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Comprehensive multiscale analysis of lactate metabolic dynamics in vitro and in vivo using highly responsive biosensors

Nature Protocols (2024)Cite this article

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Abstract

As a key glycolytic metabolite, lactate has a central role in diverse physiological and pathological processes. However, comprehensive multiscale analysis of lactate metabolic dynamics in vitro and in vivo has remained an unsolved problem until now owing to the lack of a high-performance tool. We recently developed a series of genetically encoded fluorescent sensors for lactate, named FiLa, which illuminate lactate metabolism in cells, subcellular organelles, animals, and human serum and urine. In this protocol, we first describe the FiLa sensor-based strategies for real-time subcellular bioenergetic flux analysis by profiling the lactate metabolic response to different nutritional and pharmacological conditions, which provides a systematic-level view of cellular metabolic function at the subcellular scale for the first time. We also report detailed procedures for imaging lactate dynamics in live mice through a cell microcapsule system or recombinant adeno-associated virus and for the rapid and simple assay of lactate in human body fluids. This comprehensive multiscale metabolic analysis strategy may also be applied to other metabolite biosensors using various analytic platforms, further expanding its usability. The protocol is suited for users with expertise in biochemistry, molecular biology and cell biology. Typically, the preparation of FiLa-expressing cells or mice takes 2 days to 4 weeks, and live-cell and in vivo imaging can be performed within 1–2 hours. For the FiLa-based assay of body fluids, the whole measuring procedure generally takes ~1 min for one sample in a manual assay or ~3 min for 96 samples in an automatic microplate assay.

Key points

  • This protocol describes the use of FiLa biosensors, fluorescence-based sensors for the analysis of lactate metabolism. FiLa biosensors can be used in both in vitro assays and in vivo assays, and under different nutritional and pharmacological conditions.

  • Unlike traditional methods of lactate analysis, FiLa biosensors allow subcellular analysis of lactate in real time and show a larger fluorescence ratio response than existing biosensors.

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Fig. 1: Genetically encoded lactate sensors.
Fig. 2: Imaging subcellular lactate metabolism under various nutritional conditions.
Fig. 3: Comparison study of the glycolysis stress test in live cells by Seahorse Analyzer or FiLa.
Fig. 4: Effects of different metabolic modulators on subcellular lactate levels.
Fig. 5: Imaging lactate metabolism in vivo by cell microcapsules.
Fig. 6: Imaging lactate metabolism in live mice with T1DM.
Fig. 7: Rapid and simple assay to determine the plasma lactate levels of tai chi athletes and rugby athletes.

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Data availability

The data used to generate the example results presented in Table 2 are available in the supporting primary research paper39. All other data supporting the findings of this study are available for research purposes from the authors upon reasonable request. Source data are provided with this paper.

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Acknowledgements

This research is supported by National Key Research and Development Program of China (2019YFA0904800 to Y. Zhao and 2021YFA0804900 to A.W.), National Natural Science Foundation of China (32150030, 32030065, 32121005 and 92049304 to Y. Zhao; 91857202, 21937004 and 32150028 to Y.Y.; 82030039 to Z.J; 32000920 to A.W.; 32201230 to Y. Zou), the Shanghai Science and Technology Commission (20JC1412000), the Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism (Y. Zhao), the Research Unit of New Techniques for Live-cell Metabolic Imaging (Chinese Academy of Medical Sciences, 2019-I2M-5-013 to Y. Zhao), the innovative research team of high-level local universities in Shanghai, the State Key Laboratory of Bioreactor Engineering, the Fundamental Research Funds for the Central Universities, US National Institutes of Health (HL155107, HL155096 and HL166137 to J.L.) and the American Heart Association (AHA2020CV-19 and AHA957729 to J.L.).

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Author notes

  1. These authors contributed equally: Aoxue Wang, Yejun Zou, Shuning Liu, Xiuze Zhang.

Authors and Affiliations

  1. Optogenetics & Synthetic Biology Interdisciplinary Research Center, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, State Key Laboratory of Bioreactor Engineering, School of Pharmacy, East China University of Science and Technology, Shanghai, China

    Aoxue Wang, Yejun Zou, Shuning Liu, Xiuze Zhang, Ting Li, Lijuan Zhang, Yale Xia, Xie Li, Zhuo Zhang, Yi Yang & Yuzheng Zhao

  2. Research Unit of New Techniques for Live-cell Metabolic Imaging, Chinese Academy of Medical Sciences, Beijing, China

    Aoxue Wang, Yejun Zou, Ting Li, Xie Li, Zhuo Zhang & Yuzheng Zhao

  3. School of Exercise and Health, Shanghai University of Sport, Shanghai, China

    Ruwen Wang & Ru Wang

  4. State Key Laboratory of Genetic Engineering, Department of Endocrinology and Metabolism, Institute of Metabolism and Integrative Biology, Human Phenome Institute, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, China

    Tiemin Liu

  5. Key Laboratory of Regenerative Medicine of Ministry of Education, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Institute of Aging and Regenerative Medicine, Jinan University, Guangzhou, China

    Zhenyu Ju

  6. Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Joseph Loscalzo

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Contributions

Y. Zhao, Y.Y., T. Li, A.W. and Y. Zou conceived and designed the live-cell and live-mice imaging experiments. Y. Zhao and Ru Wang designed the body fluid analysis experiment. A.W., Y. Zou, T. Li, S.L., X.Z., L.Z. and Ruwen Wang performed experiments. Y.X., X.L., Z.Z., T. Liu, Z.J. and J.L. gave technical support and conceptual advice. Y. Zhao, Y.Y., R.W., A.W., Y. Zou, T. Li and J.L. analyzed the data and wrote the manuscript.

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Correspondence to Ting Li, Ru Wang, Yi Yang or Yuzheng Zhao.

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Key references using this protocol

Li, X. et al. Cell Metab. 35, 200–211 (2023): https://doi.org/10.1016/j.cmet.2022.10.002

Jia, M. et al. Sci. Adv. 9, eadg4993 (2023): https://doi.org/10.1126/sciadv.adg4993

Dou, X. et al. Nat. Metab. 5, 1887–1910 (2023): https://doi.org/10.1038/s42255-023-00912-w

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Wang, A., Zou, Y., Liu, S. et al. Comprehensive multiscale analysis of lactate metabolic dynamics in vitro and in vivo using highly responsive biosensors. Nat Protoc (2024). https://doi.org/10.1038/s41596-023-00948-y

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