Abstract
The pathogenesis of cancer and cardiovascular diseases is subjected to spatiotemporal regulation by the tissue microenvironment. Multiplex visualization of the microenvironmental components, including immune cells, vasculature and tissue hypoxia, provides critical information underlying the disease progression and therapy resistance, which is often limited by imaging depth and resolution in large-volume tissues. To this end, light sheet fluorescence microscopy, following tissue clarification and immunostaining, may generate three-dimensional high-resolution images at a whole-organ level. Here we provide a detailed description of light sheet fluorescence microscopy imaging analysis of immune cell composition, vascularization, tissue perfusion and hypoxia in mouse normal brains and hearts, as well as brain tumors. We describe a procedure for visualizing tissue vascularization, perfusion and hypoxia with a transgenic vascular labeling system. We provide the procedures for tissue collection, tissue semi-clearing and immunostaining. We further describe standard methods for analyzing tissue immunity and vascularity. We anticipate that this method will facilitate the spatial illustration of structure and function of the tissue microenvironmental components in cancer and cardiovascular diseases. The procedure requires 1–2 weeks and can be performed by users with expertise in general molecular biology.
Key points
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The protocol covers tissue preparation, polymerization, tissue semi-clearing, immunostaining, refractive index matching and mounting, imaging, and data processing and analysis.
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The mild processing of samples ensures structural and molecular stability, preserving most of the proteins of interest for antibody-based immunostaining.
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Acknowledgements
This work was supported in part by National Institutes of Health grants R01NS094533 (to Y.F.), R01NS106108 (to Y.F.), R01CA241501 (to J.F.D. and Y.F.) and R01HL155198 (to Y.G. and Y.F.), and by American Heart Association grants Innovative Project Award (to Y.F.), Transformational Project Award (to Y.G.) and Predoctoral Fellowship (to D.Z.).
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Contributions
D.Z. and Y.F. initiated the project. D.Z. performed most of the experiments and originally developed the protocol. D.Z., A.H.C., E.K., K.H., A.L.S. and J.F.D. designed the experiments. D.Z., C.Y. and W.Z. contributed to imaging analysis. Y.G. and Y.F. supervised the project. D.Z., E.K. and Y.F. wrote the manuscript. All authors commented on the manuscript.
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Nature Protocols thanks Hiroki Ueda and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Key references using this protocol
Wang, Q. et al. Nat. Commun. 9, 559 (2018): https://doi.org/10.1038/s41467-018-03050-0
Ma, W. et al. Nat. Cancer 2, 83–97 (2021): https://doi.org/10.1038/s43018-020-00147-8
Huang, M. et al. Nat. Cardiovasc. Res. 1, 372–388 (2022): https://doi.org/10.1038/s44161-022-00047-3
Zhang, D. et al. Cell Metab. 35, 517–534 (2023): https://doi.org/10.1016/j.cmet.2023.01.010
Extended data
Extended Data Fig. 1 LSFM analysis of healthy mouse brain left hemisphere.
Healthy Cdh5-CreERT2;LSL-tdTomato mice were perfused with DyLight 649-lectin. Brain tissue was excised, followed by tissue clearing and LSFM imaging. Each minor tick on the grid represents 1 mm.
Extended Data Fig. 2 LSFM analysis of tissue samples after long-term storage.
a, GBM was induced in Cdh5-CreERT2;LSL-tdTomato mice, followed by perfused with hypoxyprobe. Tissue samples were subjected to tissue clearing, and stored in RIMS at 4°C for 2.5 years and imaged by LSFM. Each minor tick on the grid represents 1 mm. b, MI was induced in Cdh5-CreERT2;LSL-tdTomato mice, followed by perfused with lectin. Tissue samples were subjected to tissue clearing, and stored in RIMS at 4°C for 2.5 years and imaged by LSFM. Each minor tick on the grid represents 0.5 mm.
Extended Data Fig. 3 Key steps of image processing and analysis in Imaris.
a, Voxel size correction in “Image Properties” (Step 42). b, Crop out ROI in “Crop 3D” (Step 43). c, “Image Processing” module (Step 44). d, Setting deconvolution parameters in the “Image Processing” module (Step 44). e, Preprocessing the image using “Normalize Layers” and “Background Subtraction “ (Steps 45, 46). f, Creating objects for feature registration (Step 48). g, Initial parameters for creating surface objects (Step 48A(ii)). h, Thresholding of surface object creation (Step 48A(iii)). i, Filtering generated surface objects by size (Step 48A(iv)). j, Multiscale Points method of generating filament seed points (Step 48B(ii)). k, Seed point filtering by vessel diameter (Step 48B(ii)). l, Segment classification and filtering by machine learning-based model (Step 48B(iii)). m, Initial parameters for creating spot objects (Step 48C(i)). n, Spot detection by feature diameter (Step 48C(ii)). o, Filter Spot feature by quality and diameter/size (Step 48C(iii–iv)).
Supplementary information
Supplementary Software 1
3D printing file for ETC sample holder.
Supplementary Software 2
3D printing file for sample imaging holder.
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Zhang, D., Cleveland, A.H., Krimitza, E. et al. Spatial analysis of tissue immunity and vascularity by light sheet fluorescence microscopy. Nat Protoc 19, 1053–1082 (2024). https://doi.org/10.1038/s41596-023-00941-5
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DOI: https://doi.org/10.1038/s41596-023-00941-5
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