Automated Quantitative Analysis of Complex Lipidomes by Liquid

The di-18:2, 18:0/20:4, 18:0/22:6, di-16:1, di-20:1, and di-22:1 PE species as well as the .... Manual data analysis was carried out by determining th...
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Anal. Chem. 2005, 77, 2166-2175

Automated Quantitative Analysis of Complex Lipidomes by Liquid Chromatography/Mass Spectrometry Martin Hermansson,† Andreas Uphoff,† Reijo Ka 2 kela 2 , and Pentti Somerharju*

Institute of Biomedicine, Department of Biochemistry, University of Helsinki, Helsinki, Finland

Recent advances in mass spectrometry have revolutionized the analysis of lipid compositions of cells and other biomaterials by simplifying the analytical protocol dramatically and by increasing the sensitivity of detection by several orders of magnitude. However, the throughput of the published mass spectrometric methods is severely limited by data analysis, which requires extensive operator involvement. Consequently, we have developed an automated method that allows unattended identification and quantification of lipid molecular species of all the major lipid classes from a two-dimensional chromatographic/ mass spectrometric data set. More than 100 polar lipid species could be automatically quantified from different biological samples with good accuracy and reproducibility. The response was linear over ∼3 orders of magnitude with the equipment used, and ∼35 samples could be analyzed in a day. This method makes high-throughput lipidomics feasible in biology, biotechnology, and medicine. Lipids are essential not only as energy stores or structural components of cellular membranes but also because they play a key role in a variety of biological processes such as signal transduction,1 membrane trafficking and sorting,2 morphogenesis,3 prevention of water loss through the skin,4 and collapse of lung alveolae.5 This diversity of functions probably explains the presence of a great variety of different lipid species in higher organisms. However, relatively little is known about the specific structural requirements set for the lipids involved in the functions listed above. A key reason for this has been the lack of simple and sensitive methods allowing the determination of lipid compositions (and changes therein) of cells and organelles at the level of individual molecular species. Electrospray ionization mass spectrometry (ESI-MS), recently applied to lipid analysis, is now changing all this and thus lays a solid basis for functional lipidomics.6-9 * To whom correspondence should be addressed Telephone: +358-9-191 25410. Fax: +358-9-191 25444. E-mail: [email protected]. † These authors contributed equally to this study. (1) Mills, G. B.; Moolenaar, W. H. Nat. Rev. Cancer 2003, 3, 582-91. (2) Huijbregts, R. P.; Topalof, L.; Bankaitis, V. A. Traffic 2000, 1, 195-202. (3) Pavlidis, P.; Ramaswami, M.; Tanouye, M. A. Cell 1994, 79, 23-33. (4) Bouwstra, J. A.; Honeywell-Nguyen, P. L.; Gooris, G. S.; Ponec, M. Prog. Lipid Res. 2003, 42, 1-36. (5) Piknova, B.; Schram, V.; Hall, S. B. Curr. Opin. Struct. Biol. 2002, 12, 48794.

