Adaptation of Skyline for Targeted Lipidomics - Journal of Proteome

Article pubs.acs.org/jpr

Adaptation of Skyline for Targeted Lipidomics Bing Peng and Robert Ahrends* Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Str.6b, 44227 Dortmund, Germany S Supporting Information *

ABSTRACT: In response to the urgent need for analysis software that is capable of handling data from targeted high-throughput lipidomics experiments, we here present a systematic workflow for the straightforward method design and analysis of selected reaction monitoring data in lipidomics based on lipid building blocks. Skyline is a powerful software primarily designed for proteomics applications where it is widely used. We adapted this tool to a “Plug and Play” system for lipid research. This extension offers the unique capability to assemble targeted mass spectrometry methods for complex lipids easily by making use of building blocks. With simple yet tailored modifications, targeted methods to analyze main lipid classes such as glycerophospholipids, sphingolipids, glycerolipids, cholesterylesters, and cholesterol can be quickly introduced into Skyline for easy application by end users without distinct bioinformatics skills. To illustrate the benefits of our novel strategy, we used Skyline to quantify sphingolipids in mesenchymal stem cells. We demonstrate a simple method building procedure for sphingolipids screening, collision energy optimization, and absolute quantification of sphingolipids. In total, 72 sphingolipids were identified and absolutely quantified at the fatty acid scan species level by utilizing Skyline for data interpretation and visualization. KEYWORDS: lipidomics, sphingolipids, liquid chromatography, software, high throughput, selected reaction monitoring

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INTRODUCTION Lipidomics aims to study the lipid composition of a given biological system on a system-wide level to better understand their critical role as membrane components, signaling molecules, and for energy storage. Because of their broad spectrum of chemical structures, mass spectrometry (MS)based techniques have been broadly applied for the identification and quantification of lipids including sphingolipids (SPs).1−3 For untargeted liquid chromatography (LC)based lipidomics, software solutions such as LipidSearch4 and LipidBlast5 are available, and for shotgun lipidomics, direct infusion experiments, the software suite LipidXplorer6 can be utilized. LipidSearch and Lipidblast depend on spectral libraries, whereas LipidXplorer uses denovo sequencing for the lipid identification. Besides this, other identification software packages, including LipidQA,7 LIMSA,8 FAAT,9 lipID,10 LipidInspector,11 and ALEX,12 can be applied for lipid identification. Because of the high selectivity, sensitivity, and dynamic range of QqQ mass spectrometers, selected reaction monitoring (SRM) allows the simultaneous detection of very low abundant lipids next to medium and high abundant lipid species in one sample. Thanks to its low limit of detection and capability to reduce ion suppression effects, LC coupled with SRM is the method of choice for sensitive and comprehensive lipid quantification.13−15 Recent developments in MS instrumentation enable fast and sensitive screening of more than one-hundred lipids in parallel using targeted SRM based approaches.13,16 To setup SRM assays for lipids the fragmentation, behavior of target analytes © 2015 American Chemical Society

has to be studied in advance, and possible structural variations (chain length, number of double bonds) for each transition need to be precalculated. This is usually very time-consuming, as MS parameter optimization and data interpretation are not supported on monolithic software packages and require the tedious successive analysis of pure standard compounds. To further promote screening of the diverse and very dynamic sphingolipid family as well as other lipids at large scale, software tools for building transition lists based on different lipid building blocks such as headgroup (HG), fatty acids (FAs), long chain base (LCB), or their neutral losses are becoming increasingly desirable. While the lipidomics field still demands a tailor-made and powerful software solution to develop, optimize, and analyze SRM methods and data, in proteomics, such tools have been available for almost a decade, which allow the quantification of hundreds of proteins.17 More recently, Skyline,18 an open source software suite for targeted proteomics, has had a tremendous impact on this development. To develop an SRM assay for peptides, a typical automated Skyline workflow begins with importing a protein sequence list in FASTA format, which is then theoretically digested into peptides. Then suitable product ions and their m/z values are predicted based on fragmentation rules. To establish a final SRM assay, parameters such as collision energy and declustering potential can be instrument and compound-specifically adapted, the obtained results can be visually inspected and statistically evaluated, and Received: September 11, 2015 Published: November 30, 2015 291

DOI: 10.1021/acs.jproteome.5b00841 J. Proteome Res. 2016, 15, 291−301

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Journal of Proteome Research

and their corresponding species. Here, we present a systematic workflow for SRM-based lipidomics based on lipid building blocks. This “Plug and Play” system offers the unique capability to assemble targeted methods for complex lipids by the use of their structural components including FAs, LCB, and HGs without the tedious and time-consuming need of precalculations. To do so, Skyline was adapted for targeted lipidomics by implementing pseudo-PTM (post-translational modification) sequence tags, allowing the rapid generation and monitoring of transitions for main lipid classes such as glycerophospholipids (GPs), glycerolipids (GLs), sphingolipids (SPs), cholesterylesters (CEs), and cholesterol. To illustrate the benefits of our Skyline workflow, we investigated SPs in mesenchymal mouse stem cells first in qualitative and then in a quantitative fashion. Skyline was applied initially to create and subsequently to screen for the existence of different SPs species. Additionally, we optimized the collision energy to ensure the best detection capabilities of the identified SPs to finally absolutely quantify major SPs classes including ceramides (Cer(d)), sphingomyelin (SM), hexosylceramides (HexCer), dihexosylceramides (DiHexCer), sphingosine (So), and sphinganine (Sa). We envision that such tailored usage of Skyline for targeted lipidomics will clearly advance the field and improve the large-scale analysis of lipid species by LC−MS.

