Delving into the Polar Lipidome by Optimized Chromatographic

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Delving into the polar lipidome by optimized chromatographic separation, high resolution mass spectrometry and comprehensive identification with Lipostar: microalgae as case study Giorgia La Barbera, michela antonelli, Chiara Cavaliere, Gabriele Cruciani, Laura Goracci, Carmela Maria Montone, Susy Piovesana, Aldo Laganà, and Anna Laura Capriotti Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b03482 • Publication Date (Web): 11 Sep 2018 Downloaded from http://pubs.acs.org on September 12, 2018

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Analytical Chemistry

Delving into the polar lipidome by optimized chromatographic separation, high resolution mass spectrometry and comprehensive identification with Lipostar: microalgae as case study

Giorgia La Barbera1, Michela Antonelli1, Chiara Cavaliere1, Gabriele Cruciani2, Laura Goracci2, Carmela Maria Montone1, Susy Piovesana1*, Aldo Laganà1, Anna Laura Capriotti1

1

Department of Chemistry, University of Rome “La Sapienza”, Piazzale Aldo Moro 5,

Rome, Italy 2

Department of Chemistry, Biology and Biotechnology, University of Perugia, Via Elce di

Sotto 8, 06123 Perugia, Italy

Keywords Microalgae; lipidomics; High resolution mass spectrometry; chromatographic evaluation

ABSTRACT The work describes the chromatographic separation optimization of polar lipids on Kinetex-EVO, particularly focusing on sulfolipids in spirulina microalgae (Arthrospira platensis). Gradient shape and mobile phase modifiers (pH and buffer) were tested on lipid standards. Different conditions were evaluated and resolution, peak capacity and peak shape calculated both in negative mode, for sulfolipids and phospholipids, and in positive mode, for glycolipids. A high confidence lipid identification strategy was also applied. In collaboration with software creators and developers, Lipostar was implemented to improve the identification of phosphoglycerolipids and to allow the identification of glycosylmonoradyl-

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and glycosyldiradyl-glycerols classes, the last being the main focus of this work. By this

approach, an untargeted screening also for searching lipids not yet reported in the literature could be accomplished. The optimized chromatographic conditions and database search were tested for lipid identification first on the standard mixture, then on the polar lipid extract of spirulina microalgae, for which 205 lipids were identified.


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Introduction The search for bioactive compounds to be employed as nutraceuticals has become a hot field in the research community. Several chemical classes include molecules for which important bioactivities have been reported, such as phytochemicals,1 peptides and proteins,2–4 and lipids, among which Omega 3-fatty acids are the most investigated ones.5 Polar lipids also represent an important target in the investigation of bioactive lipids, especially in food, as they are ubiquitous compounds and the major components of animal and plant cell membranes (in particular phospholipids). Along with the nutritional value, phospholipids (in particular sphingolipids) have been attributed with important biological functions, such as anti-inflammatory activity, and anticholesterolemic activity,6 anticancer activity7 and regulation of the immune functions in the gut.8 Such polar lipids are mainly investigated in food, especially in milk and dairy products,9,10 but also in meat, fish, eggs, cereals and oils.11 In this context, nowadays, many species of freshwater microalgae are being investigated as a valuable source of bioactive compounds,12 including lipids.13 In particular, lipids of some microalgae species are enriched in valuable polyunsaturated fatty acids that are mainly esterified to other lipids, such as phospholipids and glycosylglycerolipids,14 which can find application in the pharmaceutical, nutraceutical and industrial fields. Lipid investigations can be performed by shotgun approaches, which rely on direct infusion of total lipid extracts and mass spectrometry (MS), or tandem MS (MS/MS) spectra acquisition,15 a strategy often preferred in high-throughput “discovery” studies due to shorter analysis times and lower sample consumption. The main shortcomings of shotgun lipidomics include significant peak overlap due to the presence of isomeric lipids.16 Liquidchromatography (LC) separation coupled to high resolution MS (HRMS) and bioinformatics provides more reliable identifications of individual lipid species, even at trace level, separation of isomers, and reduced ion-suppression effects.17 Nevertheless, many isomeric lipid species tend to coelute and the wide dynamic range of lipid concentration also causes suppression of low abundance lipids coeluting with high abundance ones.18 In this context, an efficient separation of lipid species in a complex sample is mandatory in a comprehensive lipidomic analysis, and represents a key analytical challenge in lipidomics, i.e. achieve complete analytical coverage and precise structural characterization of all the lipids that are present in a given sample.16 The most important separation mechanism exploited in lipid analysis is reversed phase (RP) LC, but normal phase LC19 and hydrophilic interaction



