Metabolomics Data- ACBP: a poor-prognosis biomarker in sepsis and a target for disease mitigation
Description
Mass spectrometry-based metabolomics performed in spleen, heart, plasma, liver and kidney of C57Bl/6J mice.
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Metabolomics analysis Sample processing. First, 30 mg of liver, spleen, kidney, and heart tissue were weighed and transferred into 2 mL homogenizer tubes containing ceramic beads (Hard Tissue Homogenizing CK28, 2.8 mm zirconium oxide beads; Precellys, Bertin Technologies). Each tube was filled with 1 mL of ice-cold extraction solvent (methanol/water, 9:1, -20 °C) containing a cocktail of internal standards. To ensure thorough extraction of endogenous metabolites, tissues were homogenized using a Precellys 24 tissue homogenizer (3 cycles of 20 seconds at 5000 rpm). Homogenates were centrifuged at 15,000×g for 10 minutes at 4°C, and the resulting supernatants were collected. For plasma samples, 25 µL of plasma was mixed with 250 µL of the same ice-cold extraction solvent. After vortexing to facilitate protein precipitation and metabolite extraction, samples were centrifuged under the same conditions (15,000×g, 10 minutes, 4°C). Supernatants from both tissue and plasma samples were collected and divided into three fractions for downstream metabolomics workflows, following protocols previously described (Motiño et al., 2022). Data analysis. Metabolomics data analysis was realized base on platform results, the analyse was conducted with the R package (R Foundation for Statistical Computing; https://www.R-project.org/) MetaboDiff (Mock et al., 2018), significant metabolite are select based on difference in means (dm), which is calculated by the package function and it is based on a t-test we used the thresholds abs(dm) ≥ 0.5 and pvalue ≤ 0.05 based on this selection we have realized Venn diagram with ggVennDiagram (Gao et al., 2024). Heatmaps was realized with ComplexHeatmap (Gu et al., 2016).
Institutions
- Centre de Recherche des CordeliersÎle-de-France, Paris
- Gustave RoussyÎle-de-France, Villejuif