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

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. Author summaryStudying the metabolism of individual cells allows us to understand the mechanisms behind a myriad of diseases. However, single-cell metabolism cannot readily be measured. Computational tools exist to predict metabolism, but validating their outputs requires metabolic measurements. To address this circular shortcoming, we created a method to automatically adapt and compare the outputs of two popular systems used to predict single-cell metabolism from genetic data. In other fields, using predictive methods in an ensemble has provided superior accuracy, and we speculated that the same may hold true for computational predictions of single-cell metabolism. Our work demonstrated that these two systems can be used together to find agreement on a broad range of metabolic processes related to disease. Further, our results, although early, suggest that system agreement may indicate genuine shifts in the underlying biology of a cell population. Owing to the methodologies used by the two systems, such changes could be studied at both a broad or granular level. As our comparison tools provide rapid readouts of such system agreement, they could potentially be used as part of an exploratory pipeline to aid in identification of candidate metabolic mechanisms as drug targets.