraction method and more recently the butanol-methanol (BUME) method were introduced by Matyash at
al [13] and Löfgren at al [14], respectively. While both methods have the advantage that
the upper layer is the lipid-rich organic phase, unsatisfactory recovery for more polar lipid classes has been observed [14]. The main objectives of lipidomics studies are to increase the number of extracted and detected lipids (the coverage of the lipidome) and to do so in a straightforward and reproducible manner to avoid bias due to technical variability. Trying to achieve these objectives, while avoiding the inherent problems of the two-phase extraction methods, one-phase lipid extractions have recently been developed [2, 3, 15, 16]. One-phase extractions focus on an “all-in-one-tube” approach eliminating the need for phase separation by denaturing proteins that are later removed by centrifugation. Methanol, butanol, isopropanol, MTBE and mixtures thereof have been used as solvents. However, up to date
these approaches have been evaluated with respect to the targeted analysis of a small set of lipid standards by comparing their recovery [2, 3, 14]. By using an untargeted lipidomics approach on plasma samples, the major aim of the current work is to explore the differences and similarities between the three most commonly used two-phase extraction systems and a more recently described one-phase system, the MMC solvent mixture (MeOH/MTBE/ CHCl3) [3], for lipid analysis. The four extraction methods were evaluated and thoroughly compared against a pooled extract that qualitatively and quantitatively was considered to represent an average standard extract.
5.2 Materials and Methods
5.2.1 Lipid extraction methodsAll extractions were performed in 2 mL Eppendorf tubes with 75 μL of human plasma each (See the Suuporting Information for blood collection). Three samples were independently prepared for each extraction method. Incubation time (1 h) and temperature of extraction (22 °C) were kept constant. Each extraction method was performed three times (n = 3) on samples independently prepared and analyzed in triplicate. Two-phase and one-phase extractions were performed as detailed below.
5.2.1.1 Two-phase extractions
Folch method (Folch): Seventy-five µL of human plasma were mixed with 187.4 µL of MeOH and vortexed for 20 s followed by addition of 375 µL of CHCl3. The mixture was incubated on a shaker at 900 rpm for 1 h. Phase separation was induced by the addition of 156.2 µL of H2O and incubation of the mixture for 10 min. Subsequently, the sample was centrifuged at 17500 RCF for 10 min at 20 °C and the lower (CHCl3) phase was collected (300 µL). The upper MeOH phase was re-extracted with 250 µL of the following solvent mixture (CHCl3/MeOH/H2O 86:14:1 v/v/v), and the lower phase was again collected (250 µL). The CHCl3 phases were combined and dried in a vacuum centrifuge at 30°C for 1 h. The extracted lipids were re-suspended in 50 µL CHCl3/MeOH/H2O (60:30:4.5 v/v/v) from which 10 µL were taken to prepare the pooled extracts (see below). The remaining 40 µL
µL MeOH/CHCl3 (2:1) and vortexed for 20 s. Subsequently, the mixture was incubated on a shaker at 900 rpm for 1 h, after which 156.2 µL of H2O were added to induce phase separation. The sample was centrifuged at 17500 RCF for 10 min at 20°C and the lower CHCl3 phase was collected (150 µL). A second extraction step was performed on the upper aqueous phase with MeOH/CHCl3 (2:1). Both organic phases were combined and dried in a vacuum centrifuge at 30°C for 1 h. The extracted lipids were re-suspended in 50 µL CHCl3/ MeOH/H2O (60:30:4.5 v/v/v) from which 10 µL were taken to prepare the pooled extracts (see below). The remaining 40 µL were diluted to the same level as the pooled extract (100 µL) and stored at -20°C.
Matyash method (MTBE): Seventy-five µL of human plasma were mixed with 187.4 µL of MeOH and vortexed for 20 s. Next, 625 µL of MTBE were added and the mixture was incubated on a shaker at 900 rpm for 1 h. Water (156.2 µL) was added to the mixture and incubated for 10 min to induce phase separation. The sample was centrifuged at 17500 RCF for 10 min at 20 °C and the upper (MTBE) phase was collected (700 µL). The lower methanol phase was re-extracted with 250 µL of MTBE/MeOH/H2O (10:3:2.5 v/v/v), and the upper phase was again collected (200 µL). The MTBE phases were combined and dried in a vacuum centrifuge at 30 °C during 1 h. The extracted lipids were re-suspended in 50 µL CHCl3/MeOH/H2O (60:30:4.5) from which 10 µL were taken to prepare the pooled extracts (see below). The remaining 40 µL were diluted to the same level as the pooled extract (100 µL) and stored at -20°C.
In order to assess the potential loss of polar lipids, the hydrophilic phase from each two-phase extraction procedures was collected and dried under a stream of N2 at room temperature overnight. The pellets obtained were re-suspended separately in 40 µL CHCl3/MeOH/H2O (60:30:4.5 v/v/v). From each pellet 10 µL were taken to prepare the pooled extract (see below). The remaining 30 µL were diluted to the same level as the pooled extract (100 µL) and stored at -20 °C.
5.2.1.2 One-phase extraction (MMC method)
Seventy-five µL of human plasma were mixed with 500 µL of MeOH/MTBE/CHCl3 (1.33:1:1 v/v/v) and vortexed for 20 s. Subsequently, the mixture was incubated on a shaker at 900 rpm for 1 h at 22 °C. The sample was vortexed during 10 s and particulate matter was pelleted by centrifugation at 17500 RCF for 10 min at 20 °C. Supernatant was collected (500 µL) and dried in a vacuum centrifuge for 1 h at 30°C. The extracted lipids were re-suspended in 50
µL chloroform/methanol/water (60:30:4.5 v/v/v) from which 10 µL were taken to prepare the pooled extract (see below). The remaining 40 µL were diluted to the same level as the pooled extract (100 µL) and stored at -20 °C.
5.2.2 Pooled lipid extracts
Two pooled extracts or quality control samples (QCs) containing the entire set of components from all extraction methods were prepared. The first pool consisted of the main set of extracted lipids (hydrophobic phases). Briefly, 10 µL of the extracts (in CHCl3/MeOH/H2O 60:30:4.5 v/v/v) from each solvent system (Folch, Bligh, MTBE and MMC methods) were mixed. Consequently, the final volume of the hydrophobic pooled sample and the Folch, Bligh, MTBE and MMC hydrophobic lipid extracts was 40 µL. Volumes of all 5 hydrophobic extracts were adjusted to 100 µL with IPA-ACN-H2O (2:1:1 v/v/v) and then subjected to LC-MS analysis.
The second pool consisted of a set of polar lipids and other components with more polar characteristics that remained in the hydrophilic phase. In short, 10 µL of the solutions (in CHCl3/MeOH/H2O 60:30:4.5 v/v/v) obtained from the hydrophilic phases of the two-phase extraction systems (Folch, Bligh and MTBE) were mixed. The final volume of the hydrophilic pooled sample and that of the Folch, Bligh and MTBE extracts was 30 µL. Final volumes were adjusted to 100 µL with IPA-ACN-H2O (2:1:1 v/v/v) and then subjected to LC/MS analysis.
5.2.3 Blank extracts
In order to evaluate whether contaminant features, that might appear as lipid signals, were part of the measured lipidomes, blank extracts were obtained using water (25 µL) instead of plasma. By following the Folch, Bligh, MTBE and MMC experimental procedures (see above), 4 blank extracts (n = 3) were obtained for comparison purposes. Contaminant features, that were found to be differentially extracted (fold change ≥ 1.5, statistical significance (P ≤ 0.05, student’s t-test), CV < 30%) in the blank extracts (both in positive and negative mode) in comparison to the various solvent systems, were removed from the data.
5.2.4 LC-MS
Lipids were separated by reversed-phase chromatography using an Acquity UPLC CSH column (1.7 µm, 100 × 2.1 mm) on an Acquity UPLC system (Waters, Manchester, UK). Mobile phases consisted of 10 mM ammonium formate in water (eluent A) and 10 mM ammonium formate in methanol (eluent B). Linear gradient elution was as follows: 0 – 5 min from 50% to 30% eluent A, 5 – 15 min from 30% to 10% eluent A, 15 – 25 min from 10% to 0% eluent A. This was followed by isocratic elution at 0% eluent A over the next 15 min. A conditioning cycle of 5 min with the initial proportions of eluents A and B was performed prior to the next analysis. The column temperature was set at 80 °C and the flow rate was 0.5 mL/min. Four or 8 µL of sample were injected in positive and negative mode, respectively. The samples were analyzed in a randomized order throughout the experiment.
Mass spectrometry detection was performed using a Synapt G2-Si high-resolution QTof mass spectrometer (Waters, Manchester, UK). Lipids were detected by electrospray ionization in positive (ESI+) and negative mode (ESI-). Nitrogen and argon were used as desolvation and collision gas, respectively. Data were acquired over the m/z range from 50 to 1750 Da in continuum and enhanced resolution modes, at an acquisition rate of 1 spectrum/0.2 s. The source temperature was set at 150 °C, the desolvation temperature at 400 °C, the cone voltage at 30 V and the capillary voltage at 2000 V. MS/MS experiments were performed with data dependent acquisition (DDA). A survey MS scan was alternated with three DDA MS/MS scans resulting in a cycle time of 1 s. Singly-charged precursor ions were selected based on abundance with a threshold of 1000 cps intensity. After being selected, a particular m/z value was excluded for 30 s from MS/MS fragmentation. The collision energy potential setting was 35 V. The system was equipped with an integral LockSpray unit with its own reference sprayer that was controlled automatically by the acquisition software to collect a reference scan every 10 s lasting 0.3 s. The LockSpray internal reference used for these experiments was a 0.2 ng/µL leucine-enkephalin solution (reference mass m/z 556.2771 in positive and m/z 554.2615 in negative mode) infused at 10 µL/min to allow operation of the instrument at high mass accuracy (<1 ppm).
