TY - GEN
T1 - Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning
AU - Steach, Holly R.
AU - Viswanath, Siddharth
AU - He, Yixuan
AU - Zhang, Xitong
AU - Ivanova, Natalia
AU - Hirn, Matthew
AU - Perlmutter, Michael
AU - Krishnaswamy, Smita
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - The ability to measure gene expression at single-cell resolution has elevated our understanding of how biological features emerge from complex and interdependent networks at molecular, cellular, and tissue scales. As technologies have evolved that complement scRNAseq measurements with things like single-cell proteomic, epigenomic, and genomic information, it becomes increasingly apparent how much biology exists as a product of multimodal regulation. Biological processes such as transcription, translation, and post-translational or epigenetic modification impose both energetic and specific molecular demands on a cell and are therefore implicitly constrained by the metabolic state of the cell. While metabolomics is crucial for defining a holistic model of any biological process, the chemical heterogeneity of the metabolome makes it particularly difficult to measure, and technologies capable of doing this at single-cell resolution are far behind other multiomics modalities. To address these challenges, we present GEFMAP (Gene Expression-based Flux Mapping and Metabolic Pathway Prediction), a method based on geometric deep learning for predicting flux through reactions in a global metabolic network using transcriptomics data, which we ultimately apply to scRNAseq. GEFMAP leverages the natural graph structure of metabolic networks to learn both a biological objective for each cell and estimate a mass-balanced relative flux rate for each reaction in each cell using novel deep learning models.
AB - The ability to measure gene expression at single-cell resolution has elevated our understanding of how biological features emerge from complex and interdependent networks at molecular, cellular, and tissue scales. As technologies have evolved that complement scRNAseq measurements with things like single-cell proteomic, epigenomic, and genomic information, it becomes increasingly apparent how much biology exists as a product of multimodal regulation. Biological processes such as transcription, translation, and post-translational or epigenetic modification impose both energetic and specific molecular demands on a cell and are therefore implicitly constrained by the metabolic state of the cell. While metabolomics is crucial for defining a holistic model of any biological process, the chemical heterogeneity of the metabolome makes it particularly difficult to measure, and technologies capable of doing this at single-cell resolution are far behind other multiomics modalities. To address these challenges, we present GEFMAP (Gene Expression-based Flux Mapping and Metabolic Pathway Prediction), a method based on geometric deep learning for predicting flux through reactions in a global metabolic network using transcriptomics data, which we ultimately apply to scRNAseq. GEFMAP leverages the natural graph structure of metabolic networks to learn both a biological objective for each cell and estimate a mass-balanced relative flux rate for each reaction in each cell using novel deep learning models.
KW - metabolomics
KW - single-cell omics
KW - transcriptomics
UR - http://www.scopus.com/inward/record.url?scp=85194279441&partnerID=8YFLogxK
U2 - 10.1007/978-1-0716-3989-4_15
DO - 10.1007/978-1-0716-3989-4_15
M3 - Conference contribution
AN - SCOPUS:85194279441
SN - 9781071639887
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 235
EP - 252
BT - Research in Computational Molecular Biology - 28th Annual International Conference, RECOMB 2024, Proceedings
A2 - Ma, Jian
PB - Springer Science and Business Media Deutschland GmbH
T2 - 28th International Conference on Research in Computational Molecular Biology, RECOMB 2024
Y2 - 29 April 2024 through 2 May 2024
ER -