Long-Term Performance and Stability of Molecular Shotgun Lipidomic

Article pubs.acs.org/ac

Long-Term Performance and Stability of Molecular Shotgun Lipidomic Analysis of Human Plasma Samples Laura A. Heiskanen, Matti Suoniemi, Hung Xuan Ta, Kirill Tarasov, and Kim Ekroos* Zora Biosciences Oy, Biologinkuja 1, Espoo, FI-02150, Finland S Supporting Information *

ABSTRACT: The stability of the lipid concentration levels in shotgun lipidomics analysis was tracked over a period of 3.5 years. Concentration levels in several lipid classes, such as phospholipids, were determined in human plasma lipid extracts. Impact of the following factors on the analysis was investigated: sample amount, internal standard amount, and sample dilution factor. Moreover, the reproducibility of lipid profiles obtained in both polarity modes was evaluated. Total number of samples analyzed was approximately 6800 and 7300 samples in negative and positive ion modes, respectively, out of which 610 and 639 instrument control samples were used in stability calculations. The assessed shotgun lipidomics approach showed to be remarkably robust and reproducible, requiring no batch corrections. Coefficients of variation (CVs) of lipid mean concentration measured with optimized analytical parameters were typically less than 15%. The high reproducibility indicated that no lipid degradation occurred during the monitored time period.

I

molecule by the characteristic fragment ion or by the resultant loss of a particular neutral fragment, respectively. Comprehensive delineations of molecular lipids have also been successfully demonstrated by utilization of high mass resolving instrumentations combined with novel MS workflows.12,13 Integration of automated sample preparations,14 sample introducing systems,15 software tools,16,17 and state-of-the-art instrumentation has furthermore enabled the performance of such analyses at high throughput and sensitivity without compromising quality.8 On the basis of these facts shotgun lipidomics has emerged as a key lipidomics methodology facilitating the solving of biological as well as clinical questions associated with metabolic alterations of the lipidome of cells or tissues. Although lipidomics has been available for about 2 decades,18 data on the stability or robustness of the analytical methodologies are still scarce. The main reason for this has been the limitations of analytical throughput. This, in turn, is a result of the shortage of suitable internal standards, which has contributed to the complications of establishing robust analytical workflows. When it comes to large-scale studies, in addition to the actual samples, various controls are needed to monitor quality of the analytical process.19 Typical controls are calibration line and instrument control (IC) samples. Since IC samples are identical and routinely analyzed, they continuously monitor the analysis performance and the long-term stability of the samples. Data on long-term performance is critical as this facilitates the standardization of the methodology. Moreover,

t is well-known that lipids have pivotal biological functions. The lipidome of eukaryotic cells contains thousands of lipid entities that structurally and chemically regulate cell membranes, store energy, or become precursors to bioactive metabolites.1,2 The proper function of such central cellular events will strongly depend on the lipids present. A defect in the lipid regulation and metabolism can therefore have a deleterious outcome on the cell or organism and take an active part in the disease pathophysiology. It is known that major diseases such as atherosclerosis,3 infectious diseases,4 diabetes,5 and Alzheimer’s disease6,7 all have a lipid component in their molecular epidemiology. Thus, lipidomic assessments are to be anticipated as such analyses could shed light on the underlying pathogenesis and bring forward new diagnostic biomarkers. Lipidomics consists of multidimensional strategies for molecular lipid characterization including bioinformatics tools for mass spectrum interpretation and quantitative measurements to study systems lipidomics in complex biological extracts. Presently, targeted and untargeted shotgun and liquid chromatography (LC)-based mass spectrometry (MS) methods are the main analytical approaches. Not only due to the high lipid diversity, but also to the extreme lipid concentration ranges that can be several million fold, multiple strategies are typically required for the delineation of lipidomes.8,9 Shotgun lipidomics has turned into an attractive method of choice for the analysis of molecular lipids (referred to as molecular shotgun lipidomics). The major benefit of this technology is that hundreds of lipids at both the sum level and at the molecular level can be directly identified and accurately quantified from total lipid extracts in a relatively short analysis time.10 Sum and molecular lipids are primarily monitored and quantified using precursor ion scanning (PIS) and neutral loss scanning (NLS),11 which measure the abundance of the parent © 2013 American Chemical Society