5.2.5 Data preprocessing
MassLynx software version 4.1 was used for data acquisition. Waters raw data files were analyzed using Progenesis QI software (Waters Corporation, Milford, MA) for peak alignment, peak picking and normalization of the LC-MS data. On the basis of normalized
peak intensities, the number of features was filtered according to 2 different sets of selection criteria (See Suuporting Information for Data preprocessing). A final table containing m/z values, retention times and normalized peak intensities was imported into Simca P v.13 (Umetrics, Umea, Sweden) for multivariate statistical analysis.
5.2.6 Multivariate statistical analysis
Using Simca P v.13, data were grouped in blocks according to the extraction methods (Folch, Bligh, MTBE and MMC), as well as to the pooled extracts (hydrophobic and hydrophilic). Principal component analysis (PCA) and partial least squares discriminant analysis (OPLS-DA) via orthogonal projection to latent structures were carried out on the filtered features. Discriminant features between lipid profiles were identified and permutation tests were carried out to determine the robustness of the multivariate models (See Supporting Information for Multivariate statistical analysis).
5.2.7 Lipid identification
An in-house data base containing retention times and accurate masses for about 600 lipid species was created by manually checking and comparing the list of lipids identified by T’Kindt et al. [17] with those present in a standard plasma sample (section on blood collection). Tentative identification of lipids was based on accurate mass determinations within a narrow m/z (1–5 mDa) and retention time (0.1 min) range. Moreover, further examination of the identified features was performed with accurate mass information present in on-line databases (LIPID MAPS, LipidBlast and HMDB).
5.3 Results and discussion
5.3.1 Pooled lipid extracts as QC samples
The reliable multicomponent analysis of complex biological samples such as plasma by HPLC-MS presents a number of challenges with respect to obtaining valid data [18]. By exploring the time dependency of the PCA scores for pooled lipid extracts (QCs), one obtains insight into trends and drifts over the course of the analysis of a batch of samples [19]. Therefore, technical performance of the LC-MS method was monitored by randomly injecting the hydrophobic QC extract several times throughout the entire study. After conditioning
as suggested elsewhere [18]. Combining aliquots of all samples to be investigated into one pooled extract to generate a QC is a common procedure in untargeted lipidomics [20]. Since the pooled extract mimics the sample matrix and lipid composition of the experimental samples both qualitatively and quantitatively, it is considered to be the average standard extract with the most comprehensive lipid composition. The pooled extract was used as a reference to test the performance of the two- and one-phase extraction methods. By using an untargeted approach with multivariate statistical data analysis, we aimed to determine whether the extraction methods produced different lipid profiles and how efficient they are for different groups of lipid species.
5.3.2 Unsupervised multivariate comparison of the extraction systems
The total number of features detected in positive mode for the hydrophobic phases followed the order: Pooled > MTBE > Folch > MMC > Bligh (3688, 3300, 3254, 3200 and 3082 features, respectively). In negative ionization, the order was as follows: Pooled > MTBE > MMC > Folch > Bligh (1082, 1030, 1029, 943 and 738 features, respectively). These features were filtered as described in the Experimental section (See Supporting Information on Data preprocessing) to eliminate low-intensity, highly variable signals and noise. Features fulfilling the filtering criteria were subjected to comparative multivariate statistical analysis (PCA).
PCA was used to display general trends, intrinsic clustering of samples, and possible outliers. The tight clustering of the pooled extracts in the middle of both PCA score plots showed that the LC-MS analysis itself introduced little technical variability compared to the extraction methods. On data from the different extraction methods in positive and negative ESI mode, PCA showed clear clustering of samples according to the extraction methods indicating that different lipid profiles were acquired with the tested extraction methods (Figure 1A, B). Since the pooled extract contains lipids derived from all 4 extraction methods, proximity of the cluster of a given extraction method with respect to the pool can be considered a readout of how comprehensive a given procedure is, but exact quantitative interpretation of this proximity is difficult. Therefore, hierarchical cluster analysis (HCA) was used to show the relationship between sample clusters according to similarities in lipid composition. On data obtained in positive ESI mode, HCA showed that the MMC cluster is closest to the pool cluster followed by the Folch, the Bligh and the MTBE clusters (Figure 1C). In the negative ESI mode, the results show a somewhat different order of proximity but the MMC cluster is again most similar to the pool cluster. The order in negative mode is Pooled = MMC > MTBE > Folch > Bligh (Figure 1D). According to our results, the MMC extraction method results
in a lipid composition that is closest to the pooled extract from a qualitative and quantitative point of view indicating that the lipid profile obtained with this method is most similar to the average standard extract. However, separation of clusters in the PCA plot indicates that there is still a considerable difference between the lipid profiles that needs to be considered. 5.3.3 Selectivity of the extraction systems for different lipid species
We employed OPLS-DA to identify lipid species that contribute to the observed molecular profile differences between the extraction methods as observed in the PCA plot. For this analysis, a different filtering approach was used consisting of solely focusing on reproducible features by taking only the contribution of signals with a CV ≤ 30% into account. These features were then analyzed on the basis of their variable importance in the projection (VIP) scores. OPLS-DA models and S-plots were used to define those features with the greatest influence on the separation of groups (Figure S2A-H for the positive and Figure S3A-H for the negative ESI mode). The VIP value is related to the importance of the contribution of a given variable to the model as a whole. Given that the average of the sums-of-squares of the VIP values is equal to 1, values larger than 1 indicate important variables and values lower than 0.5 indicate unimportant variables [21]. Furthermore, to check the robustness of the OPLS-DA models (Pooled vs Folch, Pooled vs MTBE, Pooled vs Bligh and Pooled vs MMC both in positive and negative mode), random permutation tests (n = 999) were performed (Figure S2A-H and Figure S3A-H) and compared with the default cross validation method automatically performed by the SIMCA software (see Experimental section on Multivariate statistical analysis). The results show that for all OPLS-DA models both in positive and negative ESI modes, R2 (> 0.983) and Q2 (> 0.954) values of the original models were well above the permutated models, indicating low variability and excellent predictive ability (Figure S2A-H and Figure S3A-H).
Figure 1. Comparison of lipid extraction methods by principal component analysis (PCA) after LC-MS analysis in positive (A) and negative electrospray ionization (ESI) mode (B). Hierarchical clustering analysis (HCA) of the same data in positive (C) and negative (D) ESI mode, depicting quantitative relationships between the extraction methods.
Here we used VIP values ≥ 1.5 as cutoff, allowing a better discrimination of important features. The comparisons of features considered to be mainly responsible for discrimination between the extraction methods (VIP ≥ 1.5), are shown in Figure 2 for the positive and Figure 3 for the negative ESI mode, respectively. The chromatograms were divided into 3 different retention time segments according to decreasing polarity. In positive mode segment I corresponds to lysophospholipids and monoglycerides (LPL and MG), segment II to phospholipids, sphingomyelins and diglycerides (PC, PI, PG, PE, SM and DG) and segment III to cholesterol esters and triglycerides (CE and TG) (Figure 2A). In negative mode segment I corresponds to fatty acids and lysophospholipids (FA and LPL), segment II to phospholipids and sphingomyelins (PC, PI, PG, PS, PE and SM) and segment III to some ceramides (Cer) (Figure 3A). To identify a certain number of discriminating lipids, we merged the accurate mass information from 3 on-line databases (LIPID MAPS, LipidBlast and HMDB) with our homemade database built on accurate mass and retention times. The class of lipid, adducts and the identity of individual lipids in both positive and negative ESI modes were confirmed based on matching the information using a narrow m/z window (1–5 mDa) and retention time range (0.1 min). In total, 456 distinct lipids were identified (Table
1). Glycerophospholipids were found to be the class with the largest number of species, closely followed by glycerolipids. The full list of identified lipid species and the specific groups in which the highest and lowest ion intensities were observed are shown in Table S1.
Table 1. List of lipid classes and sub-classes identified by LC-high resolution mass spectrometry in the extracted plasma sample.
Lipid class number of detection Dominant adducts Retention time range(min)
Fatty acyls 20 FAs 20 [M-H]- 6.4-16.19 Glycerophospholipids 204 LysoPCs 32 [M+H]+/[M+HCOO]- 6.32-15.91 PCs 101 [M+H]+/[M+HCOO]- 16.42-23.83 LysoPEs 10 [M+H]+/[M-H]- 8.15-11.93 PEs 36 [M+H]+/[M-H]- 17.7-21.38 LysoPSs 1 [M-H] -PSs 5 [M-H]- 17.62-19.57 LysoPGs 1 [M-H] -PGs 2 [M-H] -LysoPIs 2 [M-H] -PIs 14 [M-H]- 16.24-18.47 Sphingolipids 68 SMs 36 [M+H]+/[M+HCOO]- 15.04-22.96 Cers 32 [M+H]+/[M-H]- 15.69-24.17 Glycerolipids 154 DGs 15 [M+NH4]+ 19.44-22.44 TGs 139 [M+NH4]+ 14.88-36.98 Sterol lipids 10 CEs 10 [M+NH4]+ 27.35-31.19 Total 456
The Venn diagrams in Figures 2 and 3 show that the main difference between extraction systems is due to the lipid selectivity of each solvent system. While most of the features are common to all extraction methods (Figures 2B-C and 3B-C), there is a number of features that contributes to the formation of separate clusters upon PCA and the OPLS-DA analysis.
