SUPPORTING INFORMATION FOR:

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1 SUPPORTING INFORMATION FOR: Metabolomics and lipidomics profiling of a combined mitochondrial plus endoplasmic reticulum fraction of human fibroblasts: a robust tool for clinical studies Charlotte Veyrat-Durebex 1,2, Cinzia Bocca 2, Stéphanie Chupin 1, Judith Kouassi NZoughet 2, Gilles Simard 1, Guy Lenaers 2, Pascal Reynier 1,2, Hélène Blasco 2,3,4 1 Département de Biochimie et Génétique, Centre Hospitalier Universitaire, Angers, France 2 Equipe Mitolab, Institut MITOVASC, UMR CNRS 6015, INSERM 1083, Université d Angers, France 3 Université François-Rabelais, INSERM U930, Tours, France 4 Laboratoire de Biochimie et Biologie moléculaire, CHRU de Tours, France TABLE OF CONTENTS: - Additional methods (PDF page S-2) - Supplementary Figure S1: Metabolomics and lipidomics analyses (PDF page S-4) - Supplementary Figure S2: Metabolites identified in mitochondria-er fraction versus whole cells (PDF page S-5) - Supplementary Figure S3: Lipids identified in mitochondria-er fraction versus whole cells (PDF page S-6) - Supplementary Table S1: List of compounds identified in the mitochondria-er fraction using HRMS metabolomics and an in-house library (with coefficients of variation in ten independent samples inferior to 35%). The level of compound identification was done according to Sumner et al (Separate file TableS1.xlsx) - Supplementary Table S2: List of compounds identified in the mitochondria-er fraction using HRMS lipidomics (with coefficients of variation in ten independent samples inferior to 35%). The level of compound identification was done according to Sumner et al (Separate file TableS2.xlsx) S-1

2 ADDITIONAL METHODS Metabolomics analysis Metabolites extraction Metabolites extraction was performed in ice-cold methanol/water mixture (80/20) containing isotope labeled internal standards (17α-Hydroxyprogesterone-d8, L-Thyroxine- 13 C6, Succinic acid-2,2,3,3-d4, Pyruvic acid-1-13 C and DL-Alanine- 15 N, 10 µg/ml in methanol). Mitochondria-ER pellet was mixed with 480 µl of ice-cold methanol and 120 µl of water. After vortexing, mixture was centrifuged at g during 15 minutes at 4 C. After vacuum evaporation of the supernatant (MiVac, SP Scientific), 300 µl of methanol/water (98/2) was added to the dry pellet and 5 µl were injected for mass spectrometry analysis. Analytical system High Resolution Mass Spectrometry (HRMS) system was based on UPLC Ultimate 3000 system (Dionex) coupled to a Q-Exactive Mass Spectrometer (ThermoFisher Scientific, Bremen, Germany) using electrospray ionization in positive (ESI+) and negative (ESI-) modes. The chromatographic separation in reverse phase (RP) conditions was achieved with a Kinetex XB-C18 1.7μm 150 mm 2.1 column together with the corresponding precolumn (Phenomenex). Mobile phases consisted of water in channel A and methanol in channel B, both containing 0.1% formic acid. The elution gradient (A:B, v/v) was as follows: hold initial conditions 98:2 for 2 min, followed by a linear gradient from 98:2 to 0:100 over a 15 min period, hold at 0:100 for 3 min, return to initial conditions 98:2 over 2.5 min and then hold these conditions for a further 2 min. A constant flow rate of ml/min was used; the injection volume was 5 μl, and sample injection order was randomized. Xcalibur 2.2 software (Thermo Fisher Scientific, San Jose, CA, USA) was used for data acquisition. TraceFinder 4.1 software was used for metabolites identification and peak integration using an in-house library 1. The level of compound identification was done according to the work of Sumner et al. 2. Lipidomics analyses Lipids extraction The method of lipid extraction is based on 2 consecutive steps of liquid-liquid extraction with chloroform/methanol (1 ml, 10-1 then 2-1, v/v) on sample mixed with ammonium bicarbonate (170 µl, 155 µm), and internal standards (10 µg/ml in methanol) 3. The organic and aqueous phases were collected, evaporated to dryness and reconstituted with 250 µl of acetonitrile/isopropanol/water (65/35/5); 10 µl were injected for mass spectrometry analysis. S-2