chromatography20 also found numerous applications in the literature.17 As lipid polarity considerably varies among the different lipid species, RPLC (C8, C18, C30 types of stationary phases) allows to selectively differentiate lipids based on the different hydrophobicity of acyl- or alkyl-chain substituents, backbone structures and polarity of their head groups. Therefore, RPLC typically provides better separation efficiencies and is the most suited separation approach for comprehensive MS-based lipidomic analysis. The efficiency and high peak capacity of RPLC render it the main approach in untargeted lipidomics, also in multidimensional setups.16,18,21,22 In RPLC, typical columns are microbore (1–2.1 mm I.D.) 50–150 mm long columns with sub-2-µm or 2.6–2.8-µm (fused-core) particle size,17 with sub-2 µm core–shell columns providing a superior performance over sub2 µm porous columns for in-depth lipidomic study of Caenorhabditis elegans.23 In the literature some works have been reported, which describe optimization of chromatographic conditions in comprehensive lipid screening, with application to plasma lipids,24 gut microbiota,25 or column comparison, and application to rat plasma and liver. Gradient time was also considered, and recently it was demonstrated that the use of long gradients could improve lipid separation while minimizing undersampling, a strategy which provided the confident identification of 924 unique lipid species in murine lung tissue samples26, which is one of the largest lipidome data sets reported to date.16 Nevertheless, studies focused on the chromatographic separation of sulfolipids, belonging to the class of glycosylglycerolipids, are lacking, but they represent one fundamental class in microalgae, as they are very abundant in this matrix.14 In this work a chromatographic optimization of both sulfolipids and phosphoglycerolipids was accomplished. As sulfolipids are detected in negative mode, detection was primary in negative mode and the positive mode selected only for some classes of glycosylglycerolipids, including monogalactosylmonoacylglycerols (MGMG), monogalactosyldiacylglycerols (MGDG), digalactosylmonoacylglycerols (DGMG) and digalactosyldiacylglycerols (DGDG). Indeed, these classes of glycosylglycerolipids, representing main classes of lipids in microalgae,14 are preferably investigated in positive mode, being their fragmentation pattern more informative in positive mode and allowing a more reliable identification. The optimization of chromatographic conditions (gradient, buffer and acid modifier) was performed on Kinetex-EVO C18 column. The developed method for polar lipid characterization was based on ultra-high-performance LC (UHPLC) coupled to HRMS. HRMS is the method of choice in untargeted lipidomics, due to the possibility of high resolution acquisition of both precursors and product ions. In


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particular, the MS method is based on Orbitrap data dependent analysis, a technique which is successfully applied in proteomics,27 and that was recently reported for lipid analysis with relative quantitation as well.28 The chromatographic performance evaluation was first achieved on a complex lipid standard mixture, then the optimized conditions were applied to a complex biological polar lipid extract of spirulina microalgae. Up-to-date bioinformatics by Lipostar software29 was used for lipid identification. The software is vendor independent and allows to avoid a time consuming data analysis based on fragmentation spectra analysis and formulas to screen diagnostic fragments.30 In collaboration with software creators and developers, Lipostar was implemented in order to both improve the identification of a number of phosphoglycerolipids and to allow the identification of glycosylmonoradyl- and glycosyldiradyl-glycerols classes, the last being the main focus of this work.