Figure 2. Selectivity of the extraction systems for different lipid species delimited by time windows and analyzed in positive ESI mode. (A) LC-MS chromatogram of lipids present in plasma samples. (B) Venn diagrams of the number of extracted features present in the pooled extract in comparison to the tested extraction methods. (C) Venn diagrams of the number of extracted features in extracts of the tested approaches when compared to each other. The type of lipids in each segment of the chromatogram is as follows: segment I: lysophospholipids and monoglycerides (LPL and MG), segment II: phospholipids, sphingomyelins and diglycerides (PI, PC, PE, PG, SM and DG) and segment III: cholesterol esters and triglycerides (CE and TG).
Figure 3. Selectivity of the extraction systems for different lipid species delimited by time windows and analyzed in negative ESI mode. (A) LC-MS chromatogram of lipids present in plasma samples. (B) Venn diagrams of the number of extracted features present in the pooled extract in comparison with the tested extraction methods. (C) Venn diagrams of the number of extracted features in extracts of the tested
The main source of variation in the OPLS-DA analyses was found in the first segment of the chromatograms, comprising lipids of a polar nature (FA, LPL and MG). In order to discard the contribution of contaminating features coming from the extraction solvents, we performed a thorough comparison between blank extracts and the tested extraction approaches and found no interferences in either positive or negative ionization modes (Figures S4-S7). In positive ionization mode, the ratio of discriminant to common features was highest in segment I for all comparisons (Pooled vs Folch, Pooled vs MTBE, Pooled vs Bligh and Pooled vs MMC), followed by segment II and segment III (Figures 2B, C and Figures 3B, C). In negative mode, behavior was similar as in positive ESI mode implying that overall LPL, FA and MG are strongly affected and PC, PI, PG, PS, PE, SM and DG somewhat less by the extraction method. However, segment III in negative ESI mode can be neglected, since the number of extracted features is rather low. Regarding comparison of the extraction methods against the pooled extract in positive ESI mode, MMC showed the best results providing the broadest coverage across all lipid classes, followed by the Folch, MTBE and Bligh extraction methods (1707, 1647, 1637 and 1492 extracted features, respectively). These results are in agreement with Reis et al. [12] who reported the same decreasing order in efficiency for the two-phase extraction systems (Folch > MTBE > Bligh) but contrasts with a more recent report in which the Bligh extraction system was the most efficient in positive ESI mode [22]. In negative ESI mode on the other hand, our results showed the following coverage of extraction across all lipid classes: Folch > MMC > MTBE > Bligh (598, 572, 558 and 528 extracted lipids, respectively). This decreasing order of efficiency of the two-phase extraction systems is in agreement with the results previously reported by Lee et al. [22].
Comparing the two best extraction systems (MMC and Folch) in positive ESI mode, we observed that MMC is more efficient for medium (PI, PC, PE, PG, SM and DG) to highly apolar lipids (CE and TG), while Folch performs better for more polar lipids (LPL and MG) (Figure 2C). This result contrasts with the “all-in-one-tube” idea of the MMC extraction, in which one would expect to see the highest number of lipids with a more polar nature. The Folch extraction method appears to be better suited for the extraction of PC, PI, PG, PS, PE and SM when analyzed in negative ESI mode, while MMC and Folch show the same selectivity for FA and LPL (Figure 3C). Pellegrino et al. [3], previously introduced the MMC solvent system (MeOH/MTBE/CHCl3) as one of the most promising extraction methods for lipid analysis, since, in comparison to the popular two-phase systems (Folch, Bligh and MTBE), it increased the recovery from 79% to above 95% for a set of lipid standards covering a broad polarity range. Although, in the current work, we are not only taking a small defined
set of standard lipid compounds into account but the total extractable set of plasma lipids, our results agree with these findings. Moreover, the experimental simplicity of this one-phase approach makes it the preferred method for untargeted lipid analysis.
Previously, Sarafian et al. [23] compared a defined set of precipitation (one-phase) and extraction (two-phase) methods by untargeted lipidomics. They found that it was possible to get a repeatable extraction of the lipidome in plasma samples by using pure isopropanol (IPA) as precipitating solvent, with an enhanced lipid coverage and good recovery. On the other hand, Pellegrino et al. [3] tested a precipitating solvent based on a mixture of MeOH:IPA and found that it gave the lowest recovery for a set of lipid standards. In the current work we did not test one-phase extraction systems based on pure IPA for our untargeted method, since the high polarity of this solvent does not lend itself for lipid analysis. Our results are in line with those of Pellegrino et al. [3] that the combination of MeOH/MTBE/CHCl3 (MMC) is best adapted to extracting lipids across a wide range of polarities.
5.3.4 Polar lipids species are lost in the hydrophilic fraction of two-phase extractions systems
To gain a better understanding of the reason behind the differences observed in segment I of the chromatograms with the different two-phase lipid extraction systems, we analyzed the content of the remnant hydrophilic phases that are usually discarded. We followed a set of endogenous lysophophatidylcholines (LPC) with a C18 carbon chain and up to 2 double bonds in positive ESI mode (Figure 4) and a set of endogenous fatty acids (FA) with the same number of carbon atoms and up to 3 double bonds in the negative ESI mode (Figure S8). According to our results, the Folch extraction method shows only minor signals of LPC and FA in the more hydrophilic fraction, while the MTBE and notably the Bligh method showed significant levels of LPC- and FA-derived signals in positive and negative ESI mode, respectively. Figure 5 shows a PCA-biplot (score and loading plots are overlaid) of the extracted features in the methanol phase of all two-phase extractions systems. This plot displays similarities and dissimilarities between observations and allows us to interpret the observations in terms of the variables/features. Observations close to the origins do not contribute to the formation of the clusters and are poorly described by the model components. As highlighted in Figure 5 by the ellipses, most of the extracted lipid features present in the hydrophilic phases are related to the Bligh & Dyer extraction system. This explains the low
Time (min) 7.0 7.2 7.4 7.6 7.8 8.0 8.2 8.4 8.6 8.8 9.0 9.2 9.4 9.6 9.8 10.0 10.2 10.4 10.6 10.8 11.0 11.2 11.4 11.6 11.8 12.0 % 0 100 1: TOF MS ES+ 1.00e5 O O O PO -O O N+ CH3 C H3 CH3 OH C H3 O O O PO -O O N+ CH3 C H3 CH3 OH C H3 O O O P O -O O N+ CH3 C H3 CH3 OH C H3 I) LPC(18:2) II) LPC(18:1) III) LPC(18:0) I II III 6). Bligh - CHCl3 5). MTBE - CHCl3 4). Folch - CHCl3 3). Bligh - MeOH 2). MTBE - MeOH 1). Folch - MeOH 6 5 4 3 2 1 6 5 4 3 2 1 6 5 4 3 2 1 Peak Ion Intensity 6 6.81E+04 5 1.12E+05 4 1.11E+05 3 1.90E+04 2 1.02E+04 1 336 Peak Ion Intensity 6 5.41E+04 5 7.45E+04 4 9.69E+05 3 1.26E+04 2 4.16E+03 1 143 Peak Ion Intensity 6 6.95E+04 5 1.70E+05 4 1.26E+05 3 1.62E+04 2 3.50E+03 1 255
Figure 4. Comparison of the relative abundance of a representative set of lysophophatidylcholines in positive ESI mode present in the chloroform- and the methanol-rich aqueous phases of the 3 tested two-phase extraction methods.
Figure 5. PCA-Biplot (score and loading plots are overlaid) comparing the hydrophilic fractions of the two-phase extraction systems (Folch, Bligh and MTBE) against a hydrophilic pooled sample in positive (A) and negative (B) ESI mode. The features taken into account for this analysis are represented as “X”.
5.4 Conclusion
By comparing a pooled extract with the extracts of four different sample preparation methods for lipidomics, we tried to establish which of the methods is most comprehensive (closest to the pooled extract in terms of lipid composition) and which of the methods show significant differences. While a pooled extract might be considered most comprehensive, it is not practical to perform two or more extractions with different methods in order to increase the coverage of the number of extracted lipids. Instead, a straightforward procedure able to perform this task in a simple way is much preferred. In this regard, one-phase extraction methods and specifically in our case the MMC method (MeOH/MTBE/CHCl3) developed by Pellegrino
et al. [3] showed the best results as it turned out to be quantitatively and qualitatively most
similar to the pooled extract.
The most important differences were observed for the Bligh & Dyer extraction. Particularly, more polar lipid species like LPC or FA were lost in the methanol-rich hydrophilic phase of this extraction approach, which is usually discarded for lipid analysis.
References
[1] Sandra, K., t’kindt, R., Jorge, L., Sandra, P., in:, Metabolomics Pract., Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany, 2013, pp. 137–176.
[2] Löfgren, L., Forsberg, G.-B., Ståhlman, M., Tolley, B., Tigyi, G. J., Sci. Rep. 2016, 6, 27688. [3] Pellegrino, R. M., Di Veroli, A., Valeri, A., Goracci, L., Cruciani, G., Anal. Bioanal. Chem. 2014, 406, 7937–7948.
[4] Pizarro, C., Arenzana-Rámila, I., Pérez-Del-Notario, N., Pérez-Matute, P., González-Sáiz, J. M., Anal. Chem. 2013, 85, 12085–12092. [5] Smith, R., Anthonymuthu, T. S., Ventura, D., Prince, J. T., Bioinformatics 2013, 29, 2445–2451. [6] Gross, R. W., Han, X., Chem. Biol. 2011, 18, 284–91.
[7] Jung, H. R., Sylvänne, T., Koistinen, K. M., Tarasov, K., Kauhanen, D., Ekroos, K., Biochim. Biophys. Acta - Mol. Cell Biol. Lipids 2011, 1811, 925–934.
