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scFEA: A graph neural network model to estimate cell-wise metabolic flux using single cell RNA-seq data

Norah Alghamdi, View ORCID ProfileWennan Chang, Pengtao Dang, Xiaoyu Lu, Changlin Wan, Zhi Huang, Jiashi Wang, Melissa Fishel, Sha Cao, View ORCID ProfileChi Zhang
doi: https://doi.org/10.1101/2020.09.23.310656
Norah Alghamdi
1Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
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Wennan Chang
1Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
2Department of Electrical and Computer Engineering, Purdue University, Indianapolis, IN, 46202, USA
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  • ORCID record for Wennan Chang
Pengtao Dang
1Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
2Department of Electrical and Computer Engineering, Purdue University, Indianapolis, IN, 46202, USA
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Xiaoyu Lu
1Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
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Changlin Wan
1Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
2Department of Electrical and Computer Engineering, Purdue University, Indianapolis, IN, 46202, USA
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Zhi Huang
1Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
2Department of Electrical and Computer Engineering, Purdue University, Indianapolis, IN, 46202, USA
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Jiashi Wang
1Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
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Melissa Fishel
3Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
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Sha Cao
1Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
4Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
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  • For correspondence: czhang87@iu.edu shacao@iu.edu
Chi Zhang
1Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
2Department of Electrical and Computer Engineering, Purdue University, Indianapolis, IN, 46202, USA
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  • ORCID record for Chi Zhang
  • For correspondence: czhang87@iu.edu shacao@iu.edu
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ABSTRACT

The metabolic heterogeneity, and metabolic interplay between cells and their microenvironment have been known as significant contributors to disease treatment resistance. Our understanding of the intra-tissue metabolic heterogeneity and cooperation phenomena among cell populations is unfortunately quite limited, without a mature single cell metabolomics technology. To mitigate this knowledge gap, we developed a novel computational method, namely scFEA (single cell Flux Estimation Analysis), to infer single cell fluxome from single cell RNA-sequencing (scRNA-seq) data. scFEA is empowered by a comprehensively reorganized human metabolic map as focused metabolic modules, a novel probabilistic model to leverage the flux balance constraints on scRNA-seq data, and a novel graph neural network based optimization solver. The intricate information cascade from transcriptome to metabolome was captured using multi-layer neural networks to fully capitulate the non-linear dependency between enzymatic gene expressions and reaction rates. We experimentally validated scFEA by generating an scRNA-seq dataset with matched metabolomics data on cells of perturbed oxygen and genetic conditions. Application of scFEA on this dataset demonstrated the consistency between predicted flux and metabolic imbalance with the observed variation of metabolites in the matched metabolomics data. We also applied scFEA on publicly available single cell melanoma and head and neck cancer datasets, and discovered different metabolic landscapes between cancer and stromal cells. The cell-wise fluxome predicted by scFEA empowers a series of downstream analysis including identification of metabolic modules or cell groups that share common metabolic variations, sensitivity evaluation of enzymes with regards to their impact on the whole metabolic flux, and inference of cell-tissue and cell-cell metabolic communications.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted September 24, 2020.
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scFEA: A graph neural network model to estimate cell-wise metabolic flux using single cell RNA-seq data
Norah Alghamdi, Wennan Chang, Pengtao Dang, Xiaoyu Lu, Changlin Wan, Zhi Huang, Jiashi Wang, Melissa Fishel, Sha Cao, Chi Zhang
bioRxiv 2020.09.23.310656; doi: https://doi.org/10.1101/2020.09.23.310656
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scFEA: A graph neural network model to estimate cell-wise metabolic flux using single cell RNA-seq data
Norah Alghamdi, Wennan Chang, Pengtao Dang, Xiaoyu Lu, Changlin Wan, Zhi Huang, Jiashi Wang, Melissa Fishel, Sha Cao, Chi Zhang
bioRxiv 2020.09.23.310656; doi: https://doi.org/10.1101/2020.09.23.310656

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