Experimental section Annotation of lipid species Lipid species were annotated according to LIPID MAPS abbreviation nomenclature. Acronyms: glycerophosphocholines (PC and LPC for lyso species), glycerophosphoethanolamines (PE and LPE for lyso species), glycerophosphoserines (PS and LPS for lyso species), glycerophosphoglycerols (PG and LPG for lyso species), glycerophosphates (PA and LPA for lyso species), glycerophosphoinositols (PI and LPI for lyso species), sulfoquinovosyldiacylglycerols (SQDG), sulfoquinovosylmonoacylglycerols (SQMG), monogalactosylmonoacylglycerols (MGMG), monogalactosyldiacylglycerol (MGDG), digalactosylmonoacylglycerols (DGMG) and digalactosyldiacylglycerol (DGDG).

Chemicals and material Optima® LC-MS grade water was supplied by Thermo Fisher Scientific (Waltham, Massachusetts, USA). Ultra purity LC Methanol (MeOH) was purchased from Romil Pure Chemistry (Pozzuoli, NA). Ammonium formate, formic acid was acquired from Sigma now Merck (St. Louis, MO, USA). HPLC-grade chloroform, MeOH and water used for sample



preparation were provided by VWR International (Milan, Italy). L-αLysophosphatidylinositol sodium salt from Glycine max (soybean) was purchased from Sigma now Merck and the stock solution was prepared at 0.5 mg mL-1, the working solution at 15 µg mL-1. Phosphatidylcholine (C12) 1,2-dilauroyl-sn-glycero-3-phosphocholine, phosphatidylcholine (C14) 1,2-dimyristoil-sn-glycero-3-phosphocholine, phosphatidylglycerol (C14) 1,2-dimyristoyl-sn-glycero-3-phospho-(1'-rac-glycerol) (sodium salt) and L-α-lysophosphatidylcholine (Egg, Chicken) were purchased from Avanti Polar Lipids (Alabama, USA) and 3-sn-lysophosphatidylethanolamine from egg yolk, Type I, was purchased from Sigma now Merck. Stock solutions of all these lipid standards were dissolved at 5 mg mL-1 concentration, working solutions at 10 µg mL-1. Phosphatidylinositol (ammonium salt) solution from soybean was purchased from Sigma now Merck, the stock solution was prepared at 10 mg mL-1 concentration, the working solution at 30 µg mL-1. Phosphatidic acid (C14) 1,2-dimyristoyl-sn-glycero-3-phosphate (sodium salt) was purchased from Avanti Polar Lipids, 3-sn-phosphatidic acid (sodium salt) from egg yolk lecithin, 1,2diacyl-sn-glycero-3-phospho-L-serine from bovine brain and L-α-phosphatidylcholine from egg yolk type XVI-E were purchased from Sigma now Merck. Stock solutions for these latter lipid standards were prepared at 20 mg mL-1 and working solutions at 50 µg mL-1, except for 1,2-diacyl-sn-glycero-3-phospho-L-serine from bovine brain, which was diluted to 100 µg mL-1. The complete list of lipid standards is reported in Table S-1, with the related vendors, stock solution and working solution concentrations. Stock lipid standard solutions were prepared by dissolution in chloroform/MeOH, 90:10 (v/v); individual working solutions were prepared by dissolution of proper volumes of stock solutions in mobile phase mixture at the composition of gradient start.

Preparation of lipid extract from spinach SQDG, SQMG, MGMG, MGDG, DGMG and DGDG lipids were extracted from spinach (Spinacia oleracea) according to the following procedure,31 with some modifications. Briefly, 1 g lyophilized were sonicated in 16 mL isopropanol for 20 min. Then, 16 mL MeOH were added to the mixture sonicated with intermitted vortex shaking for other 20 min. Finally, the mixture was kept overnight at 48 °C to remove chlorophyll. Afterwards, the sample was centrifuged at 15 °C for 15 min at 3200 × g. The supernatant was discarded and the resulting pellet was extracted with 40 mL MeOH/chloroform, 1:1 (v/v) three times. The