[8] Wenk, M. R., Nat Rev Drug Discov 2005, 4, 594–610.
[9] Folch, J., Lees, M., Sloane-Stanley, G., J. Biol. Chem 1957, 226, 497–509.
[10] Bligh, E. G., Dyer, W. J., Can. J. Biochem. Physiol. 1959, 37, 911–917.
[11] Retra, K., Bleijerveld, O., Van Gestel, R., Tielens, A. G., Van Hellemond, J., Brouwers, J., Rapid Commun Mass Spectrom 2008, 22, 1853– 1862.
[12] Reis, a., Rudnitskaya, a., Blackburn, G. J., Fauzi, N. M., Pitt, a. R., Spickett, C. M., J. Lipid Res. 2013, 54, 1812–1824.
[13] Matyash, V., Liebisch, G., Kurzchalia, T. V, Shevchenko, A., Schwudke, D., J. Lipid Res. 2008, 49, 1137–46.
[14] Löfgren, L., Ståhlman, M., Forsberg, G.-B. G.-B., Saarinen, S., Nilsson, R., Hansson, G. I., Lofgren, L., Stahlman, M., Forsberg, G.-B. G.-B.,
Saarinen, S., Nilsson, R., Hansson, G. I., J. Lipid Res. 2012, 53, 1690–1700.
[15] Zhao, Z., Xu, Y., J. Lipid Res. 2010, 51, 652–659.
[16] Teo, C. C., Chong, W. P. K., Tan, E., Basri, N. B., Low, Z. J., Ho, Y. S., TrAC - Trends Anal. Chem. 2015, 66, 1–18.
[17] T’Kindt, R., Telenga, E. D., Jorge, L., Van Oosterhout, A. J. M., Sandra, P., Ten Hacken, N. H. T., Sandra, K., Anal. Chem. 2015, 87, 4957– 4964.
[18] Gika, H. G., Theodoridis, G. A., Wingate, J. E., Wilson, I. D., J. Proteome Res. 2007, 6, 3291– 3303.
[19] Gika, H. G., Macpherson, E., Theodoridis, G. A., Wilson, I. D., J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2008, 871, 299–305.
[20] Dunn, W. B., Wilson, I. D., Nicholls, A. W., Broadhurst, D., Bioanalysis 2012, 4, 2249–2264. [21] Tran, T. N., Afanador, N. L., Buydens, L. M. C., Blanchet, L., Chemom. Intell. Lab. Syst. 2014, 138, 153–160.
[22] Lee, D. Y., Kind, T., Yoon, Y.-R., Fiehn, O., Liu, K.-H., Anal. Bioanal. Chem. 2014, 406, 7275–7286.
[23] Sarafian, M. H., Gaudin, M., Lewis, M. R., Martin, F. P., Holmes, E., Nicholson, J. K., Dumas, M. E., Anal. Chem. 2014, 86, 5766–5774.
Supporting Information
Materials and MethodsChemicals
All chemicals used were analytical grade or of the highest purity commercially available. Methanol (MeOH), acetonitrile (ACN), isopropanol (IPA) and chloroform (CHCl3)were purchased from BIOSOLVE (Valkenswaard, Netherlands). Methyl tert-butyl ether (MTBE) and ammonium formate (for mass spectrometry) were purchased from Sigma-Aldrich (Zwijndrecht, The Netherlands). Ultrapure water was obtained from a Milli-Q Advantage A10 water purification system at a resistivity of 18.2 MΩ cm.(Millipore SAS, Molsheim, France). Plastic tubes that do not get altered by the use of organic solvents were purchased from Eppendorf (Order no. 0030 120.094).
Blood collection
Heparin-anticoagulated plasma samples, obtained from adult patients at the University Medical Center Groningen (UMCG) in an anonymous manner, were combined to generate a standard plasma sample. This sample was separated in aliquots that were stored at -80 °C in the dark (up to 1 year) until further use. Respective triglycerides and cholesterol concentrations in the combined plasma sample were 1.66 and 3.6 mM. The study design was in accordance with the current revision of the Helsinki Declaration (2013).
Data preprocessing
MassLynx software version 4.1 was used for data acquisition. Waters raw data files were analyzed using Progenesis QI software (Waters Corporation, Milford, MA) for peak alignment, peak picking and normalization of the LC-MS data. Peak alignment was done to correct drifts in retention times. To this end a reference LC-MS run, that was the best representative of the entire data set, was selected. All other runs were then aligned to this reference. For peak picking and feature selection the following adduct forms were used: [M+H], [M+NH4], [M+Na], [M+K], [M+H-H2O], [M+CH3OH+H], [2M+H], [2M+NH4] and [2M+Na] in positive mode; and [M-H], [M+FA-H], [M+Cl], [2M+FA-H] and [M-H2O-H] in negative mode. The peak picking limits were set at the maximum sensitivity mode. To this
A default automatic normalization approach called “normalize to all compounds” was used. This normalization automatically selects a reference LC-MS measurement and then uses ratiometric data in a log space, along with a median and mean absolute deviation outlier filtering approach, to compare and calculate a scalar factor for the remaining measurements. The metabolite features reported hereafter were entirely generated using the Progenesis software.
On the basis of normalized peak intensities, the number of features was filtered according to 2 different sets of selection criteria. The first set of selection was strict and only applied for PCA. This included observations showing a change in magnitude (Max fold change ≥ 1.5), a statistically significant difference (P ≤ 0.05, student’s t-test), while excluding features with a variation of more than 30% (CV) within each experimental group (i.e. pooled, Folch, Bligh, MTBE and MMC samples). The second set of selection, only applied for OPLS-DA, was less strict and included all observations showing a CV ≤ 30% within each experimental group. These set of selections allowed to avoid over-fitting and improved the multivariate model’s predictive ability [17]. Final results were represented in an output table containing m/z values, retention times and normalized peak intensities for each compound ion (feature) in the two pooled extracts and the samples obtained with the individual extraction systems. This table was imported into Simca P v.13 (Umetrics, Umea, Sweden) for multivariate statistical analysis.
Multivariate statistical analysis
Principal component analysis (PCA) and partial least squares discriminant analysis (OPLS-DA) via orthogonal projection to latent structures were carried out on the filtered features using Simca P v.13 (Umetrics, Umea, Sweden). Data were log10 transformed, mean-centered, pareto-scaled, and columns representing samples were grouped in blocks according to the extraction methods (Folch, Bligh, MTBE and MMC), as well as to the pooled extracts (hydrophobic and hydrophilic). Discriminating features between lipid profiles were identified for each model using s-plots [17]. Permutation testing (n = 999) was carried out to determine the robustness of the multivariate models (i.e. whether a model fits the training set well and accurately predicts the variable under study (Y) for new observations).
The latter was made possible by the sevenfold cross validation used as default by the Simca software. In this procedure 1/7th of the data is omitted from model building and the obtain model
is used to predict the omitted data class membership. In this cross validation, determination coefficient (R2) represents the fraction of the data variation explained by the multivariate regression model. A large R2 (close to 1) is directly related to a good reproducibility or low noise in the dataset. Q2 represents an estimate of the predictive ability of the model. A large Q2 (> 0.5) indicates good prediction performance.
Figure S1. PCA line plot of the Pooled sample (QC) using the first component in positive (A) and negative (B) ionization mode. The plots represent the technical variation observed for the LC-MS data across the experiments.
Figure S2. OPLS-DA scores plots and S-Plots showing the separation and discriminating features (respectively) between the pooled sample (QCs) and the Folch (A, B), MTBE (C, D), Bligh (E, F) and MMC (G, H) extracts in positive mode. Validation plots displaying 999 permutation tests for the models are shown within the S-Plots. The explained variances (R2) were 0.990, 0.993, 0.995 and 0.983, and
predictive abilities (Q2) were 0.965, 0.968, 0.960 and 0.954 for the “Pooled vs Folch”, “Pooled vs
Figure S3. OPLS-DA scores plots and S-Plots showing the separation and discriminating features (respectively) between the pooled sample (QCs) and the Folch (A, B), MTBE (C, D), Bligh (E, F) and MMC (G, H) extracts in negative mode. Validation plots displaying 999 permutation tests for the models are shown within the S-Plots. The explained variances (R2) were 0.996, 0.994, 0.990 and 0.996,
and predictive abilities (Q2) were 0.983, 0.969, 0.968 and 0.987 for the “Pooled vs Folch”, “Pooled vs
Time 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00 26.00 28.00 30.00 32.00 34.00 36.00 38.00 40.00 42.00 44.00
%
0 100
AG20161026_lipidomics_17 1: TOF MS ES-
BPI 7.00e4 10.95 1221 9.56 1088 8.87 1036 8.14 972 10.35 1164 18.26 1951 18.05 1944 17.41 1911 12.54 1399 16.50 1845 15.06 1693 19.10 1986 19.53 2001 23.07 2229 21.26 2096 20.36 2042 22.06 2147 22.51 2174 23.61 2279 Time 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00 26.00 28.00 30.00 32.00 34.00 36.00 % 0 100
AG20161011_lipidomics_17 1: TOF MS ES+
BPI 5.00e5 19.17 1588 18.34 1563 18.16 1558 17.44 1536 8.90 888 5.88 643 2.37 253 8.18 845 6.70 734 11.01 1013 9.47 922 10.68 988 16.53 1502 16.22 1478 14.05 1305 15.52 1441 18.77 1576 19.58 1600 29.58 2029 28.18 1986 20.40 1625 20.01 1613 21.32 1654 21.20 1650 27.59 1968 27.29 1959 22.07 1680 21.51 1660 26.94 1948 22.38 1690 22.47 1693 28.51 1996 29.36 2022 29.92 2042 30.12 2051 31.23 210431.522124 Folch extract Blank extract TOF MS ESI+ BPI 5.00e5 Folch extract Blank extract TOF MS ESI-BPI 7.00e4 Positive mode Negative mode
Figure S4. Comparison of plasma and blank samples extracted with Folch method in positive and negative mode. Time 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00 26.00 28.00 30.00 32.00 34.00 36.00 % 0 100
AG20161011_lipidomics_20 1: TOF MS ES+
BPI 5.00e5 19.15 1669 18.24 1642 17.42 1617 8.89 941 8.16 900 2.38 261 16.55 1587 11.01 1070 9.46 974 9.98 997 14.04 1382 16.21 156716.84 1599 18.35 1645 18.75 1657 28.50 2056 19.49 1679 20.38 1706 20.02 1695 28.13 2045 21.33 1735 21.20 1731 27.55 2027 27.28 2019 26.92 2008 22.36 1772 21.51 1741 22.46 1775 29.58 2089 29.35 2082 29.91 2100 31.54 2169 30.09 2109 31.22 2153 30.92 2139 MTBE extract Blank extract TOF MS ESI+ BPI 5.005e5 Positive mode Time 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00 26.00 28.00 30.00 32.00 34.00 36.00 38.00 40.00 42.00 44.00 % 0 100
AG20161026_lipidomics_20 1: TOF MS ES-
BPI 7.00e4 10.95 1221 9.55 1081 8.86 1022 0.55 57 7.27 868 8.64 1002 10.34 1163 18.24 1945 17.63 1917 17.41 1905 15.06 1697 12.54 1402 16.51 1837 15.44 1740 19.10 1972 19.53 1986 23.05 2200 21.25 2076 20.33 2024 22.06 2123 23.60 2249 TOF MS ESI-BPI 7.00e4 Negative mode MTBE extract Blank extract
Figure S5. Comparison of plasma and blank samples extracted with MTBE method in positive and negative mode.