if needed SRM settings can be manually optimized. As a result, targeted methods can be easily developed in an automated fashion and manually refined, with the possibility to even handle high-throughput data by using this pipeline. Unfortunately, assay development and data inspection for targeted lipidomics in LC−MS remain challenging and lack all these features. Data analysis is commonly accomplished with commercial software19,20 that is limited in the capabilities to create, optimize, inspect, and visualize large data sets. This is based on the facts that available software packages cannot predict collision energies, do not allow for the automatic optimization of instrument parameters, cannot visualize multiple analysis sets at once, and are most often limited to instrumentation of a certain vendor. Several software packages for targeted lipid analysis are available such as LipidView and MultiQuant that can be utilized in combination with vendorspecific analysis software to analyze obtained SRM results (Table 1). However, LipidView can be used to characterize and Table 1. Features of Skyline and Commonly Used Software for Targeted Lipidomics features

Skyline

LipidView

MultiQuant

develop SRM assay peak detection and integration lipid identification adaptable database support quantitation experiments high−throughput data analysis statistical analysis cross-vendor data format high-resolution data (R ≥ 40 000) open source

+ + − + + + + + + +

− − + + + − + − − −

− + − − + + + − − −

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MATERIALS AND METHODS

Chemicals and Solutions

tert-Butyl methyl ether (MTBE), formic acid, ammonium formate, and HPLC-grade phosphoric acid (85−90%) were purchased from Sigma-Aldrich. The ULC/MS-grade solvents, acetonitrile (ACN), methanol (MeOH), and isopropanol (IPA), were obtained from Biosolve (Valkenswaard, The Netherlands). All solutions were prepared with ultrapure water (18 MΩ cm at 25 °C). Phosphatidylcholine (PC) 21:0/22:6, phosphatidylserine (PS) 12:0/13:0, phosphatidylglycerol (PG) 17:0/20:4, lyso-phosphatidylcholine (LPC) 19:0, triacylglycerol (TG) 14:0/14:0/14:0, cholesteryl ester (CE) 22:0, and Ceramide/Sphingoid internal standard mixture II (CerMix) consisting of Sphingosine d17:1, Sphinganine d17:0, Sphingosine-1-P d17:1, Sphinganine-1-P d17:0, Sphingomyelin d18:1/12:0, Ceramide (Cer) d18:1/12:0, glucosylceramide (GlcCer, as internal standard for HexCer) d18:1/12:0, lactosylceramide (LacCer, as internal standard for DiHexCer) d18:1/12:0, and Ceramide-1-P (CerP) d18:1/12:0 were purchased from Avanti Polar Lipids. The CerMix was used as internal standard (IS). Serial dilutions of the CerMix stock solution were utilized for method validation. Calibration curves were created to estimate the analytical sensitivity, and all lipid standards were prepared in CHCl3/MeOH/H2O (60:30:4.5, v/v/v).

quantify lipids from MS/MS data sets, but it lacks the ability to visualize LC−MS data and therefore is limited to process data from direct infusion experiments. MultiQuant can be used to quantify LC−MS SRM data and supports high-throughput quantitation experiments; however, like LipidView, it also does not provide a built-in tool to develop and optimize SRM transition parameters. Accordingly, both packages need to be used in conjunction with other instrument-specific data acquisition software like Analyst. These limitations severely impair user-friendliness and render the optimization of dedicated lipid SRM assays a tedious and laborious task. In contrast, Skyline offers an all-in-one solution that can be used for (i) SRM assay development, (ii) parameter optimization, and (iii) data analysis at the chromatographic level including data inspection, peak integration, and statistical evaluation of high-throughput data acquired with or without high mass accuracy. Skyline is freely available and more importantly open source software, so databases can be created and adapted by users, and cross-vendor data formats including raw, wiff, mzXML, or MGF, can be easily used or implemented. Additionally, the software is continuously improved and further developed and will therefore meet the needs of the growing and vastly advancing lipidomics MS community (Table 1). Skyline already offers the possibility to handle big data sets in combination with transition lists for the analysis of small molecules, yet it still lacks the capability to systematically create screening approaches for lipids based on their building blocks. Therefore, it is desirable to create an easy-to-implement approach to generate MS transitions for various lipid classes

Cell Culture

The OP9 mesenchymal stem cell line was maintained and propagated in MEMα medium, containing 20% fetal bovine serum (FBS) and 1% penicillin/streptomycin (PSG). The culturing was carried out in T75 cell culture flasks. The media was changed every 2 days, and cells were passaged every 4 days. The OP9 cells were harvested and pelleted in triplicates and snap frozen in liquid nitrogen for storage at −80 °C. Each replicate contained 6.6 × 106 cells. The cellular protein content for normalization was quantified by bicinchoninic acid assay (Thermo Fisher Scientific). 292


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Figure 1. Modification principles for Skyline. (A) Modification principle A for lipids with multiple fragment ions. (B) Modification principle B for lipids with one fragment ion. (a, b) Representative lipid structures; (c, d) sequences formed with single-letter database codes (SLC) of amino acids in Skyline to create the lipid fragmentation pattern of interest; (e, f) equation for each amino acid part to calculate a certain lipid ion or neutral loss within the sequence; (g, h) lists of lipid classes were each principle can be applied. LCB, long chain base; HG, headgroup; FA, fatty acid chain; GP, glycerophospholipid; SP, sphingolipid; GL, glycerolipid; Ch, cholesterol; CE, cholesteryl ester; LysoGP, lyso-glycerophospholipid.

