With the rapid development of the world economy, people have changed their eating habits and lifestyles. The problems of metabolic diseases such as obesity, fatty liver and hyperlipidemia are also becoming more prominent. The development of lipidomics technology has enabled to study the mechanisms of lipid metabolism more accurately and rapidly.
There are very conserved mechanisms and other advantages among eukaryotes. Saccharomyces cerevisiae is a model organism widely used to study physiological and molecular events in eukaryotic cells. In lipid studies, due to the conserved nature among eukaryotes, Saccharomyces cerevisiae possesses strong homology with higher eukaryotes in proteins, genes, metabolic pathways and regulatory networks, providing many insights and ideas for the genetics and biochemistry of lipid-related diseases. In addition, Saccharomyces cerevisiae has a relatively simple lipidome and a large, accurate genetic database, making it easier to perform genetic manipulations that can be used to study genetics and metabolism.
Phospholipids, sterols and sphingolipids, as major components of yeast cell membranes, play an important role in maintaining cell membrane structure and biological functions. Lipidomics research methods allow the detection, identification and systematic comparative analysis of intracellular lipid metabolites. Depending on the research objectives, Creative Proteomics offers you different strategies for yeast lipidomics analysis. Take a targeted lipidomic analysis, which analyzes only specific lipids to cover specific pathways, or possibly take an untargeted lipidomic approach, which aims to detect a large number of lipid species to gain system-level insight.
With the development of mass spectrometry, many high-throughput and high-sensitivity techniques are available for lipidomics studies. We commonly use LC-MS, GC-MS for qualitative and quantitative analysis of a wide range of lipids in yeast, including PC, PE, PI, PS, PA, and many other lipids. The combination of chromatographic methods with MS can greatly increase the coverage of lipid analysis.
We have a complete technology platform for identification and quantification of yeast lipidomics, including GC-FID, GC-MS and LC-MS. Data analysis includes PCA, PLS-DA, PLS-R and univariate statistics. Generate a lipid list and classify the data based on the selected statistical test (f-test, t-test or regression and export list) for further analysis, such as enrichment and KEGG pathway analysis.
Fig1. The workflow of yeast lipidomics service.
If you have any questions about our yeast lipidomics services, please contact us.