J S S
ISSN 1615-9306 · JSSCCJ 38 (12) 2007–2192 (2015) · Vol. 38 · No. 12 · June 2015 · D 10609
JOURNAL OF
SEPARATION SCIENCE
Methods Chromatography · Electroseparation Applications Biomedicine · Foods · Environment
12 15
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2033
J. Sep. Sci. 2015, 38, 2033–2037
Kazuhiro Sonomura1,2 Shinobu Kudoh3 Taka-Aki Sato2 Fumihiko Matsuda1 1 Center
for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan 2 Life Science Research Center, Technology Research Laboratory, Shimadzu Corporation, Kyoto, Japan 3 Pharmaceuticals and Life-Science Division, Shimadzu Techno-Research, Kyoto, Japan Received December 16, 2014 Revised March 24, 2015 Accepted March 27, 2015
Short Communication
Plasma lipid analysis by hydrophilic interaction liquid chromatography coupled with electrospray ionization tandem mass spectrometry A novel method for the analysis of endogenous lipids and related compounds was developed employing hydrophilic interaction liquid chromatography with electrospray ionization tandem mass spectrometry. A hydrophilic interaction liquid chromatography with carbamoyl stationary phase achieved clear separation of phosphatidylcholine, lysophosphatidylcholine, sphingomyelin, ceramide, and mono-hexsosyl ceramide groups with good peak area repeatability (RSD% < 10) and linearity (R2 > 0.99). The established method was applied to human plasma assays and a total of 117 endogenous lipids were successfully detected and reproducibly identified. In addition, we investigated the simultaneous detection of small polar metabolites such as amino and organic acids co-existing in the same biological samples processed in a single analytical run with lipids. Our results show that hydrophilic interaction liquid chromatography is a useful tool for human plasma lipidome analysis and offers more comprehensive metabolome coverage. Keywords: Electrospray ionization tandem mass spectrometry / Hydrophilic interaction liquid chromatography / Liquid chromatography / Plasma Lipids DOI 10.1002/jssc.201401440
Additional supporting information may be found in the online version of this article at the publisher’s web-site
1 Introduction Lipids are involved in many physiological processes and their biological functions are closely related to the onset of health problems especially metabolic diseases [1,2]. The risk for and progress of metabolic diseases are traditionally evaluated by low- and high-density lipoprotein cholesterol and triglyceride levels in blood. The recent development of MS combined with various separation techniques allows the detection of individual lipid molecules, and its application to the measurement of circulating lipid concentrations has been investigated for biomarker development [3–8]. Biological lipids are generally classified into eight categories, and each category is divided into classes and subclasses precisely defined by the LIPID MAPS consortium Correspondence: Dr. Taka-Aki Sato, Life Science Research Center, Technology Research Laboratory, Shimadzu Corporation, 3-9-4, Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0237, Japan E-mail: [email protected] Fax: +81-774-95-1619
Abbreviations: NPLC, normal phase liquid chromatography; PC, phosphatidylcholine; LPC, lysophosphatidylcholine; Cer, ceramide; SM, sphingomyelin; MHC, mono-hexsosyl ceramide; SRM, selected reaction monitoring; CE, collision energy
C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
according to the backbone structure containing polar head groups [9]. Various fatty acyl chains bonded to the backbone generate numerous unique lipid molecules. A great number of lipid species have been identified in plasma, and 500 or more of them have been quantified [10]. Owing to this structural diversity of lipids, comprehensive analysis of lipids in a complex biofluid with a single method remains a challenge. Various separation techniques coupled with MS have been reported for lipidomics such as GC, LC, SFC, and CE [10–15]. Among these, LC–API-MS stands out because of its applicability to a wide range of lipid species. Two separation modes, RPLC and normal-phase liquid chromatography (NPLC) are widely used in lipid analysis. In RPLC, lipid molecules are separated according to their hydrophobicity, determined mainly by fatty acyl chain length, and lipids from different classes are sometimes co-eluted. Therefore, abundant lipid species such as phosphatidylcholine would cause ion suppression or enhancement of other co-eluted lipid species. On the other hand, lipid molecules are separated with NPLC according to the polar head group common in the relevant lipid class and roughly resolved into each lipid class. However, the chloroform or hexane used as mobile phase in NPLC are hazardous solvents and sometimes cause ion suppression. HILIC offers chromatographic separation similar to that obtained by NPLC with the use of an ESI-compatible solvent system such as acetonitrile/water. This chromatographic technique has become
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popular for the retention and separation of polar small molecular compounds. Its application has gradually been extended to lipid analysis [16–24]. The applications utilize mostly a bare or diol-modified silica gel irrespective of the fact that various types of stationary phases are available for HILIC. One of the practical difficulties in use of a bare silica gel column is that it sometimes requires a high salt concentration to achieve adequate resolution with good peak shapes, which leads to salt precipitation in the ion source of mass spectrometer and the resultant ion suppression. We have also tried several types of stationary phases including bare silica gel for lipid research and convinced with the applicability of the carbamoyl-bonded silica gel column for plasma lipid profiling. It achieved a clear separation of five lipid classes; namely, phosphatidylcholine (PC), lysophosphatidylcholine (LPC), sphingomyelin (SM), ceramide (Cer), and mono-hexsosyl ceramide (MHC). The system showed good peak area repeatability and linearity for standards from each lipid class. We applied this method to analysis of commercially available human plasma and successfully detected a total of 117 lipid molecules from the five lipid classes. Both inter- and intra-day reproducibility were then evaluated for endogenous Cer peaks normalized with a selected internal standard. In addition, we envisioned the simultaneous detection of small polar metabolites by using the same HILIC–ESI-MS/MS method in a single analytical run.