Lipid Extraction

following ESI source settings were used: curtain gas 30 arbitrary units, temperature 250 °C, ion source gas I 40 arbitrary units, ion source gas II 65 arbitrary units, collision gas medium; ion spray voltage +5500 V/ −4500 V, declustering potential +100 V/−100 V, entrance potential +10 V/−10 V, and exit potential +13 V /−10 V (positive mode/negative mode). For the scheduled SRM, Q1 and Q3 were set to unit resolution. The scheduled SRM detection window was set to 2 min, and the cycle time was set to 2 s.

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Lipid extraction was carried out according to Matyash et al. with small modifications. In brief, 225 μL of MeOH (4 °C) was added to an OP9 cell pellet in an Eppendorf tube that was placed on ice. After a few seconds of treatment with ultrasonication and vortexing, 6 μL of the internal standard mix consisting of 25 μmol/L of CerMix and 750 μL of MTBE (4 °C) was added. The mixture was incubated for 1 h at 4 °C in a thermomixer at 650 rpm. Subsequently 188 μL of ultrapure water was added to induce phase separation. The sample was centrifuged at 10 000g for 10 min at 4 °C, and then 500 μL of the upper organic layer was transferred to another Eppendorf tube and dried under continuous N2 flow. The dried lipid extract was resuspended in 1000 μL of CHCl3/MeOH/H2O (60:30:4.5, v/v/v) for further MS analysis.

Software

Data acquisition was performed by Analyst version 1.6.2 Software (AB Sciex, Concord, Ontario, Canada). Skyline (https://brendanx-uw1.gs.washington.edu/labkey/project/ home/begin.view) (64-bit, 2.6.0.7176) was used to create lipid transitions, visualize results, integrate observed signals, and quantify all lipids that were detected by MS. A tutorial regarding the workflow of adapting Skyline for lipidomic is described (Text S1). Different lipid classes were induced into Skyline by applying two adaptation principles to scan parentproduct ion pairs (Figures 1 and S1; Tables S1 and S2). Origin 9.1 was employed to create calibration curves and bar graphs. Student’s t test was used for the comparison of two normal distributed samples with the equal variance. A species or class was marked as significant different (∗,∗∗) when p was below 0.05 or below 0.01, respectively.

LC−MS/MS Analysis

The reverse-phase LC system, an UltiMate 3000-system was purchased from Thermo Fischer Scientific (Darmstadt, Germany). The chromatographic separation was performed on an Ascentis Express C18 main column (150 mm × 2.1 mm, 2.7 μm, Supelco) fitted with a guard cartridge (50 mm × 2.1 mm, 2.7 μm, Supelco). The temperatures of the autosampler and the column oven were set at 10 and 60 °C, respectively. Solvent A was ACN/H2O (3:2, v/v), and solvent B was IPA/ ACN (9:1, v/v). Both solvents contained 0.1% formic acid, 10 mM ammonium formate, and 5 μM phosphoric acid. The separation was carried out at a flow rate of 0.5 mL/min with the following 25 min long gradient: initial (30% B), 0.0−2.0 min (hold 30% B), 2.0−3.0 min (30−56.1% B), 3.0−4.0 min (56.1−58.3% B), 4.0−5.5 min (58.3−60.2% B), 5.5−7.0 min (60.2−60.6% B), 7.0−8.5 min (60.6−62.3% B), 8.5−10.0 min (62.3−64.0% B), 10.0−11.5 min (64.0−64.5% B), 11.5−13.0 min (64.5−66.2% B), 13.0−14.5 min (66.2−66.9% B), 14.5− 15.0 min (66.9−100.0% B), 15.0−19.0 min (hold 100% B), 19.0 min (5% B), 19.0−22.0 min (hold 5% B), 22.0 min (30% B), 22.0−25.0 min (hold 30% B). The injector needle was washed with 30% B and 0.1% phosphoric acid prior to each injection. Samples and standards were injected with a volume of 1 μL and analyzed in triplicates, respectively. The LC was coupled to a QTRAP 6500 (Applied Biosystems, Darmstadt, Germany), which was equipped with an electrospray ion (ESI) source (Turbo V Ion Source). The

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RESULTS AND DISCUSSION

Adaptation Principles To Adjust Skyline for Lipidomics

Skyline is broadly used as a software package for establishing and optimizing SRM assays in targeted proteomics and in this mode has not been applicable for the analysis of lipids yet. So far, Skyline has been mainly utilized to create acquisition methods for peptide fragments by the in silico digestion of proteins, the prediction of fragment ions, and fragmentation energies. When adapting Skyline for lipidomics, three key features have to be considered (i) fragment ions are calculated based on linear backbone structures as present in case of peptides; (ii) the calculation of parent and fragment ion masses from structural building blocks (e.g., amino acids) accounts for the loss of water arising from peptide bond condensation; (iii) Skyline uses the nomenclature of Roepstorff22 for peptide fragmentation when assigning fragment ions, which is a fixed 293


