Single‐Cell Metabolomics by Mass Spectrometry: Opportunities and Challenges

Abstract

Cellular heterogeneity is an inherent property of cell populations with a wide spectrum of biological manifestations, ranging from barely observable variations that enhance organismal adaptation, to life‐threatening differences. Single‐cell metabolomics can reveal molecular information and variations in metabolite concentrations between cells that are masked in cell‐population studies. These differences are quantitatively captured by the abundance distributions for the population and their statistical analysis can reveal the presence of latent subpopulations. In recent years, mass spectrometry (MS), combined with novel sampling and ionization techniques, has become an important tool for single‐cell metabolomics. These new techniques must contend with significant challenges in the form of small cell sizes and volumes, ultratrace metabolite amounts per cell, and potentially interfering high turnover rates and rapid diffusion. Owing to the ultrasmall sample volume, low abundance of some metabolites, and poor ionization efficiencies, metabolite detection, identification, and accurate quantitation remain a major challenge. The ability of some techniques to analyze tissue‐embedded cells opens the door for spatial metabolomics, potentially revealing cellular synergism in organ level metabolism. In this Concept article, we present a bird's eye view of these major themes in single cell metabolomics and some of the relevant MS‐based methods.

Single-Cell Mass Spectrometry Enables Insight into Heterogeneity in Infectious Disease

“…In contrast, single-cell mass spectrometry (SCMS) is capable of profiling metabolites in individual cells and unveiling hidden subpopulations of cells. MS-based single-cell metabolomics is capable of analyzing and determining the cellular metabolites that are altered after environmental perturbation . A series of SCMS techniques have been developed to analyze cells under vacuum (e.g., MALDI-MS (matrix-assisted laser desorption/ionization-MS) and SIMS (secondary ion mass spectrometry) or ambient environment (e.g., live single-cell video-MS, probe ESI MS, LAESI MS, and nano-DESI MS).…”

Single cell metabolism: current and future trends

“…Many algorithms toward this end have been developed for single-cell RNA data (Aibar et al, 2017;Chan et al, 2017;Pratapa et al, 2020), as well as for mass cytometry (Sachs et al, 2005;Wang et al, 2019). While in principle such methods could also be applied toward learning metabolic networks from data, we caution these may not be robust to sources of (co-) variation specific to MS-based single-cell metabolomics, such as in-source fragmentation (Dolatmoradi et al, 2021), region-dependent ion suppression (Taylor et al, 2018), or global metabolic shifts arising from cell manipulation (Llufrio et al, 2018): these may induce correlations between metabolites that are primarily technical rather than biological in origin.…”

High-Throughput f-LAESI-IMS-MS for Mapping Biological Nitrogen Fixation One Cell at a Time

“…However, they only partially capture the functional characteristics of cellular processes linked to the phenotype. Single-cell metabolomics allows for identifying differences in metabolic processes resulting in functionally distinct cells. − Statistical analysis of metabolite abundances for representative cell populations can reveal latent cellular subtypes, distinct metabolic states, and rare cell types. − To obtain statistically meaningful data, large cell numbers have to be analyzed (hundreds to thousands of cells) that can only be achieved by high-throughput techniques.…”

“…Mass spectrometry (MS) is a prime tool for single-cell metabolomics due to its high sensitivity, broad molecular coverage, and quantitation capabilities. 6 For example, secondary ion MS (SIMS), matrix-free and matrix-assisted laser desorption ionization (LDI), and laser ablation electrospray ionization (LAESI) MS-based platforms with limits of detection in the range of 800 zmol to 1 fmol have shown promising results in these applications. 9 For single-cell measurements, rapid, low perturbation, and high-precision cell selection and manipulation are required.…”