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ESI-MS-based lipid analysis has already been successfully implemented by a number of groups to investigate various issues of cell biology that would be very difficult or even impossible to study by conventional methods. The topics studied include, for example, intracellular lipid trafficking,10,11 composition of plasma membrane domains,12 and disorders in lipid-mediated signaling.13 Notably, ESI-MS, together with heavy isotope-labeled lipid precursors, has also proven most beneficial in the elucidation of lipid metabolic pathways.14-16 ESI-MS -based lipidomics is a very promising tool for medicine and biotechnology as well (see Discussion). Different approaches have been adopted for the analysis of lipidomes by ESI-MS.8 In one of these, a crude lipid extract is infused to the MS instrument and either direct MS scans or specific precursor ion or neutral-loss scans are used to identify the different lipid species.13,17-20 Another commonly used method (LC-MS) employs liquid chromatography with on-line mass spectrometric detection.21-24 Independent of the approach used, the “bottleneck” in sample throughput is the data analysis, which requires significant operator involvement and thus hampers the ability to screen of extensive sample sets. Notably, current (6) Han, X.; Yang, J.; Cheng, H.; Ye, H.; Gross, R. W. Anal. Biochem. 2004, 330, 317-31. (7) Forrester, J. S.; Milne, S. B.; Ivanova, P. T.; Brown, H. A. Mol. Pharmacol. 2004, 65, 813-21. (8) Pulfer, M.; Murphy, R. C. 2003, 22, 332-64. (9) Welti, R.; Wang, X. Curr. Opin. Plant Biol. 2004, 7, 337-44. (10) Schneiter, R.; et al. J. Cell Biol. 1999, 146, 741-54. (11) Heikinheimo, L.; Somerharju, P. Traffic 2002, 3, 367-77. (12) Blom, T. S.; et al. Biochemistry 2001, 40, 14635-44. (13) Wenk, M. R.; et al. Nat. Biotechnol. 2003, 21, 813-7. (14) DeLong, C. J.; Shen, Y. J.; Thomas, M. J.; Cui, Z. J. Biol. Chem. 1999, 274, 29683-8. (15) Hunt, A. N.; Clark, G. T.; Attard, G. S.; Postle, A. D. J. Biol. Chem. 2001, 276, 8492-9. (16) Boumann, H. A.; et al. Biochemistry 2003, 42, 3054-9. (17) Bru ¨ gger, B.; Erben, G.; Sandhoff, R.; Wieland, F. T.; Lehmann, W. D. Proc. Natl. Acad. Sci. U.S.A. 1997, 94, 2339-44. (18) Hsu, F. F.; Bohrer, A.; Turk, J. J. Am. Soc. Mass Spectrom. 1998, 9, 51626. (19) Ekroos, K.; Chernushevich, I. V.; Simons, K.; Shevchenko, A. Anal. Chem. 2002, 74, 941-9. (20) Han, X.; Gross, R. W. Proc. Natl. Acad. Sci. U.S.A. 1994, 91, 10635-9. (21) Kim, H. Y.; Wang, T. C.; Ma, Y. C. Anal. Chem. 1994, 66, 3977-82. (22) Taguchi, R.; Hayakawa, J.; Takeuchi, Y.; Ishida, M. J. Mass Spectrom. 2000, 35, 953-66. (23) Larsen, A.; Mokastet, E.; Lundanes, E.; Hvattum, E. J. Chromatogr., B Anal. Technol. Biomed. Life Sci. 2002, 774, 115-20. (24) Kurvinen, J. P.; Aaltonen, J.; Kuksis, A.; Kallio, H. Rapid Commun. Mass Spectrom. 2002, 16, 1812-20. 10.1021/ac048489s CCC: $30.25

© 2005 American Chemical Society Published on Web 02/22/2005

commercial MS software are ill-suited for quantitative lipidome analyses because of the great number of lipid species in most samples, as well as the various corrections required for precise quantification.12,25 To resolve this key problem in quantitative MS lipidomics, we have constructed a computerized method that, for the first time, allows automated analyses of large sample sets and, therefore, makes high-throughput lipidomics feasible. MATERIALS AND METHODS Nomenclature. Abbreviations used in this text are as follows: phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylserine (PS), phosphatidylinositol (PI), phosphatidic acid (PA), phosphatidylglycerol (PG), lysobisphosphatidic acid (LBPA), galactosylceramide (GalCer), R-hydroxylgalactosylceramide (R-OH-GalCer), sphingomyelin (SM), mass spectrometry (MS), liquid chromatography (LC), thin-layer chromatography (TLC), tandem MS (MS/MS), mass-to-charge ratio (m/z), and relative standard deviation (RSD). Cells and Tissues. Chinese hamster ovary cells type K-1 were cultivated as outlined previously.26 The brains and liver of C57BL mouse were kindly made available by Dr. Ulla Lahtinen (Folkha¨lsan, Department of Molecular Genetics, Helsinki, Finland). The mice were fed with Altromin Z 1324 standard mouse diet. The mice at the age of 4-6 months were stunned on dry ice and decapitated. The brain and liver were removed, washed twice in ice cold sucrose, and frozen in liquid nitrogen. The frozen organs were then homogenized in ice cold 0.25 M sucrose with an UltraTurrax homogenizer, and the lipids were extracted immediately as described below. Lipid Standards. The synthetic di-14:0, di-16:1, di-16:0, di-18: 2, di-18:1, 18:0/20:4, 18:0/22:6, di-20:1, di-21:0, and di-22:1 PC species and the di-14:0, di-16:0, di-21:0, and di-18:1 PE species were purchased from Avanti Polar Lipids (Alabaster, AL). The di-18:2, 18:0/20:4, 18:0/22:6, di-16:1, di-20:1, and di-22:1 PE species as well as the di-16:1, di-20:1, and di-22:1 PS species were synthesized from the corresponding PC species using phospholipase D (Streptomyces species, Sigma)-mediated transphosphatidylation.27 The di-14:1 and di-22:1 PA species were prepared from the corresponding PC species by phospholipase D (Streptomyces chromofuscus, Sigma)-mediated hydrolysis. The PI 34:2 and 36:2 species were isolated from total yeast PI by chromatography on a Thermo Hypersil Keystone BetaMax neutral reversed-phase column (150 × 4.6 mm, 5-µm particle size) eluted with 1-5% H2O in methanol at 1 mL/min. The 15:0, 21:0, and 25:0 SM species were synthesized from sphingosylphosphorylcholine (Matreya Inc, Pleasant Gap, PA) and the respective fatty acids (Larodan AB, Malmo¨, Sweden) as detailed previously.12 The lipid standards were dissolved in chloroform/methanol (9:1) and were stored in silanized screw-cap tubes at -20 °C. Their concentrations were determined by phosphate analysis.28 Lipid Extraction. The lipids of cells or the tissues homogenized in chloroform/methanol/water (2:1:0.05; v/v) were extracted according to Folch et al.29 but omitting the salt. The (25) Zacarias, A.; Bolanowski, D.; Bhatnagar, A. Anal. Biochem. 2002, 308, 1529. (26) Heikinheimo, L.; Somerharju, P. J. Biol. Chem. 1998, 273, 3327-35. (27) Ka¨kela¨, R.; Somerharju, P.; Tyynela¨, J. J. Neurochem. 2003, 84, 1051-65. (28) Bartlett, E. M.; Lewis, D. H. Anal. Biochem. 1970, 36, 159-67. (29) Folch, J.; Lees, M.; Sloane Stanley, G. H. J. Biol. Chem. 1957, 226, 497509.