Received: June 20, 2013 Accepted: August 6, 2013 Published: August 6, 2013 8757

dx.doi.org/10.1021/ac401857a | Anal. Chem. 2013, 85, 8757−8763

Analytical Chemistry

Article

reconstituted in chloroform/methanol (1:2, v/v) and spiked with known amounts of external lipid standards and stored shortly at −80 °C until analysis. Prior to molecular shotgun lipidomic analysis the extracts were further diluted with chloroform/methanol (1:2, v/v) containing 5 mM ammonium acetate. The analysis was explicitly performed on fresh extracts, and samples were only analyzed once. Sample analysis frequency varied in rather random fashion, but typically ranging from several times per day to once a week. Molecular Shotgun Lipidomics. Molecular lipids were analyzed on QTRAP 5500 mass spectrometers (AB Sciex, Concord, Canada) equipped with robotic nanoflow ion sources (TriVersa NanoMate, Advion Biosciences Inc., Ithaca, NJ).14 Samples were loaded into 96-well plates (twin.tec PCR plate 96, Eppendorf AG, Hamburg, Germany) and sealed with an aluminum foil (heat sealing foil, Eppendorf AG). Aliquots of 10 μL were aspirated and infused. Precursor ion and neutral loss scans were carried out in positive and negative ion modes, as described previously.8,16 On the TriVersa NanoMate electrospray ionization (ESI) voltages applied were typically 1.3 and −1.3 kV in positive and negative ion modes, respectively. The gas pressure was set typically to 0.75 psi in both polarity modes. In the positive ion mode the following MS settings were used: curtain gas, 20; collision gas, 6; interface heater, 60; declustering potential, 30; entrance potential, 10; collision cell exit potential, 20. In negative ion mode the following settings were used: curtain gas, 20; collision gas, 6; interface heater, 60; declustering potential, −100; entrance potential, −10; collision cell exit potential, −20. Data Processing and Statistical Tools. The PIS and NL data was processed using LipidView16 and in-house bioinformatic tools.8 Identified lipids were quantified by normalizing against their respective internal standard and plasma volume. The lipid concentrations are presented in micromoles per liter (μM). Total lipid class concentrations were calculated by summing-up the concentrations of lipid class within individual IC samples and molar distribution percentages by dividing all the observed lipid concentrations by the class total concentration within the sample. Data filtering was based on the frequency of individual lipid molecules observed throughout the collected long-term data within IC samples. Molecules observed from less than 75% of the IC samples and molecules lacking lipid class-specific internal standards were excluded. Filtering procedures were performed separately for both polarity modes. Only samples with sample amount of 5 or 10 μL and internal standard more than 10 pmol were included in the final calculations. All the calculations and data processing were performed using SAS 9.3 (SAS Institute).

the reliability of the methodology determines its applicability in regulatory environments. Stability assessment is a must for making lipidomics a technology applicable to regulated clinical diagnostics. We have previously observed around 10% variation (coefficient of variation) in the lipid concentrations of the human plasma instrument control extracts when analyzing over 500 samples during 30 days by molecular shotgun lipidomics.8 We therefore set out to explore the performance of the same platform by extending the number of analyses and the analytical time span. In the current study, the analysis time span covered 42 months monitoring approximately 6800 and 7300 samples in negative and positive ion modes, respectively. We demonstrate a remarkable long-term performance and stability of the applied shotgun lipidomic technique. Furthermore, the impact of the extracted plasma volume, infused sample and standard concentrations, and plasma storage stability measured per molecular lipid were investigated. As far as we know this is the first demonstration of the long-term stability of shotgun lipidomic analysis spanning a period of several years.

