Abstract

Metabolic heterogeneity between individual cells of a population harbors significant challenges for fundamental and applied research. Identifying metabolic heterogeneity and investigating its emergence require tools to zoom into metabolism of individual cells. While methods exist to measure metabolite levels in single cells, we lack capability to measure metabolic flux, i.e., the ultimate functional output of metabolic activity, on the single‐cell level. Here, combining promoter engineering, computational protein design, biochemical methods, proteomics, and metabolomics, we developed a biosensor to measure glycolytic flux in single yeast cells. Therefore, drawing on the robust cell‐intrinsic correlation between glycolytic flux and levels of fructose‐1,6‐bisphosphate (FBP), we transplanted the B. subtilis FBP‐binding transcription factor CggR into yeast. With the developed biosensor, we robustly identified cell subpopulations with different FBP levels in mixed cultures, when subjected to flow cytometry and microscopy. Employing microfluidics, we were also able to assess the temporal FBP/glycolytic flux dynamics during the cell cycle. We anticipate that our biosensor will become a valuable tool to identify and study metabolic heterogeneity in cell populations.

Highlights

  • Increasing evidence suggests that individual cells in a population can be metabolically very different (Nikolic et al, 2013; van Heerden et al, 2014; Solopova et al, 2014; Kotte et al, 2015; Takhaveev & Heinemann, 2018)

  • We employed a combination of growth substrates and two different S. cerevisiae strains: the wild type (WT) and a mutant strain (TM6), which only carries a single chimeric hexose transporter and thereby only generates low Expression of the bacterial transcriptional repressor CggR at constant levels, i.e., independent of growth rate and substrates

  • Exploiting the flux-signaling metabolite fructose-1,6-bisphosphate and the bacterial transcription factor CggR, we developed a biosensor that allows to measure glycolytic flux in individual living a 2019 The Authors yeast cells, at least under glycolytic conditions. These engineering efforts, for which we used computational protein design, biochemical, proteome, and metabolome analyses, entailed (i) development of a synthetic yeast promoter regulated by the bacterial transcriptional factor CggR, (ii) engineering of the transcription factors’ FBPbinding site toward increasing the sensor’s dynamic range, and (iii) establishment of growth-independent CggR expression levels

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Summary

Introduction

Increasing evidence suggests that individual cells in a population can be metabolically very different (Nikolic et al, 2013; van Heerden et al, 2014; Solopova et al, 2014; Kotte et al, 2015; Takhaveev & Heinemann, 2018). B The four reporter plasmids were transferred to the wild-type strain containing the CggR (R250A) under the control of the PTEFmut. Reporter activity is given by the YFP/mCherry ratio, calculated through the quantification of YFP and mCherry fluorescence along culture time using flow cytometry Both YFP and mCherry fluorescence levels were first corrected for background using the same strains harboring the YCplac plasmid (Appendix Table S8). The control is the wild type and TM6 strains expressing only the reporter plasmid without CggR. The curve fitting of the normalized values of CggR fraction bound to FBP was performed using a one-site specific binding model in GraphPad. The solid line corresponds to the wild-type CggR and the dashed line to the R250A variant.

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