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supernatant was collected and dried down in a IKA RV 8 rotary evaporator (IKA-Werke GmbH & Co. KG, Staufen, Germany). The residue was dissolved in 20 mL water/MeOH/chloroform, 3:2:1 (v/v/v), then centrifuged for 15 min at 15 °C and 3200 × g. The upper aqueous layer was collected and reduced to 100 µL in the rotatory evaporator, then diluted to 1 mL with MeOH. Before analysis, the extract was diluted 1:10 with a suitable mixture to obtain the mobile phase composition of the gradient start.

Working mix solution Individual standard stock solutions and spinach extract were mixed together to obtain the final working mix solution. The final concentration of the individual standards in the final working mix solution was the same as in the individual working solutions (Table S-1). The sulfolipid extract was added to the mix solution to obtain a final 1:10 dilution.

Preparation of lipid extracts from spirulina The extraction of spirulina microalgae (Arthrospira platensis) was performed as previously described to prepare the glycosylglycerolipids extract from spinach. In this case, however, the final extract was diluted only 1:2 with a suitable mixture to obtain the mobile phase composition of the gradient start.

RPLC-HRMS analysis of lipid samples All samples were analysed by RPLC coupled to HRMS using a Vanquish UHPLC system (Thermo Fisher Scientific) equipped with a binary pump H and an autosampler thermostatted at 14 °C. The separation was performed on a Kinetex EVO 100 × 2.1 mm, 1.7µm particle size (Phenomenex, Torrance, CA, USA), operated at 0.4 mL min-1 and 40 °C (still air option). A chromatographic evaluation was performed onto the working mix solution. In particular, four gradient elution conditions (A-D) and different modifiers (ammonium



formate, referred to as conditions D1-3, formic acid, referred to as conditions D3a-c and D4) were tested for optimization of lipid separation (Figure 1, Table S-2). The individual standard working solutions, the spinach extract and the spirulina extract were finally analysed under the optimized conditions (D3), with water as phase A and MeOH as phase B, both of which with 5 mmol L-1 ammonium formate using the following phase B linear gradient: 0 min (60% B), 0.1-5 min (60-70% B), 5.1-30 (70-99% B), followed by a 5 min washing at 99% phase B and 10 min equilibration at 60% phase B. The total analysis time including column re-equilibration was 45 min. The Vanquish system was connected to a Q Exactive hybrid quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientific) via a heated electrospray ionization (HESI) source. Tune parameters were the following: capillary temperature 275 °C in positive mode, 320 °C in negative mode; sheath gas 50 arbitrary units (a.u.) in positive mode, 35 a.u. in negative mode; auxiliary gas 15 a.u. in positive and negative mode respectively; spray voltage 3.5 kV in positive mode, 2.5 kV in negative mode; auxiliary gas heater temperature 450 °C in positive mode, 400 °C in negative mode; S-lens RF level 100%. The Q Exactive mass spectrometer was operated in top 5 data dependent mode. For full-scan spectra acquisition, the resolving power was set at 140,000, scan range 200–1200 m/z (full width at half maximum, FWHM) automatic gain control target at 5e5, maximum ion injection time at 200 ms and isolation window width of 2 m/z. MS/MS fragmentation was performed by higherenergy collisional dissociation at a normalized collision energy of 40 with a resolution FWHM of 70,000, automatic gain control target of 5e5, dynamic exclusion of 6 s. The mass spectrometer was externally calibrated within a mass accuracy of 1 ppm every two days using the commercial Pierce positive and negative calibration solutions (Thermo Fisher Scientific). Raw MS/MS data files were acquired by Xcalibur software (version 3.1, Thermo Fisher Scientific). All samples were acquired in both negative and positive polarity mode. Three technical replicates were performed for each polarity mode and run after two injections of a blank sample (H2O/MeOH, 60:40 v/v) for assuring column conditioning and for blank subtraction in data analysis. Therefore, ten chromatographic runs were performed for each condition and sample. For the evaluation of the chromatographic conditions, samples were run continuously over the time for a total of three days to minimize instrumental variability. The injection volume was 5 µL.


