Computation of single-cell metabolite distributions using mixture models

Tonn, Mona K.; Thomas, Philipp; Barahona, Mauricio; Oyarzún, Diego A.

doi:10.1101/2020.10.07.329342

Cited by 6 publications

(8 citation statements)

References 78 publications

(146 reference statements)

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“…In contrast, the mean RNA expression of ligand protein and receptor protein was frequently used to infer ligand-receptor communications, and is simple to calculate, biologically easy to interpret, and computationally efficient. Similarly, a gaussian mixture model was also reported to infer metabolite distribution based on expression of related enzymes 24 . It is yet unclear which methods might perform better when applied to infer presence of metabolite based on RNA-seq data.…”

Section: Resultsmentioning

confidence: 99%

MEBOCOST: Metabolite-mediated Cell Communication Modeling by Single Cell Transcriptome

Zheng

Zhang

Tsuji

et al. 2022

Preprint

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We developed MEBOCOST, a computational algorithm for quantitatively inferring metabolite-based intercellular communications using single cell RNA-seq data. MEBOCOST predicted cell-cell communication events for which metabolites, such as lipids, are secreted by one cell (sender cells) and traveled to interact with sensor proteins of another cell (receiver cells). The sensor protein on receiver cell might be cell surface receptor, cell surface transporter, and nuclear receptor. MEBOCOST relies on a curated database of metabolite-sensor partners, which we collected from the literatures and other public sources. Based on scRNA-seq data, MEBOCOST identifies cell-cell metabolite-sensor communications between cell groups, in which metabolite enzymes and sensors were highly expressed in sender and receiver cells, respectively. Applying MEBOCOST on brown adipose tissue (BAT) showed the robustness of predicting known and novel metabolite-based autocrine and paracrine communications. Additionally, MEBOCOST identified a set of intercellular metabolite-sensor communications that was regulated by cold exposure in BAT. Those predicted communicating metabolites and sensors may play important roles in thermogenesis regulation. We believe that MEBOCOST will be useful to numerous researchers to investigate metabolite-based cell-cell communications in many biological and disease models, thus will be useful to remove critical barriers impeding the development of new therapies to target these communications. MEBOCOST is freely available at https://github.com/zhengrongbin/MEBOCOST.

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Section: Resultsmentioning

confidence: 99%

MEBOCOST: Metabolite-mediated Cell Communication Modeling by Single Cell Transcriptome

Zheng

Zhang

Tsuji

et al. 2022

Preprint

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“…Recent discoveries have led to a renewed interest in the interplay of metabolism with other layers of the cellular machinery (Chubukov et al, 2014 ; Loftus and Finlay, 2016 ; Tretter et al, 2016 ; Reid et al, 2017 ; Tonn et al, 2020 ). Due to the complexity and scale of metabolic reaction networks, computational methods are essential to tease apart the influence of metabolic architectures on cellular function.…”

Section: Discussionmentioning

confidence: 99%

Opportunities at the Interface of Network Science and Metabolic Modeling

Dusad

Thiel

Barahona

et al. 2021

Front. Bioeng. Biotechnol.

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Metabolism plays a central role in cell physiology because it provides the molecular machinery for growth. At the genome-scale, metabolism is made up of thousands of reactions interacting with one another. Untangling this complexity is key to understand how cells respond to genetic, environmental, or therapeutic perturbations. Here we discuss the roles of two complementary strategies for the analysis of genome-scale metabolic models: Flux Balance Analysis (FBA) and network science. While FBA estimates metabolic flux on the basis of an optimization principle, network approaches reveal emergent properties of the global metabolic connectivity. We highlight how the integration of both approaches promises to deliver insights on the structure and function of metabolic systems with wide-ranging implications in discovery science, precision medicine and industrial biotechnology.

