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1 Supporting information A novel lipidomics workflow for improved human plasma identification and quantification using RPLC-MSn methods and isotope dilution strategies Evelyn Rampler 1,2,3, Angela Criscuolo 4,8, Martin Zeller 4, Yasin El Abiead 1, Harald Schoeny 1, Gerrit Hermann 1,5, Elena Sokol 6, Ken Cook 6, David Peake 7, Bernard Delanghe 4, Gunda Koellensperger 1,2,3 1 Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Währingerstr. 38, 1090 Vienna, Austria 2 Vienna Metabolomics Center (VIME), University of Vienna, Althanstraße 14, 1090 Vienna, Austria 3 Chemistry Meets Microbiology, Althanstraße 14, 1090 Vienna, Austria 4 Thermo Fisher Scientific (Bremen GmbH), Hanna-Kunath-Str. 11, Bremen, Germany 5 ISOtopic Solutions, Währingerstr. 38, 1090 Vienna, Austria 6 Thermo Fisher Scientific, 1 Boundary Park, Hemel Hempstead HP2 7GE, United Kingdom 7 Thermo Fisher Scientific, 355 River Oaks Parkway, San Jose, California, USA 8 Institute of Bioanalytical Chemistry, Faculty of Chemistry and Mineralogy, Universität Leipzig, Leipzig, Germany Supporting information This section contains the extended methods section and additional information on (S1) the combination of MS2 HCD (A) and CID (B) fragmentation, (S2) RPLC-MSn identification of TG 54:4, (S3) examples for quantification of LPC 18:0 and PC 34:0 in SRM 1950 using the 13 C labeled versions of LILY lipids, (S4) relative quantification of human serum SRM 1950 by Full- MS RPLC-MS in positive mode, (S5) linearity of label-free versus compound-specific relative quantification in human plasma, (S6) data-points per peak of TG 52:1 observed by different high-resolution MS detection strategies, (S7) RPLC-MS2 lipid class profiles of human plasma and yeast samples annotated with Lipid Search 4.1, (Table S1) lipid annotation in human plasma and yeast derived from Lipid Search 4.1 after filtering and manual assignment, (Table S2) number of phosphatidylcholine, sphingomyelin, ceramide and triglyceride annotations in human serum plasma RPLC-MS2 and RPLC-MSn and (Table S3) comparison of label-free versus compound-specific absolute quantification for 15 lipids from 10 different classes. 1

2 Lipid profiling and quantification with RPLC-MS The UPLC-system was coupled to an ultra-high-resolution accurate mass Orbitrap Fusion Lumos Tribrid Mass Spectrometer (Thermo Fisher Scientific, San Jose, CA, USA) for lipid profiling and quantification. The following optimized H-ESI source parameters were applied: Ion Transfer Tube temperature of 170 C, sheath gas flow rate of 45 (arbitrary units, a.u.), auxillary flow rate of 20 a.u., sweep gas of 1 a.u., RF Lens of 22% and Vaporizer temperature of 380 C applying a spray voltage of 3.0 kv in positive mode and 2.5 kv in negative mode. Two methods have been used for this study using these settings, one for quantification using HRAM full MS and HRAM data-dependent HCD MS2 scans and one for identification of yeast and human plasma samples using additional MS3 scans in the linear ion trap. Both methods record HRAM Orbitrap full MS scans in positive mode at resolution setting with an AGC target of 2.0e5. For the data-dependent HRAM HCD MS2 scans MS2 resolution setting and a stepped collision energy of 27 % (± 3%) was applied. Small molecule MIPS mode and Apex triggering functionality was used. The tribrid architecture of the Orbitrap Fusion Lumos enables parallel operation of both the Orbitrap and the linear ion trap to optimize duty cycles. This was used in the identification runs with an LC elution timed optimization of the MS3 trigger parameters. In the first 18 minutes of the method, HRAM HCD MS2 scans were performed only upon detection of precursor masses from a ceramide inclusion list (containing 600 suspect ceramides for human plasma and yeast samples deduced from literature) in the full MS Orbitrap scan over a scan range from m/z (30 known PEG contaminants were on an exclusion list). In order not to miss any analyte ions that are not on the inclusion list, data-dependent HCD scans with detection in the linear ion trap were carried out. Upon detection of diagnostic backbone ions in the HCD MS2 scans for the phosphatidylcholine head group at m/z , the same precursor ions were isolated again and subjected to CID fragmentation in the ion trap. CID is a more gentle fragmentation method compared to HCD and in that way predominantly the lysophospholipid backbone ions were produced. From minute 18 to 28 when predominantly triglycerides elute, the full MS mass range was limited to m/z In parallel to data-dependent HRAM HCD MS2 scans, data-dependent 2