2 Materials and methods 2.1 Reagents and standards Water used throughout the study was produced by a Milli-Q system (Millipore). LCMS-grade acetonitrile, methanol, and ammonium formate were purchased from Sigma–Aldrich (St. Louis, Missouri, USA). Chromatographic separation was conducted on a TSK amide-80 column (150 × 2.0 mm, 3 mM, Tosho). 1-Dodecanoyl2-tridecanoyl-sn-glycero-3-phosphocholine (PC_25:0), 1-tridecanoyl-sn-glycero-3-phosphocholine (LPC_C13:0), N-(dodecanoyl)-sphing-4-enine (Cer_d18:1/12:0), and N(dodecanoyl)-1-b-glucosyl-sphing-4-ene (MHC_d18:1/12:0) were obtained from Avanti polar lipids (Birmingham, AL). N-palmitoyl-D-sphingomyelin (SM_C34:0) was purchased from Sigma–Aldrich. Human plasma was purchased from KAC (Kyoto, Japan).
122, 244 nM, SM_C34:0, 1.07, 2.13, 10.7, 21.3, 107, 213 nM. For plasma lipid analysis, 180 L of methanol was added to 20 L of plasma sample and mixed vigorously. The solution was centrifuged at 16 000 × g for 10 min at 25⬚C. The supernatant was collected into a glass vial and 1 L aliquot of each sample was subjected to HILIC–ESI-MS/MS analysis.
2.3 LCMS conditions LC analysis was performed with a Shimadzu Nexera UHPLC system, which consisted of a CBM-20A controller, two LC30AD pumps, a DGU-20 A5 degassing unit, a CTO-20A column oven, and a SIL-30AC autosampler. The LC system was coupled with a triple-quadruple mass spectrometer LCMS8040 (Shimadzu, Kyoto, Japan). The mobile phase consisted of 50 mM ammonium formate aqueous solution (A) and acetonitrile (B). Separation was achieved by changing the B composition in the eluent in a stepwise manner with a time program as follows: 0–3 min, 95% B; 3–6 min, 85% B, 6–9 min, 70% B, 9–12 min, 60% B, 12–18 min, 5% B. The column was re-equilibrated with 95% B for 12 min before the next sample loading. The total flow rate was 0.2 ml/min. The column oven temperature was maintained at 40⬚C. Separation on a bare silica gel column (Ascentis Express HILIC column, 150 × 2.1 mm, 2.7 M, Sigma–Aldrich) was conducted according to the method of Zhao et al. [20]. The mobile phase consisted of 10 mM ammonium formate aqueous solution at pH 3.0 (A) and acetonitrile (B). The gradient program was as follows: 0–10 min, 92% B to 70%B; 10–15min, 70% B; 15–35 min, 92% B. LCMS-8040 was operated with the ESI in positive ion mode and selective reaction monitoring (SRM) with the following parameters: desolvation line temperature, 250⬚C; block heater, 400⬚C; nebulizing gas flow, 3 L/min; dying gas flow, 15 L/min. SRM measurement conditions were optimized by FIA with each standard solution. SRM transitions (Q1 m/z and Q3 m/z) and collision energy (CE) values are summarized in Table 1. Q3 m/z values were set to pass fragment ions originating from the polar head group. Data acquisition and peak processing were automatically performed with Labsolutions LCMS software Version 5.53 SP3 (Shimadzu) under default settings.
3 Results and discussion 3.1 Construction of LCMS method
2.2 Sample preparation Lipid standards were dissolved and diluted with methanol to make sample solutions at the required concentration levels. The concentration ranges for the linearity test were as follows: PC_25:0, 0.078, 0.156, 0.78, 1.56, 7.8, 15.6, 78, 156 nM, LPC_C13:0, 1.07, 2.13, 10.7, 21.3, 10.7, 213 nM, Cer_d18:1/12:0, 0.127, 0.254, 1.27, 2.54, 12.7, 25.4, 127, 254 nM, MHC_d18:1/12:0, 0.122, 0.244, 1.22, 2.44, 12.2, 24.4, C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
A mixture of the five standards from each lipid class was prepared to optimize LC separation. The concentrations of each standard were as follows; PC_25:0, 156 nM, LPC_C13:0, 213 nM, Cer_d18:1/12:0, 254 nM, MHC_d18:1/12:0, 244 nM, SM_C34:0, 213 nM. Of several types of stationary phases tested, carbamoyl-bonded, and bare silica gel columns showed good separation and peak shapes in our early trials, whereas zwitterionic stationary phases produced peak tailing. www.jss-journal.com
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Table 1. Lipid standards used in this study
Lipid class name
Abbreviation
Standards
SRM transitions (Q1 m/z>Q3 m/z)
CE (V)
Ceramide Monohexosylceramide Phosphatydylchoine Sphingomyelin Lysophosphatydylcholine
Cer MHC PC SM LPC
d18:1/12:0 d18:1/12:0 C25:0 C34:0 C13:0
482.40>264.40 644.50>264.40 636.40>184.10 703.60>184.10 454.30>184.10
–25 –35 –45 –25 –40
Figure 1. SRM chromatograms of the 5 lipid standards. The concentrations of each standard are as follows; PC_25:0, 156 nM, LPC_C13:0, 213 nM, Cer_d18:1/12:0, 254 nM, MHC_d18:1/12:0, 244 nM, SM_C34:0, 213 nM.
