FLUXestimator: a webserver for predicting metabolic flux and variations using transcriptomics data

• Published in: Nucleic acids research Vol. 51; no. W1; pp. W180 - W190
• Main Authors: Zhang, Zixuan, Zhu, Haiqi, Dang, Pengtao, Wang, Jia, Chang, Wennan, Wang, Xiao, Alghamdi, Norah, Lu, Alex, Zang, Yong, Wu, Wenzhuo, Wang, Yijie, Zhang, Yu, Cao, Sha, Zhang, Chi
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 05.07.2023
• Subjects: Animals; Humans; Metabolic Networks and Pathways; Metabolomics; Mice; Models, Biological; Software; Transcriptome; Web Server Issue
• ISSN: 0305-1048; 1362-4962; 1362-4962
• DOI: 10.1093/nar/gkad444

Abstract Quantitative assessment of single cell fluxome is critical for understanding the metabolic heterogeneity in diseases. Unfortunately, laboratory-based single cell fluxomics is currently impractical, and the current computational tools for flux estimation are not designed for single cell-level prediction. Given the well-established link between transcriptomic and metabolomic profiles, leveraging single cell transcriptomics data to predict single cell fluxome is not only feasible but also an urgent task. In this study, we present FLUXestimator, an online platform for predicting metabolic fluxome and variations using single cell or general transcriptomics data of large sample-size. The FLUXestimator webserver implements a recently developed unsupervised approach called single cell flux estimation analysis (scFEA), which uses a new neural network architecture to estimate reaction rates from transcriptomics data. To the best of our knowledge, FLUXestimator is the first web-based tool dedicated to predicting cell-/sample-wise metabolic flux and metabolite variations using transcriptomics data of human, mouse and 15 other common experimental organisms. The FLUXestimator webserver is available at http://scFLUX.org/, and stand-alone tools for local use are available at https://github.com/changwn/scFEA. Our tool provides a new avenue for studying metabolic heterogeneity in diseases and has the potential to facilitate the development of new therapeutic strategies. Graphical Abstract Graphical Abstract