Time
2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00 26.00 28.00 30.00 32.00 34.00 36.00
%
0 100
AG20161011_lipidomics_23 1: TOF MS ES+
BPI 5.00e5 19.15 1802 18.35 1778 18.14 1772 17.43 1750 8.88 996 8.18 944 6.67 802 16.53 1709 14.05 1476 11.03 1150 9.47 1042 16.21 168016.84 1727 18.78 1791 28.18 2215 19.59 1815 27.73 2201 20.37 1839 20.01 1828 21.32 1869 21.20 1865 27.56 2196 27.27 2187 26.91 2176 22.37 1909 21.52 1875 22.46 1912 29.61 2259 28.48 2224 29.18 2246 29.91 2270 30.11 2280 31.51 2344 31.24 2330 Bligh extract Blank extract TOF MS ESI+ BPI 5.005e5 Positive mode Time 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00 26.00 28.00 30.00 32.00 34.00 36.00 38.00 40.00 42.00 44.00 % 0 100
AG20161026_lipidomics_23 1: TOF MS ES-
BPI 7.00e4 18.27 2043 10.95 1282 8.86 1068 0.46 56 9.57 1133 10.35 1218 18.03 2036 17.42 1999 15.05 1768 12.53 1474 16.50 1924 19.07 2080 18.72 2064 23.05 2339 19.54 2099 22.06 2257 21.25 2201 20.34 2143 23.58 2387 TOF MS ESI-BPI 7.00e4 Negative mode Bligh extract Blank extract
Figure S6. Comparison of plasma and blank samples extracted with Bligh method in positive and negative mode. Time 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00 26.00 28.00 30.00 32.00 34.00 36.00 % 0 100
AG20161011_lipidomics_26 1: TOF MS ES+
BPI 5.00e5 19.15 1650 18.25 1623 18.16 1620 17.42 1598 8.88 917 8.15 875 6.68 757 14.04 1357 11.02 1052 9.48 955 9.97 982 16.55 1567 16.20 154416.861580 17.59 1603 18.33 1625 18.76 1638 19.57 1662 29.56 2098 28.17 2054 20.38 1687 20.02 1676 21.35 1717 21.21 1713 27.70 2040 27.57 2036 27.29 2027 26.92 2016 21.52 172322.36 1755 22.46 1758 26.602006 28.46 2063 29.42 2094 29.16 2086 29.91 2111 31.52 2187 31.19 2165 MMC extract Blank extract TOF MS ESI+ BPI 5.005e5 % 100
AG20161026_lipidomics_26 1: TOF MS ES-
BPI 7.00e4 10.94 1178 9.55 1045 8.83 990 7.25 10.34 1123 18.26 1913 17.62 1878 17.39 1864 19.08 1943 19.51 1962 23.04 2211 22.03 21.25 Blank extract Positive mode TOF MS ESI-BPI 7.00e4 Negative mode
Figure S8. Comparison of the relative abundance of a representative set of fatty acids in negative ESI mode present in the chloroform- and the methanol-rich aqueous phases of the 3 tested two-phase extraction methods.
Table S1. List of identified lipids in the plasma sample. Identities are proposed based on a homebuilt database.
Feature m/z Retention time
(min) Accepted Description Dominantion forms Formula Highest
Mean Lowest Mean
7.96_227.2008m/z 227,2008272 7,96 FA(14:0) M-H C14H28O2 MMC Bligh 10.37_255.2324m/z 255,2323652 10,37 FA(16:0) M-H C16H32O2 MMC Bligh 11.52_269.2476m/z 269,247571 11,52 FA(17:0) M-H C17H34O2 Folch Bligh 12.58_283.2633m/z 283,2633483 12,58 FA(18:0) M-H C18H36O2 Folch Bligh 6.40_225.1851m/z 225,1851315 6,40 FA(14:1) M-H C14H26O2 MMC Bligh 8.66_253.2165m/z 253,216534 8,66 FA(16:1) M-H C16H30O2 MMC Bligh 9.81_267.2321m/z 267,2320922 9,81 FA(17:1) M-H C17H32O2 MMC Bligh 13.03_309.2789m/z 309,2789026 13,03 FA(20:1) M-H C20H38O2 MTBE Bligh 16.19_365.3417m/z 365,3416914 16,19 FA(24:1) M-H C24H46O2 Folch Bligh 8.15_279.2319m/z 279,2319476 8,15 FA(18:2) M-H C18H32O2 Folch Pooled 9.57_279.2322m/z 279,2321683 9,57 FA(18:2) M-H C18H32O2 MTBE Bligh 11.74_307.2632m/z 307,2631592 11,74 FA(20:2) M-H C20H36O2 Folch Bligh 8.36_277.2163m/z 277,2163475 8,36 FA(18:3) M-H C18H30O2 Folch Bligh 10.51_305.2477m/z 305,2476555 10,51 FA(20:3) M-H C20H34O2 MMC Bligh 9.54_303.2320m/z 303,2320254 9,54 FA(20:4) M-H C20H32O2 MMC Bligh 11.41_331.2634m/z 331,2633572 11,41 FA(22:4) M-H C22H36O2 Folch Bligh 8.35_301.2163m/z 301,2162849 8,35 FA(20:5) M-H C20H30O2 Folch Bligh 10.29_329.2477m/z 329,2477422 10,29 FA(22:5) M-H C22H34O2 Folch Bligh 9.37_327.2321m/z 327,2321339 9,37 FA(22:6) M-H C22H32O2 MMC Bligh 15.48_351.3260m/z 351,3259688 15,48 FA(23:1) M-H C23H44O2 Folch Bligh 17.70_662.4759m/z 662,4759305 17,70 PE(30:0) M-H C35H70NO8P Pooled Folch 20.18_718.5380m/z 718,5380109 20,18 PE(34:0) M-H C39H78NO8P Bligh MMC 21.38_746.5692m/z 746,5692284 21,38 PE(36:0) M-H C41H82NO8P Bligh MMC 17.93_716.5227m/z 716,5227004 17,93 PE(34:1) M-H C39H76NO8P Bligh MTBE 19.22_716.5227m/z 716,5226918 19,22 PE(34:1) M-H C39H76NO8P Bligh MMC 20.42_744.5537m/z 744,5536888 20,42 PE(36:1) M-H C41H80NO8P Bligh MMC 20.40_772.5849m/z 772,584871 20,40 PE(38:1) M-H C43H84NO8P Bligh Pooled 18.40_714.5071m/z 714,5071007 18,40 PE(34:2) M-H C39H74NO8P Bligh Pooled 19.65_742.5380m/z 742,5379903 19,65 PE(36:2) M-H C41H78NO8P Bligh Folch 19.57_770.5690m/z 770,5690375 19,57 PE(38:2) M-H C43H82NO8P Bligh Folch 18.66_740.5224m/z 740,5223977 18,66 PE(36:3) M-H C41H76NO8P Bligh Pooled 20.06_768.5534m/z 768,5534394 20,06 PE(38:3) M-H C43H80NO8P Bligh MMC 18.35_738.5069m/z 738,506942 18,35 PE(36:4) M-H C41H74NO8P Bligh Pooled
18.91_764.5223m/z 764,5222753 18,91 PE(38:5) M-H C43H76NO8P Bligh Folch 19.81_792.5538m/z 792,5538034 19,81 PE(40:5) M-H C45H80NO8P Bligh MMC 18.24_762.5069m/z 762,5068972 18,24 PE(38:6) M-H C43H74NO8P Bligh Pooled 19.47_790.5380m/z 790,5379975 19,47 PE(40:6) M-H C45H78NO8P Bligh Folch 18.48_788.5225m/z 788,5225274 18,48 PE(40:7) M-H C45H76NO8P Bligh MMC 21.01_728.5587m/z 728,5587396 21,01 PE(O-36:2)//PE(P-36:1) M-H C41H80NO7P MTBE Folch 18.98_698.5120m/z 698,5120139 18,98 PE(O-34:3)//
PE(P-34:2) M-H C39H74NO7P Bligh MMC 20.23_726.5430m/z 726,5429737 20,23 PE(O-36:3)//PE(P-36:2) M-H C41H78NO7P MTBE Pooled 20.05_726.5432m/z 726,5431923 20,05 PE(O-36:3)//PE(P-36:2) M-H C41H78NO7P Bligh MMC 19.24_724.5275m/z 724,5275058 19,24 PE(O-36:4)//PE(P-36:3) M-H C41H76NO7P Bligh MMC 18.91_722.5121m/z 722,5120916 18,91 PE(O-36:5)//