Table 2. Range, linearity, and repeatability for the five lipid standards
Standards
Range (nM)
Linearity (R2 )
Repeatability (%RSD)
Cer_d18:1/12:0 MHC_d18:1/12:0 PC_C25:0 SM_C34:0 LPC_C13:0
0.127–254 0.122–244 0.078–156 1.08–218 1.07–213
0.9993 0.9999 0.9993 0.9970 0.9993
2.4 4.6 9.9 5.7 7.5
The carbamoyl-bonded silica column from Tosoh was employed for further method optimization because this column maintained good peak shapes for all representatives of the five lipid classes after 100 plasma sample injections, whereas a bare silica gel column did not. Separation was achieved with the mobile phase of 50 mM ammonium formate aqueous solution and acetonitrile. Acidic additives such as 0.1% formic acid, which sometimes improve sensitivity in ESI-MS detection of the positive ion mode resulted in serious peak tailing in LC and was, therefore, not employed. As shown in Fig. 1, sharp and symmetrical peaks were obtained for all the standards with the optimized HILIC–ESI-MS/MS conditions. Separation was achieved within 10 min, which is relatively short compared to the previous NPLC method [26, 27]. Peak area repeatability of five replicate measurements was less than 10% and linear responses were demonstrated between peak areas and concentrations in the broad ranges (Table 2), thus it was demonstrated that our method is applicable for the elucidation of plasma lipidome. C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
3.2 Plasma analysis The Folch [27] and Bligh and Dyer methods [28] involve LLE using water/methanol/chloroform and are the most widely used lipid extraction methods applicable for various biological samples. However, lipids are extracted in the lower chloroform phase under insoluble precipitates of denatured proteins. This “lid” hampers recovery of the objective chloroform phase and makes automation by robotics difficult. For this reason, an application for large scale sample is somewhat limited. Recently methanol has been applied for the extraction of a wide range of metabolites, including lipids from biological samples [29, 30]. We confirmed this simple pretreatment procedure is efficient for lipid extraction compared to that using the same volume of acetonitrile (Supporting Information Fig. S1) and, therefore, adopted it for the extraction of lipids from plasma samples in this study. The SRM measurement conditions for endogenous lipid molecules of five lipid classes were then determined. Since commercially available standard materials of five lipid classes are limited, it was difficult to optimize SRM transitions and CE values experimentally. Therefore, Q1 m/z values were based on theoretical [M+H]+ values calculated from the potential combination of the backbone structure, fatty acyl chain length, and double bond numbers. For Q3 m/z and CE values, the same product ions of the backbone structure common in each class were adapted. The measurement conditions are summarized in supplementary Table 1. All SRM chromatograms as a result of human plasma assay are shown in Fig. 2. In some cases, multiple peaks were detected in a single SRM chromatogram. SRM is highly specific, however, it www.jss-journal.com
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Figure 2. SRM chromatograms of lipids detected in human plasma.