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Figure 2. Workflow within Skyline for the targeted analysis of lipids. (A) Interface to edit amino acids for lipid analysis. This interface can be found by following the subsequent path: Setting → Peptide Settings > Modifications > Edit list > Edit Structural Modification. To create the transition for ceramides, glutamine (Q) can be used to represent LCB d18:1 at the first position of the sequence, cysteine (C) can be used to represent HG at the second position of the sequence for each polarity mode, and the glycine (G) modification to represent N-acyl hydroxyl fatty acid chain 16:0 at the third position of its sequence. Additionally, to obtain the mass of product fragment ion, add mass 107.0218 in neutral losses list. (B) Edit modification profiles. This interface can be subsequently used to create the lipid of interest and can be found by following the subsequent path: sequence → Modify. (C) Skyline interface of the created transitions for different types of lipids in ‘+’ positive mode, ‘−’ negative mode.

feature. To adapt Skyline for lipid analysis, we found a way to utilize these features to create a workflow for lipidomics applications. Crucially, we made use of the feature that Skyline accounts for the occurrence of highly variable amino acid (AA) modifications in nature. Thus, novel AA modifications can be implemented into Skyline to study novel/unknown PTMs. We exploited this feature to build artificial “pseudosequence tags” containing amino acids with specifically tailored modifications that can be used to represent lipid precursor and fragment ions. From the point of the molecular structure, almost every lipid can be treated as a linear array of building blocks, even triacylglycerol. Therefore, for the fast and straightforward screening of all lipid families, we introduced two different adaptation principles (termed principle A and B) to build SRM transition lists (Figure 1). For both, we used the single-letter database code (SLC) for amino acids to form molecular lipid species. To achieve this, the precursor and fragment ion masses were created by modifying individual amino acid within a small sequence tag. As masses of lipid fragments in most cases are smaller than those of peptides,23 we chose amino acids with small masses as a basis, which represent the precursor mass and comprise the information on the lipid fragment ions. A tutorial describing the workflow how to create lipid transitions by such “pseudosequence tags” in Skyline is described in Text S1. To build transition lists for multiple fragment ions, we developed adaptation principle A. Here, the basic lipid structure includes the long chain base (LCB) or FA1 (first fatty acid chain), the headgroup and FA2 (second fatty acid chain). For this adaptation principle, we applied the same SLC sequence for both polarity conditions with different amino acid modifications within the sequence. Principle A can in general be applied for lipids where more than one transition is

monitored such as SPs, GPs, and GLs (Figure S1). To define the accurate precursor mass for a distinct lipid, a sequence tag within Skyline is assembled by different modified amino acid residues adding up to the lipid precursor mass. These modified amino acids are lipid specific and were predefined by us. Accordingly, at least one residue was utilized to represent the unique lipid fragment mass to define the SRM transitions for identification and quantification within the sequence tag. In brief, to assemble a ceramide (Cer d18:1/16:0) precursor mass, three different fragments are compiled out of the long chain base (Glutamine plus 118.1762 Da), the N-acyl hydroxyl fatty acid chain (Glycine plus 92.2258 Da), and the headgroup (Cysteine plus 21.0103 Da) (Figure 2A,B). Thereby, the mass difference is introduced in the structural modification window to match the exact mass of the lipid fragment. The use of other amino acids is possible as long as it can be modified to reach targeted fragment masses. The created fragments can then be directly utilized to build the lipid SRM transitions for positive or negative mode with the exception of the fatty acid chain in SPs and the headgroup in GPs where a class-specific fixed mass must be subtracted from the amino acid residues via the neutral loss option in Skyline (Figures 2 and S1; Tables S1 and S2). Equally, the specific fragment ions for GLs are obtained. For cardiolipin (CL), one sequence tag generates only two fatty acid fragments. If three or four unique fatty acid chains must be compiled, an extra sequence tag is necessary with different order of the fatty acid chain modification. This principle can be employed to build transition sets with one to multiple fragment ions that are generated from a single representative “pseudosequence tag”. To screen for a wide range of SPs, the chain length and the number of double bonds can be easily adjusted in the sequence tag by introducing 294


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Figure 3. Lipid transitions for different lipid classes and their chromatograms in Skyline. (A) Transition and combined chromatograms of Cer d18:1/ 16:0 as well as the transition in positive (LCB) and negative mode (FA) and the separated chromatograms of each mode in ‘+’ (positive mode) and ‘−’ (negative mode). (B) Skyline chromatogram of HexCer d18:1/22:0 in positive mode. (C) Skyline chromatogram chart of DiHexCer d18:1/16:0 in positive mode. (D) Combined chromatograms of PG 17:0/20:4 with the separate chromatograms for each mode. (E) Combined chromatograms of LPC 19:0 and the separated chromatograms of each mode. (F) Chromatogram of TG 14:0/14:0/14:0 in positive mode. (G) Chromatogram of CE 22:0 in positive mode. (H) Combined chromatogram of SM d18:1/12:0 in positive mode.