■

EXPERIMENTAL SECTION Chemicals. Methanol, ultrapure water, ammonium acetate, acetic acid (all LC−MS grade), and 2,6-di-tert-butyl-4methylphenol (BHT) were purchased from Sigma-Aldrich GmbH (Steinheim, Germany), and chloroform (HPLC grade) was from Rathburn Chemicals Ltd. (Walkerburn, Scotland). Synthetic lipid standards were purchased from Avanti Polar Lipids Inc. (Alabaster, AL) (LPC 17:0, PC 17:0/17:0, PA 17:0/ 17:0, PE 17:0/17:0, PG 17:0/17:0, PS 17:0/17:0, SM (d18:1/ 12:0)), Larodan Fine Chemicals AB (Malmö, Sweden) (DG 17:0/17:0, D6-PC 16:0/16:0, D3-LPC 16:0), and C/D/N Isotopes Inc. (Pointe-Claire, Quebec, Canada) (D6-CE 18:0). Samples. Fresh-frozen human plasma from two healthy volunteers (batch 1 and batch 2), approximately 300 mL each, was purchased from Finnish Red Cross Blood Service (Helsinki, Finland). The plasma batches were aliquoted into Eppendorf Safe-Lock tubes (Eppendorf AG) and stored at −80 °C until lipidomic analysis. Lipid Extraction. Robotic-assisted 96-well sample preparation and extraction was performed using a Hamilton Microlab Star system (Hamilton Robotics AB, Kista, Sweden). A modified Folch protocol, using chloroform, methanol, and acetic acid for liquid−liquid extraction, was applied to extract a broad lipid-type spectrum.14,20 This extraction procedure is efficient and robust over a wide lipid concentration range,21 and applicable for the extraction of acid-labile lipids such as plasmalogens since no dramatic formation of their lyso species have been observed (data not shown). Optionally, plasmalogens could be readily extracted as described by Yang et al.22 Briefly, fresh-frozen human plasma was thawed on ice and aliquots of 5 or 10 μL were manually pipetted onto the 96-well plates and spiked with known amounts of synthetic lipid standards prior to extraction.14 A volume of 200 μL of ice-cold methanol containing 0.1% BHT was added to the samples to prevent lipid oxidation. Chloroform (450 μL) was added to samples, followed by addition of 120 μL of 20 mM aqueous acetic acid. The samples were thoroughly mixed by a repeated aspiration−dispense sequence followed by centrifugation for 5 min at 500g, after which the lower organic phase was collected. Another round of extraction was repeated for the remaining aqueous phase. The organic phases were combined and evaporated under nitrogen until dryness. The extracts were

■

RESULTS AND DISCUSSION Shotgun lipidomics is a routine analysis in our laboratory. Sample amount used in lipid extraction and the amounts of internal standards (IS) vary depending on the matrix. In case of plasma, sample amounts of 5 or 10 μL are typically used, although volumes down to 1 μL have been reported.9 Primarily, lipid class selective nonendogenous synthetic standards are used as ISs, serving for the quantification of the endogenous lipids.16 The amount of IS is typically adjusted to retrieve similar ion abundances as the endogenous lipids of the same class. Lastly, the final lipid extract is diluted in preferred MS solvents to retrieve the optimal response of the monitored lipids.10 Data presented here include lipid concentrations in IC 8758



Article

producing coefficient of variations (CV) in the range of 5.1− 13%. When compared to PCs that were monitored in negative ion mode, the CVs in CE species, monitored in positive ion mode, are in the same range, 8.9−38%. Similar variations were observed for the other molecular PCs and CEs (data not shown). These results demonstrate that the applied molecular shotgun lipidomics workflow is highly robust generating consistent and reproducible plasma lipid profiles. Furthermore, the consistent profiles throughout the time span indicate that of the lipids monitored minimal or no lipid degradation occurred during sample preparation, analysis, or during storage. Additionally, although the need for batch correction is widely recognized in high-throughput large-scale studies,23,24 the data presented here indicate that batch correction is not needed in shotgun lipidomic analysis. Factors Affecting Lipid Concentration Signal Stability. The results in Figure 1 include IC samples in which the extracted plasma amount is either 10 or 5 μL, IS concentrations vary, and infused plasma concentration varies from the most diluted 1/600 to the least diluted 1/40. These factors do not dramatically interfere in the reproducibility of lipid profiles as demonstrated in Figure 1. However, they have higher impact on the reproducibility in lipid concentrations (micromoles per liter in plasma). For example, in case of PC 16:0−18:2 in negative ion mode, the mean concentration CV in IC samples from plasma batch 2 is 45%, while the mol % CV is only 5.1%. Therefore, we next explored if any of these factors was causing the high analytical variation in the concentrations. To keep the text concise only plasma sample batch 2 is discussed here, since it comprised more IC samples than plasma sample batch 1. Plasma Sample Amount. Figure 2a shows mean concentrations CVs of selected PC species in negative ion mode, sorted by extracted plasma volume. Figure 2b shows similar data of all CE species in positive ion mode. All IC samples were diluted in the same ratio (infused plasma concentration 1/200) (see detailed discussion about dilution below). Supporting Information Table S-1 shows numeric values of mean concentrations and mean concentration CVs for all the detected lipids. Mean PC lipid concentrations shown by green bars (Figure 2a) belong to the sample set where the extracted plasma amount is 10 μL. Mean concentration CVs for this sample set are in the range from 6.6% to 18%. When extracted plasma amount is decreased to 5 μL (marked as purple bars in Figure 2a), mean concentration CVs increase dramatically to the range of 28−37%. The results clearly indicate that larger sample volumes are preferred as they produce more accurate results. In the applied workflow the IC samples have been manually aliquoted onto the 96-well plates, whereas the lipid extraction has been done in an automated fashion by the robotic setup.14 Thus, it is most likely the manual pipetting and handling that give rise to the obtained variations. The pipetting has been done by various users, which is likely a major contributing factor to the obtained variations. This has previously been observed in the context of manual extraction by different persons.8 Obviously, 10 μL is a more representative sample that can be more reproducibly pipetted even between different users. This finding is surprising and will be more thoroughly investigated in future studies. Even though such extensive data as presented in this manuscript cannot be quickly collected, undoubtedly different sample volumes need to be evaluated as well as sample aliquoting using robotics that expectedly should significantly reduce the analytical variations.