3 low-resolution CID scans in the linear ion trap were carried out and the top 3 fragment ions in the mass range of 33-95% relative to the precursor ions were subjected to low-resolution HCD MS3 scans. In that way triggering of MS2 precursor related ions (unfragmented precursor or neutral loss of triglycerides thereof) was excluded and detailed structural information about the fatty acid backbone of the triglycerides was obtained. All profiling samples were measured in positive mode (n=6) and lipids identified in blank runs (n=6) were removed. Data evaluation was performed using Lipid Search SP1 (Thermo Fisher Scientific) for the ddms2 and enhanced lipid profiling by ddmsn. For the ddmsn identification runs the complex CID/HCD analysis option in the configuration was activated. Lipid Search results were filtered for 5 ppm in MS1, 7 ppm in MS2 and 0.5 Da in MSn. Separate Lipid Search alignments for the ddmsn runs of blanks (n=4) with human plasma samples (n=6) and blanks (n=4) with yeast samples (n=6) were performed using a retention time tolerance of +/ min. The lipids were only considered if the areas were 3 x higher than in the blank samples or not present in the blanks. The main adduct ion was set to H+ for PC, PS, PE, PA, LPGs, Cer, HexCer, CerP, SM and for DG, TG, PG, PI, the main adduct ions were set to M+NH 4 /Na, for Cer, HexCer and CerP additionally adduct ion with loss of H 2 O were considered. The main grade was set to A and B for all lipid classes except PC, Cer, HexCer, CerP and SM, there A, B and C grade were allowed (description of A, B, C grade can be found in Supplementary Table S1 and S2). After additional manual curation of the data, the lipid identification lists for human serum plasma and yeast were matched and compared by their lipid class and lipid species levels 1 (e.g. not including fatty acid scan information on TGs) using a retention time tolerance of +/ min. Tracefinder 4.1 (Thermo Fisher Scientific) was used for Full-MS quantification based on peak areas obtained from extraction ion chromatograms (± 5 ppm) with two different external calibrations: one without internal standardization and one with internal standardization using 500 µl LILY 13 C lipids. The calibration was performed over three orders of magnitude (standard concentrations of 50 nm, 100 nm, 500 nm, 750 nm, 1000 nm, 2500 nm, 5000 nm). 3

4 Supporting Figure S1: The combination of MS2 HCD (A) and CID (B) fragmentation leads to enhanced fatty acyl level information for phosphatidylcholine and sphingomyelin lipids as exemplary shown for PC (16:0_18:1) and SM (d18:1_16:0). 4

Supporting Figure S2: RPLC-MSn identification of TG 54:4

1 annotation and fragmentation pattern derived via CID in

A: Endogenous human plasma (SRM 1950) LPC 18:0 versus 13 C

5 Supporting Figure S2: RPLC-MSn identification of TG 54:4 based Lipid Search 4.1 annotation and fragmentation pattern derived via CID in MS2 and in HCD in MS3. Supporting Figure S3. A: Endogenous human plasma (SRM 1950) LPC 18:0 versus 13 C labeled LPC 18:0 LILY lipids spiked to human plasma Supporting Figure S3. B: External calibration using the endogenous LPC 18:0 standard and the 13 C labeled LPC 18:0 LILY yeast lipid for compound-specific internal standardization 5

6 Supporting Figure S3. C: Endogenous human plasma (SRM 1950) PC 34:0 versus 13 C labeled PC 34:0 LILY lipids spiked to human plasma Supporting Figure S3. D: External calibration using the endogenous PC 34:0 standard and the 13 C labeled PC 34:0 LILY yeast lipid for compound-specific internal standardization 6