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Figure 1. Scheme of the optimization scheme followed for polar lipid separation, first on standards then applied to polar lipid extract from spirulina microalgae. For details on gradients A-C, see the supporting information. For gradient D description, see section “RPLC-HRMS analysis of lipid samples”.

Data analysis and lipid identification For each chromatographic condition, accurate mass ion chromatograms obtained from three consecutive injections of the working mix solution and the blank were analysed for a preliminary lipid identification, by Lipostar,29 a commercially available software for lipidomic analysis. Firstly, the platform Lipostar DB Manager was used to build a customized home-made database of lipid precursors and MS/MS theoretical ions. By means of the Lipid Builder Tool several lipid polar heads were combined with the desired acyl chains. In particular PC, PG, PA, PS, PI polar heads were chosen together with SQ, MG and DG additional glycosylglycerolipid classes.14,32–35 All polar heads were combined with 1 or 2 acyl chains ranging from a length of C3 to C35 and from 0 to 9 double bonds. Typical fragmentation rules were also applied to generate the related MS/MS spectra. After building the customized database baseline and noise reduction, peak extraction, smoothing, signal to noise ratio filtering, deisotoping and deconvolution were performed setting the parameters reported in Table S-3. Finally, database search of experimental MS and MS/MS spectra was performed with the following parameters for lipid identification: 5 ppm precursor ion mass tolerance and 10 ppm product ion mass tolerance. All lipid identifications based on both precursor and MS/MS were manually checked.



In order to evaluate the best chromatographic condition, several parameters related to the identified chromatographic peaks, such as FWHM and asymmetry factor, had to be extrapolated from the analysed chromatographic runs. However, neither FWHM nor asymmetry factor were explicitly provided by Lipostar, requiring the support of another software. For each chromatographic condition, accurate mass ion chromatograms obtained from three consecutive injections of the working mix solution and the blank were then analysed by MZmine 2.32 (http://mzmine.github.io)36 as follows: the targeted peak detection module was used for searching into the list of identified lipids with a precursor mass tolerance of 0.001 uma or 5 ppm and a retention time tolerance of 0.1 min. Peaks integration was checked and manually corrected when needed. Retention time, peak area, FWHM, asymmetry factor, peak capacity and resolution were calculated and used for the chromatographic evaluation. The individual working solutions, the spinach extract and the spirulina extract were finally analysed by RPLC-HRMS in the optimized chromatographic conditions. The obtained chromatographic runs were processed by Lipostar with the same workflow and parameters as described before, allowing the final identification of the individual working solutions, the spinach extract and the spirulina extract.

Results and discussion Optimization of chromatographic conditions In the literature several works describing optimization of chromatographic conditions for comprehensive glycerophospholipids screening have been reported37. Most of these works perform lipidomic analysis in positive ionization mode, due to the higher signal response compared to negative ionization mode38. At the same time, no work has been published on the chromatographic optimization for the analysis of sulfolipids, which can be detected in negative polarity mode only. Considering that sulfolipids are one of the main characteristic classes present in microalgae, in this work an optimization of the chromatographic conditions was performed to obtain a more comprehensive lipidomic coverage in microalgae, including both glycerophospholipid and sulfolipid analysis. However, due to the presence of other glycosylglycerolipids in microalgae, i.e. MGMG, MGDG, DGMG and DGDG, typically