extracts were spiked at the one-phase stage with a cocktail of standards for different lipid classes. After evaporation under nitrogen flow, the lipids were dissolved either in the LC solvent or in chloroform/methanol (1:2) for direct infusion experiments. In the latter case, 1% NH4OH was added just prior to the analysis. Thin-Layer Chromatography. Thin-layer chromatography was carried out on silica gel plates (Merck, Darmstadt, Germany) developed with chloroform/methanol/acetic acid/formic acid/ water (70/30/12/4/2; v/v). After visualization with iodine vapor, the lipids were scraped from the plate and their phosphate content was determined.28 Reported data represent the mean of three replicate analyses. Liquid Chromatography-Mass Spectrometry. The on-line chromatographic separations were carried out isocratically at room temperature by using the Ultimate nano-HPLC system equipped with the Famos autosampler (LC Packings, Amsterdam, The Netherlands) and a Lichrosphere diol-modified silica column (250 × 1 mm; 5-µm particles). The autosampler system was rinsed between injections. In most experiments, the solvent consisted of hexane/2-propanol/water/formic acid/triethylamine (628:348: 24:2:0.8; v/v) and was eluted at 50 µL/min. In the cone fragmentation experiments, the solvent proportions were changed to 705: 280:15:2:0.8 in order to increase the separation of the lipid classes. This system has several advantages such as nearly symmetric peak shape27 and a good retention time stability ((2%), both being useful features when the computerized signal detection and assignment was used. This column matrix is robust, allowing hundreds of injections with only modest changes in peak shape or retention times. Isocratic, rather than gradient, elution was used to avoid complications due to solvent composition-dependent variations of lipid ionization efficiency (unpublished data). An additional advantage of the isocratic system is that no equilibration time is needed after the run. Although some polar lysolipids elute after the normally used run time of 40 min, thus appearing in the subsequent sample, this does not interfere with the analysis of the intact lipids due to the much lower masses of the lysolipids. If such lysolipids are of interest, they can be readily analyzed by extending the elution time by 20-30% or by using a shallow flow gradient. As a technical note, we have found that the recovery of lipids from a new column is initially significantly smaller than from a used one. This can be avoided, for instance, by saturating the column by multiple injections of a relatively concentrated extract of natural lipids. The column eluent was introduced to the standard electrospray source of a Quattro Micro triple-quadrupole mass spectrometer (Micromass, Manchester, U.K.) operated in the negative ion mode, which allowed the detection of all the glycerophospholipid classes and some sphingolipid classes as well. PA, PE, PS, PI, and sulfatide species were detected as [M - H]- ions and PC, SM, and GalCer species as [M + HCOO]- ions. Nitrogen was used as the nebulizer (500 L/h at 130 °C) and cone gas (50 L/h). The source temperature was set at 90 °C, and the potentials of the cone, extractor, and rf lens were 40, 2, and 0.3 V, respectively. For cone fragmentation, a cone voltage of 80 V was used. The capillary voltage was 3.8 kV. The spectra were scanned from 200 or 500 to 1000 m/z with a frequency of 1 scan/2 s. Analytical Chemistry, Vol. 77, No. 7, April 1, 2005

