Tripletof 6600 q tof

Cells seeded into 6-well plates (2 × 10⁵ cells/well) were cultured for 24 h before collecting conditioned culture medium sample (200 μL) and extracting in water:methanol:acetonitrile (1:2:7) containing 2-chloro-l-phenylalanine after vortexing for 30 s. After further sonication for 10 min and centrifugation at 13,000 × g at 4 °C for 15 min, the supernatant was transferred into a fresh 2-mL liquid chromatography–mass spectrometry glass vial for ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) analysis. Supernatants (100 μL) were used for UHPLC-Q-TOF/MS analysis and 10 μL was used for quality control samples. Supernatant metabolic profiling analysis was performed using a UHPLC system (1290; Agilent Technologies, Palo Alto, CA, USA) with a UPLC BEH Amide column coupled to a Triple TOF 6600 (Q-TOF; AB SCIEX, Foster City, CA, USA).

The metabolomics of plasma was performed on a UHPLC system (1290, Agilent Technologies, Santa Clara, CA, USA) with a UPLC BEH Amide column (1.7 μm 2.1 ×* 100 mm, Waters) coupled to TripleTOF 6600 (Q-TOF, AB Sciex, Foster City, CA, USA). MS raw data files (wiff) were converted to the mzXML format by using ProteoWizard software (AB Sciex, Foster City, CA, USA). Retention time alignment, peak discrimination, data filtering, alignment and matching were carried out by using R package XCMS (version 3.2), which generated a data matrix that consisted of the retention time (RT), mass-to-charge ratio (m/z) values, and peak intensity. Then, CAMERA R package was used for peak annotation. An in-house MS2 database was applied to identify the metabolites. We obtained 1918 annotated metabolites and 1016 unknown analytes in the positive and negative ion model, and chose the annotated metabolites for subsequent analysis.
The partial least square discriminant analysis (PLSDA) of the metabolomics data was conducted by the mixOmics R package.

The analysis of LC-MS/MS was performed using an UHPLC system (Agilent Technologies) with an UPLC BEH amide column (1.7 μm 2.1 mm*100 mm, Waters) coupled to Triple TOF 6600 (Q-TOF, AB Sciex). The fluid-phase that was consisted of 25 mM NH4Ac and 25 mM NH4OH in water (pH = 9.75) (A) and acetonitrile (B), was performed according to the following gradient elution method: 0 min(95% B); 0.5 min(95% B); 7 min(65% B); 8 min(40% B); 9 min(40% B); 9.1 min(95% B); 12 min(95% B), delivered at 0.5 mL/min, 2 μL of injection. The triple TOF mass spectrometer was applied for its ability to acquire MS/MS spectra on an information-dependent basis (IDA) during a LC/MS experiment. The acquisition software (Analyst TF 1.7, AB Sciex) continuously measures the full scan survey MS data as it collected and triggered the acquisition of MS/MS spectra that depend on preselected criteria. 12 precursor ions whose intensity greater than 100 were chosen for fragmentation at collision energy (CE) of 30 V (15 MS/MS events with product ion accumulation time of 50 msec each) in each cycle. Then, we set ESI source conditions: ion source gas 1 as 60 Psi, ion source gas 2 as 60 Psi, curtain gas as 35 Psi, source temperature 650 °C, Ion Spray Voltage Floating (ISVF) at 5000 V or − 4000 V in positive or negative modes, respectively [17 , 18 (link)].

Analysis data were acquired using a UHPLC-high definition quadrupole time-of-flight MS instrument (UHPLC-qTOF SYNAPT G1 HD-MS system, Waters Co., Ltd., Milford, MA, USA), equipped with a TripleTOF 6600 (Q-TOF, AB Sciex). A binary solvent method consisting of eluent A (25 mM NH4Ac and 25 mM NH4OH in water pH = 9.75) and acetonitrile (B) was carried out with an elution gradient as follows: 0 min, 95% B; 0.5 min, 95% B; 7 min, 65% B; 8 min, 40% B; 9 min, 40% B; 9.1 min, 95% B; 12 min, 95% B, delivered at 0.5 mL min⁻¹. The Triple TOF mass spectrometer was used for its ability to acquire MS/MS spectra on an information-dependent basis (IDA) during an LC/MS experiment. In this mode, the acquisition software (Analyst TF 1.7, AB Sciex) continuously evaluated the full-scan survey MS data as it collected and triggered the acquisition of MS/MS spectra depending on preselected criteria. In each cycle, 12 precursor ions, whose intensity was greater than 100, were chosen for fragmentation at a collision energy (CE) of 30 V (15 MS/MS events with product ion accumulation time of 50 ms each). ESI source conditions were set as follows: ion source gas 1 as 60 psi, ion source gas 2 as 60 psi, curtain gas as 35 psi, source temperature 600 °C, Ion Spray Voltage Floating (ISVF) 5000 V or −4000 V in positive or negative modes, respectively.

LC-MS/MS analyses were performed using a UHPLC system (1290, Agilent Technologies) with a UPLC BEH Amide column (1.7 μm 2.1×100 mm, Waters) coupled to a TripleTOF 6600 (Q-TOF, AB Sciex) and QTOF 6550 (Agilent). MS raw data files were converted to the mzXML format using ProteoWizard and processed by the R package XCMS (version 3.2). The R package CAMERA was used for peak annotation after XCMS data processing. MetaboAnalyst (Pang et al., 2021 (link)) (https://www.metaboanalyst.ca/) and Kyoto Encyclopedia of Genes and Genomes (Kanehisa and Goto, 2000 (link)) (KEGG, https://www.kegg.jp/) were used for bioinformatics analysis.

The 100 uL sample mixture was precipitated with 200 uL methanol. All samples were centrifuged at 4℃, 14,000 g for 10 min. The supernatant was analyzed by UHPLC system (1290, Agilent Technologies) with UPLC BEH column (1.7 μm 2.1 × 100 mm, Waters), TripleTOF 6600 (Q-TOF, AB Sciex) and QTOF 6550 (Agilent). MS raw data files were converted to mzXML format by R package XCMS (version 3.2). A data matrix consisting of retention time (RT), mass/charge ratio (m/z) and peak intensity was generated. MetaboAnalyst platform (https://www.metaboanalyst.ca/) was used for bioinformatics analysis [22 (link)]. Statistical analysis (single factor) included multivariate statistics - PCA, partial least squares-discriminant analysis (PLS-DA), etc. PCA uses an “unsupervised mode” to clearly demonstrate the redundancy within data groups and the differences between groups, thereby assessing the replicability of the data. PLS-DA uses partial least squares regression to effectively reduce the number of variables (metabolites) in high-dimensional metabolomics data to assess the relationship between metabolite expression levels and sample categories. The Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www.kegg.jp/) pathway database was used for functional prediction in the MetaboAnalyst platform.