PE(P-36:4) M-H C41H74NO7P Bligh Folch 20.38_776.5585m/z 776,5584717 20,38 PE(O-40:6)//PE(P-40:5) M-H C45H80NO7P Bligh MMC 20.12_750.5428m/z 750,5428163 20,12 PE(P-38:4)//PE(O-38:5) M-H C43H78NO7P Bligh MMC 21.37_778.5745m/z 778,5745033 21,37 PE(P-40:4)//
PE(O-40:5) M-H C45H82NO7P Bligh MMC 18.22_720.4965m/z 720,4964949 18,22 PE(P-36:5)//
PE(O-36:6) M-H C41H72NO7P Bligh MMC 19.18_748.5274m/z 748,5273748 19,18 PE(P-38:5)//PE(O-38:6) M-H C43H76NO7P Bligh MMC 19.46_748.5275m/z 748,527478 19,46 PE(P-38:5)//PE(O-38:6) M-H C43H76NO7P MTBE Folch 18.77_746.5120m/z 746,511999 18,77 PE(P-38:6)//
PE(O-38:7) M-H C43H74NO7P Bligh Folch 20.01_774.5426m/z 774,5425936 20,01 PE(P-40:6)//
PE(O-40:7) M-H C45H78NO7P Bligh MMC 19.08_772.5275m/z 772,5274532 19,08 PE(P-40:7)//PE(O-40:8) M-H C45H76NO7P Pooled MMC 8.97_452.2774m/z 452,2773841 8,97 LPE(16:0) M-H C21H44NO7P MMC Bligh 8.39_480.3088m/z 480,3087513 8,39 LPE(18:0) M-H C23H48NO7P MMC Bligh 8.87_480.3089m/z 480,3089193 8,87 LPE(18:0) M-H C23H48NO7P Folch Bligh 9.56_478.2931m/z 478,293117 9,56 LPE(18:1) M-H C23H46NO7P MMC Bligh 9.44_506.3244m/z 506,3244246 9,44 LPE(20:1) M-H C25H50NO7P MMC Pooled 8.23_476.2775m/z 476,2775166 8,23 LPE(18:2) M-H C23H44NO7P MMC Bligh 8.15_504.3087m/z 504,3086999 8,15 LPE(20:2) M-H C25H48NO7P MMC Bligh 8.25_500.2774m/z 500,277446 8,25 LPE(20:4) M-H C25H44NO7P MMC Bligh 8.28_524.2779m/z 524,2779047 8,28 LPE(22:6) M-H C27H44NO7P Pooled Bligh 11.93_464.3140m/z 464,3139663 11,93 LPE(P-18:0)//
LPE(O-18:1) M-H C23H48NO6P MMC Bligh 17.85_760.5124m/z 760,5123788 17,85 PS(34:1) M-H C40H76NO10P MMC Folch 17.62_760.5122m/z 760,5122352 17,62 PS(34:1) M-H C40H76NO10P MMC Folch 17.87_786.5279m/z 786,5278869 17,87 PS(36:2) M-H C42H78NO10P MMC Folch 19.57_834.5256m/z 834,5255938 19,57 PS(40:6) M-H C46H78NO10P Bligh Folch 18.22_830.4941m/z 830,4941187 18,22 PS(40:8) M-H C46H74NO10P Bligh Pooled 7.28_508.2672m/z 508,2672447 7,28 LPS(17:1) M-H C23H44NO9P MMC Folch 19.52_777.5637m/z 777,5637428 19,52 PG(36:0) M-H C42H83O10P Pooled Folch 17.90_773.5329m/z 773,532925 17,90 PG(18:2/18:0) M-H C42H79O10P MTBE Folch 8.26_509.2872m/z 509,2872281 8,26 LPG(18:1) M-H C24H47O9P MMC Folch 16.24_807.5018m/z 807,5018364 16,24 PI(32:1) M-H C41H77O13P MTBE Folch
17.32_835.5322m/z 835,5322083 17,32 PI(34:1) M-H C43H81O13P MTBE Folch 18.47_863.5648m/z 863,564832 18,47 PI(36:1) M-H C45H84O13P MMC Folch 16.63_833.5174m/z 833,5174042 16,63 PI(34:2) M-H C43H78O13P MTBE Folch 17.74_861.5487m/z 861,5486708 17,74 PI(36:2) M-H C45H83O13P MTBE Folch 16.87_859.5329m/z 859,5328578 16,87 PI(36:3) M-H C45H81O13P MTBE Folch 18.12_887.5643m/z 887,5642911 18,12 PI(38:3) M-H C47H85O13P MMC Folch 18.05_915.5968m/z 915,5968498 18,05 PI(40:3) M-H C45H81O13P Bligh MTBE 16.60_857.5167m/z 857,5166943 16,60 PI(36:4) M-H C45H79O13P MTBE Folch 17.69_885.5490m/z 885,5490101 17,69 PI(38:4) M-H C47H83O13P MTBE Folch 16.91_883.5322m/z 883,532219 16,91 PI(38:5) M-H C47H81O13P MTBE Folch 17.92_911.5640m/z 911,5639851 17,92 PI(40:5) M-H C49H85O13P Pooled Folch 16.52_881.5183m/z 881,518304 16,52 PI(38:6) M-H C47H79O13P MTBE Folch 17.60_909.5485m/z 909,5484777 17,60 PI(40:6) M-H C49H82O13P MTBE Folch 7.04_583.2883m/z 583,2883342 7,04 LPI(17:1) M-H C26H49O12P MMC Folch 9.52_599.3193m/z 599,3193394 9,52 LPI(18:0) M-H C27H53O12P MMC Folch 15.69_481.4487n 526,4468391 15,69 Cer(d30:0) M+FA-HM-H, C30H59NO3 Bligh Folch 19.93_612.5564m/z 612,5564036 19,93 Cer(d36:0) M+FA-H C36H73NO3 Bligh MTBE
22.37_623.6212n 668,6191609 22,37 Cer(d40:0) M-H,
M+FA-H C40H81NO3 Bligh MTBE 22.94_637.6367n 682,6346493 22,94 Cer(d41:0) M-H,
M+FA-H C41H83NO3 Bligh MMC 23.51_651.6518n 696,6500662 23,51 Cer(d42:0) M-H, M+Cl, M+FA-H C42H85NO3 Bligh MMC 16.91_509.4801n 554,4783393 16,91 Cer(d32:1) M+FA-HM-H, C32H63NO3 Bligh Pooled 17.54_568.4940m/z 568,493959 17,54 Cer(d33:1) M+FA-H C33H65NO3 Bligh MMC
18.20_537.5113n 582,509837 18,20 Cer(d34:1) M-H,
M+FA-H C34H67NO3 Bligh Folch 19.50_565.5425n 610,5409053 19,50 Cer(d36:1) M+FA-HM-H, C36H71NO3 Bligh Folch 20.79_593.5742n 638,5721267 20,79 Cer(d38:1) M+FA-HM-H, C38H75NO3 Bligh MMC 21.99_621.6053n 666,6037172 21,99 Cer(d40:1) M-H, M+Cl,
M+FA-H C40H79NO3 Bligh MMC 22.35_680.6184m/z 680,6183841 22,35 Cer(d41:1) M+FA-H C41H81NO3 Bligh MTBE 22.56_635.6208n 680,6193079 22,56 Cer(d41:1) M+FA-HM-H, C41H81NO4 Bligh Folch 22.59_670.5903m/z 670,5903429 22,59 Cer(d41:1) M+Cl C41H81NO4 Bligh Folch
23.13_649.6365n 694,6349917 23,13 Cer(d42:1) M-H, M+Cl,
M+FA-H C42H83NO3 Bligh Folch 23.49_663.6521n 708,6501685 23,49 Cer(d43:1) M-H, M+Cl, M+FA-H C43H85NO3 Bligh MMC 23.66_663.6523n 708,6501895 23,66 Cer(d43:1) M-H, M+Cl, M+FA-H C43H85NO4 Bligh MMC 24.17_722.6656m/z 722,6656235 24,17 Cer(d44:1) M+FA-H C44H87NO3 Bligh MMC
17.24_535.4958n 580,4939758 17,24 Cer(d34:2) M-H,
M+FA-H C34H65NO3 Bligh MMC 18.52_608.5254m/z 608,52545 18,52 Cer(d36:2) M+FA-H C36H69NO3 Bligh MTBE 20.98_619.5898n 664,5876608 20,98 Cer(d40:2) M+FA-HM-H, C40H77NO3 Bligh Folch
22.14_647.6208n 646,6134964 22,14 Cer(d42:2) M-H, M+Cl C42H81NO3 Bligh Folch 22.49_661.6360n 706,6344865 22,49 Cer(d43:2) M+FA-HM-H, C43H83NO3 Bligh Pooled 22.65_661.6367n 706,63472 22,65 Cer(d43:2) M-H,
M+FA-H C43H83NO4 Bligh MMC 23.23_675.6517n 720,6498637 23,23 Cer(d44:2) M+FA-HM-H, C44H85NO3 Bligh MMC 21.26_645.6051n 690,6032663 21,26 Cer(d42:3) M+FA-HM-H, C42H79NO3 Bligh Folch 21.34_684.6136m/z 684,6136134 21,34 Cer(t40:0) M+FA-H C40H81NO4 Bligh MMC
