Toward modeling metabolic state from single-cell transcriptomics

Abstract

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.

Keywords: Constraint-based modeling; Kinetic modeling; Metabolic modeling; Pathway analysis; Single-cell RNA-seq.

Graphical abstract

Figure 1

Bulk versus single-cell analysis. Bulk analysis assumes that cells are identical and can model the exchange between cells and environment. Single-cell analysis accounts for cellular heterogeneity and can model exchange between different cells and between individual cells and environment.

Figure 2

Metabolic modelling approaches. (A) Pathway-level analysis assumes that gene expression maps directly to enzyme concentration or reaction activity. It can be used for defining pathways enriched in differentially expressed genes or for calculating the activity of individual pathways by propagating activity through the pathway (mechanistic pathway analysis). (B) Constraint-based analysis assumes that metabolite concentrations are constant and based on this defines possible fluxes that would result in such a system, as further described in the main text. Namely, metabolite fluxes, which are constrained to be zero, are obtained by multiplying reaction fluxes with the stoichiometric matrix that defines metabolite conversions involved in each reaction. This gives an undetermined system of equations that defines possible reaction flux distributions. Optimal flux configuration is then selected based on optimization objectives, such as correspondence between gene expression and enzyme activity. (C) Kinetic models predict metabolic changes based on detailed prior metabolic knowledge and information on metabolite and enzyme availability. Gene expression can be used for the prediction of enzyme concentration.

Figure 3

Inputs across modelling approaches. Modelling approaches require different information about gene–reaction associations, reaction mechanisms, and measured data. This figure shows only the most commonly used inputs, although different input information may be also incorporated in the models.

Figure 4

Knowledge-primed neural networks. (A) Prior knowledge about genes encoding enzymes and enzymes catalyzing metabolite conversions. (B) Neural network architecture can be based on metabolic knowledge. Different omics layers can be represented by different components or layers of the neural network so that connections between nodes correspond to prior metabolic knowledge.