DOI: 10.5061/dryad.qv9s4mwgx
title: Lipidomic data of the kidney cortex from diabetic mice fed MUFA-HFD and SFA-HFD

description: In diabetic patients, dyslipidemia frequently
 contributes to organ damage such as diabetic kidney
 disease (DKD). DKD is associated with excessive renal deposition of
 triacylglycerol (TAG) in lipid droplets (LD). Yet, it is unclear whether
 LDs play a protective or damaging role and how this might be influenced by
 dietary patterns. By using a diabetes mouse model, we find here that
 high-fat diet enriched in the unsaturated oleic acid (OA) caused more
 lipid storage in LDs in renal proximal tubular cells (PTC) but less
 tubular damage than a corresponding butter diet with the saturated
 palmitic acid (PA). In order to study the changes in the lipidome, we
 performed shotgun lipidomics on the kidney cortex of these mice.
description: C57BL/6NCrl male 7-week-old
 mice were put on a control diet, MUFA-HFD or SFA-HFD with free access to
 food and water. At 11 weeks old, insulin deficiency was induced by
 intraperitoneal administration of streptozotocin (50 mg/kg per day for 5
 consecutive days).  Control mice were injected with sodium citrate
 buffer. The animals were
 sacrificed by cervical dislocation 16 weeks after STZ treatment. Kidney
 cortex was dissected and processed for lipidomic
 analysis.  <strong>Lipid extraction for mass spectrometry
 lipidomics </strong> Mass spectrometry-based lipid analysis was
 performed by Lipotype GmbH (Dresden, Germany) as
 described (Sampaio et al,
 2011). Lipids were
 extracted using a two-step chloroform/methanol
 procedure (Ejsing et al,
 2009). Samples were spiked
 with internal lipid standard mixture containing: cardiolipin
 14:0/14:0/14:0/14:0 (CL), ceramide 18:1;2/17:0 (Cer), diacylglycerol
 17:0/17:0 (DAG), hexosylceramide 18:1;2/12:0 (HexCer), lyso-phosphatidate
 17:0 (LPA), lyso-phosphatidylcholine 12:0 (LPC),
 lyso-phosphatidylethanolamine 17:1 (LPE), lyso-phosphatidylglycerol 17:1
 (LPG), lyso-phosphatidylinositol 17:1 (LPI), lyso-phosphatidylserine 17:1
 (LPS), phosphatidate 17:0/17:0 (PhA), phosphatidylcholine 17:0/17:0 (PC),
 phosphatidylethanolamine 17:0/17:0 (PE), phosphatidylglycerol 17:0/17:0
 (PG), phosphatidylinositol 16:0/16:0 (PI), phosphatidylserine 17:0/17:0
 (PS), cholesterol ester 20:0 (CE), sphingomyelin 18:1;2/12:0;0 (SM),
 sulfatide d18:1;2/12:0;0 (Sulf), triacylglycerol 17:0/17:0/17:0 (TAG) and
 cholesterol D6 (Chol). After extraction, the organic phase was transferred
 to an infusion plate and dried in a speed vacuum concentrator. 1st step
 dry extract was re-suspended in 7.5 mM ammonium acetate in
 chloroform/methanol/propanol (1:2:4, V:V:V) and 2nd step dry extract in
 33% ethanol solution of methylamine in chloroform/methanol (0.003:5:1;
 V:V:V). All liquid handling steps were performed using Hamilton Robotics
 STARlet robotic platform with the anti-droplet control feature for organic
 solvents pipetting. 
 <strong>MS data
 acquisition </strong> Samples were analyzed by direct infusion on a
 QExactive mass spectrometer (Thermo Scientific) equipped with a TriVersa
 NanoMate ion source (Advion Biosciences). Samples were analyzed in both
 positive and negative ion modes with a resolution of Rm/z=200=280000 for
 MS and Rm/z=200=17500 for MSMS experiments, in a single acquisition. MSMS
 was triggered by an inclusion list encompassing corresponding MS mass
 ranges scanned in 1 Da increments (Surma et al, 2015). Both MS and MSMS data were combined to monitor
 CE, DAG and TAG ions as ammonium adducts; PC, PC O-, as acetate adducts;
 and CL, PA, PE, PE O-, PG, PI and PS as deprotonated anions. MS only was
 used to monitor LPA, LPE, LPE O-, LPI and LPS as deprotonated anions; Cer,
 HexCer, SM, LPC and LPC O- as acetate adducts and cholesterol as ammonium
 adduct of an acetylated derivative (Liebisch et al, 2006).
 <strong>Data analysis
 and post-processing </strong>
 Data were analysed with
 in-house developed lipid identification software based on
 LipidXplorer (Herzog et
 al, 2012; Herzog et al, 2011). Data post-processing and normalization were
 performed using an in-house developed data management system. Only lipid
 identifications with a signal-to-noise ratio &gt;5, and a signal
 intensity 5-fold higher than in corresponding blank samples were
 considered for further data analysis.