has a limitation in distinguishing isomers and lipid classes producing common product ions. In the general process for peak assignment, retention times in a sample and those in the corresponding authentic chemical standard analyses are confirmed to be identical. Owing to the limited availability of lipid standards, an alternative process was adopted in the present study. Peak assignment was based on comparison of the measured retention times with those in the representative standard analyses and consistency of the elution profile with the theoretical prediction that lipids having a longer fatty acyl chain would elute earlier in HILIC within the same class. According to this process, a total of 117 peaks were simply assigned although the identification reliability needs to be fortified further such with a different unique fragment ion as a qualifier ion. Retention times and their repeatability are summarized in supplementary Table 1. The peaks assigned for each lipid class were well resolved with sufficient repeatability. It was demonstrated that class separation of lipids was achieved by HILIC using the carbamoyl-bonded silica gel column. The performance of our method was compared with that of the method using a bare silica gel column, which is one of the most widely used stationary phase in HILIC analysis, in terms of inter- and intra-class separation of lipid. The intraclass separations of Cer and MHC were achieved much better by our presented HILIC method (Fig. S2) with no clear differences for the interclass separation of the five lipid classes. For a practical application, Cer_d18:1/12:0 as an internal standard, which was not detected in human plasma used in this research was evaluated. Plasma samples from five different subjects were fortified with Cer_d18:1/12:0 at the concentration of 254 nM, and the peak areas of nine endogenous Cer peaks were measured by our HILC–ESI-MS/MS method and normalized by the area of the Cer_d18:1/12:0. The same measurement was repeated on three separate days, and the intra- and inter-day reproducibility were calculated as summarized in Table 3. These values indicate that Cer_d18:1/12:0 can serve as an internal standard for the quantification of ceramides having different alkyl chain lengths. C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Table 3. Ceramide analysis of human plasma
Lipid
Intra-day reproducibility (%RSD)
Inter-day reproducibility (%RSD)
Cer_d18:1/16:0 Cer_d18:1/18:0 Cer_d18:0/18:0 Cer_d18:1/20:0 Cer_d18:1/22:0 Cer_d18:1/24:1 Cer_d18:1/24:0 Cer_d18:1/26:1 Cer_d18:1/26:0
6.4 7.8 15.7 7.3 4.3 4.9 6.3 3.3 12.2
1.7 6.5 7.2 9.2 2.1 4.0 3.7 5.8 9.9
In addition, we confirmed that polar small metabolites such as amino and organic acids co-existing in the same biological samples retained on the analytical column well and eluted with good peak shapes reproducibly after the elution of all the lipids (Supporting Information Fig. S2 and Table S1). The small molecular metabolites can, therefore, be determined simultaneously in a single analytical run with the lipids and more comprehensive metabolome information is possibly obtained in a single analysis.
4 Conclusion In this report, we applied HILIC–MS/MS using the carbamoyl-bonded silica gel column to lipid analysis of human plasma and successfully obtained clear group separation of five lipid classes. The method developed in this study showed good validation parameters both in the analysis of lipid standards and commercially available human plasma. In addition, the simultaneous detection of small polar metabolites was achieved in a single run with lipids. Analysis can be conducted with a simple pretreatment and in relatively short measurement time. With these advantages, the method www.jss-journal.com
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proposed here would be a useful tool for lipidome analysis in large population-based cohorts. The authors have declared no conflict of interest.
5 References [1] Quehenberger, O., Dennis, E. A., N. Engl. J. Med. 2011, 365, 1812–1823. [2] Hegele, R. A., Nat. Rev. Genet. 2009, 10, 109–121. [3] Rhee, E. P., Cheng, S., Larson, M. G., Walford, G. A., Lewis, G. D., McCabe, E., Yang, E., Farrell, L., Fox, C. S., O’Donnell, C.J., Carr, S. A., Vasan, R. S., Florez, J. C., Clish, C. B., Wang, T. J., Gerszten, R. E., J. Clin. Invest. 2011, 12, 1402–1411. [4] Weir, J. M., Wong, G., Barlow, C. K., Greeve, M. A., Kowalczyk, A., Almasy, L., Comuzzie, A. G., Mahaney, M. C., Jowett, J. B. M., Shaw, J., Curran, J. E., Blangero, J., Meikle, P., J. Lipid Res. 2013, 54, 2898–2908. [5] Castro-Perez, J. M., Kamphorst, J., DeGroot, J., Lafeber, F., Goshawk, J., Yu, K., Shockcor, J. P., Vreeken, R. J., Hankemeier, T., J. Proteome Res. 2010, 9, 2377–2389. ¨ [6] Drobnik, W., Liebisch, G., Audebert, F. X., Frohlich, D., ¨ Gluck, T., Vogel, P., Rothe, G., Schmitz, G., J. Lipid Res. 2003, 44, 754–761. [7] Ichi, I., Nakahara, K., Miyashita, Y., Hidaka, A., Kutsukake, S., Inoue, K., Maruyama, T., Miwa, Y., Harada-Shiba, M., Tsushima, M., Kojo, S., Lipids 2006, 41, 859–863. [8] Haus, J. M., Kashyap, S. R., Kasumov, T., Zhang, R., Kelly, K. R., DeFronzo, R. A., Kirwan, J. P., Diabetes 2009, 58, 337–343. [9] Fahy, E., Subramaniam, S., Brown, H. A., Glass, C. K., Merrill, A. H. Jr., Murphy, R. C., Raetz, C. R. H., Russell, D. W., Seyama, Y., Shaw, W., Shimizu, T., Spener, F., Meer, G. V., VanNieuwenhze, M. S., White, S. H., Witztum, J. L., Dennis, E. A., J. Lipid Res. 2005, 46, 839–862. [10] 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. [11] Li, M., Yang, L., Bai, Y., Liu, H., Anal. Chem. 2013, 86, 161–175.