samples of fresh-frozen human plasma monitored during 42 months, counting altogether 610 and 639 lipids in negative and positive ion modes, respectively. Data Filtering. The resulting IC lipidomic data set was filtered to exclude potential artifacts, such as lipids observed only in a few control samples. We decided to exclude lipid species with more than 25% missing observations, summing-up to 119 lipids. Furthermore, we included only lipid classes where absolute quantification was possible. This resulted in the inclusion of 34 lipids belonging to four lipid classes and 51 lipids belonging to four lipid classes in negative and positive ion modes, respectively. The 85 lipid species included are presented in Supporting Information Table S-1. Concentrations of included lipids are in agreement with those presented previously.9 Overall Lipid Signal Stability within a Lipid Class. We first set out to explore the overall stability of individual molecular species within a respective lipid class. We calculated the molar distribution (mol %) of the individual species within the major phosphatidylcholine (PC) and cholesteryl ester (CE) lipid classes and plotted the values by time. PCs and CEs were chosen as they represent the major classes in plasma.9 The mol % levels of selected high- and low-abundant molecular PC and CE species obtained during the 42 month period are shown in Figure 1, parts a and b, respectively. The human plasma batch

Figure 1. Molar distribution (mol %) of selected (a) PC and (b) CE species within their respective lipid classes during a 42 month period. The reference human plasma batch was changed at 14 months’ time. (a) Two PC species analyzed in 610 IC samples in negative ion mode: PC 16:0−18:2 (green) and PC 18:0−20:4 (black). (b) Two CE species analyzed in 639 IC samples in positive ion mode: CE 18:1 (purple) and CE 20:5 (orange).

was changed at 14 months’ time, which is seen as a change in the mol % levels of the measured lipids. After the batch change the PC 16:0−18:2 proportion decreases while PC 18:0−20:4 slightly increases (Figure 1a). Similarly, CE 18:1 proportion increases, whereas CE 20:5 slightly decreases after plasma batch change. These changes are due to individual differences in plasma donor lipidomes. The analytical variation per batch was calculated separately for batches 1 (0−14 months) and 2 (15−42 months). Within both IC batches, PC lipid signals show to be highly stable 8759



Article

volume extracted. Furthermore, regardless of the polarity mode applied similar concentrations of LPCs are obtained (Supporting Information Table S-1), demonstrating the robustness of the applied molecular lipidomic workflow. IS Concentration. We next investigated if the IS concentration in the infused sample extract had any impact on the analytical variation. The examined IS concentrations were generated by altering the IS amounts added to the IC samples during the lipid extraction. The IS concentrations ranged from 0.1 to 0.8 μM in PC analysis and from 0.2 to 1.6 μM in CE analysis (10 μL IC samples in plasma batch 2). Parts a and b of Figure 3 show the mean lipid concentration CVs as a

Figure 2. Y-axis shows mean concentrations (pmol/μL) and the x-axis CVs for the mean concentrations in IC samples (human plasma). Lipid extracts are from the second human plasma batch only, and they are all diluted in the same ratio for shotgun lipidomics analysis; original plasma volume is diluted 1/200 in the final infusion extract. (a) PC species analyzed in negative ion mode as described in the Experimental Section. Green bars indicate a sample set (n = 52) where extracted plasma volume is 10 μL, and purple bars indicate a sample set (n = 21) where extracted plasma volume is 5 μL. (b) CE species analyzed in positive ion mode as described in the Experimental Section. Blue bars indicate a sample set (n = 50) where extracted plasma volume is 10 μL, and red bars indicate a sample set (n = 24) where extracted plasma volume is 5 μL.