standards. Thus, SPs, GPs, and their lyso-forms, GLs, as well as CEs were successfully integrated into Skyline as displayed by their extracted ion chromatograms (EICs) in Figure 3. As presented, the inspection of the multiple fragment ions can be easily carried out for Cer, HexCer, DiHexCer, SM, PG, TG, CE, and LPC. Additionally, we were able to observe different ionization efficiencies simultaneously from both polarity modes for individual species (Figure 3). This facilitated the evaluation and validation of fragmentation patterns in different lipid classes, which can substantially boost the development of novel targeted MS strategies for the analysis of SPs and other lipids in high-throughput experiments. By applying this target-oriented adaptation principle to Skyline, we demonstrated how Skyline can be adapted and utilized for targeted lipidomics without the need for specific databases. Two different types of adaptation principles (here named A and B) were utilized for the tailored design of specific transitions for all major lipid classes. Adaptation principle B is the ideal choice whenever a single transition is sufficient to identify and quantify a specific lipid. However, if a more confident strategy for the identification of a targeted lipid species can be applied or is needed, principle A, where multiple fragment ions are generated (e.g., HG, FA, LCB), is recommended. In addition, to observe product ions that are generated from the loss of the fatty acid as ketone (for GP), it is possible to add extra neutral loss masses to the modification to display these fragments. This intuitive direct assembly allows to

additional fragment ions in the structural modification windows (Figure S2). To directly build transition lists for lipid precursors with only a single fragment ion such as lyso-GPs, we applied the adaptation principle B. In principle B, the basic lipid structure only contains the HG and the FA part. The product ion representing the HG, which carries the charge of the precursor ion, can be formed in either positive mode or negative mode. Thus, the FA part can be adapted to obtain the targeted precursor mass. Since principle B aims to build transition sets with only one fragment ion generated from the sequence tag, it can be applied for SPs, GPs, GLs, lyso-GPs, cholesterol, and cholesteryl esters as well as other lipid classes. For instance, to create the correct transition for a phosphatidylcholine headgroup fragment ion of PC, the delta masses generating the precursor and the correct fragment ion mass are added to the individual amino acids in the sequence tag (Figure 2C). Thereby, the delta mass is introduced in the structural modification window to adjust the exact mass of the lipid fragment (Figure 1A). Hence, the two principles introduced here can be applied in a highly flexible and adaptive way to meet both the MS/MS identification strategy and the lipid class of interest. To demonstrate the general applicability of our adaptation principles, we applied our “pseudo-PTM sequence tag strategy” to create tailored transitions for further lipid classes and validated our approach by analyzing representative lipid 295


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Journal of Proteome Research create transitions with various lipid building blocks (e.g., FA, LCB, HG) within a lipid class and therefore to screen for a variety of distinct lipid species at once. MS/MS Identification Strategy for Sphingolipids

After the general adaptation of the Skyline software for the lipid analysis, we applied the workflow to generate an SRM transition list for SPs screening to analyze OP9 mouse mesenchymal stem cells. Usually SLs are investigated in the positive ionization mode at the species level based on their long chain base (LCB).24 Since the hydroxy/nonhydroxy/FAs in ceramides have been reported to have a direct unique functional effect on cell signaling25 and for a further structural validation, an additional analysis at the negative ionization mode at the fatty acid scan species level was applied. According to PubMed, most of the SPs studies are performed in combination with reverse phase (RP) chromatography (60%) and just a fair part with normal phase liquid chromatography (30%). Thus, we chose RP chromatography on C18 material, the most frequently used technique, with a nonlinear gradient for SPs separation, screening, and quantification. To create the transitions, for shorthand purposes, a nomenclature similar to fatty acids was employed for the LCB. Here, the LCB was annotated with the prefix ‘d’ or ‘t’, for di- or tri-hydroxy bases, respectively. For a theoretical assessment of SP, we made the following assumptions (i) the LCB has to range from d14−d22 or from t16−t24; (ii) the number of double bonds should be between 0 and 2; and (iii) the length of the FA should range between 8 and 36 carbon atoms with zero and three double bonds per FA. To identify sphingolipids in an extensive way, we first scanned for the LCB fragment ions in positive mode for all SP classes followed by a second scan for the class specific fragment ions (Figure 4). In detail, to distinguish Cer(d) from Cer(t), which contains one additional hydroxyl group, the presence of distinct LCB fragment ions for Cer(d) or Cer(t) was utilized and the individual FA fragment ion was identified in negative mode for structural elucidation. For the differentiation of hCer from Cer(d/t), a further criterion was employed, as the fragmentation of both classes results in the generation of a pseudoisobaric fragment ion representing the FA. For this purpose, we scanned for the specific neutral loss fragment of the LCB to identify hCer.26 The identification of CerP was achieved by scanning for the phosphate groups in negative mode. To identify HexCer and DiHexCer, we scanned for the neutral loss (es) of hexose(s), which is specific for hexose containing ceramides in positive mode. To identify SM, the choline phosphate headgroup was utilized for identification in addition to the LCB. Finally, to identify So and Sa, we monitored for the loss of one water molecule and formaldehyde molecule27 (Figure 4). In total, we analyzed transitions for >20 000 sphingolipids permutations including Cer(d), Cer(t), hCer, CerP, SM, HexCer, and DiHexCer. By the application of this SRM LC− MS screening approach and the method setup of principle A, we unambiguously identified 72 sphingolipids including 36 Cer(d) species, 14 SM species, four HexCer species, 16 DiHexCer species, So (18:1) and Sa (18:0). The screen was carried out with at least two specific fragments ions at the fatty acid scan species level in combination with Skyline for visualization and quality control (Table S3). For further validation, these identified SP species were additionally verified

