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Single-cell level measurements are necessary to characterize the intrinsic biological variability in a population of cells. In this study, we demonstrate that, with the microarrays for mass spectrometry platform, we are able to observe this variability. We monitor environmentally (2-deoxy-D-glucose) and genetically (ΔPFK2) perturbed Saccharomyces cerevisiae cells at the single-cell, few-cell, and population levels. Correlation plots between metabolites from the glycolytic pathway, as well as with the observed ATP/ADP ratio as a measure of cellular energy charge, give biological insight that is not accessible from population-level metabolomic data.single-cell measurements | MALDI mass spectrometry | baker's yeast E ven genetically identical cells present in the same microenvironment can express different phenotypes, for a number of reasons: cell-to-cell heterogeneity can stem from differences in the cell age and differences in the cell cycle stage, and stochastic effects together with feedback mechanisms can lead to distinctively different phenotypes, too (1-6). As population-level measurement techniques inherently average out such cell-to-cell differences, biochemical mechanisms underlying a studied system cannot be deduced from such measurements. Thus, to detect and exploit this heterogeneity, new analytical platforms with a sensitivity at the single-cell level and the ability to perform quantitative analyses must be developed and validated.Motivated by advances of mass spectrometry (MS) in metabolomics, the analytical chemistry community has stepped up its efforts toward realizing MS-based single-cell metabolomics (1, 2). A number of analytical approaches were developed with detection limits low enough for single-cell metabolite analyses [e.g., nanostructured surfaces (7, 8), postionization techniques (9, 10), modified laser optics (11), the use of microsampling tools (12, 13), microarrays for MS measurements (14, 15), etc.]. Until now, however, most MS studies targeting single-cell metabolite analysis have only shown the analytical capabilities, but have not demonstrated that true biological information can be retrieved from studying the metabolism of single cells.Here, using the unicellular eukaryotic model organism Saccharomyces cerevisiae, we present an analytical validation of a single-cell metabolite analysis using the microarrays for mass spectrometry (MAMS) platform. This validation concerns both the analytical methodology and the biological information, by monitoring expected cellular responses upon an environmental and a genetic perturbation. Furthermore, we present examples of biological insight that are only accessible with a platform such as MAMS. Specifically, we unravel metabolite-metabolite correlations, and visualize coexisting subpopulations in an isogenic cell culture. This technology can now be used to reveal metabolic differences in cells of isogenic cell populations, such as differences caused by cell cycles stages, cell ages, or stochastically induced phenotypic differences. Results and ...

Single-cell metabolomics is an emerging field that addresses fundamental biological questions and allows one to observe metabolic phenomena in heterogeneous populations of single cells. In this review, we assess the suitability of different detection techniques and present considerations on sample preparation for single-cell metabolomics. Although targeted analysis of single cells can readily be conducted using fluorescent probes and optical instruments (microscopes, fluorescence detectors), a comprehensive metabolomic approach requires a powerful label-free method, such as mass spectrometry (MS). Mass-spectrometric techniques applied to study small molecules in single cells include electrospray MS, matrix-assisted laser desorption/ionization MS, and secondary ion MS. Sample preparation is an important aspect to be taken into account during further development of methods for single-cell metabolomics.

Functional high-density micro-arrays for mass spectrometry enable rapid picolitre-volume aliquoting and ultrasensitive analysis of microscale samples, for example, single cells.