Targeted lipidomics platform

  • PI:
  • Dr. Maria Fedorova
  • Project title:
  • Extension of the Lipidomics technology platform to include targeted high-throughput phenotyping of lipid metabolism and its remodelling
  • Funding period:
  • March 2022 – December 2022
  • Funding organization:
  • TG70 by Sächsische Aufbaubank and Sächsisches Staatsministerium für Wissenschaft, Kultur und Tourismus (SMWK; Saxon State Ministry for Science, Culture and Tourism), the measure is co-financed with tax funds on the basis of the budget passed by the Saxon state parliament.

Project description:

Despite the significant role of lipid metabolism in biomedical research and the high potential in clinical application, effective monitoring of dynamic remodelling of the natural lipidome is difficult. On the one hand, this is due to the structural complexity of the lipids and, on the other hand, due to the large dynamic range of endogenous lipid concentrations. Modern bioanalytical methods based on mass spectrometry (MS) are able to resolve lipidome complexity but requires tailored solutions aiming either in-depth lipidome profiling or high-throughput screening. The goal of the project was to extend the existing MS technology platform to include an LC-MS/MS-based application for high-throughput quantitative analysis for phenotyping lipid metabolism. New QqQ instrument was integrated into the existing lipidomics infrastructure at ZML which allows now streamlined combination of untargeted and targeted lipidomics workflows under the same data analysis pipeline. New instrumentation was integrated into the Dresden Technology Portal.

Current projects:

Understanding dynamics of lipid metabolism and oxidation in ferroptotic cell death programme

Ferroptosis as a common underlying pathomechanism in tissue ischemia/reperfusion injury

Pan-European Network in Lipidomics and EpiLipidomcis

Dysregulated systemic release of metabolic and bioactive lipids along hepatocyte-VLDL axis

Extension of the Lipidomics technology platform to include targeted high-throughput phenotyping of lipid metabolism and its remodelling

Apply machine learning and deep learning methods to (epi)lipidomics data analysis

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