NSI MS Refines Lipidomics by Enhancing Lipid Coverage

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Nano-LC/NSI MS refines lipidomics by enhancing lipid coverage, measurement sensitivity, and linear dynamic range Niklas Danne-Rasche, Cristina Coman, and Robert Ahrends Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b01275 • Publication Date (Web): 24 May 2018 Downloaded from http://pubs.acs.org on May 25, 2018

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Analytical Chemistry

Niklas Danne-Rasche, Cristina Coman and Robert Ahrends* Leibniz-Institut für Analytische Wissenschaften-ISAS e.V. Otto-Hahn-Str. 6b, 44227 Dortmund, Germany

ABSTRACT: Nano-liquid chromatography (nLC) - nano-electrospray (NSI) is one of the cornerstones of mass spectrometry based bioanalytics. Nevertheless, the application of nLC is not prevailing lipid analyses so far. In this study, we established a reproducible nLC separation for global lipidomics and describe the merits of using such a miniaturized system for lipid analyses. In order to enable comprehensive lipid analyses that is not restricted to specific lipid classes, we particularly optimized sample preparation conditions and reversed phase separation parameters. We further benchmarked the developed nLC system to a commonly used high flow HPLC/ESI MS system in terms of lipidome coverage and sensitivity. The comparison revealed an intensity gain between two and three orders of magnitude for individual lipid classes and an increase in the linear dynamic range of up to two orders of magnitude. Furthermore, the analysis of the yeast lipidome using nLC/NSI resulted in more than a 3-fold gain in lipid identifications. All in all, we identified 447 lipids from the core phospholipid lipid classes (PA, PE, PC, PS, PG and PI) in Saccharomyces cerevisiae. 6

. However, until now nLC systems did not attract much attention for lipid analyses and are far away from being as routinely used as this is the case for proteomics. The reasons are manifold and range from easily introducible and arduously detectable leaks and dead volumes, which cause peak broadening, to fast clogging of columns and emitters by sub microscopic solids or salts7-8. Consequently, the general challenges in handling nLC systems are demanding and require well trained operators to generate reproducible results. Nevertheless, nano-scale analytics payoff with unprecedented efficiency and sensitivity that is essential when access to biological material is limited 9 or tissue slices need to be analyzed10. In comparison to conventional electrospray ionization (ESI), nano electrospray (NSI) generates smaller, heavily charged initial droplets, which maximizes desolvation and ionization processes11-12. Furthermore, NSI exhibits higher tolerances for nonvolatile salts13, which are often used in LC-MS based lipid analyses as mobile phase modifiers14. Hence, nLC-MS systems offer the possibility to enhance both ionization efficiencies and signal to noise levels and thereby increase the overall measurement sensitivity for target analytes.

Lipidomics is a fast growing discipline of the metabolomics field and targets the comprehensive and quantitative analysis of all lipid compounds in a given biological system1. Current lipidomics studies are predominantly based on mass spectrometry and either use a direct infusion of lipid extracts (i.e. shotgunlipidomics) or rely on liquid chromatography (LC) based separation of lipids prior to MS detection. Thereby, LC has the potential to overcome drawbacks of direct infusion approaches such as strong ion suppression and the missing separation of isobaric species. Currently, two major strategies are employed for LC separation. If a separation of lipid classes is desired (separation mainly by head group polarity), hydrophilic interaction liquid chromatography (HILIC) or normal phase LC (NPLC) approaches are carried out. Compared to this, individual lipid species can be separated by means of reversed phase chromatography2. Most LC-based lipidomics studies are currently performed using micro, narrow- or analytical bore columns2 as opposed to the proteomics field, where nano-bore chromatography is applied by default for non-targeted discovery approaches. In proteomics, the miniaturization of the chromatography successfully enhanced protein coverage and measurement sensitivity3-

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The merits of nano-scale analytics do not only arise from better ionization efficiencies, but are also explained by chromatographic advantages: When the sample is injected onto the column, it is radially diluted on a narrow bore LC system. This goes along with a decrease in the peak concentrations of target analytes that pass a concentration sensitive detector. By downscaling the inner column diameter, a suchlike chromatographic dilution is reduced. Hence, a 440 fold gain in sensitivity can be expected by replacing a 2.1 mm ID column with a 100 µm ID column15 (concentration factor = [2.1 mm/0.1 mm]²). In a similar fashion, solvent consumption is drastically reduced, which diminishes waste disposal and costs as well operator´s time to maintain the system. All in all, nLC/NSI systems provide a strategy to come closer towards the goal of comprehensive lipidome coverage even when sample availability is limited. However, despite the apparent advantages of nLC-MS systems for lipid analyses, this promising technology has not yet settled down in the lipidomics community even though several labs already applied nano/capillary chromatographic systems for (often class restricted) lipid analyses22, 38-43. Nano-scale chromatography for lipidomics appears only to be routinely used by the Moon lab so far35-36, 4446 . In this study, we demonstrate an advanced nLC system for lipidomics and present solutions for constrains in sample loading and separation. We further demonstrate the robustness of the developed system and benchmark the performance of the nLC system to a comparative narrow bore LC system in terms of sensitivity and lipidome coverage. For the assessment of the lipidome coverage, we chose Saccharomyces cerevisiae as a model system, since S. cerevisiae serves as a general eukaryotic model system, most lipid classes can be found in it, a decent lipidome coverage has been reported, and the lipidome complexity is supposed to be small due to the presence of solely saturated and monounsaturated fatty acids16-18.