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For display, the data were smoothed using a 4- or 2-point running average in time and mass direction, respectively. Tandem Mass Spectrometry. The crude lipid extracts in 1:2 chloroform/methanol were infused into the source at the flow rate of 6 µL/min. The instrument settings were essentially as described above, except the collision energy was set to 25-65 eV and positive ion mode was used as well. The PC, SM, PE, PS, PI, GalCer, and sulfatide species were selectively detected using classspecific MS/MS scanning modes.17,18,30 Argon was used as the collision gas. Data Analysis. The computerized data analysis protocol developed here is rather involved and is therefore described in detail in the Supporting Information. Briefly the process goes as follows. First, all relevant signals are extracted from the data by sequentially fitting a three-dimensional signal model (a massdependent generic isotope pattern) to the highest peak, subtracting the fit, and repeating the process. The process is repeated until the signal height reaches a threshold of 2 times the background level or until 10 consequent signals have been identified as noise spikes. After removing artifacts from the found signals, the fit is refined to account for signal overlap and deviations of the isotopic pattern from the assumed generic pattern. Second, the found signals are assigned to lipid species. The assignment relies on the manual assignment of at least two lipid species (reference compounds) in a class for one chromatogram (reference chromatogram) in a series of related runs. The algorithm first finds the reference compounds based on (i) their relative retention times within the respective class and (ii) the relative retention time order of lipid classes. When the reference compounds have been found, a polynomial relating retention time to fatty acid chain length is created to predict the retention time of other species in the same class. The prediction assumes a relative retention time shift per double bond that is the same for all classes in the chromatogram. In the next step, all signals that have the correct m/z and lie near the predicted retention time are assigned to the appropriate lipid species. Third, the assigned signals are analyzed quantitatively. The signal areas are first adjusted according to the compound-specific isotope pattern by dividing the area of the first isotope by its theoretical relative abundance.31 Then an instrument response versus m/z function is derived based on the intensities obtained for the internal standards,17,32 and finally, this function is used to calculate the amounts of the analytes. The algorithm has been realized using MatLab 6.5 (The MathWorks) and Smodels, a logic programming system developed at the Helsinki University of Technology.33 Manual data analysis was carried out by determining the elution range of each PL class by visual inspection of the data in the 2-D “map” format provided by the MassLynx 4.0 software. The spectra corresponding to the elution range of each lipid class were combined, and the peak intensities were corrected for any (30) Sullards, M. C.; Merrill, A. H., Jr. Sci. STKE 2001, 2001, PL1. (31) Han, X.; Gross, R. W. Anal. Biochem. 2001, 295, 88-100. (32) Koivusalo, M.; Haimi, P.; Heikinheimo, L.; Kostiainen, R.; Somerharju, P. J. Lipid Res. 2001, 42, 663-72. (33) Simons, P.; Niemela¨, I.; Soininen, T. Artif. Intelligence 2002, 138, 181234.

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overlap by isotope or adduct peaks of other molecular species after careful inspection of the respective ion chromatograms. The peak intensities were then corrected to account for the 13C-isotope effect,31 and since the 13C-isotopic correction does not fully account for the acyl chain length dependency of the instrument response under the conditions used, an additional correction was performed based on the peak intensities of the included standards.12,27 RESULTS A typical two-dimensional MS chromatogram obtained from 2 nmol of mouse brain extract is shown in Figure 1a. A threedimensional version of this plot can be found in Figure S-1 (Supporting Information). The chromatogram contains ∼800 peaks, not all of which are readily visible in the printed figure. Since each compound gives rise to a pattern of several isotopic peaks (hereafter referred to as “signal”), these 800 peaks could in principle represent some 200-300 different lipid species. However, not all of these signals arise from different species, since some lipids can occur, for example, as a deprotonated molecule as well as an adduct, typically a formate adduct. The signals of different species frequently appear as overlapping patterns as shown in the magnified view in Figure 1c,d. This overlap, the large number of signals, the presence of adducts and (unavoidable) inter-run retention time variations ((2%) make the identification and accurate quantification of the individual lipid species a challenging and very time-consuming task. Therefore, we constructed a computer program, which after an initial supervised assignment of so-called “reference compounds” (added and/or endogenous lipids) in a reference chromatogram fully automatically (i) finds and determines the area of all relevant signals, (ii) assigns these signals to particular lipid species, and (iii) quantifies them based on a calibration function defined by the lipid standards included in the sample. As a result, an unlimited number of samples that are compositionally similar (or simpler) than the reference sample can be processed without any operator involvement. A detailed description of this program is given in the Supporting Information. The method developed here is taking advantage of the twodimensional nature of LC-MS data, which makes deconvolution and integration of complex signal patterns more accurate than if only spectral data are available. The quality of signal fitting obtained was very good as shown in Figure 1e,f and evidenced by tests carried out with simulated data (see Figure S-3, Supporting Information). In the MS chromatogram shown in Figure 1a, the automatic method detected 353 potential lipid signals (peak patterns), of which 136 were assigned to different lipid species belonging to 11 different lipid classes (color coded in Figure 1b). When the assignment was carried out manually, seven additional, very minor signals could be assigned. On the other hand, the computerized method correctly assigned five minor signals missed upon manual assignment. Among the 136 automatically assigned signals only 3 were found incorrect, while 2 signals were assigned to 2 alternative species one of which was the correct one. The average number of assigned signals in a set of 5 replicates was 137 out of which 112 were assigned in all cases. The assignments were compared and in a good agreement with data obtained using class-specific precursor ion or neutral loss MS/MS scanning.17,18,30 The reproducibility of the assignment was examined by analyzing 22 replicates of a mouse brain lipid extract (0.5-10 nmol

