Metabolomics Data - Integrated multi-omics analysis using MENTOR reveals metabolic reprogramming in the Niemann-Pick type C mouse brain

Mice were euthanized with isoflurane followed by decapitation. Brains were collected rapidly from 7 week old mice from each genotype (WT and Npc1-/-), and the forebrains were quickly dissected and flash frozen in liquid nitrogen. Tissue samples were removed from -80 °C storage and maintained on wet ice throughout the processing steps. Tissues were carefully weighed to 30 mg +/- 2 mg and the extraction solvent (1:1:1:1: Methanol:Acetone:Acetonitrile:Water) containing internal standards was scaled to the tissue weight (30:1). Tissue was disrupted using a probe sonicator at 40% output power, 40% duty cycle for 20 seconds. Samples were allowed to rest on wet ice for 10 min, then centrifuged at 4° C, 14,000 rpm for 10 min. 10 µL of each sample was removed and pooled in a separate autosampler vial for quality control purposes. 200 µL of supernatant was transferred to an autosampler vial and brought to complete dryness using a nitrogen drier in ambient conditions. Samples and pools were reconstituted with 100 µL and 150 µL of water: methanol (8:2 by volume).

Analysis was performed on an Infinity Lab II UPLC coupled with a 6545 QTof mass spectrometer (Agilent Technologies) using a JetStream ESI source in negative mode. The following source parameters were used: Gas Temp: 250 °C, Gas Flow: 13 L/min, Nebulizer: 35 psi, Sheath Gas Temp: 325 °C, Sheath Gas Flow: 12 L/min, Capillary: 3500 V, Nozzle Voltage: 1500 V.

The UPLC was equipped with a 10-port valve configured to allow the column to be either eluted to the mass spectrometer or back-flushed to waste. The chromatographic separation was performed on an Agilent ZORBAX RRHD Extend 80Å C18, 2.1 × 150 mm, 1.8 μm column with an Agilent ZORBAX SB-C8, 2.1 mm × 30 mm, 3.5 μm guard column. The column temperature was 35 °C. Mobile phase A consisted of 97:3 water/ methanol and mobile phase B was 100% methanol; both A and B contained tributylamine and glacial acetic acid at concentrations of 10mM and 15mM, respectively. The column was back-flushed with mobile phase C (100% acetonitrile, no additives) between injections for column cleaning.The LC gradient was as follows: 0-2min, 0%B; 2-12 min, linear ramp to 99%B; 12-17.5 min, 99%B. At 17.5 min, the 10-port valve was switched to reverse flow (back-flush) through the column, and the solvent composition changed to 99%C. From 20.5-21 min the flow rate was ramped to 0.8 mL/min, held until 22.5 min, then reduced to 0.6mL/min. From 22.7-23.5 min the solvent was ramped from 99% to 0% C while flow was simultaneously ramped down from 0.6-0.4mL/min and held until 29.4 min, at which point flow rate was returned to starting conditions at 0.25mL/min. The 10-port valve was returned to restore forward flow through the column at 28.5 min. An isocratic pump was used to introduce reference mass solution through the reference nebulizer for dynamic mass correction. Total run time was 30 min. The injection volume was 5 uL.

Data analysis for this platform follows a hybrid targeted/non-targeted approach.  Semi-quantitative data for known compounds is obtained by manual integration using Profinder v8.00 (Agilent Technologies, Santa Clara, CA.) Metabolites were identified by matching the retention time (+/- 0.1 min), mass (+/- 10 ppm) and isotope profile (peak height and spacing) to authentic standards. Non-targeted data analysis was performed using Agilent’s MassHunter Find by Molecular Feature workflow (v7.0) with recursion using Agilent’s Mass Profiler Pro (v8.0).

A combined feature set was generated by merging untargeted features and named metabolites into a single feature list. The combined feature set underwent data reduction using Binner (M. Kachman et al., 2020). Briefly, Binner first performs RT-based binning, followed by clustering of features by Pearson’s correlation coefficient, and the assignment of isotopes, adducts or in-source fragments by searching for known mass differences between highly correlated features. After Binner data reduction in-house software was used to search Refmet (https://www.metabolomicsworkbench.org/databases/refmet/index.php) to provide MS1 (L. W. Sumner et al., 2007) Level III identifications, or to an in-house library of authentic standards to provide MS1 Level I identifications.

Iterative Data Dependent Acquisition (iDDA) ms/ms analysis was performed on the pooled sample material. iDDA captures ms/ms in stepwise fashion, with rolling excluded precursors. For untargeted platforms, we collect 8 rounds of iDDA at 3 different collision energies. At each collision energy, ~8000 ms/ms spectra are collected, which represent ms/ms spectra for approximately 75-95% of the features obtained by the untargeted data analysis. Analysis of iDDA spectra using NIST2020 was performed to provide MS1 Level II identifications for statistically significant features.