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wash steps) are available in Table S2. The injection volume was 1 µL. Narrow bore HPLC was performed using an Ultimate™ 3000 System (Thermo Scientific, Germany) equipped with a WPS3000TFC Analytical Autosampler, a Flow Manager FLM3200B and a HPG-3400SD binary pump. An Ascentis Express C18 column (150 mm × 2.1 mm, 2.7 µm; 90 Å; Supelco) was used as the main column, which was protected with a guard cartage (5 mm × 2.1 mm, 2.7 µm, Supelco). Separation was performed at 60 °C at a flow rate of 500 µL/min. Samples were injected at a volume of 1 or 5 µL. Detailed gradient information is given in Table S2. The total method duration was 35 min. Mass spectrometry High resolution mass spectrometric analyses were performed using a QExactive Plus (Thermo Scientific, Bremen, Germany) mass spectrometer either using data dependent acquisition (DDA) or parallel reaction monitoring (PRM) (Table S3). S-Lens RF level was set to 60 and capillary temperature were 250 °C (nLC) and 320 °C (HPLC), respectively. Data were collected in both positive and negative ionization modes. Collision energies for targeted analyses were optimized by direct infusion experiments (Table S4). Data analysis Data from targeted experiments were analyzed using Skyline (64-bit) 3.5.0.931919-20. Product ion or neutral loss fragments were monitored with a tolerance of 5 mDa and were manually integrated. Semi-automated data analysis of non-targeted data was performed as described in the results part utilizing Skyline and the in-house developed tool LipidCreator.

Sample loading Although modern definitions categorize lipids based on biosynthetic origin21, lipids are commonly regarded as hydrophobic, hardly water-soluble biomolecules. For this reason, lipid extracts should be dissolved in organic solvents that are best capable of (i) fully dissolving all lipids, (ii) preventing degradations and oxidations, and (iii) are compatible with the initial HPLC mobile phase2. So far, no universal applicable solvent appears to prevail lipidomics studies considering the vast number of solvent mixtures that are applied to dissolve lipid extracts2 and the over 30 orders of magnitude on the octanol/water partitioning scale to cover14. In initial experiments, isopropanol (IPA) was tested as a resuspension solvent for lipid extracts. Lipid solutions in IPA:ACN 9:1 could be injected in low volumes (1-5 µL) on a narrow bore HPLC without having an impact on chromatography. However, when testing this solvent on the reversed phase nLC system, it was observed that lipids are not properly retained, but are rather flushed with the injection volume through the column resulting in a distorted chromatography or even an elution within the injection peak (Figure S3). In order to avoid such a loss of lipids during loading procedure, injections of higher volumes using a trap column 22 were not considered as applicable for nLC based lipid analyses. Instead, the analytical column was directly connected to the pump via the autosampler valve, allowing a direct injection of small volumes (1 µL) onto the column without collapsing the flow.

Chemicals and Materials Lipid standards, solvents, reagents and components of buffer solutions are listed in the supporting information (Table S1). The utilized lipid notation is visualized in Figure S2. Both lipids, and associated mass spectra were notated according to Pauling et al. (2017)37. Yeast Extraction Extraction of yeast (S. cerevisiae FW1511) lipids was performed after Ejsing et al. (2009)17. Extraction details are described in the supporting information. Liquid chromatography For nLC separations, an Ultimate™ 3000 System (Thermo Scientific, Germany) was used (Nano/Cap System NCS -3500RS pump with a WPS-3000T PL RS Pulled-Loop-Autosampler). The flowrate was set to 600 nL/min and the column compartment was heated to 60 °C. Inhouse packed nano-bore columns (100 µm x 30 cm) were prepared with Ascentis Express C18 core shell material (2.7 µm; 90 Å; Supelco) as described in the supplementary information. ACN:H2O 60:40 (v/v) was used as weak eluent (A), while ACN:IPA 10:90 (v/v) was used as the strong eluent (B). Both eluents were supplemented with 0.1 % formic acid and 10 mM ammonium formate. Details of the 110 min gradient (including


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Figure 1. Lipid detectability is dependent on resuspension solvent. Selected lipids of different hydrophobicity were dissolved in the indicated solvents (Ref.1: Sarafian et al.25; Ref.2: Bird et al.24) and 1 µL of a 240 nM solution were directly injected onto the nano bore column without using a precolumn. A) The chromatograms show that peak shape and recovery are strongly influenced by the resuspension solvent. B) For replicate evaluation (n=3), the peak height in resuspension condition with the highest signal was set to 100% and the other obtained peak heights relative to that. C) The hydrophobicity of the used lipid standards can be estimated on predicted octanol/water partition coefficient XlogP3 values27. A complete set of chromatograms can be found in Figure S4; statistical evaluations are shown in Figure S5.