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[12] Lee, J. W., Yamamoto, T., Uchikata, T., Matsubara, A., Fukusaki, E., Bamba, T., J. Sep. Sci. 2011, 34, 3553– 3560. [13] Zamfir, A., Vukelic, Z., Peter-Katalinic, J., Electrophoresis 2002, 23, 2894–2903. [14] Bogusz, S., Hantao, L. W., Braga, S. C. G. N., de Matos Franc¸a, V. D. C., da Costa, M. F., Hamer, R. D., Ventura, D. F., Augusto, F., J. Sep. Sci. 2012, 35, 2438–2444. [15] Hellmuth, C., Uhl, O., Segura-Moreno, M., Demmelmair, H., Koletzko, B., J. Sep. Sci. 2011 34, 3470–3483. [16] Ikeda, K., Taguchi, R., Rapid Commun. Mass Spectrom. 2010, 24, 2957–2965. ¨ [17] Scherer, M., Bottcher, A., Schmitz, G., Liebisch, G., Biochim. Biophys. Acta, Mol. Cell Biol. Lipids, 2011, 1811, 68–75. [18] Okazaki, Y., Kamide, Y., Hirai, M. Y., Saito, K., Metabolomics 2013, 9, 121–131. [19] Schwalbe-Herrmann, M., Willmann, J., Leibfritz, D., J. Chromatogr. A, 2010, 1217, 5179–5183. [20] Zhao, Y. Y., Xiong, Y., Curtis, J. M., J. Chromatogr. A 2011, 1218, 5470–5479. [21] Donato, P., Cacciola, F., Cichello, F., Russo, M., Dugo, P., Mondello, L., J. Chromatogr. A 2011, 1218, 6476–6482. [22] Zhu, C., Dane, A., Spijksma, G., Wang, M., Vander Greef, J., Luo, G., Hankemeier, T., Vreeken, R. J., J. Chromatogr. A 2012, 1220, 26–34. ¨ ¨ [23] Wormer, L., Lipp, J. S., Schroder, J. M., Hinrichs, K. U., Org. Geochem. 2013, 59, 10–21. [24] Jian, W., Edom, R. W., Xu, Y., Weng, N., J. Sep. Sci. 2010, 33, 681–697. [25] Shui, G., Stebbins, J. W., Lam, B. D., Cheong, W. F., Lam, S. M., Gregoire, F., Kusonoki, J., Wenk, M. R., PLoS One 2011, 6, e19731. [26] Shui, G., Guan, X. L., Gopalakrishnan, P., Xue, Y., Goh, J. S. Y., Yang, H., Wenk, M. R., PLoS One 2010, 5, e11956. [27] Folch, J., Lees, M., Sloane-Stanley, G. H., J. Biol. Chem, 1957, 226, 497–509. [28] Bligh, E. G., Dyer, W. J., Can. J. Biochem. Physiol. 1959, 37, 911–917. [29] Want, E. J., O’Maille, G., Smith, C. A., Brandon, T. R., Uritboonthai, W., Qin, C., Trauger, S. A, Siuzdak, G., Anal. Chem. 2006, 78, 743–752. [30] Yuan, M., Breitkopf, S. B., Yang, X., Asara, J. M., Nat. Protoc. 2012, 7, 872–881.
www.jss-journal.com
ISSN 1615-9306 · JSSCCJ 38 (12) 2007–2192 (2015) · Vol. 38 · No. 12 · June 2015 · D 10609
JOURNAL OF
SEPARATION SCIENCE
Methods Chromatography · Electroseparation Applications Biomedicine · Foods · Environment
12 15
www.jss-journal.com
2033
J. Sep. Sci. 2015, 38, 2033–2037
Kazuhiro Sonomura1,2 Shinobu Kudoh3 Taka-Aki Sato2 Fumihiko Matsuda1 1 Center
for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan 2 Life Science Research Center, Technology Research Laboratory, Shimadzu Corporation, Kyoto, Japan 3 Pharmaceuticals and Life-Science Division, Shimadzu Techno-Research, Kyoto, Japan Received December 16, 2014 Revised March 24, 2015 Accepted March 27, 2015
Short Communication
Plasma lipid analysis by hydrophilic interaction liquid chromatography coupled with electrospray ionization tandem mass spectrometry A novel method for the analysis of endogenous lipids and related compounds was developed employing hydrophilic interaction liquid chromatography with electrospray ionization tandem mass spectrometry. A hydrophilic interaction liquid chromatography with carbamoyl stationary phase achieved clear separation of phosphatidylcholine, lysophosphatidylcholine, sphingomyelin, ceramide, and mono-hexsosyl ceramide groups with good peak area repeatability (RSD% < 10) and linearity (R2 > 0.99). The established method was applied to human plasma assays and a total of 117 endogenous lipids were successfully detected and reproducibly identified. In addition, we investigated the simultaneous detection of small polar metabolites such as amino and organic acids co-existing in the same biological samples processed in a single analytical run with lipids. Our results show that hydrophilic interaction liquid chromatography is a useful tool for human plasma lipidome analysis and offers more comprehensive metabolome coverage. Keywords: Electrospray ionization tandem mass spectrometry / Hydrophilic interaction liquid chromatography / Liquid chromatography / Plasma Lipids DOI 10.1002/jssc.201401440
Additional supporting information may be found in the online version of this article at the publisher’s web-site
1 Introduction Lipids are involved in many physiological processes and their biological functions are closely related to the onset of health problems especially metabolic diseases [1,2]. The risk for and progress of metabolic diseases are traditionally evaluated by low- and high-density lipoprotein cholesterol and triglyceride levels in blood. The recent development of MS combined with various separation techniques allows the detection of individual lipid molecules, and its application to the measurement of circulating lipid concentrations has been investigated for biomarker development [3–8]. Biological lipids are generally classified into eight categories, and each category is divided into classes and subclasses precisely defined by the LIPID MAPS consortium Correspondence: Dr. Taka-Aki Sato, Life Science Research Center, Technology Research Laboratory, Shimadzu Corporation, 3-9-4, Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0237, Japan E-mail: [email protected] Fax: +81-774-95-1619