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detected in positive mode, acquisition in positive polarity mode was also performed for this lipid class only. As the first step in developing an optimized chromatographic method for polar lipid investigation in microalgae, the mixture of polar lipid standards was analysed under different conditions to assess the chromatographic performance. Four gradients conditions were tested (Table S-2, Figure 1). Initially, water and MeOH were used as phase A and B, respectively, both with 2.5 mmol L-1 ammonium formate. Gradient A was found not suitable for polar lipid separation, as lyso lipid species (LPC, LPE, LPI, LPS) and SQMG (16:0/0:0), (0:0/16:0), (18:0/0:0), and (0:0/18:0) lipids were not sufficiently retained and the separation of the isomers could not be accomplished. On the other hand, the most retained analytes were eluted during the washing, thus no separation was possible. Increasing phase A starting percent from 20% to 30% and lowering the final B percent from 80% to 70% did not improve lipid retention or separation. A different gradient profile was devised (C), comprising in a 2-min isocratic step at increased phase A percent (40%), to improve retention of the less retained analytes. To increase analyte separation, an additional step was introduced at 70 % B before the final 99% B. As no elution was observed in such 2-min isocratic step, it was removed in the final gradient (D). Gradient D did not only allow retention of the most polar lipids, but also it allowed the separation of isomers. The intermediate step also speeded up the elution of the most apolar lipids, avoiding their elution with the washing. After assessing the best gradient shape (D), mobile phase composition was considered. First, buffer was considered, and three concentrations of ammonium formate were tested, i.e. 1 mmol L-1, 2.5 mmol L-1 and 5 mmol L-1 (D1, D2 and D3, respectively), for both phase A and B (Figures S-1, S-2 and S-3 display the extracted ion chromatograms of one representative lipid for each class under conditions D1, D2 and D3, respectively). The buffer and pH modifiers were expected to affect not only lipid separation but also ESI, both of which in turn have a significant impact on final lipid identification. Thus, different parameters were calculated for comparison of the different tested conditions. Resolution, peak capacity, peak area and peak asymmetry factors were used for the chromatographic evaluation and calculated for the negative mode (Table S-4) and positive mode (Table S-5). The resolution was calculated on every couple of resolved isomers according to the formula (1):



= (, − , )/ 1.7 ∗ 0.5., + .,

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(1)

where tR is the retention time and w0.5 represents an average peak width at FWHM. Resolution for sulfolipids was significantly affected by mobile phase composition, and for sulfolipid standards was 9.2 for condition D1, 3.8 for condition D3 and 2.9 for condition D2 (Table S-6). For MGDG, DGMG and DGDG, average values ranged between 2.3 and 3.0, the latter for condition D3. As for the other lipid classes, conditions D1 and D3 appeared similar, with average resolution of 4.9 and 5.3, respectively. No value could be calculated for LPS under condition D1 and an overall good resolution of lipid isomers was achieved using 5 mmol L-1 formate (D3). Peak capacity (nc) is the theoretical number of peaks that may be separated with a given resolution under a given set of conditions. It is one of the most important parameters to evaluate separation power. The peak capacity was calculated from (2): = 1 +

.×!"#$

(2)

For sulfolipids, peak capacity increased with ammonium formate concentration and was maximum at conditions D3 (259, Table S-7). For MGDG, DGMG and DGDG, there was no trend and peak capacity was larger at D1 for MGDG (222), at D2 for DGMG (272) and equal D1 with D2 for DGDG (201) with average best condition at D2. Extending evaluation to the other polar lipids, peak capacity was larger at D3 (163). Average area for conditions D1-3 was found to be 1.24e8-2.18e8 for the glycerophospholipisd and sulfolipids and 7.65e7-9.27e7 for glycosylglycerolipids. For sulfolipids response was significantly lower (4.76e6-2.13e7, Table S-8) with best results at condition D2 followed by D3 (1.90e7). MGDG, DGMG, DGDG had their maximum area at condition D3 (MGDG with 6.09e7 and DGMD with 6.28e6), except DGDG, for which area was maximum at D1 (2.28e8) followed by D3 (2.13e8). Peak asymmetry factors were measured according to the formula (3): %=

&

'

(3)

where a is the width of the front half of the peak, and b is the width of the back half of the peak measured at 10% of the peak height. For conditions D1-3, the mean asymmetry