Additional tests revealed that non-polar and hydrophobic lipids such as steryl esters and triglycerides do not dissolve in the often utilized initial mobile phase (ACN:H2O 6:4)23-26, while stronger organic resuspension solvents24-25, provoke similar distorting effects as terminal HPLC conditions (ACN:IPA 1:9) (Figure 1A). To obtain both sharp chromatographic peak shapes and acceptable recoveries for all lipids, different solvent mixtures were screened (Table S5) and finally, a one-phasic mixture of 1-BuOH:IPA:H2O 8:23:69 (8Bu) was chosen as a reasonable compromise for a global lipidomics application (Figure 1A/B). Notably, hydrophilic lipids that are dissolved in 8Bu such as LPS 17:1, produced even sharper peaks (30% reduction of FWHM) when compared to a dissolving in the initial mobile phase (60 % ACN) (Figure S5). The peak intensity of LPS 17:1 could thus be increased by a factor of 2.3. However, the chosen solvent mixture is still a concession, since strong hydrophobic lipids such as TAGs and steryl esters were not completely dissolvable in 8Bu. The biggest proportion of lipids though, i.e. lipids ranging from LPS 17:1 to DAG 32:0 (spanning 13 orders of hydrophobicity (XlogP3)27; Figure 1C), were effectively dissolvable in 8Bu without losing analytes or having a negative impact on chromatography, which demonstrates its applicability for a global lipidomics approach. Improving chromatography for lipids with free phosphate groups Even though the peak shape of most lipids was improved by usage of the resuspension solvent 8Bu, some lipids

remained hardly detectable and could neither be properly separated, nor quantified. Those lipids´ shared features were terminal phosphate groups at the sn3 position of glycerol backbones (PA, LPA) or phosphorylations of the 1-hydrodxy group of sphingoid bases (LCBP, CerP). Some authors report that addition of phosphoric acid to mobile phases and the injector rinsing solvent can reduce peak tailing of these lipids with terminal phosphates28-29, which can be attributed to a blocking of metalphosphate interactions on flow passage material such as the injection needle30-31. However, the addition of 5 µM phosphoric acid to the eluents did not improve the detection properties of lipids with terminal phosphates (Figure S6). Interestingly, when 5 mM phosphoric acid was supplemented to the sample resuspension solvent, lipids with terminal phosphates became detectable even when no H3PO4 was present in the elution solvents (Figure 2). The phosphoric acid supplementation drastically reduced the chromatographic tailing and thereby increased the peak height between 8 and 68 fold for LCBP, LPA, CerP and PA. Remarkably, also peak area and height of cardiolipins increased 20- and 55- fold, respectively. Furthermore, phosphoric acid addition to the samples did not only condense the retention, but also the peak width of CL, (L)PS, PE, and of especially choline group carrying lipids such as (L)PC and SM thus increasing the peak height of these choline analytes over 50 % (Figure S7).



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Figure 2. Phosphoric acid influences peak shape of lipids with terminal phosphate groups. Selected lipids of different lipids classes were dissolved in 8Bu (1-BuOH:IPA:H2O 8:23:69) or 8Bu+ (1-BuOH:IPA:H2O 8:23:69 + 5 mM H3PO4). Addition of phosphoric acid to the samples clearly improved peak shape and MS response of both lipids with terminal phosphates and cardiolipins (black chromatograms), while other lipids like PG were not affected by a sample supplementation with phosphoric acid (grey chromatograms). The analysis was performed under standard elution conditions. The supplementation of phosphoric acid to the LC eluents did not display a distinguishable effect compared to the detection properties without eluent supplementation (Figure S6).

A long term memory effect of phosphoric acid sample supplementation was not observed since successive measurements without phosphoric acid supplementation, which were injected after analyses of samples with phosphoric acid, exhibited poor chromatographic behavior again. nLC allows reproducible lipid separation Especially, quantitative analytical studies require a high repeatability of separations. In order to benchmark the stability of the developed nLC/MS method, a yeast lipid extract was prepared and spiked with 23 standards from all major lipid classes. Aliquots of this extract were analyzed in 25 repetitive measurements by targeted analyses of both 43 endogenous lipids and 23 spiked in standards. Over a measuring period of two days, an average retention time (RT) standard deviation of 5.2 ± 2.3 s (determined based on 66 lipids) was achieved, which demonstrates the RT stability of the nLC method. The maximal RT deviations over the 25 repetitive analyses ranged from 8.4 to 45.6 seconds. Apparently, RT stability is strongly dependent on the slope of the gradient, as lipids that were separated in the flatter gradient regime showed higher deviations in their retention time (Figure S8, Table S6). By contrast, MS response was most robust in the middle gradient regime (20-60 min; elution of PL and Cer; RSD < 10 %), while it was slightly less stable at the beginning of the gradient (elution of lysoPL and sphingoid bases; RSD ~ 10%) (Figure S8, Table S6). In gradient regimes over 90 % B, where almost no water was present, the spray stability was significantly reduced, which explains why late eluting hydrophobic lipids such as long chain TAGs and steryl esters were measured in a less reproducible manner (RSD > 15%). Interestingly, the detection