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“…Our results have helped to identify the sources of variation observed in experimental measurements of important metabolites in single cells, indicating that reaction network coupling contributes to population heterogeneity by amplifying or attenuating the sources noise in an integrative fashion. SSA-FBA can therefore be used to probe metabolic heterogeneity in a manner complementary to alternative existing experimental and computational methods [11, 22, 25, 27].…”

Section: Discussionmentioning

confidence: 99%

“…The existing approaches to stochastic modelling of single-cell metabolism can be organised into two categories: (a) (semi-)analytical treatment of rigorous models of individual metabolic pathways involving the expression of one [24, 25] or several [26, 27] enzymes catalysing a handful of metabolic reactions, or (b) ad hoc simulation of whole-cell models [28, 29] involving hybrid methods that combine ordinary differential equations (ODEs), particle-based stochastic simulation algorithms (SSA, [30, 31]), and dynamic FBA (DFBA, [32]). Although the shared aim of these approaches is to relate single-cell behaviour to genotype, the two categories fall at opposite ends of a wide spectrum: whole-cell modelling attempts to accommodate as much detail as possible, but no framework is yet available to rigorously simulate entire cells.…”

Section: Introductionmentioning

confidence: 99%

Simulating single-cell metabolism using a stochastic flux-balance analysis algorithm

Tourigny

Goldberg

Karr

2020

Preprint

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Stochasticity from gene expression in single cells is known to drive metabolic heterogeneity at the population-level, which is understood to have important consequences for issues such as microbial drug tolerance and treatment of human diseases like cancer. Experimental methods for probing metabolism in single cells currently lag far behind advancements in single-cell genomics, transcriptomics, and proteomics, which motivates the development of computational techniques to bridge this gap in the systems approach to single-cell biology. In this paper, we present SSA-FBA (stochastic simulation algorithm with flux-balance analysis embedded) as a modelling framework for simulating the stochastic dynamics of metabolism in individual cells. SSA-FBA extends the constraint-based formalism of metabolic network modelling to the single-cell regime, providing a suitable approach to simulation when kinetic information is lacking from models. We also describe an advanced algorithm that significantly improves the efficiency of exact SSA-FBA simulations, which is necessary because of the computational costs associated with stochastic simulation and the observation that approximations can be inaccurate and numerically unstable. As a preliminary case study we apply SSA-FBA to a single-cell model of Mycoplasma pneumoniae, and explore the use of simulation to understand the role of stochasticity in metabolism at the single-cell level.Recent experimental advances are driving a data explosion in systems biology by enabling researchers to profile single cells, including their genome, transcriptome, and proteome [1,2]. Such single-cell measurements can yield information on thousands of individual cells in a single experiment. This can provide insights on intracellular function and the role of intercellular heterogeneity in a variety of biological systems ranging from microbial populations [3,4] to human diseases such as cancer [5,6].Relatively less advanced are methodologies to probe the metabolism of single cells [7,8,9,11,12]. This presents a barrier to studying metabolic reprogramming in tumour biology for example, which is now understood to be a central hallmark of cancer [13,14]. Single-cell metabolism is challenging due to low abundances of many metabolites, compartmentalisation in eukaryotic cells, and the wide diversity of intracellular metabolites that lack regular structure [9]. Moreover, metabolism is more dynamic than many cellular processes such as DNA replication and gene expression, which means attempts to capture the metabolic state of an individual cell is susceptible to perturbation by changes in cellular behaviour and the surrounding environment. Current experimental obstacles to studying single-cell metabolism combined with its fundamental biological importance necessitates the development of computational techniques that infer the metabolism of single cells from other sources, such as single-cell transcriptomic or proteomic data and information about metabolism at the population-level [10].While our current capacity ...

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Computation of single-cell metabolite distributions using mixture models

Cited by 6 publications

References 78 publications

MEBOCOST: Metabolite-mediated Cell Communication Modeling by Single Cell Transcriptome

MEBOCOST: Metabolite-mediated Cell Communication Modeling by Single Cell Transcriptome

Opportunities at the Interface of Network Science and Metabolic Modeling

Simulating single-cell metabolism using a stochastic flux-balance analysis algorithm

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