Figure 1. Two-dimensional display of LC-MS data obtained for mouse brain lipid extract. (a) Negative ion mode 2-D LC-MS data. (b) Assignment of the signals shown in panel a. The lipid classes from left to right are as follows: blue at ∼7 min, PA; black, GalCer; gray, R-OHGalCer; red, PE plasmalogens, green at ∼12 min, PE; orange, PC; violet, SM; yellow, sulfatides; pink, R-hydroxysulfatides; green at ∼25 min, PS and blue at ∼38 min, PI. (c) Enlarged view of the region indicated by the rectangle in panel a. (d) A 3-D view of the data in panel c showing the projections in the time (left wall) and mass (right wall) directions. (e) Recorded (blue line) and fitted (red line) ion chromatogram for m/z 774.2 (PE alkenyl-40:6) at 7-14 min. (f) Recorded (blue line) and fitted (red line) mass spectra at 10.5 min corresponding to the area shown in panel a.

of total phospholipid injected). It was found that all of the species representing >3 pmol were assigned correctly at least 20 times out of 22. Even many less abundant species (>1 pmol) were found reliably (g19/22). Statistical analysis indicated that signals exceeding 10 000 counts in height, i.e., ∼2-4 pmol depending on the peak shape and instrument response for the lipid in question, were correctly assigned in >95% of the replicates (see Figure S-5, Supporting Information). Accuracy and Reproducibility of Quantification. The quantitative accuracy and reproducibility of the automated analysis were determined by repeated analysis of an equimolar mixture of synthetic PC species (1-400 pmol each) with varying acyl chain length and unsaturation. In addition, internal standards, i.e., di-

16:1, di-20:1, and di-22:1 PC, were included at 5-fold concentrations in order to mimic the situation with the biological samples. Figure 2a shows that a good accuracy and reproducibility, as indicated by the error bars, was obtained with the automated method when 30 pmol of each PC species was injected. On average, the determined value deviated from the nominal one by 3.1% (n ) 10), the highest deviation being 13%. However, it is important to note that these deviations are not related to the computerized data analysis since essentially identical results were obtained with manual analysis. The deviations are probably due to several factors including inaccuracies in pipetting as well as acyl chain structure-dependent differences in instrument response.12 Analytical Chemistry, Vol. 77, No. 7, April 1, 2005

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Figure 2. Test of Accuracy and Reproducibility. The dashed horizontal lines indicate the targeted value. Error bars indicate the standard deviation. (a) Accuracy of the method. (b) The effect of the total lipid concentration on the accuracy. (cf. Figure 2a). The total amount of phospholipid is indicated on the X-axis. (c) Comparison of the phospholipid class compositions of CHO-K1 obtained with the LC-MS method with either computerized (white bars) or manual (gray bars) data analysis or with TLC and phosphate analysis (black bars).

We also tested the effect of the total lipid concentration on the accuracy of the method. The ratios of the determined to the nominal value, averaged over all the PC species (n ) 80; 8 species, 10 determinations), are plotted against the amount of total of PC injected in Figure 2b. As can be seen, the determined values were 2170 Analytical Chemistry, Vol. 77, No. 7, April 1, 2005