of some lipids such as cardiolipins seems to be less error prone since they could be quantified with RSD below 15 %. In order to decrease the observed intensity deviations, retention time based and class based normalization approaches were carried out post analysis. For some lipids, RT based normalization (Figure S8D) seems to be favorable. The peak area RSD of LPC 16:1 for instance could be reduced from 14.8 % to 3.8 % by RT based normalization. Other lipids such as cardiolipins should be normalized by a standard of the same lipid class. Measurement instabilities could thus be reduced from 14.3 % to 7.1 % RSD for CL 72:4. In a best case scenario, isotopically labeled internal standards32 could serve for normalization purposes, but were not applied in this study. In general, it should be considered to reduce the measurement instabilities for late eluting hydrophobic molecules e.g. by optimizing the commonly used eluent system for lipidomics (A: ACN:H2O 6:4; B: ACN:IPA 1:9)23-26 or by applying a sheath liquid at high organic solvent compositions. Measurement sensitivity and dynamic range are enhanced by usage of nLC Lipids of different categories and classes vary in extraction, ionization and fragmentation efficiencies. Differences in physico-chemical properties thus determine both class and species specific detection limits (LOD/LOQ) as well as the linear dynamic quantification range. Since application of nLC was already shown to maximize measurement sensitivity and analyte coverage for proteomics3, 5, similar effects can be expected for lipid analyses. So far, to the best of our knowledge, no direct comparison between a nano and narrow bore HPLC system was conducted for a lipidomics approach. In order to benchmark the expected gain in sensitivity, a yeast lipid extract was spiked with 22


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Figure 3. Benchmark of the linear range. A) The sensitivity and the linear dynamic range of the nLC-MS method were benchmarked against a corresponding narrow bore HPLC method in both positive and negative ionization mode for different lipid standards that were supplemented to a yeast lipid extract. The dynamic ranges of the respective classes were extrapolated using following standards: LCB_a from LCB 17:1;2 LCB_b from LCB 17:0;2, LCBP_a from LCBP 17:0;2; LCBP _b from LCBP 17:1;2; Cer and CerP from 18:1;2/12:0;0; LPS, LPG and LPA from their 17:1 species, LPC from LPC 13:0; PA, PC, PE, PG, PI and PS from their 17:0/14:1 species; CL from CL 15:0(3)/16:1; DAG from DAG 17:0/17:0 d5; TAG from TAG 17:0-17:1-17:0 d5. A more detailed summary of the sensitivity evaluation is depicted in Table S7. B) and C) exemplary show the MS response (peak area) of the standards GlcCer 18:1;2/12:0;0 and PE 17:0/14:1 in dependency of the injection amount. Correlation plots for the other lipids are shown in Figure S9.

(nLC). Notably, 15 out of 22 lipids could be measured in positive mode on the nLC over four orders of magnitude ranging from 640 amol to 10 pmol. However, especially in positive mode, a strong matrix response was recorded for phospholipids that was not observed for other lipid categories (Figure 3B/C). PE 17:0/14:1 for example was only linearly quantifiable over 2 orders of magnitude using the HPLC system (80-10000 fmol) and almost three orders of magnitude on nLC system (16-10000 fmol) in positive mode (Figure 3B). This is due to the fact, that coeluting isomers generate the same principal head group loss related fragment which was used for analysis of phospholipids in positive ion mode (e.g. –PE(141)). In case different coeluting isomers of the same lipid species were present in the yeast lipid extract such as for PE 31:1 (Figure S10), the dynamic range was reduced even if the standards were “true” artificial standards and were not endogenously identifiable in the yeast lipid extract, which was the case for several lipids as well. Therefore, before starting quantitative assay development, internal standards should be carefully chosen. Furthermore, before quantitative analysis of low abundant species potential carry over effects from previously analyzed samples should be considered.

standards from 20 lipid classes to yield final standard concentrations between 640 amol/µL and 10 pmol/µL within a constant sample matrix. These samples were analyzed on both systems comparatively. For both LC methods, the same stationary and mobile phase system was used. However, a shorter and steeper gradient was selected for the narrow bore HPLC method, as peak broadening and loss of intensity was observed for late eluting lipids when the nLC gradient was applied on the narrow bore HPLC column. Additionally, lipids were measured in both positive mode and negative mode in order to curtail the working range in both ion modes comparatively. nLC-MS increased the linear dynamic range in 16 of 22 cases in negative mode and in 15 of 22 cases in positive mode for at least one order of magnitude (Figure 3A). PE 17:0/14:1 for instance, could be measured over two orders of magnitude (162000 fmol on column) in negative mode on the HPLC system while it could be linearly detected over 3 orders of magnitude (0.64-2000 fmol on collumn) on the nLC system. Additionally, the linear range was also greater in positive than in negative mode for 14/22 lipids (HPLC) and 17/22 lipids



In most cases the carryover was below 1% as determined from peak areas of the foremost wash-blank following extract analysis (Figure S13).

