OMIC based studies often consist of large scale cohorts in order to allow for more confidence in statistically relevant findings. The LC-MS system and associated methodologies used to perform these experiments need to demonstrate robustness, reproducibility and stability over the entire analysis. These characteristics then allow for the identification of statistically relevant markers and reliable quantification. In this study, we show demonstrate these features for a multi-omic study using data independent approaches over multiple platforms between different laboratories.
Samples consisted of liver tissue and plasma extracts derived from mice which had been administered statins. Appropriate sample preparation protocols provided proteomic, lipidomic and metabolomic sample sets. LC-MS data were collected using standard flow chromatographic conditions coupled to a Synapt XS mass spectrometer, operating in DIA (SONAR or HDMSE). For ion mobility (IMS) based acquisitions, collision cross section (CCS) values were generated. The IMS was calibrated using a mixture of compounds, covering a range of m/z values and selected for use in either positive or negative ESI. Data were processed using either Progenesis QI for Proteomics or QI for proteomic and lipidomic/metabolomic data respectively. Identifications resulting from Uniprot (proteomics), LipidMaps (lipids) and HMDB (metabolites) were appended to the processed data. Multivariate statistical analysis was conducted using SIMCA P. In order to assess the reproducibility aspect, data was collected for the same sample sets using replica chromatographic conditions over multiple platforms in different laboratories.
Assessment of the data across all OMIC experiments from the multiple platforms, resulted in low %CV’s for normalised abundance and hence provided high quantitative accuracy. High mass accuracy was also maintained across the various datasets with a significant amount of the collected data being less than 2 ppm. CCS measurements were also shown to be highly reproducibility for both day-to-day (based on the same platform) and laboratory-to-laboratory.