22.47_667.6469n 712,6450494 22,47 Cer(t42:0) M-H,
M+FA-H C42H85NO4 Bligh MMC 16.71_721.5491m/z 721,5490693 16,71 SM(d32:0) M+FA-H C37H77N2O6P Folch MMC 17.95_749.5803m/z 749,5803153 17,95 SM(d34:0) M+FA-H C39H81N2O6P Pooled MTBE 19.23_777.6102m/z 777,6102239 19,23 SM(d36:0) M+FA-H C41H85N2O6P Pooled MMC 21.68_833.6738m/z 833,6737679 21,68 SM(d40:0) M+FA-H C45H93N2O6P Bligh MMC 15.04_691.5022m/z 691,5022482 15,04 SM(d30:1) M+FA-H C35H71N2O6P MTBE Folch 16.22_674.5354n 719,5336144 16,22 SM(d32:1) 2M+FA-HM+FA-H, C37H75N2O6P Bligh MTBE 16.84_733.5489m/z 733,5489323 16,84 SM(d33:1) M+FA-H C38H77N2O6P Folch Pooled
17.43_702.5669n 747,5651056 17,43 SM(d34:1) M+FA-H,
2M+FA-H C39H79N2O6P Bligh Pooled 18.74_715.5750m/z 715,5750325 18,74 SM(d35:1) M-H C40H81N2O6P Bligh MTBE 17.87_761.5785m/z 761,5785404 17,87 SM(d35:1) M+FA-H C40H81N2O6P Folch MTBE 18.09_761.5802m/z 761,5801857 18,09 SM(d35:1) M+FA-H C40H81N2O6P MMC MTBE
18.73_730.5978n 775,5960426 18,73 SM(d36:1) M+FA-H,
2M+FA-H C41H83N2O6P Bligh MTBE 19.37_789.6117m/z 789,6116801 19,37 SM(d37:1) M+FA-H C42H85N2O6P Pooled MMC 20.01_803.6270m/z 803,6270226 20,01 SM(d38:1) M+FA-H C43H87N2O6P Bligh MTBE 20.63_817.6427m/z 817,6427083 20,63 SM(d39:1) M+FA-H C44H89N2O6P Bligh MTBE 21.81_845.6742m/z 845,6742195 21,81 SM(d41:1) M+FA-H C46H93N2O6P Bligh MTBE 21.37_797.6533m/z 797,65329 21,37 SM(d41:2) M-H C46H91N2O6P MMC Pooled 21.82_859.6891m/z 859,6890645 21,82 SM(d42:1) M+FA-H C47H95N2O6P Bligh Pooled 22.36_859.6898m/z 859,6897941 22,36 SM(d42:1) M+FA-H C47H95N2O6P Bligh MTBE 22.73_873.7052m/z 873,7052234 22,73 SM(d43:1) M+FA-H C48H97N2O6P Bligh MMC 22.96_873.7055m/z 873,7055066 22,96 SM(d43:1) M+FA-H C48H97N2O6P Pooled MMC 15.35_717.5177m/z 717,5176994 15,35 SM(d32:2) M+FA-H C37H73N2O6P MTBE Bligh 17.54_745.5514m/z 745,55141 17,54 SM(d34:2) M+FA-H C39H77N2O6P Pooled MMC 16.57_700.5510n 745,549193 16,57 SM(d34:2) 2M+FA-HM+FA-H, C39H77N2O6P Bligh Pooled 17.82_773.5801m/z 773,5801341 17,82 SM(d36:2) M+FA-H C41H81N2O6P Bligh Pooled 19.08_801.6115m/z 801,6114648 19,08 SM(d38:2) M+FA-H C43H85N2O6P Pooled Folch 20.33_769.6215m/z 769,6214784 20,33 SM(d39:2) M-H C44H87N2O6P Bligh Folch 20.33_829.6426m/z 829,6426238 20,33 SM(d40:2) M+FA-H C45H89N2O6P Bligh Pooled 20.95_843.6582m/z 843,6581817 20,95 SM(d41:2) M+FA-H C46H91N2O6P Bligh Folch 21.37_812.6758n 857,6740217 21,37 SM(d42:2) 2M+FA-HM+FA-H, C47H93N2O6P Bligh Pooled 22.13_871.6898m/z 871,6897654 22,13 SM(d43:2) M+FA-H C48H95N2O6P Folch MMC 21.73_871.6900m/z 871,6899526 21,73 SM(d43:2) M+FA-H C48H95N2O6P Bligh Pooled 19.32_827.6270m/z 827,6270244 19,32 SM(d40:3) M+FA-H C45H87N2O6P Bligh Folch
20.48_810.6602n 855,6583608 20,48 SM(d42:3) 2M+FA-HM+FA-H, C47H91N2O6P Bligh Pooled 21.67_883.6890m/z 883,6890113 21,67 SM(d44:3) M+FA-H C49H95N2O6P Bligh MMC 20.69_875.6845m/z 875,6845092 20,69 SM(t42:1) M+FA-H C47H95N2O7P Bligh MMC
37.32_1653.2534n 1654,260689 37,32 24:1(3)-14:1 CA
M+H, M+NH4,
M+Na
C95H178O17P2 Bligh Folch 21.01_1246.8007n 1247,808211 21,01 14:1(3)-15:1 CA
M+H, M+NH4,
M+Na C66H120O17P2 Pooled Folch 24.51_1308.9104n 1309,917693 24,51 15:0(3)-16:1 CA
M+H, M+NH4,
M+Na C70H134O17P2 Pooled Folch 30.82_1569.1609n 1570,168098 30,82 22:1(3)-14:1
CA
M+H, M+NH4,
M+Na
C89H166O17P2 Bligh Folch 14.88_470.3612n 493,3503847 14,88 TG(24:0) M+NH4, M+Na C27H50O6 MMC Folch 22.63_638.5497n 661,538451 22,63 TG(36:0) M+NH4,
M+Na C39H74O6 MMC MTBE
24.82_694.6119n 712,6456869 24,82 TG(40:0) M+NH4,
M+Na C43H86NO6 MMC Pooled 25.78_722.6428n 740,6765964 25,78 TG(42:0) M+NH4, M+Na, M+K, 2M+Na C45H90NO6 MMC MTBE 26.25_754.6925m/z 754,692548 26,25 TG(43:0) M+NH4 C46H88O6 MMC Bligh 26.70_750.6740n 768,7078684 26,70 TG(44:0) M+NH4, M+Na, 2M+Na C47H94NO6 MMC MTBE 27.18_782.7237m/z 782,7236746 27,18 TG(45:0) M+NH4 C48H92O6 MMC MTBE 27.86_778.7056n 796,7394319 27,86 TG(46:0) M+NH4, M+Na C49H98NO6 MMC MTBE 28.50_810.7553m/z 810,7553362 28,50 TG(47:0) M+NH4 C50H100NO6 MMC MTBE
29.26_806.7373n 824,7711685 29,26 TG(48:0)
M+NH4, M+Na, M+K,
2M+Na C51H102NO6 Pooled Folch 31.02_834.7682n 852,8020044 31,02 TG(50:0)
M+NH4, M+Na, M+K,
2M+Na C53H106NO6 Bligh Pooled 31.67_866.8174m/z 866,8173814 31,67 TG(51:0) M+NH4 C54H104O6 MMC Folch 32.06_866.8171m/z 866,8170657 32,06 TG(51:0) M+NH4 C54H104O6 MMC Pooled 33.24_862.7989n 885,7886255 33,24 TG(52:0) M+NH4, M+Na, M+K, 2M+Na C55H110NO6 MMC Pooled 21.75_636.5337n 654,567497 21,75 TG(36:1) M+NH4,
M+Na C39H72O6 MMC Pooled 22.91_664.5649n 687,5542437 22,91 TG(38:1) M+Na, M+KM+NH4, C41H76O6 MMC Folch 23.21_664.5646n 687,5542248 23,21 TG(38:1) M+NH4, M+Na C41H76O6 MMC Folch 24.03_692.5961n 715,5851677 24,03 TG(40:1) M+NH4,
M+Na C43H80O6 MMC Folch 25.04_720.6275n 738,6612878 25,04 TG(42:1) M+NH4,
M+Na, M+K C45H88NO6 Bligh Folch 25.48_734.6430n 752,6768172 25,48 TG(43:1) M+NH4, M+Na C46H90NO6 MMC Pooled 25.90_748.6586n 766,6923741 25,90 TG(44:1) M+NH4, M+Na, M+K, 2M+Na C47H92NO6 MMC Pooled 26.43_780.7078m/z 780,7078269 26,43 TG(45:1) M+NH4 C48H94NO6 MMC Bligh 26.90_776.6901n 794,7239641 26,90 TG(46:1) M+Na, M+K, M+NH4, 2M+Na C49H96NO6 MMC Pooled 27.48_790.7055n 808,7392835 27,48 TG(47:1) M+NH4, M+Na C50H98NO6 MMC Pooled 28.73_818.7376n 836,7714157 28,73 TG(49:1) M+NH4,
M+Na C52H102NO6 Folch Pooled 29.52_832.7530n 850,7867858 29,52 TG(50:1)
M+H,