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All in all, using a combination of nLC and NSI increased the sensitivity about 2-3 orders of magnitude thereby granting subfemtomol quantification limits for 7 out of 22 lipids in negative mode and 11 out of 22 lipids in positive mode (Table S7).

Figure 4. Lipid identification exemplified on PC. Using the semi-automated workflow, lipids can be characterized by the three analytical dimension retention time, exact precursor mass and exact MS/MS fragments. A) A complete separation of lipid isomers of the same species was not achieved. Hence, mixed spectra derived from several coeluting isomers were often identified in a single MS2 spectrum. B) Lipids with longer acyl chains clearly separate by the cumulative amount of carbon atoms and degree of unsaturation in the fatty acyl chains, allowing gradient and exact precursor mass dependent identification of lipids, even when no MS2 is available. These putative IDs were marked by hollow shapes. C) The lipidome can be easily visualized by the depicted heat map. The left part visualizes the peak area [log10] of the IDs on species level, while the right part discloses the number of detected isomers on molecular species level per lipid species. Exemplary chromatograms and MS/MS spectra for further phospholipid classes are depicted in Figure S13.


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Figure 5. Benchmark of the yeast lipidome coverage. Using the semi-automated analysis approach, a total of 447 lipids on molecular species level could be identified belonging to the principal phospholipid classes (PC, PI, PA, PG, PE and PS). A) The Venn diagram shows the unique and shared identifications in the different analysis conditions. B) depicts the summarized phospholipid identifications across (PC, PI, PA, PG, PE and PS) in the different analysis conditions on both molecular species level and species level. C) The distribution of lipid molecular species is shown for the respective phospholipid classes more precisely; for individual values including putative IDs refer to Table S9 and the supplementary excel sheets. Exemplary nLC chromatograms and MS/MS spectra of lipid classes/categories that were not considered for method comparison purposes, but were well detectable in the yeast lipid extract using the nLC method are depicted in Figure S14.

over 90 % of the relative PC moiety (based on total summarized PC intensities), while the smallest proportion (< 1 % of PC overall intensity) was cumulated from 60 % of the species. A similar distribution was also observed for the other phospholipid classes, which were generally not that diverse compared to PC (and PI) (Figure S11). The yeast phospholipidome is dominated by PC, PI and PE (Figure S12) as previously quantitatively described by shotgunlipidomics17. However, the application of nLC reveals a more comprehensive picture of lipid diversity on both species level and molecular species level. Especially odd chain fatty acids do not seem occur exceptionally, but are rather commonly incorporated into all phospholipid classes (supplementary excel sheets). Nevertheless, odd chain lipid species were generally less abundant than (double-) even chain counterparts. Interestingly, lipid species are not uniformly distributed within the different phospholipid classes: While species distribution of PA, PE, PG and PS exhibited a similar pattern, the array of PC and PI was much more diverse and displayed an altered distribution of lipid species within the respective classes (Figure S11, supplementary excel sheets). Notably, PI lipids might be subdivided into three clusters: (i) short and saturated PIs, (ii) mono- and di-unsaturated species (C30-C36) and (iii) very long monounsaturated species that are composed of an often unsaturated long chain fatty acid (C16-C18) and a very long, mostly saturated fatty acyl chain (VLCFA) with a carbon load of C22-C28. The latter were also found for phosphatidylcholines, but were less common in other lipid classes. nLC increases the lipidome coverage In order to compare the number of lipid identifications between a state-of-the-art HPLC set-up and the nLC system, the same sample aliquots from the nLC based analyses were also used on narrow bore HPLC. Furthermore, the same extract aliquots was measured on the narrow bore HPLC in a 25 times higher concentration to achieve similar base peak intensities on the HPLC system compared the nLC system (Table S8). This procedure allowed to compare the performance of both systems, when i) same extract

