Regulation of cell growth by nutrients is governed by highly conserved signaling pathways, yet mechanisms of nutrient sensing are still poorly understood. In yeast, glucose activates both the Ras/PKA pathway and TORC1, which coordinately regulate growth through enhancing translation and ribosome biogenesis and suppressing autophagy. Here, we show that cytosolic pH acts as a cellular signal to activate Ras and TORC1 in response to glucose availability. We demonstrate that cytosolic pH is sensitive to the quality and quantity of the available carbon source (C-source). Interestingly, Ras/PKA and TORC1 are both activated through the vacuolar ATPase (V-ATPase), which was previously identified as a sensor for cytosolic pH in vivo. V-ATPase interacts with two distinct GTPases, Arf1 and Gtr1, which are required for Ras and TORC1 activation, respectively. Together, these data provide a molecular mechanism for how cytosolic pH links C-source availability to the activity of signaling networks promoting cell growth.
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 metabolite analysis provides valuable information on cellular function and response to external stimuli. While recent advances in mass spectrometry reached the sensitivity required to investigate metabolites in single cells, current methods commonly isolate and sacrifice cells, inflicting a perturbed state and preventing complementary analyses. Here, we propose a two-step approach that combines nondestructive and quantitative withdrawal of intracellular fluid with subpicoliter resolution using fluidic force microscopy, followed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. The developed method enabled the detection and identification of 20 metabolites recovered from the cytoplasm of individual HeLa cells. The approach was further validated in C-glucose feeding experiments, which showed incorporation of labeled carbon atoms into different metabolites. Metabolite sampling, followed by mass spectrometry measurements, enabled the preservation of the physiological context and the viability of the analyzed cell, providing opportunities for complementary analyses of the cell before, during, and after metabolite analysis.
Non-covalent interactions are essential for the structural organization of biomacromolecules and play an important role in molecular recognition processes, such as the interactions between proteins, glycans, lipids, DNA, and RNA. Mass spectrometry (MS) is a powerful tool for studying of non-covalent interactions, due to the low sample consumption, high sensitivity, and label-free nature. Nowadays, native-ESI MS is heavily used in studies of non-covalent interactions and to understand the architecture of biomolecular complexes. However, MALDI-MS is also becoming increasingly useful. It is challenging to detect the intact complex without fragmentation when analyzing non-covalent interactions with MALDI-MS. There are two methodological approaches to do so. In the first approach, different experimental and instrumental parameters are fine-tuned in order to find conditions under which the complex is stable, such as applying non-acidic matrices and collecting first-shot spectra. In the second approach, the interacting species are "artificially" stabilized by chemical crosslinking. Both approaches are capable of studying non-covalently bound biomolecules even in quite challenging systems, such as membrane protein complexes. Herein, we review and compare native-ESI and MALDI MS for the study of non-covalent interactions.
In order to investigate metabolic properties of single cells of freshwater algae (Haematococcus pluvialis), we implement matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) in combination with microspectroscopic mapping. Straightforward coupling of these two detection platforms was possible thanks to the self-aliquoting properties of micro-arrays for mass spectrometry (MAMS). Following Raman and fluorescence imaging, the isolated cells were covered with a MALDI matrix for targeted metabolic analysis by MALDI-MS. The three consecutive measurements carried out on the same cells yielded complementary information. Using this method, we were able to study the encystment of H. pluvialis - by monitoring the adenosine triphosphate (ATP) to adenosine diphosphate (ADP) ratio during the build-up of astaxanthin in the cells as well as the release of β-carotene, the precursor of astaxanthin, into the cytosol.
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