very close to the nominal ones when 0.23-2.3 nmol of total PC was injected. At higher amounts, the determined value exceeded the nominal one by ∼10%, most likely due to differential leveling off of the instrument response for the standards versus the analytes.12 Essentially identical results were obtained for PE species (data not shown). These data suggest that the amount of lipid injected should be kept within certain limits to obtain optimal accuracy. To further assess the accuracy of the computerized method, we determined the phospholipid class composition of CHO-K1 cells and compared the results with those obtained by manual analysis of the LC-MS data and by TLC/phosphorus analysis (Figure 2c). A close agreement among all three methods was found, thus indicating that automated LC-MS provides accurate and reproducible data on complex samples as well. The average RSD for all the analyzed species in the CHO-K1 lipid extract was 6.9% when 2 nmol was injected (Table S-1). Dynamic Range and Detection Limits. The dynamic range/ linearity of the method was determined by analyzing different amounts of mouse liver lipid extract. Even though the peak intensities started to level off above ∼5 nmol of total phospholipid injected (data not shown), the instrument response for the analyte species studied relative to those for the internal standards remained essentially constant up to at least 12 nmol of total phospholipid injected (Figure 3). Thus, the range of linear relative response covered at least 3 orders of magnitude. The detection limit of the method was determined by injecting variable amounts of the mixture of synthetic PC species. The detection limit was defined as the minimum amount of a lipid species required to maintain the error of determination below 30%. By this definition, the detection limit was found to be ∼1 pmol of a species injected. Since PC has the lowest response factor of all classes studied, all significant (>2‰ of total phospholipid) species of PC, PE, PS, PI, SM, and PA can be determined with reasonable accuracy from only 0.5 nmol of total phospholipid. Notably, the detection limit could be pushed down remarkably by using a microcolumn and a nanospray interface (see Discussion). The time of analysis is determined by the time required for the chromatographic separation of the lipid classes and is ∼40 min. Thus, ∼35 samples can be analyzed per day. The throughput could be increased significantly by, for example, increasing the polarity of the solvent, the rate of elution, or both. However, we found that such modifications may result in some loss of performance, particularly with complex samples such as the mouse brain lipid extract (data not shown). Application to Biological Samples. During the development of the method, different biological samples, i.e., the mouse brain and liver as well as a CHO cell line, were analyzed to obtain information on the performance. The capability of the method is particularly well demonstrated with mouse brain, which has a complex polar lipid composition (cf. Figure 1). Beside the common phospholipid classes, galactosylceramides and sulfatides containing nonhydroxy or hydroxy fatty acid residues were present. As shown in Table 1, the results obtained with the automated method were in close agreement with those obtained from manual data analysis and, when applicable, with those published previously,34 thus supporting the reliability of the computerized method. The (34) Guan, Z.; et al. J. Neurochem. 1996, 66, 277-85.

species (Figure 4b). PS 40:6 gave abundant PA 40:6 (due to neutral loss of serine36), lyso-PA 18:0, FA 22:6, and FA 18:0 fragments, thus identifying the parent lipid as the 18:0/22:6 species (Figure 4c). Parallel results were obtained for the other natural glycerophospholipids studied. The analysis of synthetic positional isomers showed that the lysolipid with the sn-1 chain attached was >5fold more abundant than the lysolipid with the sn-2 acyl chain (data not shown), which is consistent with previous studies.35,37,38 While the identification and quantification of the fragment ions is straightforward in the case of, for example, PS and PI, the situation is more complex with classes such as PA and PE because several species eluting close to each other produce the same lysolipid or fatty acid fragment. Due to these and other complications (see Discussion), quantification of isobaric species or positional isomers was not attempted here.

Figure 3. Linearity and dynamic range. The amount of found species (pmol) vs the total lipid injected. (a) For a minor PC and PE species, (b) for a major PC and PE species, and (c) for a PS and a PI-species. The straight lines represent linear fits to the data. The coefficients of determination are also shown. The error bars indicate the standard deviation (n ) 5).

mouse liver and CHO-K1 lipid compositions are shown in Tables S-1 and S-2 (Supporting Information), respectively. Cone Fragmentation Provides Information on the Acyl Residues. As is the case with most of the commonly used MS methods, the present method does not directly identify the acyl residues or their positions in the lipid molecules, but rather provides the total number of acyl chain carbons and double bonds. However, information on the acyl chain composition can be obtained by inducing partial fragmentation of the lipid molecules at the cone, i.e., before they enter the MS analyzer.23,35 As shown in Figure 4, significant amounts of diagnostic fragments, such as lysolipids and fatty acids, “coeluting” with the unfragmented parent glycerophospholipids were observed under such conditions. For example, PE 38:4 fragmented to lyso-PE 18:0, FA 20:4, and FA 18:0, thus allowing the identification of this lipid as the 18:0/20:4 (35) Brouwers, J. F.; Vernooij, E. A.; Tielens, A. G.; van Golde, L. M. J. Lipid Res. 1999, 40, 164-9.

DISCUSSION We describe here a computerized LC-MS method that, for the first time, allows for a fully automated, quantitative analysis of complex lipidomes. Such an automatic method is a necessity for any large-scale routine application simply because the great body of data produced in any MS-based analysis precludes manual data analysis. The method was shown to be generally applicable as compositionally different samples could be analyzed reliably (see also Supporting Information). Data derived from various mammalian tissues and cell lines are shown in Results and in the Supporting Information. We also used this method successfully to analyze the lipid composition of bacteria and bacteriophages (data not shown). The accuracy and reproducibility of the method were found to be good and the linearity and sensitivity fully adequate for a great majority of anticipated applications. We will now discuss the various aspects of this method in more detail and also indicate some potential applications. The computerized data analysis protocol is the key component of the present method and it consists of three main tasks: (1) finding and fitting of the relevant chromatographic signals, (2) assignment of the signals to specific lipid species, and (3) quantification of those species. Signal Finding and Fitting. Although analysis of chromatographic peaks has been addressed in many previous studies,39,40 the present study represents, to the best of our knowledge, the first attempt to fit LC-MS data in its native 2-D format. The advantage of this approach is that complex lipidomes, like that of the mouse brain, can be analyzed more accurately than if the LCMS data are analyzed in the spectral format.24,27 This is due to the fact that even when signals of different species overlap in the mass dimension, they are usually separated in time (see Figure 1c) to a degree that allows reliable assignment and quantification (see Supporting Information). The automated signal detection protocol tended to miss some very minor lipid signals (3%) was 4.0% and for the small ones (100-fold) increase in sensitivity is likely to be achieved by using a capillary column (41) Rissanen, J. IEEE Trans. Inf. Theory 2000, 46, 2537-2543.