Figure 2b shows similar data for CEs in positive ion mode as discussed above for PCs in negative ion mode. Mean concentration CVs for the sample set where extracted plasma amount is 10 μL (blue bars in Figure 2b) are in the range from 8.2% to 41%, although most of the CVs are below 20%. Smaller sample volume, 5 μL, produces higher CVs; from 20% to 62% (red bars in Figure 2b). Thus, difference in the mean concentration CVs caused by different extraction volumes is clear, but less pronounced than in negative ion mode. Mean concentrations CVs in lipid species of other classes are listed in Supporting Information Table S-1. The CVs of molecular phosphatidylethanolamines (PEs) and phosphatidylserines (PSs) are originating from negative ion mode analyses, and diacylglycerols (DGs) and sphingomyelins (SMs) from positive ion mode analyses. Lysophosphatidylcholines (LPCs) were obtained from both polarity modes. These data include IC samples with plasma volume 5 and 10 μL. Final plasma concentration in the samples is 1/200, the same as in the data presented in Figure 2. In negative ion mode the mean concentration CVs per lipid species within each class are typically lower when 10 μL of plasma volume is extracted compared to 5 μL (Supporting Information Table S-1), which is in line with Figure 2. The same pattern applies for positive ion mode data. In summary, the chosen sample volume impacts the analytical variation of quantified lipid species, where higher sample volumes produce less variable results. Despite that the analytical variability is higher in low-volume data, similar lipid concentrations can still be obtained irrespective of plasma

Figure 3. Correlation between mean concentration CV and IS concentration for (a) total PCs and (b) total CEs. The size of a circle shows the relative number of IC samples included in each point; the largest circle stands for (a) 140 and (b) 151 and the smallest for four IC samples analyzed with a specific IS concentration. The data consists of 10 μL IC samples from plasma batch 2.

function of IS concentration (micromoles per liter in infused extracts) for total PCs and total CEs, respectively. Corresponding data for selected molecular PCs and CEs is shown in Supporting Information Figure S-1. The size of a circle corresponds to the number of IC samples analyzed with a selective IS concentration; the largest circle corresponds to 140 (PCs, Figure 3a) and 151 (CEs, Figure 3b) analyzed IC samples and the smallest for four (both PCs and CEs, Figure 3, parts a and b). Data presented in parts a and b of Figure 3 do not show any clear dependence of lipid class mean concentration CV on IS concentration. Similar results were observed in other molecular lipids as well. Thus, varying IS concentrations, at least in the range investigated here, have no significant role in the analytical variability. This can therefore be applied in shotgun lipidomics analyses without sacrificing the signal stability and determination of the final lipid concentrations. Infused Plasma Concentration. It has been suggested that total lipid concentration in infused lipid extract in shotgun MS analysis should be less than 100 pmol/μL (100 μM) to avoid lipid aggregation in analysis.25 In our data infused plasma 8760



Article

mean concentration CV and final plasma concentration for total PCs and total CEs. Corresponding data for selected molecular PCs and CEs is shown in Supporting Information Figure S-2. As depicted in Figure 4, final plasma concentration in our data ranges from the most diluted 1/600 to least diluted 1/80. Since the total lipid concentration in plasma is approximately 8000 pmol/μL (Supporting Information Table S-1), all the samples were diluted to the recommended range, total lipid concentrations in infused samples ranging from approximately 13 to 100 pmol/μL. The size of a circle in Figure 4 correlates with number of IC samples included in each point, where the largest circle stands for 129 (PCs, Figure 4a) and 140 (CEs, Figure 4b) analyzed IC samples and the smallest for four (both PCs and CEs, Figure 4, parts a and b). On the basis of the data shown in parts a and b of Figure 4, the infused plasma concentration, i.e., dilution rate, does not have any significant effect on the signal stability in respective polarity mode. A dilution around 1/200, corresponding to 40 pmol/μL total lipid, seems to be optimal for both polarity modes. Similar results were observed for other molecular lipids as well (Supporting Information Figure S-2). Thus, the dilutions applied in this study do not have any significant role in the analytical variability. With the current instrumentations, shotgun lipidomics analyses can safely be performed by applying a 1/200 dilution of the plasma samples in both polarity modes. At this setting, the signal stability is robust leading to mean lipid concentrations CVs around 15% of 10 μL plasma aliquots. Additionally, we studied if infused plasma concentration affects the number of detected molecular lipids. Supporting Information Figure S-3 shows detected number of lipids of respective classes per infused plasma concentration. Infused plasma ratios with less than 10 IC samples were excluded from this data. Differences in the number of lipids were found