Assessment of the lipidome coverage One of the core motivations to develop a nLC/MS based lipidomics approach was to pursue comprehensive and in-depth lipid identification. For this reason, a non-targeted analysis of a yeast lipid extract was performed to benchmark the lipidome coverage on the example of phospholipids. However, contrasting to the expectations of an eased data analyses by commercial software, an enormous number of false positive annotations of lipid spectra and peaks was observed, while many true positive lipids were omitted and not discovered. Therefore, a discrete workflow was developed using the in-house developed software tool LipidCreator, which enabled semiautonomous data analyses of non-targeted data. Briefly, target phospholipid classes (PI, PG, PA, PE, PC and PS) were permutated in their fatty acid combinations (FA 8:0 – FA 30:2 generating PL 16:0 to PL 60:4) and imported into Skyline. Subsequently, the relevant lipid identification information was manually analyzed by (i) exact precursor mass on MS1 level (Δ m/z < 3 ppm), (ii) qualifier ions that identify the lipid class and (iii) fatty acyl fragments that verify the lipid identification on molecular species level (e.g. PC 14:1-15:0) (Figure S2). Critical spectra were additionally manually confirmed using Xcalibur 3.0.63. The workflow is more precisely described in the supplementary methods. The yeast phospholipidome Exemplary for a phospholipid lipid class, the distribution of phosphatidylcholines (PC) in yeast is shown in Figure 4. A total of 137 PCs on molecular species level were detected, which are associated to 59 PCs on species level (e.g. PC 29:1). Up to 6 different isomers on molecular species level were identified for a single PC species (Figure 4A). Furthermore, 9 putative PCs (no MS2 triggered) on species level were annotated by RT and exact mass (Figure 4B). Principal lipid species were even chain species (C30-C36) with one or two double bonds (Figure 4C). Single fatty acids with more than 2 double bonds were not found, while fatty acids with 2 double bonds were extremely rare (Fig S11). Interestingly, few PC species dominate this phospholipid class: Only a few major PC species (13% of IDs) were found contribute to



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cluding 381 molecular species level phospholipids)33, the presented data still significantly exceed the number of detected phospholipids. Notably, also other researchers pursued a miniaturization of the chromatography for lipid analysis pioneering from Taguchi et al. (2000)34 and Bang et al. (2006)35. Gao et al. (2012)22 were able to describe 238 species level phospholipids (446 species level lipids) in a human cell line, while Park et al. (2017)36 analyzed 169 (mixed species level and molecular species level) phospholipids (412 total lipids) in skeletal muscle tissue. Hence, this study is likely setting a new benchmark for confident comprehensive phospholipid datasets.

concentrations are used, and ii) when LC system specific suitable sample concentrations are used. In total, 447 esterified phospholipids on molecular species level were identified in yeast belonging to the central phospholipid classes (PC, PI, PA, PG, PE and PS), whereas only 123 of those IDs were identified in both sample concentrations on both chromatographic systems (Figure 5A). 150 molecular lipid species were not detected in the low concentrated samples on the narrow bore HPLC, but using the nLC-MS system, or in high concentrated samples using the narrow bore HPLC. 162 molecular lipid species were exclusively identified on the nLC-MS system, while only 11 lipids were solely detected on the narrow bore HPLC system. The 436 molecular species lipids, which were found on the nano-system, can be attributed to 202 species level lipids (Figure 5B) (ratio: 2.16 isomers per species). This ratio was much lower on the HPLC system with concentrated sample conditions (284 molecular species level IDs / 151 species level IDs ≙ 1.88 isomers per species). Moreover, compared to the high concentrated sample, less than half of the lipids on molecular species level were detected when the nLC-suitable concentration was measured on the narrow bore HPLC (128 molecular species level IDs / 76 species level IDs ≙ 1.68 isomers per lipid species) (Table S8). Most lipids were identified for PC and PI (137 & 134 IDs) on the nLC system, while the other classes contained significantly less different molecular species (24-64 IDs; Figure 5C). With exception of PA, application of the nLC system yielded about two times more molecular species at comparative MS intensities, while about four times more IDs could be annotated by using the nLC system, when the same extract concentration was used on both chromatographic systems. Taken together, 153 % more molecular species level IDs (≙120% species level IDs) were annotated on the nLC system at comparative intensity to the narrow bore HPLC, whereas 343 % molecular lipid species (≙162% species level IDs) were identified when the same sample amount was used. This strongly stresses the advantage of the introduced nLC-MS in cases when it is essential to identify lipids on the molecular species level such as in identifying precursors of lipid mediators. In summary, application of nLC clearly increased the sensitivity by two to three orders of magnitude that went along with an increase of the linear dynamic range of one to two orders of magnitude. Furthermore, low abundant coeluting lipid isomers, which are likely to be suppressed under standard HPLC conditions, became distinctively detectable due to enhanced ionization efficiencies. On this basis, an exceptional gain of lipid identifications on molecular species level was achieved. The 436 discovered phospholipids on molecular species level belonging to PA, PE, PC, PI, PG and PS (202 species level lipids + 52 putative species level IDs) clearly exceed the quantitative yeast lipidome on molecular lipid level by Ejsing et al. (250 lipids, 81 molecular species level phospholipids), Klose et al. (63 species level phospholipids) and Casanovas et al. (67 species level phospholipids)16-18. Compared to current benchmark lipidomics studies of lung tissue (924 confident lipids in-

Here we demonstrate that nLC is applicable as an analytical tool for global lipid analyses. Nano-flow LC permits comprehensive in-depth analyses of lipid extracts and particularly enables the identification of minor, coeluting species(isomers) within complex samples, which are often not detectable under standard LC conditions as demonstrated by direct comparison to a narrow bore analytical system. These advantages especially emerge when sample availability is limited or low concentrated samples need to be analyzed. All in all, we are convinced that this study is paving the way to implement nLC as a generally applicable analytical tool for deep screening of lipid extracts that require unprecedented lipidome coverage and sensitivity.