Figure 4. SRM identification strategy for sphingolipids. In an initial step, SPs are identified by varying the precursor mass and screening for different LCB fragment ions in positive mode. Subsequently, to distinguish the different SP classes, a class specific fragment ion screen was carried out. Thereby, all experiments were performed on LC scale using SRM. After the successive screening of multiple ions, a lipid was counted as validated if at least two transitions were identified (Validated list). Rx (x = 1 or 2) can be saturated, monounsaturated, or polyunsaturated fatty acid chain. “√” represents the former fragment, which was identified by SRM. ‘X’ represents the former fragment, which was not identified by SRM. ‘+’ represents positive ion mode, and ‘−’ represents negative ion mode. NL, neutral loss; Cer(d/t), ceramide and phytoceramide; hCer, 2-hydroxy ceramide; CerP, ceramide-1-phosphate; HexCer, hexosylceramide; DiHexCer, dihexosylceramide; SM, sphingomyelin; So, sphingosine; Sa, sphinganine.

by PRM on a high-resolution Q-Exactive Plus mass spectrometer. Notably, also the PRM assay was created in Skyline using principles A and B. The additional information obtained from PRM about exact precursor and fragment masses allowed us to further verify the SRM results. Importantly, this additional validation might decrease the total number of identified SPs but will increase the number of true positive fragment and precursor ion signatures for SPs analysis (data not shown). Optimization of Instrumental Parameter

To gain a higher sensitivity for SPs in targeted lipidomics experiments, the collision energy optimization for individual SP species is an important requirement since relative proportions of distinct product ions are dependent on the collision energy. However, the fragmentation efficiency for different molecular 296


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Journal of Proteome Research species and fragment ions is not equal;28 therefore, collision energy values have to be individually adjusted to maximize their intensity. The prediction of collision energy in Skyline depends on a simple linear equation model, which empirically relies on the precursor m/z (mass-to-charge ratio) and signal intensity. Compared with automated flow injection based collision energy optimization algorithm of different instrument manufactures, the collision energy optimization feature in Skyline is more efficient as it can be carried out for multiple targets on LC scale; thereby, standards are dispensable since an empirical collision energy prediction for an endogenous lipid can be utilized.29 To adjust the prediction of collision energy for lipids, we used a commonly applied collision energy (40 V)30 for SPs analysis and utilized 23 of the identified SPs (Table S4) and their LCB fragment ions from the OP9 sample and prepared an experiment to revise the default linear equation (created for peptide analysis). This experiment covered a precursor m/z range from 500−1000. With the default equation, collision energy optimization was carried out with a total of ten steps based on the predicted collision energy value, that is, ± 5 V, ± 4 V, ± 3 V, ± 2 V, and ± 1 V. As proposed by Sherwood et al.,31 the precursor ion mass can be used to code for transition and collision energy simultaneously; therefore, each m/z value can be altered with a subtle mass alteration, with no effect on precursor ion selection, and be assigned to a different collision energy. The initial predicted collision energy value was assigned to the exact product m/z value, while the remaining 10 collision energy values were assigned to m/z varying in 5-hundredths from the true product m/z. These transitions were exported from Skyline with the collision energy optimization feature (Table 2). In total, we monitored 253 transitions with 11

the improvement of the optimized collision energy equation, we compared the peak areas acquired from optimized collision energy and nonoptimized collision energy (40 V) for the LCB fragment ions of different SPs (Figure 5C). The observed improvements were significant; especially for the detection of DiHexCer, an intensity increase between 200 and 550% was achieved. Additionally, we conducted further adjustments of equation coefficients for a specific fragment ion of hexose containing ceramides (Table 2) and obtained a 40% increase in intensity for the neutral loss of a hexose derived fragment ion in DiHexCer. This enabled us to scan for two distinct fragment ions from individual DiHexCer with respectively optimized collision energy. Following the same procedure, another instrument parameter such as declustering potential (DP) can be optimized. We optimized the DP for 23 SPs but achieved only a modest intensity increase (10%) compared to the nonoptimized DP (100 V) and conclude that DP optimization can be neglected in this case (data not shown). However, it might be useful for other lipids, as in case of sphingosines, where DP optimization led to a considerable increase of 70% (Figure S4). Nonetheless, it should be mentioned that currently only one linear equation at a time can be employed, which makes it still necessary to export multiple method files from Skyline to use all optimized features at once. All in all, the versatility of the here-applied method to employ different collision energy to maximize sensitivity on multiple fragment ions was demonstrated, and therefore collision energy optimization “on the fly” has a great potential to generate large sets of optimized lipid species for targeted lipidomics in high-throughput assays.

