| Autor: |
von Gerichten J; School of Chemistry and Chemical Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, GU2 7XH Guildford, U.K., Saunders KDG; School of Chemistry and Chemical Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, GU2 7XH Guildford, U.K., Kontiza A; School of Chemistry and Chemical Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, GU2 7XH Guildford, U.K., Newman CF; Cellular Imaging and Dynamics, GlaxoSmithKline, Stevenage SG1 2NY, U.K., Mayson G; School of Bioscience, Faculty of Health and Medical Sciences, University of Surrey, GU2 7XH Guildford, U.K., Beste DJV; School of Bioscience, Faculty of Health and Medical Sciences, University of Surrey, GU2 7XH Guildford, U.K., Velliou E; Centre for 3D Models of Health and Disease, University College London, Division of Surgery and Interventional Science, London W1W 7TY, U.K., Whetton AD; vHive, School of Veterinary Medicine, School of Biosciences and Medicine, University of Surrey, Guildford GU2 7XH, U.K., Bailey MJ; School of Chemistry and Chemical Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, GU2 7XH Guildford, U.K. |
| Abstrakt: |
We report the development and validation of an untargeted single-cell lipidomics method based on microflow chromatography coupled to a data-dependent mass spectrometry method for fragmentation-based identification of lipids. Given the absence of single-cell lipid standards, we show how the methodology should be optimized and validated using a dilute cell extract. The methodology is applied to dilute pancreatic cancer and macrophage cell extracts and standards to demonstrate the sensitivity requirements for confident assignment of lipids and classification of the cell type at the single-cell level. The method is then coupled to a system that can provide automated sampling of live, single cells into capillaries under microscope observation. This workflow retains the spatial information and morphology of cells during sampling and highlights the heterogeneity in lipid profiles observed at the single-cell level. The workflow is applied to show changes in single-cell lipid profiles as a response to oxidative stress, coinciding with expanded lipid droplets. This demonstrates that the workflow is sufficiently sensitive to observing changes in lipid profiles in response to a biological stimulus. Understanding how lipids vary in single cells will inform future research into a multitude of biological processes as lipids play important roles in structural, biophysical, energy storage, and signaling functions. |