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factors were 2.4-2.9 for glycerophospholipids and sulfolipids, and 1.2-1.4 for glycosylglycerolipids. Sulfolipids were slightly better than the average, with values in the range1.3-1.7 and better condition at D2 followed by D3 (1.4, Table S-9). For glycosylglycerolipids, the effect of formate concentration on peak shape was generally moderate, with the only exception of MGDG, for which fronting was observed in conditions D3. Extending consideration to the other polar lipid classes, PS and PA had the worst peak asymmetry factors (3.2-4.2 for PS, 5.9-9.8 for PA) and on average better results were obtained with D2 (2.5). From the above considerations, the heterogeneity of lipid structures and classes does not allow to identify a single condition as the best for all chromatographic parameters considered and a compromise was necessary. Bearing this in mind, and focusing on sulfolipids first as chromatography was never optimized for this class, sulfolipid species could be better separated at condition D3 (5 mmol L-1 formate). D3 allowed better peak capacity and the second better condition for resolution, area and asymmetry factor. Condition D3 was also a compromise for the other microalgae main lipid species and polar lipids in general. For further improvement, the presence of an acid modifier was evaluated. Formic acid was added at three concentration levels, i.e. 2.5 mmol L-1, 25 mmol L-1 and 100 mmol L-1 (D3a, D3b, D3c, for which extracted ion chromatograms of one representative lipid for each class are reported in Figures S-4, S-5 and S-6, respectively). One additional experiment, where ammonium formate was omitted and formic acid used at 100 mmol L-1 concentration was also performed (D4, Figure S-7), to assess any improvement due to buffer removal. As far as resolution was concerned, a minor improvement was obtained only for glycosylglycerolipids, with condition D3a (average resolution 3.6, Table S-6). Peak capacity significantly decreased for glycerophospholipids and sulfolipids and a minor improvement was observed only for glycosylglycerolipids (222 for condition D4). Peak area was the parameter which varied the most due to the acid modifier. Average area was larger at 100 mmol L-1 formic acid (D4) for glycerophospholipids and sulfolipids (8.06e8), being the best for sulfolipid detection among the several conditions (2.13e8, Table S-8). For glycosylglycerolipids, acid modifiers did not improve peak area. D4 was also the best condition for most polar lipids, i.e. LPE (3.18e8), LPS (4.71e8), PA (2.41e9), PC (4.04e8), PG (1.96e9), PI (9.40e8) and PS (9.76e8). Finally, for peak asymmetry factors tailing was



still observed for most lipid classes with no major improvement. As previously observed, no clear trend could be observed and no condition met the best values for all considered parameters. Still, condition D3 performed on the average slightly better, as it met the best condition for resolution and peak capacity and the second best condition for area. Condition D3c also performed quite well, i.e. the best condition for asymmetry factors and the second best condition for area and peak capacity. However, after comparing average values of D3 to D3c peak capacity was significantly lower in D3c (average value of 163 vs 182) and no clear improvement was provided for area, asymmetry factors or resolution. At the same time, PC were resolved at D3 but not at D3c and asymmetry factors increased from an average of 9.8 to 15.4 for PA. Thus, the best compromise for sulfolipid analysis along with polar lipids in microalgae was provided by condition D3 (Figure 2). In comparison to C30 optimization39, peak capacities could be improved both for glycosylglycerolipids (68 vs 210) and for glycerophospholipids and sulfolipids (63.3 vs173).


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Figure 2. Total ion current chromatograms of lipid standards (A), spinach extract (B) and spirulina extract (C) acquired under conditions D3 and negative ESI.

The best final conditions were chosen evaluating three chromatographic replicates for each condition and running all samples continuously over the time for a total of three days, in order to minimize inter-day instrumental variability in terms of sensitivity and mass accuracy. Variability of retention time over the three replicates, instead, was evaluated for the assessment of chromatographic performance and, for the final conditions, excellent retention time reproducibility was obtained, with the standard deviation always

Delving into the Polar Lipidome by Optimized Chromatographic

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