Model-based assessment of mammalian cell metabolic functionalities using omics data

Richelle, Anne; Kellman, Benjamin P.; Wenzel, Alexander T.; Chiang, Austin W. T.; Reagan, Tyler; Gutierrez, Jahir M.; Joshi, Chintan; Li, Shangzhong; Liu, Joanne K.; Masson, Helen O.; Lee, Jooyong; Li, Zerong; Heirendt, Laurent; Trefois, Christophe; Juarez, Edwin F.; Bath, Tyler; Borland, David; Robasky, Kimberly; Lewis, Nathan E.

doi:10.1016/j.crmeth.2021.100040

Cited by 34 publications

(49 citation statements)

References 57 publications

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“…GSMMs also lack widely accepted standardized protocols for workflow and the validation of model outputs. Genome-scale networks require manual and laborious curation, a process that can introduce significant variability in predictions [ 79 , 112 ]. To overcome this, Richelle et al recently developed a computational framework, CellFie, that incorporates a list of metabolic tasks alongside knowledge databases and transcriptomics data [ 112 ].…”

Section: Shortcomings Of Current Network-based Modeling Approachesmentioning

confidence: 99%

“…Genome-scale networks require manual and laborious curation, a process that can introduce significant variability in predictions [ 79 , 112 ]. To overcome this, Richelle et al recently developed a computational framework, CellFie, that incorporates a list of metabolic tasks alongside knowledge databases and transcriptomics data [ 112 ]. Although this work was restricted to select metabolic tasks in human transcriptomics data, this is applicable to other high-dimensional datasets from humans and other species.…”

Section: Shortcomings Of Current Network-based Modeling Approachesmentioning

confidence: 99%

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Systems-based approaches to study immunometabolism

et al. 2022

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Technical advances at the interface of biology and computation, such as single-cell RNA-sequencing (scRNA-seq), reveal new layers of complexity in cellular systems. An emerging area of investigation using the systems biology approach is the study of the metabolism of immune cells. The diverse spectra of immune cell phenotypes, sparsity of immune cell numbers in vivo, limitations in the number of metabolites identified, dynamic nature of cellular metabolism and metabolic fluxes, tissue specificity, and high dependence on the local milieu make investigations in immunometabolism challenging, especially at the single-cell level. In this review, we define the systemic nature of immunometabolism, summarize cell- and system-based approaches, and introduce mathematical modeling approaches for systems interrogation of metabolic changes in immune cells. We close the review by discussing the applications and shortcomings of, and challenges faced by metabolic modeling techniques. With systems-oriented studies of metabolism expected to become a mainstay of immunological research, an understanding of current approaches toward systems immunometabolism will help investigators make the best use of current resources and push the boundaries of the discipline.

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Section: Shortcomings Of Current Network-based Modeling Approachesmentioning

confidence: 99%

Section: Shortcomings Of Current Network-based Modeling Approachesmentioning

confidence: 99%

Systems-based approaches to study immunometabolism

et al. 2022

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“…Currently, differential expression analysis followed by pathway enrichment is the most commonly used method for metabolic analysis based on scRNA-seq data [ 20 , 55 ], and uses general purpose enrichment methods [ [56] , [57] , [58] ]. Alternatively, pathway activity can be inferred from gene expression of pathway-associated genes [ 14 , 59 , 60 ].…”

Section: Modelling Approachesmentioning

confidence: 99%

Toward modeling metabolic state from single-cell transcriptomics

Hrovatin

Fischer

Theis

2022

Molecular Metabolism

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Background Single-cell metabolic studies bring new insights into cellular function, which can often not be captured on other omics layers. Metabolic information has wide applicability, such as for the study of cellular heterogeneity or for the understanding of drug mechanisms and biomarker development. However, metabolic measurements on single-cell level are limited by insufficient scalability and sensitivity, as well as resource intensiveness, and are currently not possible in parallel with measuring transcript state, commonly used to identify cell types. Nevertheless, because omics layers are strongly intertwined, it is possible to make metabolic predictions based on measured data of more easily measurable omics layers together with prior metabolic network knowledge. Scope of Review We summarize the current state of single-cell metabolic measurement and modeling approaches, motivating the use of computational techniques. We review three main classes of computational methods used for prediction of single-cell metabolism: pathway-level analysis, constraint-based modeling, and kinetic modeling. We describe the unique challenges arising when transitioning from bulk to single-cell modeling. Finally, we propose potential model extensions and computational methods that could be leveraged to achieve these goals. Major Conclusions Single-cell metabolic modeling is a rising field that provides a new perspective for understanding cellular functions. The presented modeling approaches vary in terms of input requirements and assumptions, scalability, modeled metabolic layers, and newly gained insights. We believe that the use of prior metabolic knowledge will lead to more robust predictions and will pave the way for mechanistic and interpretable machine-learning models.

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“…We adjust for the enzyme promiscuity by dividing the expression value of a gene by the number of reactions the gene has participated in. A similar approach has been seen in [75]. The steps of deriving MRAS are the following: Let 𝑤 𝑖 be the number of reactions 𝐸𝑛𝑧𝑦𝑚𝑒 𝑖 participate and…”

Section: Underlying Genome-scale Metabolic Model (Gem) 42 Inference O...mentioning

confidence: 99%

Characterizing metabolism from bulk and single-cell RNA-seq data using METAFlux

Huang

Monhanty

Dede

et al. 2022

Preprint

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Cells often alter metabolic strategies under nutrient-deprived conditions to support their survival and growth. Characterizing metabolic reprogramming in the TME (Tumor Microenvironment) is of emerging importance in ongoing cancer research and therapy development. Recent developments in mass spectrometry (MS)-based technologies allow simultaneous characterization of metabolic features of tumor, stroma, and immune cells in the TME. However, they only measure a subset of metabolites and cannot provide in situ measurements. Computational methods such as flux balance analysis (FBA) have been developed to estimate metabolic flux from bulk RNA-seq data and have recently been extended to single-cell RNA-seq (scRNA-seq) data. However, it is unclear how reliable the results are, particularly in the context of tissue TME characterization. To investigate this question and fill the analytical gaps, we developed a computational program METAFlux (METAbolic Flux balance analysis), which extends the FBA framework to infer metabolic fluxes from either bulk or single-cell transcriptomic TME data. We benchmarked the prediction accuracy of METAFlux using the exometabolomics data generated on the NCI-60 cell lines and observed significant improvement over existing approaches. We tested METAFlux in bulk RNA-seq data obtained from various tumor types including those in the TCGA. We validated previous knowledge, e.g., lung squamous cell carcinoma (LUSC) has higher glucose uptake than lung adenocarcinoma (LUAD). We also found a novel subset of LUAD samples with unique metabolic profiles and distinct survival outcome. We further examined METAFlux on scRNA-seq data obtained from coculturing tumor cells with CAR-NK cells and observed high consistency between the predicted and the experimental (i.e., Seahorse extracellular) flux measurements. Throughout our investigation, we discovered various modes of metabolic cooperation and competition between various cell-types in TMEs, which could lead to further target discovery and development.

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Model-based assessment of mammalian cell metabolic functionalities using omics data

Cited by 34 publications

References 57 publications

Systems-based approaches to study immunometabolism

Systems-based approaches to study immunometabolism

Toward modeling metabolic state from single-cell transcriptomics

Characterizing metabolism from bulk and single-cell RNA-seq data using METAFlux

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