IsoSolve: An Integrative Framework to Improve Isotopic Coverage and Consolidate Isotopic Measurements by Mass Spectrometry and/or Nuclear Magnetic Resonance

Stable-isotope
labeling experiments are widely used to investigate
the topology and functioning of metabolic networks. Label incorporation
into metabolites can be quantified using a broad range of mass spectrometry
(MS) and nuclear magnetic resonance (NMR) spectroscopy methods, but
in general, no single approach can completely cover isotopic space,
even for small metabolites. The number of quantifiable isotopic species
could be increased and the coverage of isotopic space improved by
integrating measurements obtained by different methods; however, this
approach has remained largely unexplored because no framework able
to deal with partial, heterogeneous isotopic measurements has yet
been developed. Here, we present a generic computational framework
based on symbolic calculus that can integrate any isotopic data set
by connecting measurements to the chemical structure of the molecules.
As a test case, we apply this framework to isotopic analyses of amino
acids, which are ubiquitous to life, central to many biological questions,
and can be analyzed by a broad range of MS and NMR methods. We demonstrate
how this integrative framework helps to (i) clarify and improve the
coverage of isotopic space, (ii) evaluate the complementarity and
redundancy of different techniques, (iii) consolidate isotopic data
sets, (iv) design experiments, and (v) guide future analytical developments.
This framework, which can be applied to any labeled element, isotopic
tracer, metabolite, and analytical platform, has been implemented
in IsoSolve (available at https://github.com/MetaSys-LISBP/IsoSolve and https://pypi.org/project/IsoSolve), an open-source software that can be readily integrated into data
analysis pipelines.