A method to identify high consensus predictions of single-cell metabolic flux

Abstract

Altered metabolism is a key contributor to pathology in numerous disease states, including cancer. These changes can occur within certain pathological cells, or within a population of cells. Two recently developed single-cell flux prediction tools, Single-cell Flux Estimation Analysis ('scFEA') and Compass, have shown success in predicting cellular metabolism using readily available transcriptome data. By adapting the outputs of these tools, we sought to determine if they can work in concert to identify higher confidence consensus flux predictions. We created a set of reaction modules for the two systems. By testing multiple function composites with sets of modularized Compass outputs, we identified a method that showed the highest global similarity to the outputs of scFEA. Our analysis showed broad biological areas of agreement between the results of the two systems when applied to single-cell data arising from both pathological and healthy samples, with pathological samples increasing system consensus. Consensus testing on matched transcriptome and metabolomics data suggested that agreement between the two systems could indicate at least a minimal degree of coherence between both systems and direct metabolite measurements. Overall, we demonstrated that automated Comparisons between the outputs of Compass and scFEA are possible, applicable to data arising from pathological samples, and that such a consensus approach can reveal strongly correlated predictions between these two systems.