Modeling metabolic variation with single-cell expression data

Abstract

Cellular metabolism encompasses the biochemical reactions and transportation of various metabolites in cells and their surroundings, which are integrated at all levels of cellular functions. We developed a method to systematically simulate cellular metabolism using single-cell RNA-seq (scRNA-seq) data through constraint-based context specific metabolic modeling. We simulated the NAD + biosynthesis activity in 7 different mouse tissues, and the simulated NAD + biosynthesis flux levels showed significant linear correlation with experimental measurements in previous research. We also show that the simulated NAD + biosynthesis fluxes are reproducible using two additional scRNA-seq datasets.

Toward modeling metabolic state from single-cell transcriptomics

“…Multiple methods have been proposed to adapt FBA to single-cell inference. The simplest approach is the construction of context-specific GEMs based on scRNA-seq data, followed by classical FBA on each context-specific GEM, as proposed by [ 75 ], using specific metabolite production as the optimization objective. Another option is MERGE, which optimizes for reactions with lowly expressed genes carrying less flux and vice versa as well as for low overall flux [ 50 ].…”

“…An important aspect of single-cell metabolic modeling in contrast to bulk is to overcome noise and sparsity of the scRNA-seq input, which can be achieved in multiple ways. The most common approach is to share information across cells, either by using pseudobulk, by sharing flux distribution information across similar cells, or by jointly modelling metabolism of all cells [ 20 , 45 , 75 ]. Data can be also pooled across genes by modeling pooled reaction modules rather than individual reactions or by assuming that neighboring genes affect each other and thus share information on expression variation among them.…”

Immunometabolism in the Single-Cell Era

“…due to gene knock-out or tissue-specific pattern of expression. Indeed, Zhang et al have demonstrated scRNA-seq-based analysis of NAD+ biosynthesis in different tissues using an FBA-based approach (Zhang et al, 2020). Conceptually, this work follows from the approach of studying tissue-specific metabolism based on bulk RNA-seq data from different tissues (Bordbar et al, 2011).…”

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

“…Single cell RNA-Seq (scRNA-seq) data has been widely utilized to characterize cell type specific transcriptional states in a complex tissue. Large amount of scRNA-seq data carry the potential to deliver information on a cell’s functioning state and its underlying phenotypic switches (Vasdekis and Stephanopoulos 2015; Damiani et al 2019a; Evers et al 2019a; Honkoop et al 2019; Saurty-Seerunghen et al 2019; Xiao et al 2019a; Levine et al 2020; Rohlenova et al 2020; Xiao et al 2020; Zhang et al 2020). Realizing the strong connections between transcriptomic and metabolomic profiles (Hirayama et al 2009; Lee et al 2012; Mehrmohamadi et al 2014; Damiani et al 2019b; Xiao et al 2019b; Wagner et al 2020), scRNA-Seq has found its application in portraying metabolic variations.…”

“…Most of the existing studies examined single cell metabolic changes relying on differential expression and enrichment analysis of key metabolic enzymes and pathways (Vasdekis and Stephanopoulos 2015; Evers et al 2019a; Honkoop et al 2019; Saurty-Seerunghen et al 2019; Xiao et al 2019a; Levine et al 2020; Rohlenova et al 2020; Xiao et al 2020), without considering individual metabolite nodes in a metabolic pathway, or the mass balance constraints of metabolic network. Studies coupling single cell transcriptomics data and the Flux Balance Analysis (FBA) at steady-state framework have only recently emerged (Damiani et al 2019a; Zhang et al 2020). The FBA describes the potential flux over the topological structure of a metabolic network, with a set of equations governing the mass balance at steady state.…”