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with a diameter of 75-100 µm and a nanospray interface.42 Then, a full lipid profile could be determined accurately from a sample containing less than 20 pmol of total phospholipid. (42) Abian, J.; Oosterkamp, A. J.; Gelpı´, E. J. Mass Spectrom. 1999, 34, 244254.

Figure 4. On-line fragmentation of mouse brain extract. (a) Negative ion mode 2-D LC-MS data. (b) Ion chromatograms for masses corresponding to PE 38:4, LPE 20:4, LPE 18:0, FA 20:4, and FA 18:0. All chromatograms are scaled identically, and their peak positions in the 2-D map are indicated by arrows. The vertical line indicates the peak of elution of PE 38:4. (c) Ion chromatograms from masses corresponding to PS 40:6 and its fragments PA 20:4, LPA 22:6, LPA 18:0, FA 22:6, and FA 18:0. The vertical line indicates the peak of elution of PS 40:6.

Signal Assignment. Assignment of the signals was by far the most demanding task while the present method was being developed. This is largely due to the method being designed to be generally applicable (i.e., independent of the number of the lipid classes and/or individual species present in the samples) and to accommodate both inter-run variations of retention times and the absence of certain reference lipids, i.e., assignment standards. Ambiguous assignments may arise when two lipid classes elute close to each other and contain species of equal mass. However, once the operator has resolved such ambiguities for the reference chromatogram, which typically takes only a few minutes, all subsequent samples can be analyzed fully automatically. Quantification. For accurate quantification of lipids by MS, it is essential to use internal standards for which the instrument response is as similar as possible to that of the analytes.8 Previous studies indicate that the response factor is very sensitive to

variations in headgroup structure as well as acyl chain length and unsaturation, particularly at higher total lipid concentrations.12,25 Thus, for best accuracy, several standards for each lipid class should be included. To avoid the complication of obtaining several standards for each lipid class, some investigators have chosen to operate at low total lipid concentration. While relatively accurate results could be obtained this way, we prefer to include several internal standards per lipid class since (i) a greater number of minor lipid species can be analyzed when using higher total lipid concentrations and (ii) this relaxes the requirement to strictly control the total lipid concentration. Yet another important benefit of several added standards, particularly in the present context, is that they are very helpful for the assignment. While the current version of the method does not allow one to quantify isobaric species with different fatty acid residues or positional isomers, it seems possible to develop it further to accomplish this as well. This is indicated by the data in Figure 4 Analytical Chemistry, Vol. 77, No. 7, April 1, 2005

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showing fatty acid and lyso fragments “coeluting” with the parent lipid, thus allowing positive identification of the fatty acid residues. The marked prevalence of the lyso fragment with the sn-1 acyl chain attached over that with the sn-2 acyl chain in turn allows one to determine the sn position of the acyl residues. Automated analysis of the cone fragmentation data is outside the scope of the present study since extensive work would be needed to determine the structure-dependent fragmentation efficiencies36 of different molecular species as well as to extend the method to allow the analysis of the more complex data sets produced. In addition, it is likely that reliable quantification of isobaric species and positional isomers requires the use of a different chromatographic matrix, e.g., the reversed phase, providing better separation of the species within a lipid class.23,35 Comparison with MS/MS Methods. Beside LC-MS, MS/ MS methods involving direct infusion of a crude lipid extract to a triple-quadrupole instrument and specific precursor ion and neutral-loss scans are commonly used for lipidome analysis.43 Although not yet implemented, such methods could also be computerized as proposed recently.6 The particular virtues of the MS/MS methods are that no chromatographic preseparation is needed, and second, metabolic studies with heavy isotope-labeled lipid precursors are straightforward.11,14-16 Furthermore, the data acquisition time is probably shorter as compared to LC-MS, albeit this is clearly dependent on the instrument available and the number of different MS/MS scans needed. On the other hand, the MS/MS approach has some disadvantages as compared to the LC-MS method used here. First, the quantification of some SM species by MS/MS is hampered due to extensive mass overlap with the much more abundant PC species. Thus, prior removal of the PC species by alkaline hydrolysis seems necessary for accurate determination of SM by this method. In contrast, the LC-MS method allows facile analysis of all SM and PC species, since the classes are well separated in time (cf. Figure 1). Another problematic case for the MS/MS method is the analysis of PG and LBPA classes, since PG and LPBA species with identical fatty acids are isobaric. These species can, however, be readily resolved with the LC-MS approach due to their different elution times. Finally, when the MS/MS method is used, the major lipid species or impurities present in the lipid extract can hamper the detection of the minor species by suppressing their ionization.8 In our experience, particularly PA, PS, and PI species can be analyzed with higher sensitivity with LC-MS than MS/MS (unpublished data). However, this may be dependent on the instrumentation and therefore warrants further studies. Analysis of Other Lipid Classes. While the present method is focused on phospholipids, it could be applied to analyze other lipid classes as well. For instance, preliminary tests show that by replacing triethylamine by ammonia, several neutral lipid classes, e.g., triacylglycerols, diacylglycerols, cholesterol esters, and ceramides, could be detected with high sensitivity in the positive ion mode. (Incidentally, also galactosyl- or glucosylceramides, lactosylceramides, and sphingomyelins as well as phosphatidylcholines can be detected with higher sensitivity in this mode.) However, automated analysis of these less polar lipids would require modifications to the chromatographic system, i.e., to use (43) Han, X.; Gross, R. W. J. Lipid Res. 2003, 44, 1071-9.

