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bioRxiv · 10.1101/2022.05.03.490288

Probing single cell fermentation flux and intercellular exchange networks via pH-microenvironment sensing and inverse modeling

Abstract

The homeostatic control of their environment is an essential task of living cells. It has been hypothesized that when microenvironmental pH inhomogeneities are induced by high cellular metabolic activity, diffusing protons act as signaling molecules, driving the establishment of cross-feeding networks sustained by the cell-to-cell shuttling of overflow products such as lactate. Despite their fundamental role, the extent and dynamics of such networks is largely unknown due to the lack of methods in single cell flux analysis. In this study we provide direct experimental characterization of such exchange networks. We devise a method to quantify single cell fermentation fluxes over time by integrating high-resolution pH microenvironment sensing via ratiometric nanofibers with constraint-based inverse modeling. We apply our method to cell cultures with mixed populations of cancer cells and fibroblasts. We find that the proton trafficking underlying bulk acidification is strongly heterogeneous, with maximal single cell fluxes exceeding typical values by up to 3 orders of magnitude. In addition, a crossover in time from a networked phase sustained by densely connected "hubs" (corresponding to cells with high activity) to a sparse phase dominated by isolated dipolar motifs (i.e. by pair-wise cell-to-cell exchanges) is uncovered, which parallels the time course of bulk acidification. Our method promises to shed light on issues ranging from the homeostatic function of proton exchange to the metabolic coupling of cells with different energetic demands, and paves the way for real-time non-invasive single cell metabolic flux analysis.

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BibTeXRIS

Onesto, V., Forciniti, S., Alemanno, F., Narayanankutty, K., Chandra, A., Prasad, S., Azzariti, A., Gigli, G., Barra, A., De Martino, A., de Martino, D., del Mercato, L. L.. 2022-05-04. Probing single cell fermentation flux and intercellular exchange networks via pH-microenvironment sensing and inverse modeling. https://doi.org/10.1101/2022.05.03.490288

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