Isotope dilution with isotopically labeled biomass: An effective alternative for quantitative metabolomics.

Background: State-of-the-art quantitative metabolomics relies on isotope dilution using internal standards (IS) derived from fully C labeled biomass. By spiking samples and external standards with known amounts of IS, the spike characterization demands are kept to a minimum. In fact, it is sufficient to experimentally assess the isotopic enrichment of the IS. This study develops the yeast derived IS toolbox further, (1) by characterizing the concentration levels of hydrophilic metabolites in a yeast fermentation batch and (2) by exploring the analytical figures of merit of one-point IS versus multipoint external calibration using IS, the established gold-standard for quantitative metabolomics.

Results: Independent reverse isotope dilution experiments using different chromatographic methods over a period of several months, delivered a list of 83 C-labeled metabolites with fully characterized concentration and their uncertainty, covering 5 orders of magnitude, from the nanomolar to the low millimolar range. The C-labeled yeast-derived IS showed excellent intermediate stability with 92 % of molecules showing inter-method RSDs ≤30 % (75 % of molecules showed RSDs ≤15 %) over a timeframe of five months. One-point internal standardization with the characterized labeled biomass achieved figures of merit equivalent to multipoint calibrations for the majority of metabolites.

Significance: The proposed calibration workflow rationalizes time and standard expenditure and is particularly beneficial for laboratories dealing with wide-target assays and small analysis batches. The present assessment serves as a seminal study for further developments of the concept towards absolute quantification from archive high-resolution MS data of UC-biomass-spiked samples and the implementation of quick biomass recalibration with each experiment, promising seamless transition between internal standards derived from different fermentation batches.