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a less polar solvent or a reversed-phase column in order to obtain adequate chromatographic separation. On the other hand, a more polar solvent would be needed for the analysis the polyphosphoinositides and other highly polar lipids such as gangliosides. In the case of complex gangliosides or other lipids with a high molecular weight, some modification of the signal fitting algorithm (designed for the analysis of lipids with a molecular mass of less than ∼1500 Da) would be needed. Modifications would also be necessary for lipids appearing as doubly or multiply charged species. Such lipids, however, seem to be rare as also indicated by the fact that even polyphosphoinositides appear as singly charged species.13 Applications. The automated LC-MS-MS method developed here will be most useful whenever analysis of large sample sets is necessary. In cell biology, this method allows one to screen the effects of a large number of effectors, mutations, or growth conditions on the cellular lipid composition. Such studies are instrumental for resolving the mechanisms of lipid compositional regulation. In the clinic, the method could be used for routine screening of blood or other tissue samples in order to diagnose hereditary diseases,27,44-46 certain cancers,47 or multifactorial diseases such as the metabolic syndrome and arteriosclerosis.48 In biotechnology, the method would allow high-throughput screening of engineered lipases and, with some modifications, monitoring and processing of fats, oils, or bioproducts containing these substances. It could also provide a powerful tool for the identification of bacteria and other microorganisms due to their unique lipidomes.49 ACKNOWLEDGMENT We are grateful to Tomi Janhunen for assistance with the Smodels system, to Jukka Heikkonen for advice on data processing and optimization with Matlab, to Ulla Lahtinen for providing the mouse tissues, to Tarja Grundstro¨m for skillful technical assistance, and to Perttu Haimi, Sarah Siggins, Vesa Olkkonen, Tuomas Haltia, and Elina Ikonen for critical reading of the manuscript. This study was supported by grants from the Finnish Academy (44236), from the University of Helsinki Funds to P.S., and from the Sigrid Juselius Foundation to M.H. and P.S. SUPPORTING INFORMATION AVAILABLE Detailed description of the data analysis algorithm. Tables showing the lipid composition of CHO cells (Table S-1) and the lipid composition of mouse liver (Table S-2); 3-D display of the LC-MS data shown in Figure 1 (Figure S-1); Figure showing the weighting factors used in data fitting (Figure S-2); figure illustrating the signal fitting procedure using simulated data with added noise (Figure S-3); figure illustrating the ordering of lipid classes done in the algorithm (Figure S-4), and figure showing the (44) Han, X.; Holtzman, D. M.; McKeel, D. W., Jr. J. Neurochem. 2001, 77, 116880. (45) Valianpour, F.; Wanders, R. J.; Barth, P. G.; Overmars, H.; van Gennip, A. H. Clin. Chem. 2002, 48, 1390-7. (46) Dombrowsky, H.; Clark, G. T.; Rau, G. A.; Bernhard, W.; Postle, A. D. Pediatr. Res. 2003, 53, 447-54. (47) Shen, Z.; et al. Gynecol. Oncol. 2001, 83, 25-30. (48) Clish, C. B.; et al. Omics 2004, 8, 3-13. (49) Laurinavicˇius, S.; Ka¨kela¨, R.; Somerharju, P.; Bamford, D. H. Virology 2004, 322, 328-36.

probability of finding a signal as function of the signal height (Figure S-5). This material is available free of charge via the

Received for review October 13, 2004. Accepted January 13, 2005.

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