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Analysis of redox landscapes and dynamics in living cells and in vivo using genetically encoded fluorescent sensors

Nature Protocolsvolume 13pages23622386 (2018) | Download Citation


Cellular oxidation–reduction reactions are mainly regulated by pyridine nucleotides (NADPH/NADP+ and NADH/NAD+), thiols, and reactive oxygen species (ROS) and play central roles in cell metabolism, cellular signaling, and cell-fate decisions. A comprehensive evaluation or multiplex analysis of redox landscapes and dynamics in intact living cells is important for interrogating cell functions in both healthy and disease states; however, until recently, this goal has been limited by the lack of a complete set of redox sensors. We recently reported the development of a series of highly responsive, genetically encoded fluorescent sensors for NADPH that substantially strengthen the existing toolset of genetically encoded sensors for thiols, H2O2, and NADH redox states. By combining sensors with unique spectral properties and specific subcellular targeting domains, our approach allows simultaneous imaging of up to four different sensors. In this protocol, we first describe strategies for multiplex fluorescence imaging of these sensors in single cells; then we demonstrate how to apply these sensors to study changes in redox landscapes during the cell cycle, after macrophage activation, and in living zebrafish. This approach can be adapted to different genetically encoded fluorescent sensors and various analytical platforms such as fluorescence microscopy, high-content imaging systems, flow cytometry, and microplate readers. A typical preparation of cells or zebrafish expressing different sensors takes 2–3 d; microscopy imaging or flow-cytometry analysis can be performed within 5–60 min.

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We thank S.J. Remington for the roGFP1 vector; V.V. Belousov for the HyPer and HyPerRed vectors; J. Du for the pTol2 vector; J. Yi for the psPAX2 and pMD2.G vectors; N. Su, L. Huang, Q. Wang, P. Ni, and H. Zi for technical assistance; and S.C. Tribuna for secretarial assistance. This research was supported by the National Key Research and Development Program of China (2017YFC0906900, 2017YFA050400, 2016YFA0100602, and 2017YFA0103302), the NSFC (31722033, 91649123, 31671484, 31225008, 31470833, 91749203, 81525010, and 81420108017), the Shanghai Science and Technology Commission (14XD1401400, 16430723100, and 15YF1402600), the Young Elite Scientists Sponsorship Program by Cast, Shanghai Young Top-notch Talent, the State Key Laboratory of Bioreactor Engineering, the Fundamental Research Funds for the Central Universities, the US National Institutes of Health (HL061795, HG007690, and GM107618 to J.L.), and the American Heart Association (D700382 to J.L.).

Author information

Author notes

  1. These authors contributed equally: Yejun Zou and Aoxue Wang


  1. Synthetic Biology and Biotechnology Laboratory, State Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation Center for Biomanufacturing Technology, East China University of Science and Technology, Shanghai, China

    • Yejun Zou
    • , Aoxue Wang
    • , Mei Shi
    • , Xianjun Chen
    • , Renmei Liu
    • , Ting Li
    • , Chenxia Zhang
    • , Zhuo Zhang
    • , Yi Yang
    •  & Yuzheng Zhao
  2. Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China

    • Yejun Zou
    • , Aoxue Wang
    • , Mei Shi
    • , Xianjun Chen
    • , Renmei Liu
    • , Ting Li
    • , Chenxia Zhang
    • , Zhuo Zhang
    •  & Yuzheng Zhao
  3. Institute of Fine Chemicals, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, China

    • Linyong Zhu
  4. Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Aging and Regenerative Medicine, Jinan University, Guangzhou, China

    • Zhenyu Ju
  5. Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    • Joseph Loscalzo
  6. Optogenetics & Synthetic Biology Interdisciplinary Research Center, CAS Center for Excellence in Brain Science, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China

    • Yi Yang


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Y. Zhao, Y.Y., Y. Zou, and M.S. conceived and designed the live-cell and zebrafish imaging experiments. Y. Zhao, Y.Y., and A.W. designed the flow-cytometry analysis experiment. Y. Zou, A.W., M.S., X.C., R.L., T.L., and C.Z. performed experiments. Z.Z., L.Z., Z.J., and J.L. gave technical support and conceptual advice. Y.Y., Y. Zhao, Y. Zou, A.W., M.S., and J.L. analyzed the data and wrote the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Yi Yang or Yuzheng Zhao.

Integrated supplementary information

  1. Supplementary Figure 1 Effect of diamide or oxamate treatment on subcellular pH.

    (a and b) Fluorescence imaging (top) and fluorescence changes (bottom, n = 6 cells) in HeLa cells simultaneously expressing cytosol-localized iNapc and cytosol-localized pHRFP in response to 200 µM diamide (a) or 2 mM oxamate (b). (c and d) Fluorescence imaging (top) and fluorescence changes (bottom, n = 6 cells) in HeLa cells simultaneously expressing mitochondria-localized iNapc, nuclear-localized iNapc, and mitochondrial-localized pHRFP in response to 200 µM diamide (c) or 2 mM oxamate (d). Data are the mean ± s.d. All p values were obtained using unpaired two-tailed Student’s t test. *p < 0.05, ***p < 0.001. Scale bars, 10 µm.

  2. Supplementary Figure 2 pH fluorescence imaging during the cell cycle.

    (a and b) Fluorescence images (a) and quantification (b) of pH dynamics during cell division. Scale bars, 10 µm.

  3. Supplementary Figure 3 Example gating strategy.

    (a) Sample gating strategy for forward and side scatter (FSC/SSC). (b and c) Sample gating strategy for negative cells (b) and positive cells (sensor-expressing cells, c).

  4. Supplementary Figure 4 Flow cytometry analysis of the pH state in resting and activated mouse macrophages.

    (a) Cytosolic or mitochondrial pH detection in resting or activated RAW264.7 mouse macrophages by flow cytometry. (b) Quantitative data for cytosolic or mitochondrial pH sensor fluorescence were obtained from three or more independent detections by flow cytometry. Data are the mean ± s.e.m. All p values were obtained using unpaired two-tailed Student’s t test. *p < 0.05, **p < 0.01, ***p < 0.001.

  5. Supplementary Figure 5 pH fluorescence imaging of zebrafish larvae.

    (a and b) In vivo fluorescence imaging of zebrafish larvae expressing iNapc in response to 50 mM H2O2 (a) or 5 µM rotenone (b).

Supplementary information

  1. Supplementary Figures 1–5

    Supplementary Figures 1–5 and Supplementary Table 1: The numerical analysis of all cells and positive cells.

  2. Reporting Summary

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