Improved experimental data processing for UHPLC–HRMS/MS lipidomics applied to nonalcoholic fatty liver disease

摘要: Untargeted metabolomics workflows include numerous points where variance and systematic errors can be introduced. Due to the diversity of lipidome, manual peak picking quantitation using molecule specific internal standards is unrealistic, therefore quality algorithms further feature processing normalization are important. Subsequent normalization, data filtering, statistical analysis, biological interpretation simplified when acquisition employed. Metrics for QC important throughout workflow. The robust workflow presented here provides techniques ensure that checks implemented sample preparation, acquisition, pre-processing, analysis. untargeted lipidomics includes standardization prior blocks blanks run at intervals between randomized experimental data, blank filtering (BFF) remove features not originating from sample, analysis processing. was successfully applied mouse liver samples, which were investigated discern lipidomic changes development nonalcoholic fatty disease (NAFLD). workflow, including a novel method, BFF, allows improved confidence in results conclusions applications. Using model developed study transition NAFLD an early stage known as simple steatosis, later stage, steatohepatitis, combination with our we have identified phosphatidylcholines, phosphatidylethanolamines, triacylglycerols may contribute onset and/or progression.