Deciphering Metabolic Heterogeneity by Single-Cell Analysis

Abstract

Single-cell analysis provides insights into cellular heterogeneity and dynamics of individual cells. This Feature highlights recent developments in key analytical techniques suited for single-cell metabolic analysis with a special focus on mass spectrometry-based analytical platforms and RNA-seq as well as imaging techniques that reveal stochasticity in metabolism.

Signaling lipids as diagnostic biomarkers for ocular surface cicatrizing conjunctivitis

“…Immunometabolism is an emerging field in biomedicine that explores the crosstalk between inflammatory processes and metabolic signaling. Metabolic profiling is increasingly used to find novel disease biomarkers that inform on disease activity and therapeutic outcomes [11,12], and provide insights into phenotypical variations between individual cells [13,14]. The importance of metabolic and immunometabolic processes in ocular surface diseases has been increasingly reported [15][16][17].…”

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

“…We experimented with the relative scaling of SSA-FBA and SSA-only propensity values, and identified a range of stoichiometry and scaling factors yielding simulations that produced biologically-realistic trajectories after a runtime of just 7-9 seconds on the same machine ( Figure 3). The SSA-FBA model generally displayed a wide range of different behaviours, where alternative groups of metabolite or protein species would tend to accumulate more than others in each simulation instance, supporting the notion that stochasticity at the single-cell level is a driver for metabolic heterogeneity [10,21,24].…”

“…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 to probe or model the metabolism of single-cells is limited, considerable attention has been devoted to the metabolism of cellular populations, where metabolic network modelling has received a great deal of success combining limited experimental data and computational simulation [15,16,17]. Extensions of these population-based frameworks, such as dynamic metabolism expression models [18] or dynamic enzyme-cost flux-balance analysis [19], have also been developed to incorporate the dynamics of gene expression.…”

“…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].…”

Development and Application of High-Throughput Single Cell Lipid Profiling: A Study of SNCA-A53T Human Dopamine Neurons

“…Lipids are the most abundant class of metabolites in the cell, and the measurement of lipids by mass spectrometry in bulk samples is well described. A handful of studies have previously described proof of principle for single cell lipid profiling (Evers et al, 2019); however, these are not platforms capable or suitable for robust high-throughput readouts of cell activity. Ellis et al used a low-throughput approach where cell droplets were printed onto a glass slide, which were imaged and analyzed using liquid extraction surface analysis coupled with mass spectrometry (LESA-MS) (Ellis et al, 2012).…”