Table 2. Equation Coefficients for Collision Energy Optimization

To demonstrate the applicability of our developed and optimized SP assay, we absolutely quantified the identified SP species at the fatty acid scan species level in OP9 cells. Therefore, we analyzed 144 transitions (108 in positive mode, 36 in negative mode) in a scheduled SRM analysis with optimized collision energy (Figure 6). To evaluate the limit of detection (LOD), the influence of matrix components was investigated. Therefore, calibration was conducted for each SP class (Cer, SM, GlcCer, LacCer, So, and Sa) by combining the dried CerMix standards with OP9 cell extracts32 using amounts ranging from 0.2−5000 fmol (0.2, 1, 5, 10, 50, 500, 5000 fmol), respectively (Table 3). Peak integration was done directly in Skyline after visual inspection of the obtained EICs. The linear equations were derived from calibration curves (Figure S3), which covered a dynamic range of 4−5 orders of magnitude with excellent coefficient of determination values (0.990− 1.000) (Figure S3). The limit of quantification (LOQ) was defined as the lowest amount in the calibration curve with a signal-to-noise ratio of 10. As a result, LOQs of 0.2 fmol for Cer d18:1/12:0 and GlcCer d18:1/12:0, 1.0 fmol for SM d18:1/ 12:0, LacCer d18:1/12:0, So 17:1, and Sa 17:0 were determined for the used conditions. The LOD was calculated as 0.06 fmol for Cer d18:1/12:0 and GlcCer d18:1/12:0, 0.3 fmol for SM d18:1/12:0, LacCer d18:1/12:0, So 17:1, and Sa 17:0 by applying a signal-to-noise ratio of 3 (Table 3). For quantification of SPs, a strategy described by Ogiso et al. and Lieser et al.27,33 was applied. Briefly, for each ceramide class, an internal standard was spiked in, and all samples were normalized first to the lipid internal standard and then to the protein content (1.1 mg/1 × 106 OP9 cells). In the end, because of the use of (i) multiple fragments per species, (ii)

setting ABI 5500 Qtrap ABI 5500 Qtrap (revised) Qtrap 6500 SP LCB posa Qtrap 6500 hexose posb

charge

slope

intercept

step size

step count

1 1

0.036 0.036

8.857 8.857

1 4

5 5

1

0.0402

16.1538

1

5

1

0.0179

17.7522

1

5

Quantitative Analysis of Sphingolipids in OP9 Cells

a

To optimize collision energy for transitions of LCB fragments in positive mode. bTo optimize collision energy for transitions of neutral loss of hexose fragments in positive mode.

collision energies per species in a single LC−ESI−MS experiment. Notably, the 11 evaluated collision energy values were not covering the optimal collision energy range for the targeted lipids since the default equation was set for peptides. Therefore, we changed the step size to 4 V. The precursor ion with the exact mass was assigned to the center with a collision energy of 40 V, and from this center, additional five steps to lower and higher collision energy were applied to define the optimal collision energy for the fragment ion of interest (Figure 5A). On the basis of the acquired MS data, we performed the adjustment of the equation coefficients for lipids, allowing Skyline to automatically select the appropriate collision energy of the specific SP transitions (Figure 5B). The new linear equation parameters (Table 2) can now be used to predict the correct collision energy values for further SP species. To verify 297


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Figure 5. Adjustments of the collision energy (CE) optimization function in Skyline for sphingolipids. (A) The workflow includes three parts: changing the peptide settings, parameter adjustment for CE optimization, and accepting the final lipid settings. Twenty-three SPs and their LCB fragment ions were chosen from OP9 cells to be optimized. The transition lists were exported with revised setting for an “ABI 5500 QTrap”. After the SRM data were aquired and imported, Skyline automatically selected the CE for each ceramide transition that gave best intensity. Subsequently Skyline calculated the equation coefficients based on the precursor m/z and the selected CE of chosen SPs. (B) Skyline peak area view of the LCB of Cer d18:1/16:0. The peak areas were obtained at different CE voltages and normalized according to the highest peak area. (C) Comparison of intensities between nonoptimized CE (40 V) and optimized CE. Each measurement was carried out in triplicate. ∗p < 0.05, ∗∗p < 0.01.

Figure 6. SRM total ion chromatogram of sphingolipids in OP9 cells in positive mode. The SRM chromatogram displays all transitions in positive mode. Thereby, the SPs were separated on an Ascentis Express C18 main column (150 mm × 2.1 mm, 2.7 μm, Supelco) at 60 °C with a flow rate of 0.5 mL/min using a nonlinear gradient.

collision energy optimization, and (iii) internal SP standards, we were able to achieve a highly confident quantification of 72

SP ranging from 0.1−3778.55 pmol per mg of protein, resulting in a dynamic range of five orders of magnitude and 298


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Journal of Proteome Research Table 3. Dynamic Range and Limit of Detection and Quantification for Sphingolipid Standards linear equationsa

ceramide classes Cer d18:1/12:0 SM d18:1/12:0 GlcCer d18:1/12:0 LacCer d18:1/12:0 So 17:1 Sa 17:0

y y y y y y

= = = = = =

0.7590x 0.8273x 0.7848x 0.8483x 0.6750x 0.8171x

+ + + + + +

16.1416 15.4288 16.3723 16.6866 13.5688 15.5021

r2b

range (fmol)

LOQc (fmol)

LODd (fmol)

RSDe(%)

0.990 0.999 0.995 1.000 0.996 0.997

0.2−5000 1−5000 0.2−5000 1−5000 1−5000 1−5000

0.2 1.0 0.2 1.0 1.0 1.0

0.06 0.30 0.06 0.30 0.30 0.30

1.7 0.18 9.32 1.09 4.51 10.43

a y = lg10(peak area), x = lg10(mol). br2, correlation coefficient. cLOQ, limit of quantification. dLOD, limit of detection. eRSD, relative standard deviation, at loading amount of 0.05 pmol.