Abbreviations: NPLC, normal phase liquid chromatography; PC, phosphatidylcholine; LPC, lysophosphatidylcholine; Cer, ceramide; SM, sphingomyelin; MHC, mono-hexsosyl ceramide; SRM, selected reaction monitoring; CE, collision energy
C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
according to the backbone structure containing polar head groups [9]. Various fatty acyl chains bonded to the backbone generate numerous unique lipid molecules. A great number of lipid species have been identified in plasma, and 500 or more of them have been quantified [10]. Owing to this structural diversity of lipids, comprehensive analysis of lipids in a complex biofluid with a single method remains a challenge. Various separation techniques coupled with MS have been reported for lipidomics such as GC, LC, SFC, and CE [10–15]. Among these, LC–API-MS stands out because of its applicability to a wide range of lipid species. Two separation modes, RPLC and normal-phase liquid chromatography (NPLC) are widely used in lipid analysis. In RPLC, lipid molecules are separated according to their hydrophobicity, determined mainly by fatty acyl chain length, and lipids from different classes are sometimes co-eluted. Therefore, abundant lipid species such as phosphatidylcholine would cause ion suppression or enhancement of other co-eluted lipid species. On the other hand, lipid molecules are separated with NPLC according to the polar head group common in the relevant lipid class and roughly resolved into each lipid class. However, the chloroform or hexane used as mobile phase in NPLC are hazardous solvents and sometimes cause ion suppression. HILIC offers chromatographic separation similar to that obtained by NPLC with the use of an ESI-compatible solvent system such as acetonitrile/water. This chromatographic technique has become
www.jss-journal.com
2034
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K. Sonomura et al.
popular for the retention and separation of polar small molecular compounds. Its application has gradually been extended to lipid analysis [16–24]. The applications utilize mostly a bare or diol-modified silica gel irrespective of the fact that various types of stationary phases are available for HILIC. One of the practical difficulties in use of a bare silica gel column is that it sometimes requires a high salt concentration to achieve adequate resolution with good peak shapes, which leads to salt precipitation in the ion source of mass spectrometer and the resultant ion suppression. We have also tried several types of stationary phases including bare silica gel for lipid research and convinced with the applicability of the carbamoyl-bonded silica gel column for plasma lipid profiling. It achieved a clear separation of five lipid classes; namely, phosphatidylcholine (PC), lysophosphatidylcholine (LPC), sphingomyelin (SM), ceramide (Cer), and mono-hexsosyl ceramide (MHC). The system showed good peak area repeatability and linearity for standards from each lipid class. We applied this method to analysis of commercially available human plasma and successfully detected a total of 117 lipid molecules from the five lipid classes. Both inter- and intra-day reproducibility were then evaluated for endogenous Cer peaks normalized with a selected internal standard. In addition, we envisioned the simultaneous detection of small polar metabolites by using the same HILIC–ESI-MS/MS method in a single analytical run.
2 Materials and methods 2.1 Reagents and standards Water used throughout the study was produced by a Milli-Q system (Millipore). LCMS-grade acetonitrile, methanol, and ammonium formate were purchased from Sigma–Aldrich (St. Louis, Missouri, USA). Chromatographic separation was conducted on a TSK amide-80 column (150 × 2.0 mm, 3 mM, Tosho). 1-Dodecanoyl2-tridecanoyl-sn-glycero-3-phosphocholine (PC_25:0), 1-tridecanoyl-sn-glycero-3-phosphocholine (LPC_C13:0), N-(dodecanoyl)-sphing-4-enine (Cer_d18:1/12:0), and N(dodecanoyl)-1-b-glucosyl-sphing-4-ene (MHC_d18:1/12:0) were obtained from Avanti polar lipids (Birmingham, AL). N-palmitoyl-D-sphingomyelin (SM_C34:0) was purchased from Sigma–Aldrich. Human plasma was purchased from KAC (Kyoto, Japan).
122, 244 nM, SM_C34:0, 1.07, 2.13, 10.7, 21.3, 107, 213 nM. For plasma lipid analysis, 180 L of methanol was added to 20 L of plasma sample and mixed vigorously. The solution was centrifuged at 16 000 × g for 10 min at 25⬚C. The supernatant was collected into a glass vial and 1 L aliquot of each sample was subjected to HILIC–ESI-MS/MS analysis.