32.03_874.7992n 892,8330534 32,03 TG(53:1) M+NH4, M+Na C56H110NO6 MMC Pooled 32.42_874.7990n 897,7880242 32,42 TG(53:1) M+Na, M+KM+NH4, C56H110NO6 Folch Bligh 33.61_888.8151n 906,8489246 33,61 TG(54:1) M+NH4,
M+Na, M+K C57H112NO6 Folch Bligh 22.06_680.5831m/z 680,5831266 22,06 TG(38:2) M+NH4 C41H74O6 MMC MTBE 25.25_746.6429n 764,6767159 25,25 TG(44:2) M+NH4, M+Na, M+K, 2M+Na C47H90NO6 MMC Pooled 26.11_774.6744n 792,7082698 26,11 TG(46:2) M+NH4, M+Na, M+K, 2M+Na C49H94NO6 MMC Pooled 27.12_802.7060n 820,7395394 27,12 TG(48:2) M+NH4, M+Na, M+K, 2M+Na C51H98NO6 MMC Pooled 27.63_834.7552m/z 834,7552077 27,63 TG(49:2) M+NH4 C52H100NO6 MMC Pooled 28.27_830.7373n 848,7711507 28,27 TG(50:2) M+Na, M+KM+NH4, C53H102NO6 Folch Pooled 29.04_844.7521n 862,7865978 29,04 TG(51:2) M+K, 2M+NaM+NH4, C54H104NO6 Folch Pooled 29.81_858.7683n 881,7575101 29,81 TG(52:2) M+Na, M+K, 2M+Na C55H106NO6 Bligh Pooled 29.74_858.7687n 876,8025321 29,74 TG(52:2)
M+H, M+NH4,
2M+NH4 C55H106NO6 Folch Pooled 30.61_872.7838n 890,8176046 30,61 TG(53:2) M+NH4,
M+Na, M+K C56H108NO6 Folch Pooled 31.64_886.7996n 904,8334513 31,64 TG(54:2) M+H, M+NH4, M+Na C57H110NO6 MMC Pooled 34.00_914.8309n 932,8643592 34,00 TG(56:2) M+NH4,
M+Na, M+K C59H114NO6 MMC Pooled 36.98_942.8613n 965,850544 36,98 TG(58:2) M+Na, M+KM+NH4, C61H114O6 MMC Pooled 24.54_744.6273n 762,6610867 24,54 TG(44:3) M+Na, M+KM+NH4, C47H84O6 MMC Folch 25.43_772.6586n 790,692404 25,43 TG(46:3) M+Na, M+KM+NH4, C49H92NO6 Bligh Pooled 26.40_800.6899n 818,7237214 26,40 TG(48:3)
M+NH4, M+Na,
2M+Na C51H96NO6 MMC Pooled 26.81_814.7058n 832,7396449 26,81 TG(49:3) M+NH4,
M+Na C52H98NO6 MMC Pooled 27.39_828.7215n 846,755374 27,39 TG(50:3) M+H, M+NH4, M+Na, M+K, 2M+Na C53H100NO6 MMC Pooled 27.96_842.7370n 860,7708665 27,96 TG(51:3) M+Na, M+KM+NH4, C54H102NO6 MMC Pooled
28.61_856.7531n 874,7869043 28,61 TG(52:3) M+H- H2O2, M+H, M+NH4, M+Na, M+K, 2M+NH4, 2M+Na
C55H104NO6 Folch Pooled
29.38_870.7686n 888,8024256 29,38 TG(53:3) M+NH4, M+Na C56H106NO6 MTBE Pooled 30.04_884.7836n 902,8179748 30,04 TG(54:3) M+NH4M+H, C57H108NO6 Bligh Pooled 30.90_898.7992n 916,8330141 30,90 TG(55:3) M+NH4,
M+Na C58H106O6 MMC Pooled 31.91_930.8486m/z 930,8486372 31,91 TG(56:3) M+NH4 C59H112NO6 MMC Pooled 34.34_940.8461n 958,8798835 34,34 TG(58:3) M+NH4, M+K C61H112O6 MMC Pooled 26.60_826.7053n 844,7391358 26,60 TG(50:4) M+H, M+NH4, M+Na, M+K, 2M+Na
C53H98NO6 Bligh Pooled
27.67_854.7367n 872,7705652 27,67 TG(52:4) M+H, M+NH4, M+Na C55H102NO6 MMC Pooled 28.13_868.7517n 886,7855537 28,13 TG(53:4) M+NH4,
M+Na C56H104NO6 MMC Pooled 28.87_900.8024m/z 900,8023643 28,87 TG(54:4) M+NH4 C57H106NO6 Folch Pooled 32.44_938.8303n 956,8641112 32,44 TG(58:4) M+NH4, M+Na C61H110O6 MMC Pooled 26.95_852.7208n 870,7551151 26,95 TG(52:5) M+H,
27.35_884.7702m/z 884,7702344 27,35 TG(53:5) M+NH4 C56H102NO6 MMC Pooled 27.86_880.7520n 898,7858088 27,86 TG(54:5) M+NH4, M+H,
M+Na
C57H104NO6 MMC Pooled 28.40_880.7528n 898,78663 28,40 TG(54:5) M+Na, M+KM+NH4, C57H104NO6 Bligh Pooled 29.09_912.8025m/z 912,8024607 29,09 TG(55:5) M+NH4 C58H106NO6 Folch Pooled 29.43_926.8175m/z 926,817458 29,43 TG(56:5) M+NH4 C59H108NO6 MTBE Pooled 29.89_908.7840n 926,8178285 29,89 TG(56:5) M+Na, M+KM+NH4, C59H108NO6 Folch Pooled 31.38_954.8484m/z 954,8484347 31,38 TG(58:5) M+NH4 C61H112NO6 Folch Pooled
27.13_878.7355n 896,7692764 27,13 TG(54:6) M+NH4,
M+Na C57H102NO6 MMC Pooled 27.48_878.7370n 896,7707896 27,48 TG(54:6) M+NH4,
M+Na, M+K C57H102NO6 MTBE Pooled 28.54_924.8022m/z 924,8021803 28,54 TG(56:6) M+NH4 C59H106NO6 Folch Pooled 28.76_920.7838n 938,8176528 28,76 TG(57:6) M+NH4, M+Na C60H108NO6 Bligh Pooled 30.15_952.8336m/z 952,8335662 30,15 TG(58:6) M+NH4 C61H110NO6 Folch Pooled
26.43_876.7204n 894,7542287 26,43 TG(54:7) M+H, M+NH4, M+Na, M+K C57H100NO6 MMC Pooled 26.78_876.7211n 894,7549212 26,78 TG(54:7) M+NH4,
M+Na C57H100NO6 Bligh Pooled 27.63_922.7863m/z 922,7863019 27,63 TG(56:7) M+NH4 C59H104NO6 Folch Pooled 30.20_978.8486m/z 978,8485562 30,20 TG(60:7) M+NH4 C63H108O6 MMC Pooled 25.83_913.6679m/z 913,6679314 25,83 TG(54:8) M+K C57H98NO6 MMC Folch
25.84_874.7058n 892,7396329 25,84 TG(54:8) M+NH4,
M+Na C57H98NO6 MMC Pooled 26.25_892.7381m/z 892,73814 26,25 TG(54:8) M+NH4 C57H98NO6 MTBE Pooled 27.17_920.7700m/z 920,7699717 27,17 TG(56:8) M+NH4 C59H102NO6 MMC Pooled 28.27_948.8018m/z 948,8018211 28,27 TG(58:8) M+NH4 C61H106NO6 MMC Pooled 29.13_976.8321m/z 976,8320507 29,13 TG(60:8) M+NH4 C63H106O6 Folch Pooled 27.44_928.7525n 946,7863344 27,44 TG(58:9) M+NH4, M+Na C61H104NO6 MMC Pooled 28.76_974.8171m/z 974,8171069 28,76 TG(60:9) M+NH4 C63H104O6 MTBE Pooled
26.64_926.7357n 944,7695028 26,64 TG(58:10) M+NH4,
M+Na C61H102NO6 Bligh Pooled 27.08_926.7365n 944,7702801 27,08 TG(58:10) M+NH4,
M+Na C61H102NO6 MTBE Pooled 27.58_972.8012m/z 972,8012457 27,58 TG(60:10) M+NH4 C63H102O6 MTBE Pooled 28.13_972.8018m/z 972,8017601 28,13 TG(60:10) M+NH4 C63H102O6 MTBE Pooled
27.17_952.7523n 970,7861137 27,17 TG(60:11) M+NH4,
M+Na C63H104NO6 MTBE Pooled 26.51_968.7701m/z 968,7701447 26,51 TG(60:12) M+NH4 C63H98O6 Folch Pooled 26.80_950.7353n 968,769079 26,80 TG(60:12) M+NH4, M+Na C63H98O6 Bligh Pooled 26.31_992.7704m/z 992,7704165 26,31 TG(62:14) M+NH4 C65H98O6 Folch Bligh
26.31_850.7059n 868,7391069 26,31 TG(52:6) M+NH4,
M+Na, M+K C55H94O6 Bligh Pooled 30.21_914.8173m/z 914,8173103 30,21 TG(55:4) M+NH4 C58H104O6 MMC Pooled 30.75_910.7992n 928,8330521 30,75 TG(56:4) M+NH4, M+Na C59H106O6 Folch Pooled 26.27_900.7203n 918,7541216 26,27 TG(56:9) M+NH4, C59H96O6 MMC Pooled
