Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Protocol
  • Published:

Multispectral confocal 3D imaging of intact healthy and tumor tissue using mLSR-3D

Abstract

Revealing the 3D composition of intact tissue specimens is essential for understanding cell and organ biology in health and disease. State-of-the-art 3D microscopy techniques aim to capture tissue volumes on an ever-increasing scale, while also retaining sufficient resolution for single-cell analysis. Furthermore, spatial profiling through multi-marker imaging is fast developing, providing more context and better distinction between cell types. Following these lines of technological advance, we here present a protocol based on FUnGI (fructose, urea and glycerol clearing solution for imaging) optical clearing of tissue before multispectral large-scale single-cell resolution 3D (mLSR-3D) imaging, which implements ‘on-the-fly’ linear unmixing of up to eight fluorophores during a single acquisition. Our protocol removes the need for repetitive illumination, thereby allowing larger volumes to be scanned with better image quality in less time, also reducing photo-bleaching and file size. To aid in the design of multiplex antibody panels, we provide a fast and manageable intensity equalization assay with automated analysis to design a combination of markers with balanced intensities suitable for mLSR-3D. We demonstrate effective mLSR-3D imaging of various tissues, including patient-derived organoids and xenografted tumors, and, furthermore, describe an optimized workflow for mLSR-3D imaging of formalin-fixed paraffin-embedded samples. Finally, we provide essential steps for 3D image data processing, including shading correction that does not require pre-acquired shading references and 3D inhomogeneity correction to correct fluorescence artefacts often afflicting 3D datasets. Together, this provides a one-week protocol for eight-fluorescent-marker 3D visualization and exploration of intact tissue of various origins at single-cell resolution.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Schematic summary of the mLSR-3D imaging protocol and envisioned applications.
Fig. 2: mLSR-3D equalization assay and FUnGI clearing.
Fig. 3: mLSR-3D imaging of fetal kidney, Wilms tumor, breast cancer xenograft and organoids.
Fig. 4: mLSR-3D imaging of brain tissue and organoids.
Fig. 5: mLSR-3D imaging of deparaffinized FFPE tissue.
Fig. 6: mLSR-3D guided comparative spatio-phenotypic analysis of human brain tumors.

Similar content being viewed by others

Data availability

All data described in this protocol are available from the corresponding author upon reasonable request. Representative subsetted datasets and analyses are made publicly available through demos on the STAPL-3D GitHub page (https://github.com/RiosGroup/STAPL3D).

Code availability

All source codes of STAPL-3D are publicly available through GitHub (https://github.com/RiosGroup/STAPL3D).

References

  1. Belle, M. et al. Tridimensional visualization and analysis of early human development. Cell 169, 161–173.e12 (2017).

    Article  CAS  PubMed  Google Scholar 

  2. Hannezo, E. et al. A unifying theory of branching morphogenesis. Cell 171, 242–255.e27 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Rios, A. C., Fu, N. Y., Lindeman, G. J. & Visvader, J. E. In situ identification of bipotent stem cells in the mammary gland. Nature 506, 322–327 (2014).

    Article  CAS  PubMed  Google Scholar 

  4. Scheele, C. L. G. J. et al. Identity and dynamics of mammary stem cells during branching morphogenesis. Nature 542, 313–317 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Murakami, T. C. et al. A three-dimensional single-cell-resolution whole-brain atlas using CUBIC-X expansion microscopy and tissue clearing. Nat. Neurosci. 21, 625–637 (2018).

    Article  CAS  PubMed  Google Scholar 

  6. Messal, H. A. et al. Tissue curvature and apicobasal mechanical tension imbalance instruct cancer morphogenesis. Nature 566, 126–130 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Rios, A. C. et al. Intraclonal plasticity in mammary tumors revealed through large-scale single-cell resolution 3D imaging. Cancer Cell 35, 953 (2019).

    Article  CAS  PubMed  Google Scholar 

  8. Brown, M. et al. Lymph node blood vessels provide exit routes for metastatic tumor cell dissemination in mice. Science 359, 1408–1411 (2018).

    Article  CAS  PubMed  Google Scholar 

  9. Almagro, J., Messal, H. A., Thin, M. Z., van Rheenen, J. & Behrens, A. Tissue clearing to examine tumour complexity in three dimensions. Nat. Rev. Cancer 21, 718–730 (2021).