2.3 LCMS conditions LC analysis was performed with a Shimadzu Nexera UHPLC system, which consisted of a CBM-20A controller, two LC30AD pumps, a DGU-20 A5 degassing unit, a CTO-20A column oven, and a SIL-30AC autosampler. The LC system was coupled with a triple-quadruple mass spectrometer LCMS8040 (Shimadzu, Kyoto, Japan). The mobile phase consisted of 50 mM ammonium formate aqueous solution (A) and acetonitrile (B). Separation was achieved by changing the B composition in the eluent in a stepwise manner with a time program as follows: 0–3 min, 95% B; 3–6 min, 85% B, 6–9 min, 70% B, 9–12 min, 60% B, 12–18 min, 5% B. The column was re-equilibrated with 95% B for 12 min before the next sample loading. The total flow rate was 0.2 ml/min. The column oven temperature was maintained at 40⬚C. Separation on a bare silica gel column (Ascentis Express HILIC column, 150 × 2.1 mm, 2.7 M, Sigma–Aldrich) was conducted according to the method of Zhao et al. [20]. The mobile phase consisted of 10 mM ammonium formate aqueous solution at pH 3.0 (A) and acetonitrile (B). The gradient program was as follows: 0–10 min, 92% B to 70%B; 10–15min, 70% B; 15–35 min, 92% B. LCMS-8040 was operated with the ESI in positive ion mode and selective reaction monitoring (SRM) with the following parameters: desolvation line temperature, 250⬚C; block heater, 400⬚C; nebulizing gas flow, 3 L/min; dying gas flow, 15 L/min. SRM measurement conditions were optimized by FIA with each standard solution. SRM transitions (Q1 m/z and Q3 m/z) and collision energy (CE) values are summarized in Table 1. Q3 m/z values were set to pass fragment ions originating from the polar head group. Data acquisition and peak processing were automatically performed with Labsolutions LCMS software Version 5.53 SP3 (Shimadzu) under default settings.
3 Results and discussion 3.1 Construction of LCMS method
2.2 Sample preparation Lipid standards were dissolved and diluted with methanol to make sample solutions at the required concentration levels. The concentration ranges for the linearity test were as follows: PC_25:0, 0.078, 0.156, 0.78, 1.56, 7.8, 15.6, 78, 156 nM, LPC_C13:0, 1.07, 2.13, 10.7, 21.3, 10.7, 213 nM, Cer_d18:1/12:0, 0.127, 0.254, 1.27, 2.54, 12.7, 25.4, 127, 254 nM, MHC_d18:1/12:0, 0.122, 0.244, 1.22, 2.44, 12.2, 24.4, C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
A mixture of the five standards from each lipid class was prepared to optimize LC separation. The concentrations of each standard were as follows; PC_25:0, 156 nM, LPC_C13:0, 213 nM, Cer_d18:1/12:0, 254 nM, MHC_d18:1/12:0, 244 nM, SM_C34:0, 213 nM. Of several types of stationary phases tested, carbamoyl-bonded, and bare silica gel columns showed good separation and peak shapes in our early trials, whereas zwitterionic stationary phases produced peak tailing. www.jss-journal.com
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Table 1. Lipid standards used in this study
Lipid class name
Abbreviation
Standards
SRM transitions (Q1 m/z>Q3 m/z)
CE (V)
Ceramide Monohexosylceramide Phosphatydylchoine Sphingomyelin Lysophosphatydylcholine
Cer MHC PC SM LPC
d18:1/12:0 d18:1/12:0 C25:0 C34:0 C13:0
482.40>264.40 644.50>264.40 636.40>184.10 703.60>184.10 454.30>184.10
–25 –35 –45 –25 –40
Figure 1. SRM chromatograms of the 5 lipid standards. The concentrations of each standard are as follows; PC_25:0, 156 nM, LPC_C13:0, 213 nM, Cer_d18:1/12:0, 254 nM, MHC_d18:1/12:0, 244 nM, SM_C34:0, 213 nM.