concentration describes the amount of plasma in the lipid extract, typically diluted, that is analyzed in shotgun lipidomics. For example, the value 1/10 means that the original plasma volume has been diluted 10-fold prior to the shotgun lipidomics analysis. Parts a and b of Figure 4 show dependence between

Figure 4. Correlation between mean concentration CV and infused plasma concentration for (a) total PC and (b) total CE. The data includes only batch 2 IC samples. The size of a circle shows the relative number of IC samples included in each point; the largest circle stands for (a) 129 and (b) 140 analyzed IC samples and the smallest for four IC samples. The data consists of 10 μL IC samples from plasma batch 2.

Figure 5. Y-axis shows mean concentrations (pmol/μL) and the x-axis CVs for the mean concentrations in 10 μL IC samples. Lipid extracts are from the second human plasma batch only, and they are all diluted in the same ratio for shotgun lipidomics analysis; original plasma volume is diluted 1/ 200 in the final infusion extract. Time span of analysis is five months. (a) PC (marked with green Δ, n = 52) and LPC (marked with orange ○, n = 49) species analyzed in negative ion mode. (b) CE (marked with blue □, n = 50) and SM (marked with red ◇, n = 50) species analyzed in positive ion mode. 8761



Article

degradation in the measured molecular species during the 3.5 years’ tracking period applied in this study. Thus, this indicates high stability in the molecular species measured even after long storage periods. In concert with the high analytical reproducibility these findings are foremost fundamental in the context of such as clinical trials, clinical diagnostics, and biomarker discovery programs. Expectedly, this will not only improve such assessments but also make molecular shotgun lipidomics a standard technology platform in these environments.

insignificant with no clear dependence between infused plasma concentration and detected number of lipids. Lipid Concentration Stability with Optimized Analysis Parameters. As the 10 μL plasma sample volume and the sample dilution of 1/200 were found to affect lipid signal stability, we further studied the variations in individual lipid signals within the 10 μL sample set. Figure 5 shows lipid mean concentration and mean concentration CVs for molecular PCs and LPCs in negative ion mode (Figure 5, parts a and b) and CEs and SMs in positive ion mode (Figure 5, parts c and d). Numerical values of mean concentrations and mean concentration CVs are listed in Supporting Information Table S-1. The sample set with 10 μL plasma volume for molecular PCs (Figure 5a) shows clear dependence between CV and mean concentration level; CV increases as mean concentration decreases, which is expected because lower signal level is more affected by background noise resulting in insufficient ion statistics. This dependence is not as clear in the case of molecular LPCs (Figure 5b); high-abundant LPC 16:0 has higher mean concentration CV than other detected lowerabundant LPC species. Molecular CEs and SMs analyzed in positive ion mode (Figure 5, parts c and d) show similar trends as for PCs analyzed in negative ion mode; mean concentration CV increases as mean concentration decreases. The largest part of the quantified molecular lipids displays an analytical variation around 15% or less. Importantly, this variation is maintained over a 3 orders of magnitude concentration range, which is a typical linearity range on triple quadrupole instrumentations.14,26 Furthermore, the result shows that the measured lipids do not degrade or alter due to other reasons in the applied workflow. An observed higher variation in CE 19:2 (41%) and CE 21:0 (35%) mean concentrations is not due to degradation of lipids by time or exceptionally low peak areas compared to other CE species (data not shown), but more likely due to the nature of these molecules as these are rather uncommon and could potentially be hydroxylated CEs. This still needs to be verified in future work. Taken together, the performance of applied shotgun lipidomics shows extraordinarily good stability and robustness throughout the complete instrument linearity range when using optimized analysis parameters. Stability of Shotgun Lipidomics versus LC-Driven Lipidomics. Direct comparison of shotgun lipidomics stability to LC-driven lipidomics stability is not possible in this study since our data does not include LC−MS data. Yang et al. recently presented long-term stability data of plasma metabolites analyzed by LC−MS.27 The authors reported remarkable instability of the analytes, for example, LPCs, during five years’ storage at −80 °C. In our shotgun lipidomics data from 3.5 years’ time such instability is not observed. Compared to LC− MS methods, shotgun lipidomics methods are likely less prone to variation caused by matrix, e.g., ionization suppression, since the background from matrix remains the same during shotgun lipidomics analyses unlike in LC−MS. In this study, we typically analyzed the fresh-frozen plasma on a daily and/or weekly basis. This in itself creates a unique data collection compared to existing studies such as the work of Yang et al.,27 typically containing only a few time points (e.g., start and end points). From our data we can in detail study the daily or weekly alteration in each measured endogenous molecular lipid over the time monitored. To our knowledge this is the first study of such kind. Even though more analyses are needed, the initial results show no significant lipid

