MAIMS: a software tool for sensitive metabolic tracer analysis through the deconvolution of ¹³C mass isotopologue profiles of large composite metabolites

Introduction: Metabolic tracer analysis (MTA) is a collection of principles, rules and tools for the interpretation of stable isotope incorporation patterns. One example is the GAIMS algorithm for the deconvolution of the UDP-GlcNAc ¹³C mass isotopologue profile. GAIMS has been presented as a powerful, yet currently unavailable, proof-of-concept-only technique. Objectives: We aimed to build a tool inspired by the original GAIMS algorithm, providing identical functionality and straightforward extensibility towards alternative composite metabolites. Methods: We implemented MAIMS by applying Multistart metaheuristics combined with an efficient hybrid stopping rule to solve the non-convex optimization underlying the deconvolution problem. By testing our tool on several theoretical datasets, we were able to confirm its robust and reproducible performance. Results: MAIMS is capable of finding the individual contributions of specifically labeled molecular subunits to large composite metabolites (such as UDP-GlcNAc and ATP) upon U-¹³C-glucose administration and thereby hinting on the activity of several metabolic pathway activities. Applied to proliferating endothelial cells (ECs), MAIMS led to several interesting metabolic insights and generally proved to be a sensitive way for relatively measuring specific pathway activities and for detecting compartmentalized pools of precursor metabolites. Conclusion: MAIMS is a powerful and extendible tool for isotopologue profile deconvolution tasks and is freely available on github as an open-source Python (Python 2.7 and 3.5 + compliant) script for command line usage. (http://github.com/savantas/MAIMS).