7 Figure S4: Relative quantification of human serum SRM 1950 by Full-MS RPLC-MS in positive mode. Comparison of label-free (A) versus compound-specific (B) relative quantification for 40 lipids out of 6 classes (DG, LPE, PC, PE, PI, TG). The set fold change of prepared dilution series was compared to the experimentally observed fold changes. The 40 lipids were chosen as they were medium to high-abundant (in terms of ion intensity) in human plasma allowing a dilution series over four orders of magnitude for a part of the lipids*. * As can be observed five out of 40 lipids showed significant trueness bias already at a dilution factor of 2, which can be explained by low abundance and high background. Supporting Figure S5: Linearity of label-free versus compound-specific relative quantification in human plasma (SRM1950 samples) using RPLC-MS. 7

8 Supporting Figure S6: Data-points per peak of TG 52:1 observed by different high-resolution MS detection strategies. A) Full-MS (MS ) with 35 data points per TG 52:1 peak B) MS1 (120000) trace derived from ddms2 detection with simultaneous fragmentation in the linear ion trap led to 22 data-points, C) ddmsn (MS1: , MS2/MS3:30000) profiling method for triglycerides led to 6 data points. The varying retention times can be explained by different measurement days and individually prepared eluents. 8

9 Supporting Figure S7: Number of lipids per class identified in human plasma (SRM 1950) and yeast (Pichia pastoris) samples using RPLC-MS 2. A) Human plasma samples lipid profile without and with 13 C LILY lipids added as internal standard (IS) B) Yeast sample lipid profile without and with 13 C LILY lipids added as internal standard (IS) 9

10 Supporting Table S1: Lipid annotation in human plasma (SRM 1950) and yeast (Pichia pastoris). Lipid IDs were derived from Lipid Search 4.1 after filtering (detailed filters can be found in the lipid profiling section of the supporting information) and manual assignment. 390 lipids were identified in human plasma, 212 lipids were annotated in yeast. MS2 Number Grade A Grade B Grade C Lipids 184 PC SM Cer TG MSn Number Grade A Grade B Grade C Lipids 390 PC SM Cer TG Main Grade A B C RPLC-HRMS was analysed by Lipid Search 4.1 Explanation lipid class and fatty acids are completely identified lipid class and some fatty acids are identified lipid class or fatty acids are identified Supporting Table S2: Number of phosphatidylcholine, sphingomyelin, ceramide and triglyceride annotations in human serum plasma (SRM 1950). Comparison of state of the art data-dependent RPLC-MS2 and enhanced lipid profiling by RPLC-MSn derived via Lipid Search 4.1 after filtering and manual inspection 10

11 A Yeast B Human Plasma Lipids nmol/ml Label-free Compound-specific Label-free Compound-specific CER d36:1 < LOQ ± < <LOQ 0.15 ± 0.01 DG 34: ± ± < ± ± < 0.1 LPC 16: ± ± < ± 2 51 ± < 1 LPC 18:0 < LOQ ± < ± 2 18 ± < 1 PA 34:1 19 ± ± 0.1 <LOD <LOD PC 34:0 < LOQ < LOQ 1.2 ± ± 0.1 PC 34:1 1.1 ± ± < ± ± < 1 PC P-35:1/34:2 4.6 ± ± < ± ± < 1 PE 34:1 1.8 ± ± ± ± 0.03 PE 36:2 1.7 ± ± ± ± < 0.1 PS 34:1 3.8 ± ± < 0.1 <LOD <LOD PS 36: ± < ± < 0.01 <LOD <LOD TG 52: ± ± < ± < ± 0.2 Supporting Table S3: Comparison of label-free versus compound-specific absolute quantification for a panel of lipids from different classes (Cer, DG, LPC, PA, PC, PE, PI, PS, TG) determined by MS1 quantification by C30 reversed-phase chromatography coupled to high resolution MS. A. Lipid concentrations of Pichia pastoris samples determined by label-free and compound-specific quantification. B. Lipid concentrations of human plasma SRM 1950 samples determined by label-free and compound-specific quantification SI References (1) Liebisch, G.; Vizcaíno, J. A.; Köfeler, H.; Trötzmüller, M.; Griffiths, W. J.; Schmitz, G.; Spener, F.; Wakelam, M. J. O. J. Lipid Res. 2013, 54 (6),

Increased Identification Coverage and Throughput for Complex Lipidomes

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