3 Analytical system HRMS acquisitions were based on UPLC Ultimate 3000 system (Dionex) coupled to a Q- Exactive Mass Spectrometer (ThermoFisher Scientific, Bremen, Germany) using electrospray ionization in positive (ESI+) and negative (ESI-) modes. The chromatographic separation was achieved with a Kinetex Evo-C18 1.7μm 150 mm 2.1 column together with the corresponding precolumn (Phenomenex). Mobile phases consisted of isopropanol/acetonitrile (90/10) in channel A and acetonitrile/water (60/40) in channel B, both containing 0.1% formic acid and 10 mm ammonium formiate. The elution gradient (A:B, v/v) was as follows: hold initial conditions 32:68 for 2 min, followed by a linear gradient from 98:2 to 0:100 over a 15 min period, hold at 0:100 for 3 min, return to initial conditions 98:2 over 2.5 min and then hold these conditions for a further 2 min. A constant flow rate of 0.3 ml/min was used; the injection volume was 10 μl, and sample injection order was randomized. Xcalibur 2.2 software (Thermo Fisher Scientific, San Jose, CA, USA) was used for data acquisition. LipidSearch was employed for lipids identification using MS and MS² spectra, and TraceFinder 4.1 software (Thermo Fisher Scientific) for data processing. The level of compound identification was done according to Sumner et al References: 1. Kouassi Nzoughet, J.; Bocca, C.; Simard, G.; Prunier-Mirebeau, D.; Chao de la Barca, J. M.; Bonneau, D.; Procaccio, V.; Prunier, F.; Lenaers, G.; Reynier, P., A Nontargeted UHPLC-HRMS Metabolomics Pipeline for Metabolite Identification: Application to Cardiac Remote Ischemic Preconditioning. Anal Chem 2017, 89 (3), Sumner, L. W.; Amberg, A.; Barrett, D.; Beale, M. H.; Beger, R.; Daykin, C. A.; Fan, T. W.; Fiehn, O.; Goodacre, R.; Griffin, J. L.; Hankemeier, T.; Hardy, N.; Harnly, J.; Higashi, R.; Kopka, J.; Lane, A. N.; Lindon, J. C.; Marriott, P.; Nicholls, A. W.; Reily, M. D.; Thaden, J. J.; Viant, M. R., Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 2007, 3 (3), Ejsing, C. S.; Sampaio, J. L.; Surendranath, V.; Duchoslav, E.; Ekroos, K.; Klemm, R. W.; Simons, K.; Shevchenko, A., Global analysis of the yeast lipidome by quantitative shotgun mass spectrometry. Proc Natl Acad Sci U S A 2009, 106 (7), S-3

4 SUPPLEMENTARY FIGURES Supplementary Figure S1 Figure S1: Metabolomics and lipidomics analyses A and C. Venn Diagram of compounds detected in both negative and positive acquisition modes in metabolomics (A) and lipidomics (C) analyses. B and D. PCA score plots and DModX plots showing the distance to the model for each observation, build from the metabolomics (B) and lipidomics (D) profiles of replicated mito- ER samples. S-4

5 Supplementary Figure S2 Figure S2: Metabolites identified in mitochondria-er fraction versus whole cells. Venn diagram highlighting differences in compounds reproducibly identified in mitochondria- ER fraction versus whole cells using metabolomics analysis. S-5

6 Supplementary Figure S3 Figure S3: Lipids identified in mitochondria-er fraction versus whole cells. Venn diagram highlighting differences in compounds reproducibly identified in mitochondria- ER fraction versus whole cells using lipidomics analysis. Cer ceramides; CerG neutral glycosphingolipids; CL cardiolipins, DG diglycerides; GM3 gangliosides; LP lysophospholipids; PC phosphatidylcholine; MePC methyl-pc; PE phosphatidylethanolamine; PG phosphatidylglycerol; PI phosphatidylinositol; PS phosphatidylserine; SM sphingomyelin; TG triglycerides; So sphingosine; ChE cholesterol ester; dmepe dimethyl-pe; ZyE zymosterol. S-6