The Supporting Information is available free of charge on the ACS Publications website. Supplementary material (.pdf) Lipidome coverage (.xslx)

* Robert Ahrends. Leibniz-Institut für Analytische Wissenschaften ISAS - e.V., Otto-Hahn-Str. 6b, 44227 Dortmund, Germany. Phone: +49 231 1392 4173. Email: [email protected]. The authors declare no competing financial interest.

This work was supported by the „Ministerium für Innovation, Wissenschaft und Forschung des Landes Nordrhein-Westfalen“, the „Senatsverwaltung für Wirtschaft, Technologie und Forschung des Landes Berlin“, and the „Bundesministerium für Bildung und Forschung“ (de.NBI program code 031L0108A). We further thank Bing Peng and Dominik Kopczynski for the development of LipidCreator (manuscript in preparation) and Philipp Westhoff for proofreading the manuscript and valuable discussions.

(1) Rustam, Y. H.; Reid, G. E. Anal Chem 2018, 90, 374-397. (2) Cajka, T.; Fiehn, O. Trends Analyt Chem 2014, 61, 192-206.


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Insert Table of Content artwork here


Page 10 of 15

Solvent used for lipid resuspension

A

25%B

50 % B

100 % B

ACN:H2O 60:40

IPA:ACN:H2O 23:37:30


IPA:ACN 90:10

Ref.1

Ref.2

8Bu


IPA:ACN:H2O 30:65:5

110

BuOH:IPA:H2O 8:23:69

Intensity [106]

1

100 90

A 25%B 50%B 100%B Sarafian Bird 8Bu

80 70 60 50 40 30 20

0

0

10

20 0

10

20

0

10

20 0

10

20 0

10

20

0

10

20 0

10

10

20

0

10 Intensity [106]

130 120

2

C)

5

0 20 10

30

40 20

30

40 20

30

40 20

30

40 20

30

40 20

30

40 20

30

LPS 17:1

PG 17:0/14:1

DAG 16:0/16:0 TAG 14:0/14:0/14:0

Hydrophobicity [logP] 0 LPS 17:1 logP 1.6

40

Intensity [106]

5 5

10 0

50

60

70 50

60

70 50

60

70 50

60

70 50

60

70 50

60

70 50

60

70

PG 17:0/14:1 logP 10.7

100 Intensity [106]

1 2 LPS 3 17:1 4 5 6 7 PG 17:0/14:1 8 9 10 11 DAG 12 16:0/16:0 13 14 15 16 TAG 1714:0/14:0/14:0 18 19 20

B)


Normalized height [%]

A) 11 of 15 Page

15

50

0 60

ACS Paragon Plus Environment 70 RT [min]

80 60

70 RT [min]

80 60

70 RT [min]

80 60

70 RT [min]

80 60

70 RT [min]

80 60

70 RT [min]

80 60

70 RT [min]

80

20

DAG 16:0/16:0 logP 14.0

TAG 14:0/14:0/14:0 logP 18.7

Sample without phosphoric acid & eluents without phosphoric acid

Signal increase (height)

NL: 1.4 E4

50 0 100

1 2 3 4 5 6 7 8 9

NL: 1.6 E4

50

Relative Abundance [%]

0 100

NL: 1.7 E4

50

0 100

NL: 6.7 E3

50

0 100

NL: 5.4 E4

50

Page 12 of 15

100

LCBP 17:1;2 21.0x

NL: 2.7 E5

50 0 100

LPA 17:1 7.9x

NL: 2.0 E5

50 0 100

CerP 18:1;2/12:0;0 39.5x

NL: 5.7 E5

50 0 100

PA 17:0/14:1 67.9x

NL: 3.3 E5

50 0 100

CL 15:0(3)-16:1 55.3x

NL: 2.5 E6

50

ACS Paragon Plus Environment

0 100

NL: 2.3 E6

50 0

Sample with 5 mM phosphoric acid & eluents without phosphoric acid


100

0

10

20

30

40 Time (min)

50

60

70

80

0 100

PG 17:0/14:1 1.0x

NL: 2.4 E6

50 0

0

10

20

30

40 Time (min)

50

60

70

80

Page A) 13 of 15 HPLC NEG DAG

1 CL 2 PS 3PI 4 5PG 6 PE 7 PC 8 PA 9 B) 10 11 10 12 10 13 14 10 15 10 16 17 10 18 10 19 20 10 21 22 10 23 10 24 25 26

TAG

LCB_a 10000 1000

LCB_b LCBP_a

DAG

Cer

10 1

CerP

0.1 [fmol]