Table 2. Range, linearity, and repeatability for the five lipid standards
Standards
Range (nM)
Linearity (R2 )
Repeatability (%RSD)
Cer_d18:1/12:0 MHC_d18:1/12:0 PC_C25:0 SM_C34:0 LPC_C13:0
0.127–254 0.122–244 0.078–156 1.08–218 1.07–213
0.9993 0.9999 0.9993 0.9970 0.9993
2.4 4.6 9.9 5.7 7.5
The carbamoyl-bonded silica column from Tosoh was employed for further method optimization because this column maintained good peak shapes for all representatives of the five lipid classes after 100 plasma sample injections, whereas a bare silica gel column did not. Separation was achieved with the mobile phase of 50 mM ammonium formate aqueous solution and acetonitrile. Acidic additives such as 0.1% formic acid, which sometimes improve sensitivity in ESI-MS detection of the positive ion mode resulted in serious peak tailing in LC and was, therefore, not employed. As shown in Fig. 1, sharp and symmetrical peaks were obtained for all the standards with the optimized HILIC–ESI-MS/MS conditions. Separation was achieved within 10 min, which is relatively short compared to the previous NPLC method [26, 27]. Peak area repeatability of five replicate measurements was less than 10% and linear responses were demonstrated between peak areas and concentrations in the broad ranges (Table 2), thus it was demonstrated that our method is applicable for the elucidation of plasma lipidome. C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
3.2 Plasma analysis The Folch [27] and Bligh and Dyer methods [28] involve LLE using water/methanol/chloroform and are the most widely used lipid extraction methods applicable for various biological samples. However, lipids are extracted in the lower chloroform phase under insoluble precipitates of denatured proteins. This “lid” hampers recovery of the objective chloroform phase and makes automation by robotics difficult. For this reason, an application for large scale sample is somewhat limited. Recently methanol has been applied for the extraction of a wide range of metabolites, including lipids from biological samples [29, 30]. We confirmed this simple pretreatment procedure is efficient for lipid extraction compared to that using the same volume of acetonitrile (Supporting Information Fig. S1) and, therefore, adopted it for the extraction of lipids from plasma samples in this study. The SRM measurement conditions for endogenous lipid molecules of five lipid classes were then determined. Since commercially available standard materials of five lipid classes are limited, it was difficult to optimize SRM transitions and CE values experimentally. Therefore, Q1 m/z values were based on theoretical [M+H]+ values calculated from the potential combination of the backbone structure, fatty acyl chain length, and double bond numbers. For Q3 m/z and CE values, the same product ions of the backbone structure common in each class were adapted. The measurement conditions are summarized in supplementary Table 1. All SRM chromatograms as a result of human plasma assay are shown in Fig. 2. In some cases, multiple peaks were detected in a single SRM chromatogram. SRM is highly specific, however, it www.jss-journal.com
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K. Sonomura et al.
Figure 2. SRM chromatograms of lipids detected in human plasma.
has a limitation in distinguishing isomers and lipid classes producing common product ions. In the general process for peak assignment, retention times in a sample and those in the corresponding authentic chemical standard analyses are confirmed to be identical. Owing to the limited availability of lipid standards, an alternative process was adopted in the present study. Peak assignment was based on comparison of the measured retention times with those in the representative standard analyses and consistency of the elution profile with the theoretical prediction that lipids having a longer fatty acyl chain would elute earlier in HILIC within the same class. According to this process, a total of 117 peaks were simply assigned although the identification reliability needs to be fortified further such with a different unique fragment ion as a qualifier ion. Retention times and their repeatability are summarized in supplementary Table 1. The peaks assigned for each lipid class were well resolved with sufficient repeatability. It was demonstrated that class separation of lipids was achieved by HILIC using the carbamoyl-bonded silica gel column. The performance of our method was compared with that of the method using a bare silica gel column, which is one of the most widely used stationary phase in HILIC analysis, in terms of inter- and intra-class separation of lipid. The intraclass separations of Cer and MHC were achieved much better by our presented HILIC method (Fig. S2) with no clear differences for the interclass separation of the five lipid classes. For a practical application, Cer_d18:1/12:0 as an internal standard, which was not detected in human plasma used in this research was evaluated. Plasma samples from five different subjects were fortified with Cer_d18:1/12:0 at the concentration of 254 nM, and the peak areas of nine endogenous Cer peaks were measured by our HILC–ESI-MS/MS method and normalized by the area of the Cer_d18:1/12:0. The same measurement was repeated on three separate days, and the intra- and inter-day reproducibility were calculated as summarized in Table 3. These values indicate that Cer_d18:1/12:0 can serve as an internal standard for the quantification of ceramides having different alkyl chain lengths. C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Table 3. Ceramide analysis of human plasma
Lipid
Intra-day reproducibility (%RSD)
Inter-day reproducibility (%RSD)
Cer_d18:1/16:0 Cer_d18:1/18:0 Cer_d18:0/18:0 Cer_d18:1/20:0 Cer_d18:1/22:0 Cer_d18:1/24:1 Cer_d18:1/24:0 Cer_d18:1/26:1 Cer_d18:1/26:0
6.4 7.8 15.7 7.3 4.3 4.9 6.3 3.3 12.2
1.7 6.5 7.2 9.2 2.1 4.0 3.7 5.8 9.9
In addition, we confirmed that polar small metabolites such as amino and organic acids co-existing in the same biological samples retained on the analytical column well and eluted with good peak shapes reproducibly after the elution of all the lipids (Supporting Information Fig. S2 and Table S1). The small molecular metabolites can, therefore, be determined simultaneously in a single analytical run with the lipids and more comprehensive metabolome information is possibly obtained in a single analysis.
4 Conclusion In this report, we applied HILIC–MS/MS using the carbamoyl-bonded silica gel column to lipid analysis of human plasma and successfully obtained clear group separation of five lipid classes. The method developed in this study showed good validation parameters both in the analysis of lipid standards and commercially available human plasma. In addition, the simultaneous detection of small polar metabolites was achieved in a single run with lipids. Analysis can be conducted with a simple pretreatment and in relatively short measurement time. With these advantages, the method www.jss-journal.com
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proposed here would be a useful tool for lipidome analysis in large population-based cohorts. The authors have declared no conflict of interest.
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