Robust Sample Generation and Data Analysis for the Application of Metabolomics in Bioprocessing

In recent years there has been a rise in –omics approaches to understand cellular phenotype with applications in bioprocessing. In particular, metabolomics involves the identification of small molecule metabolites which offer a more accurate snapshot of the cellular metabolism as a result of process and environmental parameters. Few instances in the literature have looked at quench and extraction methods to better preserve the cell metabolism. This work will emphasize the importance of a well hypothesized study design as a first step for the successful application of global metabolite profiling. Further, the development of a robust method for sample collection will be reviewed by discussing the impact of factors such as cell pellet handling, quenching of metabolism, time of exposure and cell pellet resuspension. The amount of data generated from such a study is typically large and insights will be provided on data analysis from sample studies.