Metabolic modeling of single Th17 cells reveals regulators of autoimmunity

Wagner, Allon; Wang, Chao; Fessler, Johannes; DeTomaso, David; Ávila-Pacheco, Julián; Kaminski, James J.; Zaghouani, Sarah; Christian, Elena; Thakore, Pratiksha I.; Schellhaass, Brandon; Akama-Garren, Elliot H.; Pierce, Kerry A.; Singh, Vasundhara; Ron-Harel, Noga; Douglas, Vivian Paraskevi; Bod, Lloyd; Schnell, Alexandra; Puleston, Daniel J.; Sobel, Raymond A.; Haigis, Marcia C.; Pearce, Erika L.; Soleimani, Manoocher; Clish, Clary B.; Regev, Aviv; Kuchroo, Vijay K.; Yosef, Nir

doi:10.1016/j.cell.2021.05.045

Cited by 230 publications

(257 citation statements)

References 119 publications

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“…These results also support the finding that combination therapies involving inhibitors of mTOR, which sits downstream of RAS signaling, is a viable therapeutic opportunity in KRAS- driven non-small-cell lung cancer (Vasan, Boyer and Herbst, 2014). Future work may investigate if this therapy-sensitive population is mainly characterized by differential metabolic signaling, perhaps leveraging recent work modeling metabolic profiles from scRNA-seq data (Wagner et al ., 2021).…”

Section: Resultsmentioning

confidence: 99%

PhyloVision: Interactive Software for Integrated Analysis of Single-Cell Transcriptomic and Phylogenetic Data

Jones

Rosen

Yosef

2021

Preprint

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Recent advances in CRISPR-Cas9 engineering and single-cell assays have enabled the simultaneous measurement of single-cell transcriptomic and phylogenetic profiles. However, there are few computational tools enabling users to integrate and derive insight from a joint analysis of these two modalities. Here, we describe PhyloVision: an open source software for interactively exploring data from both modalities and for identifying and interpreting heritable gene modules whose concerted expression are associated with phylogenetic relationships. PhyloVision provides a feature-rich, interactive, and shareable web-based report for investigating these modules, while also supporting several other data and meta-data exploration capabilities. We demonstrate the utility of PhyloVision using a published dataset of metastatic lung adenocarcinoma cells, whose phylogeny was resolved using a CRISPR/Cas9-based lineage-tracing system. Together, we anticipate that PhyloVision and the methods it implements will be a useful resource for scalable and intuitive data exploration for any assay that simultaneously measures cell state and lineage.

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Section: Resultsmentioning

confidence: 99%

PhyloVision: Interactive Software for Integrated Analysis of Single-Cell Transcriptomic and Phylogenetic Data

Jones

Rosen

Yosef

2021

Preprint

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“…Genome-scale metabolic models can be obtained from BiGG [ 44 ], BioModels, and other repositories listed in [ 27 ]. The modelling of human metabolism often relies on GEMs [ 27 , [45] , [46] , [47] ] from Recon [ 37 ] and Human Metabolic Reaction series [ 48 ] and the resources are being constantly improved [ 49 ]. GEMs can be also constructed de novo from KEGG or other resources and are often updated with manual curation [ 20 , 46 , 50 ].…”

Section: Resources For Metabolic Modelingmentioning

confidence: 99%

“…First, the curated pathway definitions aim to capture the whole biological process under different biological contexts, disregarding complex regulatory interplay, and thus contain more genes than are involved in any specific cellular state or type [ 39 , 40 ]. Predefined pathways may be composed of genes responding to perturbation in different directions, leading to false negatives in enrichment analysis, which could be solved by defining context-specific pathways [ 45 ]. Second, pathway enrichment does not account for biological changes spanning multiple pathways [ 40 ] and does not leverage the information about regulatory and reaction interactions.…”

Section: Modelling Approachesmentioning

confidence: 99%

“…On the other hand, some models rely on system-specific data, such as RNA-sequencing, partial metabolomics measurements, or information from literature. Measurement-based objectives are of great use when theory-based biological objective is unknown or simple objectives, such as growth maximization, are not applicable due to the complexity of the system [ 45 , 46 , 69 , 72 , 74 ]. Integration of measured data, such as from scRNA-seq, enables context-specific predictions, such as for specific cell type, developmental stage, or patient [ 27 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

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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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“…As commonly done (e.g., in [39,49,65]), we combined the RNA-seq datasets with the GPR rules through the employment of the Reaction Activity Score (RAS). The RAS is based on the assumption that enzyme isoforms contribute additively to the reaction activity, whereas the least expressed enzyme subunit is limiting.…”

Section: Reaction Activity Score Computationmentioning

confidence: 99%

INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation

et al. 2022

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Metabolism is directly and indirectly fine-tuned by a complex web of interacting regulatory mechanisms that fall into two major classes. On the one hand, the expression level of the catalyzing enzyme sets the maximal theoretical flux level (i.e., the net rate of the reaction) for each enzyme-controlled reaction. On the other hand, metabolic regulation controls the metabolic flux through the interactions of metabolites (substrates, cofactors, allosteric modulators) with the responsible enzyme. High-throughput data, such as metabolomics and transcriptomics data, if analyzed separately, do not accurately characterize the hierarchical regulation of metabolism outlined above. They must be integrated to disassemble the interdependence between different regulatory layers controlling metabolism. To this aim, we propose INTEGRATE, a computational pipeline that integrates metabolomics and transcriptomics data, using constraint-based stoichiometric metabolic models as a scaffold. We compute differential reaction expression from transcriptomics data and use constraint-based modeling to predict if the differential expression of metabolic enzymes directly originates differences in metabolic fluxes. In parallel, we use metabolomics to predict how differences in substrate availability translate into differences in metabolic fluxes. We discriminate fluxes regulated at the metabolic and/or gene expression level by intersecting these two output datasets. We demonstrate the pipeline using a set of immortalized normal and cancer breast cell lines. In a clinical setting, knowing the regulatory level at which a given metabolic reaction is controlled will be valuable to inform targeted, truly personalized therapies in cancer patients.

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Metabolic modeling of single Th17 cells reveals regulators of autoimmunity

Cited by 230 publications

References 119 publications

PhyloVision: Interactive Software for Integrated Analysis of Single-Cell Transcriptomic and Phylogenetic Data

PhyloVision: Interactive Software for Integrated Analysis of Single-Cell Transcriptomic and Phylogenetic Data

Toward modeling metabolic state from single-cell transcriptomics

INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation

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