■

CONCLUSIONS AND FUTURE PERSPECTIVES Shotgun lipidomics shows excellent long-term stability. The most affecting factor on lipid concentration level stability in IC samples was found to be plasma sample volume. Other studied parameters did not have a remarkable effect on signal stability, which shows that the applied shotgun lipidomics method is robust and produces stable and repeatable results throughout the complete linearity range of used instrumentation. This leads to lipid concentration CVs less than 15%, which in fact directly meets the good laboratory practice (GLP) method requirements. Undoubtedly, the outstanding performance in concert with the standardization makes shotgun lipidomics a tempting method of choice for regulatory lipidomic analyses, such as in clinical diagnostics and the food industry. Another aspect is its direct applicability to large-scale studies. Finally, the setup can be supplemented with techniques such as differential mobility spectrometry (DMS) 28 and ozone-induced dissociation (OzID).29 Taken together, this paves the way to biomarker discovery, target discovery programs, and nutrition programs as it will prospectively shed new insights into the affected metabolic and signaling pathways and the transition of lipidomics into clinical laboratories.

■

ASSOCIATED CONTENT

S Supporting Information *

Additional information as noted in text. This material is available free of charge via the Internet at http://pubs.acs.org.

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: kim.ekroos@zora.fi. Phone: +358-40-7448997. Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS We thank all members of our laboratory and partner organizations for valuable discussions and suggestions and the Finnish Funding Agency for Technology and Innovation (Tekes) for financial support.

■

REFERENCES

(1) Shevchenko, A.; Simons, K. Nat. Rev. Mol. Cell Biol. 2010, 11, 593−598. (2) Lipidomics Technologies and Applications; Ekroos, K., Ed.; WileyVCH: Weinheim, Germany, 2012. (3) Hiukka, A.; Ståhlman, M.; Pettersson, C.; Levin, M.; Adiels, M.; Teneberg, S.; Leinonen, E. S.; Hultén, L. M.; Wiklund, O.; Oresic, M.; Olofsson, S.-O.; Taskinen, M.-R.; Ekroos, K.; Borén, J. Diabetes 2009, 58, 2018−2026. (4) Morita, M.; Kuba, K.; Ichikawa, A.; Nakayama, M.; Katahira, J.; Iwamoto, R. Cell 2013, 1−14. (5) Gross, R. W.; Han, X. Methods Enzymol. 2007, 433, 73−90.

