Webinar Multivariate Time Series Analysis of Metabolomics Data

Multivariate Time Series Analysis of Metabolomics Data Targeting Productivity and Cell Death in Cell Culture Processes

Chinese hamster ovary (CHO) cells are the most common mammalian production system for biopharmaceuticals. There is considerable variation between CHO cell clones, causing productivity and cell death differences between production batches, ultimately affecting the product’s amount and quality. The underlying biological and environmental determinants are not fully understood. Comprehensively characterizing the various metabolites in the system can shed light on this biology.

Metabolomics data collected from CHO bioprocesses consist of numerous metabolites and process parameters measured for several batches and across multiple time points. Such multidimensional time series data are difficult to analyze as both metabolite levels and many process variables are correlated with elapsed process time, confounding the metabolite-process variable relation. Conventional correlation-based statistical techniques are inadequate to resolve this time dependence.

To remedy this, we have expanded on an existing hierarchical method based on orthogonal partial least squares (OPLS) regression that handles the time dependence by analyzing each time point separately. We have modified this method with effect projection (EP) to highlight metabolites with a consistent correlation to a process variable over the given timeframe. It should be noted that the method can be applied in other omics settings with ample time points (e.g. 1x/day) as well.

The presentation’s objective is to showcase how the method works and why, and the biochemical insight that could be gleaned from applying it to a metabolomics data set.

What You Will Learn:

How hierarchical modelling of time series data and OPLS-EP work in conjunction to overcome the time dependence
How this method compares against conventional, global OPLS (all time points at once)
How this method can find biologically relevant metabolites

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Presenter

This webinar is presented by Andreas Eriksson, doctoral student at the Department of Chemistry, Umeå University, Sweden, working in collaboration with Sartorius Digital Solutions. Andreas holds dual MSc degrees in environmental chemistry and molecular biology, with an overarching foundation in analytical chemistry. His research involves omics characterization of CHO-based biopharmaceutical production processes and developing appropriate MVDA methods to do so.