Figure 7. Absolute quantification of different sphingolipids. Lipids were extracted by the MTBE/MeOH protocol with the corresponding class specific internal standards for SPs. Thereby, the different ceramide classes were identified and quantified by LC−MS using scheduled SRM. The sphingolipids were quantified by applying internal standards and using normalization to the detected protein content. (A) Absolute amount of ceramides; (B) sphingomyelin, sphingosine, sphinganine, hexosylceramide, and dihexosylceramide in pmol/mg of protein in OP9 cells.

demonstrating the versatility and flexibility of this approach for targeted lipidomics (Figure 7). The building blocks for creation of transitions for various lipid classes as well as the optimized collision energy for SPs are provided in Figure S5 and Table S3. Overall, our results are in agreement with previously published reports of screening for similar SP classes. In the study of Ogiso et al., 36 SPs were identified in tissues from SM synthase 2-knockout mice through a typical sphingolipid profiling strategy by monitoring only the sphingoid base backbone (LCB fragment ion).33 However, in our study, multiple fragment ions (class-specific fragments and fatty acid

chain-specific fragments) were monitored to increase the confidence of the results. The work presented here resulted in the characterization of 72 SPs using multiple fragment ions and therefore is the most stringent SRM lipid approach for this cell system to date. A previous presentation of Skyline developers indicated that a targeted analysis of small molecules is feasible by applying Skyline; however, their efforts focused solely on single transitions rather to develop a comprehensive toolbox to create various transitions per target analyte. Hence, to develop more confident assays, we created a general approach to target 299



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multiple fragment ions for single species among all major lipid classes. In addition, the workflow presented here is also applicable for targeted workflows carried out on high mass accuracy instruments, as Skyline can utilize masses with up to four decimal places and can be applied for both positive and negative polarity modes.

CONCLUSION In summary, we present a “Plug and Play” workflow for lipidomics in response to the demand of a dedicated highthroughput targeted software platform. The adaptation of the targeted method design, data analysis, and evaluation procedures of Skyline for lipidomics demonstrated the applicability of this workflow for the deep profiling of sphingolipids in mouse mesenchymal stem cells. The adaptation of Skyline for cross-vendor targeted lipidomics allowed in a time efficient way to (i) create transitions and assays, (ii) optimize collision energies, (iii) visually review the obtained results, and (iv) quantify the lipids of interest. For future directions, the here presented building block based adaptation of Skyline breaks ground since this workflow will not only simplify method design, analysis, and data evaluation in targeted lipidomics, but also further support the introduction of spectral libraries and the sharing of experimental data to build up a broad vendor independent exchange platform for the lipidomics research community. The here presented Skyline approach should be regarded as an interim technique that would be made even easier if Skyline natively supported lipidomics. As a rising scientific discipline, along with the growing interest for lipidomics, robust and easy accessible tools for method development and data processing will be the critical subject in this research field. Therefore, Skyline will become an important cornerstone of lipid research in the near future.

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ABBREVIATIONS LCB, long chain base; HG, headgroup; FA, fatty acid chain; SLC, single-letter database codes; NL, neutral loss; Cer(d), ceramide; Cer(t), phytoceramide; hCer, 2-hydroxy ceramide; CerP, ceramide-1-phosphate; HexCer, hexosylceramide; DiHexCer, dihexosylceramide; SM, sphingomyelin; GP, glycerophospholipid; PC, phosphatidylcholine; PG, phosphatidylglycerol; PS, phosphatidylserine; CL, cardiolipin; GL, glycerolipids; TG, triacylglycerol; So, sphingosine; Sa, sphinganine; CE, cholesteryl ester; LysoGP, lyso-glycerophospholipid; SP, sphingolipid; ESI, electrospray ionization; MS/MS, tandem mass spectrometry; SRM, selected reaction monitoring; LC− MS, liquid chromatography coupled mass spectrometry

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REFERENCES

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ASSOCIATED CONTENT

* Supporting Information S

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jproteome.5b00841. Workflow for Skyline adaption; adaptation principle A/B for different lipid classes; structural modification window in Skyline; calibration curves for sphingolipid standards in OP9 cell extracts; DP optimization for sphingosine (d18:1) standard; example of the Skyline interface for targeted lipidomics analysis of sphingolipids; constants for amino acid modification; modified mass list of adaptation principles for sphingolipids, phosphoglycerolipids, phosphatidylcholine, sphingomyelin; transitions of identified sphingolipids with optimized collision energy; sphingolipids and their LCB fragment ions utilized for parameter optimization; supporting files can be downloaded at https://cld.isas.de/index.php/s/ t1nViP6XIsfCWcP. Password: skyline (PDF)

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ACKNOWLEDGMENTS

The authors thank Michelle Protzek for help on cell culture. We also thank Cristina Coman for helpful discussions and Andreas Hentschel, Ulf Bergmann, and René P. Zahedi for critical review of the manuscript. This work is supported by the Ministry for Innovation, Science, and Research of the Federal State of North Rhine-Westphalia and the Federal Ministry of Education and Research in Germany.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Phone: +49(0)231 1392 4173. Notes

The authors declare no competing financial interest. 300


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