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Optical sensors for monitoring dynamic changes of intracellular metabolite levels in mammalian cells


Knowledge of the in vivo levels, distribution and flux of ions and metabolites is crucial to our understanding of physiology in both healthy and diseased states. The quantitative analysis of the dynamics of ions and metabolites with subcellular resolution in vivo poses a major challenge for the analysis of metabolic processes. Genetically encoded Förster resonance energy transfer (FRET) sensors can be used for real-time in vivo detection of metabolites. FRET sensor proteins, for example, for glucose, can be targeted genetically to any cellular compartment, or even to subdomains (e.g., a membrane surface), by adding signal sequences or fusing the sensors to specific proteins. The sensors can be used for analyses in individual mammalian cells in culture, in tissue slices and in intact organisms. Applications include gene discovery, high-throughput drug screens or systematic analysis of regulatory networks affecting uptake, efflux and metabolism. Quantitative analyses obtained with the help of FRET sensors for glucose or other ions and metabolites provide valuable data for modeling of flux. Here we provide a detailed protocol for monitoring glucose levels in the cytosol of mammalian cell cultures through the use of FRET glucose sensors; moreover, the protocol can be used for other ions and metabolites and for analyses in other organisms, as has been successfully demonstrated in bacteria, yeast and even intact plants. The whole procedure typically takes 4 d including seeding and transfection of mammalian cells; the FRET-based analysis of transfected cells takes 5 h.

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Figure 1: Cartoon of a FRET sensor.
Figure 2: Assembly of the perfusion chamber.
Figure 3: Diagram of the perfusion system for FRET imaging.
Figure 4: Overlap of the excitation and emission spectra of CFP and YFP.
Figure 5: Screenshot of data acquisition for FRET imaging in SlideBook 5.1.
Figure 6: Screenshot of data analysis for FRET imaging in SlideBook 5.1.
Figure 7: MATLAB screenshot of the user interface for the FRET analysis script for correction of spectral bleed-through (SBT) and baseline fitting.
Figure 8: In vivo analysis of FLII12Pglu-700μδ6 glucose sensors expressed in the cytosol of HEK293T cells.


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This research was supported by the National Institutes of Health (National Institute of Diabetes and Digestive and Kidney Diseases; 1RO1DK079109). G.G. was supported by the European Molecular Biology Organization. X.-Q.Q. was supported by a stipend from the Scholarship Program of the Chinese Scholarship Council (file no. 2009635108). We thank the members in our lab for the corrections and suggestions in this paper. Special thanks go to K.-J. Huang (Stanford University) for his intensive support in writing and discussion during the preparation of this manuscript.

Author information

Authors and Affiliations



B.-H.H. and H.T. conducted experiments and data analysis. H.T. had a major role in the development of this FRET imaging technology. L.-Q.C., G.G., X.-Q.Q., A.M.J. and S.L. assisted in the preparation of the paper and figures. O.S. and W.W. wrote the FRET script for MATLAB. B.-H.H. and W.B.F. wrote the paper.

Corresponding author

Correspondence to Wolf B Frommer.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Data 1

Example of data (.txt) exported from the software SlideBook. (TXT 146 kb)

Supplementary Data 2

The script (FRETnorm) for FRET imaging in Matlab. (ZIP 419 kb)

Supplementary Data 3

The manual of the Matlab script, FRETnorm. (PDF 273 kb)

Supplementary Data 4

Example of the loading instruction (external-load.csv). The column provides the glucose concentrations at a given time (corresponding to time points in the input data file, cf Supplemental Data 5) for a defined perfusion experiment, e.g. as shown in Figure 8. (CSV 1 kb)

Supplementary Data 5

Example of an input data file (.csv) recognized by FRETnorm. This file provides the time, the background and the actual fluorescence data for each ROI. It is generated from the Supplementary file 1 (.txt). The data in this file were further analyzed by Matlab and plotted in Figure 8b. (CSV 95 kb)

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Hou, BH., Takanaga, H., Grossmann, G. et al. Optical sensors for monitoring dynamic changes of intracellular metabolite levels in mammalian cells. Nat Protoc 6, 1818–1833 (2011).

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