LacCer

LCB_a 10000 1000

LCB_b LCBP_a

PS

Cer

10 1

PI

DAG

CerP

0.1 [fmol]

LacCer

PA

LPC

LPG

PS

Cer

10 1

CerP

0.1 [fmol]

LacCer

LPS

C) 10 12

PE 17:0/14:1

PS

10 11

LCB_b LCBP_a LCBP_b Cer

10 1

CerP

0.1 [fmol]

GlcCer LacCer

PE SM

PC PA

LPC

LPG

10000

100

PI

SM

LPC

LCB_a 1000

CL

LPA

PA

TAG

GlcCer PG

PC

11

DAG

LCBP_b

PE

12

LPA LPS

LPG

LPC

GlcCer 18:1;2/12:0;0 nLC pos

nLC pos 10 10

10

nLC neg

9

HPLC pos

8

HPLC neg

7

Intensity [au]

Intensity [au]

LCB_b LCBP_a

100

LPA LPS

10000

PI

SM

PC

LPA

LCB_a 1000

GlcCer PG

PE

nLC POS

HPLC POS

TAG

CL

LCBP_b

100

GlcCer PG

SM LPG

TAG

CL

LCBP_b

100

LPS


nLC NEG

10 9

nLC neg

10 8

HPLC pos

10 7

HPLC neg

6

6

10

5

10 5

ACS Paragon Plus Environment 10 4

4

M

1

10

100

Injecton amount [fmol]

1000

10000

M

1

10

100

1000

Injection amount [fmol]

10000


A)

Relative Abundance [%]

100 80

+

[PC 29:1 +H]+ m/z 690.5068 ± 3ppm

100

RT 29.49 min hcd 27.5 < 3 ppm

PC (184)

80

60

60

40

40

20

20

PC 29:1

0 100 Relative Abundance [%]

PC 11:0 -18:1 PC 12:0 -17:1

0 28.0

80

-

29.0

30.0

31.0

[PC 29:1 +HCOO]m/z 734.4976 ± 3ppm

100

100 80

60

60

40

40

20

20

0

200

300

pairs of FA

400

500

600

PC 13:0 -16:1

700

PC 14:0 -15:1

RT 29.59 min hcd 30.00 < 3 ppm

FA 15:1 (+O)

FA 14:0 (+O)

PC 14:1-15:0 PC 13:1-16:0

-PC (60)

FA 13:0 (+O) FA 14:1 (+O) FA 12:0 (+O) FA 11:0 (+O)

FA 16:1 (+O) FA 15:0 (+O) FA 17:1 (+O) FA 18:1 (+O)

0 28.0

29.0 30.0 Time [min]

31.0

100

200

300

400 m/z

500

C)

B)

600

700

Area

Isomer counts

Number of double bonds 100

48 3 DB 2 DB 1 DB 0 DB

44 42

90 80

40

70

38

34

50

32

40

30 28

30

26

20

24 10

22 20

0 0

5

10 15 20 25 30 35 40 45 50 55 60 65 70 75 RT [min]

[% B]

60

36

Carbon number in FA chains

46

Carbon number in FA chains

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 14 of 15

22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

0 6.7

1 7.1

7.5 6.7 8.7 7.9 9.3 7.5 9.1 7.3 8.6 6.8 8.7 6.4 7.9

7.6 6.1 8.9 7.6 9.9 8.4 10.0 9.1 10.2 9.0 10.2 8.9 9.9 7.2 8.4

6.7 6.0 7.0 6.2 7.2

4.3

8.4 8.0 9.0 8.0 8.5 7.3 7.7

2 6.8

5.7 8.0 7.3 9.9 9.3 10.7 8.9 10.6 8.7 10.0 7.4 8.7 7.2 5.4 7.4 5.2 6.8

7.1 6.7 8.9 6.9 8.6 7.6

0 1

1 1

2 n/d

3 1 3 3 3 3 2 2 2 2 1 n/d 2

2 n/d 4 4 5 6 3 4 3 4 2 4 3 2 3

3 1 3 2 3 2 2 3 3 1 2

2 n/d 2 n/d 1

n/d 5.4

Peak area [log10]

ACS Paragon Plus Environment

3

3 2 2 2 2 1 1

3

1

1 1 2 1 2 1

3 n/d 2 n/d 2 n/d

Number of isomers/lipid species n/d: no MS2 available

HPLC 25x

Analytical Chemistry 500

molecular species level species level

450

8

123 1

3 0

Nano HPLC 25x HPLC

120 100

350 300 250 200 150

80 60 40

100 20

50 0

HPLC

140

400

150

162

C) Number of IDs

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Nano

B) Number of IDs

A)

Page 15 of 15

Nano

ACS Paragon Plus Environment HPLC 25x

Analysis method

HPLC

0 PC

PI

PA

PG

Lipid Class

PE

PS

NSI MS Refines Lipidomics by Enhancing Lipid Coverage

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