Stable isotope tracer experiments are invaluable in broadening our understanding of the dynamic nature of metabolism. However, data analysis and interpretation for these experiments is often difficult and cumbersome. We have used software engineering expertise and cloud technology to automate and speed up many flux workflows.
Investigating cellular metabolism to develop new therapeutics has proven to be a promising approach to drug discovery for both cancer and immunometabolism. Despite these successes, broader integration of metabolism studies in drug discovery programs has been limited due to computational and logistical challenges of translating raw data into metabolic insights
The paper talks about determination of metabolic fluxes by measurement of time-dependent sampling of isotopic enrichments during the administration of labeled substrates provides rich information. Because such experiments are resource-intensive and frequently push the limits of sensitivity of the measurement techniques, optimization of experiment design can improve feasibility with respect to financial and labor costs, time to completion, and increase precision and accuracy of the results.
A 2016 paper published by Vander Heiden Lab at MIT in collaboration with Elucidata highlighted the indisputable role of metabolic studies in identifying pathways and drug targets within lung tumors in mice. With the aid of LC-MS and GC-MS experiments using 13C glucose and 13C-glutamine feeds, the authors investigated nutrient utilization in TCA cycle of tumor cells in vivo and in vitro. The experiments revealed that environment determines the utilization of nutrients by tumor cells. The investigators found glutamine to be the main source of carbon for TCA in cells growing in-vitro whereas glucose oxidation was the primary source of carbon in-vivo.
Elucidata’s tools for processing, analysis, and visualization of labeling experiments (LC-MS, GC-MS, and LC-MS/MS) allow our partners to use similar approaches to study their biological systems of interest.
This 2015 paper was the result of a collaboration between researchers at Agios Pharmaceuticals and Washington University and was highlighted on the cover of the journal Immunity. The paper used a systems-level multi-omics approach to highlight metabolic and transcriptional changes that occur in macrophages during M1 and M2 polarization. The investigators combined mass spectrometry based metabolomic data with RNA-seq based transcriptional data in Combi-T analysis to make data-driven predictions about metabolic rewiring in macrophages. They validated their predictions using 13C labeling experiments as well as other targeted perturbation experiments.
We have since used similar multi-omics approach in many projects for comprehensive understanding of biological processes and to identify potential drug targets.