Abstract
Tissue microenvironments are extremely complex and heterogeneous. It is challenging to study metabolic interaction between the different cell types in a tissue with the techniques that are currently available. Here we describe a multimodal imaging pipeline that allows cell type identification and nanoscale tracing of stable isotope-labeled compounds. This pipeline extends upon the principles of correlative light, electron and ion microscopy, by combining confocal microscopy reporter or probe-based fluorescence, electron microscopy, stable isotope labeling and nanoscale secondary ion mass spectrometry. We apply this method to murine models of hepatocellular and mammary gland carcinomas to study uptake of glucose derived carbon (13C) and glutamine derived nitrogen (15N) by tumor-associated immune cells. In vivo labeling with fluorescent-tagged antibodies (B220, CD3, CD8a, CD68) in tandem with confocal microscopy allows for the identification of specific cell types (B cells, T cells and macrophages) in the tumor microenvironment. Subsequent image correlation with electron microscopy offers the contrast and resolution to image membranes and organelles. Nanoscale secondary ion mass spectrometry tracks the enrichment of stable isotopes within these intracellular compartments. The whole protocol described here would take ~6 weeks to perform from start to finish. Our pipeline caters to a broad spectrum of applications as it can easily be adapted to trace the uptake and utilization of any stable isotope-labeled nutrient, drug or a probe by defined cellular populations in any tissue in situ.
Key points
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To understand complex tissues, it is useful to map metabolic processes to specific cell types. Nanoscale secondary ion mass spectrometry (NanoSIMS) can differentiate between isotopes derived from labeled metabolites at subcellular resolution.
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This protocol combines three imaging modalities: confocal microscopy of fluorescent probes (antibodies), electron microscopy and NanoSIMS enabling cell type identification, characterization of the cellular architecture and extraction of metabolic information.
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Data availability
The raw images associated with the SEM and NanoSIMS for the different cell types (Fig. 2) are available at https://doi.org/10.25418/crick.24989841.
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Acknowledgements
This work was supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK, the UK Medical Research Council and the Wellcome Trust FC001223 (M.Y.) and by the CRUK Grand Challenge Award 2015 C57633/A25043 (J.B. and M.Y.). We would like to thank all the animal technicians from the Francis Crick Institute’s Biological Research Facility for their dedicated work. LLMs (Microsoft Editor, ChatGPT and Grammarly) were solely used for grammar checks and reformatting purposes during the writing of this manuscript.
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S.V.V., P.K., C.M.L. and G.M. developed the protocol with critical input from M.Y., L.C. and J.B. The paper was written by S.V.V., P.K., C.M.L. and G.M. Further edits and suggestions for the manuscript were provided by G.G., L.C. and M.Y. The animal experiments and confocal microscopy imaging was performed by S.V.V. The sample preparation and EM imaging was carried out by C.M.L. All the NanoSIMS experiments and data analysis were performed by G.G. and G.M.
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Key reference
Kreuzaler, P. et al. Nat. Metab. 5, 1870–1886 (2023): https://doi.org/10.1038/s42255-023-00915-7
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Venkateswaran, S.V., Kreuzaler, P., Maclachlan, C. et al. A multimodal imaging pipeline to decipher cell-specific metabolic functions and tissue microenvironment dynamics. Nat Protoc 20, 1678–1699 (2025). https://doi.org/10.1038/s41596-024-01118-4
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DOI: https://doi.org/10.1038/s41596-024-01118-4