8762



Article

(6) Chan, R. B.; Oliveira, T. G.; Cortes, E. P.; Honig, L. S.; Duff, K. E.; Small, S. A.; Wenk, M. R.; Shui, G.; Di Paolo, G. J. Biol. Chem. 2012, 287, 2678−2688. (7) Han, X. Curr. Alzheimer Res. 2005, 2, 65−77. (8) Jung, H. R.; Sylvänne, T.; Koistinen, K. M.; Tarasov, K.; Kauhanen, D.; Ekroos, K. Biochim. Biophys. Acta 2011, 1811, 925−934. (9) Quehenberger, O.; Armando, A. M.; Brown, A. H.; Milne, S. B.; Myers, D. S.; Merrill, A. H.; Bandyopadhyay, S.; Jones, K. N.; Kelly, S.; Shaner, R. L.; Sullards, C. M.; Wang, E.; Murphy, R. C.; Barkley, R. M.; Leiker, T. J.; Raetz, C. R. H.; Guan, Z.; Laird, G. M.; Six, D. A.; Russell, D. W.; McDonald, J. G.; Subramaniam, S.; Fahy, E.; Dennis, E. A. J. Lipid Res. 2010, 51, 3299−3305. (10) Ekroos, K.; Chernushevich, I. V; Simons, K.; Shevchenko, A. Anal. Chem. 2002, 74, 941−949. (11) Brügger, B.; Erben, G.; Sandhoff, R.; Wieland, F. T.; Lehmann, W. D. Proc. Natl. Acad. Sci. U.S.A. 1997, 94, 2339−2344. (12) Schuhmann, K.; Herzog, R.; Schwudke, D.; Metelmann-Strupat, W.; Bornstein, S. R.; Shevchenko, A. Anal. Chem. 2011, 83, 5480− 5487. (13) Simons, B.; Kauhanen, D.; Sylvänne, T.; Tarasov, K.; Duchoslav, E.; Ekroos, K. Metabolites 2012, 2, 195−213. (14) Ståhlman, M.; Ejsing, C. S.; Tarasov, K.; Perman, J.; Borén, J.; Ekroos, K. J. Chromatogr., B 2009, 877, 2664−2672. (15) Ekroos, K.; Ejsing, C. S.; Bahr, U.; Karas, M.; Simons, K.; Shevchenko, A. J. Lipid Res. 2003, 44, 2181−2192. (16) Ejsing, C. S.; Duchoslav, E.; Sampaio, J.; Simons, K.; Bonner, R.; Thiele, C.; Ekroos, K.; Shevchenko, A. Anal. Chem. 2006, 78, 6202− 6214. (17) Herzog, R.; Schwudke, D.; Schuhmann, K.; Sampaio, J. L.; Bornstein, S. R.; Schroeder, M.; Shevchenko, A. Genome Biol. 2011, 12, R8. (18) Han, X.; Gross, R. W. Proc. Natl. Acad. Sci. U.S.A. 1994, 91, 10635−10639. (19) Dunn, W. B.; Broadhurst, D.; Begley, P.; Zelena, E.; FrancisMcIntyre, S.; Anderson, N.; Brown, M.; Knowles, J. D.; Halsall, A.; Haselden, J. N.; Nicholls, A. W.; Wilson, I. D.; Kell, D. B.; Goodacre, R. Nat. Protoc. 2011, 6, 1060−1083. (20) Folch, J.; Lees, M.; Stanley, G. H. S. J. Biol. Chem. 1957, 226, 497−509. (21) Iverson, S. J.; Lang, S. L.; Cooper, M. H. Lipids 2001, 36, 1283− 1287. (22) Yang, K.; Zhao, Z.; Gross, R. W.; Han, X. PLoS One 2007, 2, e1368. (23) Leek, J. T.; Scharpf, R. B.; Bravo, H. C.; Simcha, D.; Langmead, B.; Johnson, W. E.; Geman, D.; Baggerly, K.; Irizarry, R. A. Nat. Rev. Genet. 2010, 11, 733−739. (24) Wang, S.-Y.; Kuo, C.-H.; Tseng, Y. J. Anal. Chem. 2013, 85, 1037−1046. (25) Han, X. Front. Biosci. 2007, 12, 2601−2615. (26) Liebisch, G.; Binder, M.; Schifferer, R.; Langmann, T.; Schulz, B.; Schmitz, G. Biochim. Biophys. Acta 2006, 1761, 121−128. (27) Yang, W.; Chen, Y.; Xi, C.; Zhang, R.; Song, Y.; Zhan, Q.; Bi, X.; Abliz, Z. Anal. Chem. 2013, 85, 2606−2610. (28) Trimpin, S.; Tan, B.; Bohrer, B. C.; O’Dell, D. K.; Merenbloom, S. I.; Pazos, M. X.; Clemmer, D. E.; Walker, J. M. Int. J. Mass Spectrom. 2009, 287, 58−69. (29) Poad, B. L. J.; Pham, H. T.; Thomas, M. C.; Nealon, J. R.; Campbell, J. L.; Mitchell, T. W.; Blanksby, S. J. J. Am. Soc. Mass Spectrom. 2010, 21, 1989−1999.

8763


Long-Term Performance and Stability of Molecular Shotgun Lipidomic

Recommend Documents