    Article  CAS  PubMed  Google Scholar 

  10. Ertürk, A. et al. Three-dimensional imaging of solvent-cleared organs using 3DISCO. Nat. Protoc. 7, 1983–1995 (2012).

    Article  PubMed  Google Scholar 

  11. Messal, H. A. et al. Antigen retrieval and clearing for whole-organ immunofluorescence by FLASH. Nat. Protoc. 16, 239–262 (2021).

    Article  CAS  PubMed  Google Scholar 

  12. Dekkers, J. F. et al. High-resolution 3D imaging of fixed and cleared organoids. Nat. Protoc. 14, 1756–1771 (2019).

    Article  CAS  PubMed  Google Scholar 

  13. Bernier-Latmani, J. & Petrova, T. V. High-resolution 3D analysis of mouse small-intestinal stroma. Nat. Protoc. 11, 1617–1629 (2016).

    Article  CAS  PubMed  Google Scholar 

  14. Kusumbe, A. P., Ramasamy, S. K., Starsichova, A. & Adams, R. H. Sample preparation for high-resolution 3D confocal imaging of mouse skeletal tissue. Nat. Protoc. 10, 1904–1914 (2015).

    Article  CAS  PubMed  Google Scholar 

  15. Susaki, E. A. et al. Advanced CUBIC protocols for whole-brain and whole-body clearing and imaging. Nat. Protoc. 10, 1709–1727 (2015).

    Article  CAS  PubMed  Google Scholar 

  16. Tomer, R., Ye, L., Hsueh, B. & Deisseroth, K. Advanced CLARITY for rapid and high-resolution imaging of intact tissues. Nat. Protoc. 9, 1682–1697 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Renier, N. et al. iDISCO: a simple, rapid method to immunolabel large tissue samples for volume imaging. Cell 159, 896–910 (2014).

    Article  CAS  PubMed  Google Scholar 

  18. Susaki, E. A. & Ueda, H. R. Whole-body and whole-organ clearing and imaging techniques with single-cell resolution: toward organism-level systems biology in mammals. Cell Chem. Biol. 23, 137–157 (2016).

    Article  CAS  PubMed  Google Scholar 

  19. Tainaka, K., Kuno, A., Kubota, S. I., Murakami, T. & Ueda, H. R. Chemical principles in tissue clearing and staining protocols for whole-body cell profiling. Annu. Rev. Cell Dev. Biol. 32, 713–741 (2016).

    Article  CAS  PubMed  Google Scholar 

  20. Richardson, D. S. & Lichtman, J. W. Clarifying tissue clearing. Cell 162, 246–257 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Li, W., Germain, R. N. & Gerner, M. Y. High-dimensional cell-level analysis of tissues with Ce3D multiplex volume imaging. Nat. Protoc. 14, 1708–1733 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Ku, T. et al. Elasticizing tissues for reversible shape transformation and accelerated molecular labeling. Nat. Methods 17, 609–613 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968–981.e15 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Seo, J. et al. PICASSO allows ultra-multiplexed fluorescence imaging of spatially overlapping proteins without reference spectra measurements. Nat. Commun. 13, 2475 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Murray, E. et al. Simple, scalable proteomic imaging for high-dimensional profiling of intact systems. Cell 163, 1500–1514 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. van Ineveld, R. L. et al. Revealing the spatio-phenotypic patterning of cells in healthy and tumor tissues with mLSR-3D and STAPL-3D. Nat. Biotechnol. 39, 1239–1245 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Stoltzfus, C. R. et al. CytoMAP: a spatial analysis toolbox reveals features of myeloid cell organization in lymphoid tissues. Cell Rep. 31, 107523 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Alon, S. et al. Expansion sequencing: spatially precise in situ transcriptomics in intact biological systems. Science 371, eaax2656 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Valm, A. M. et al. Applying systems-level spectral imaging and analysis to reveal the organelle interactome. Nature 546, 162–167 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Coutu, D. L., Kokkaliaris, K. D., Kunz, L. & Schroeder, T. Multicolor quantitative confocal imaging cytometry. Nat. Methods 15, 39–46 (2018).

    Article  CAS  PubMed  Google Scholar 

  32. Zimmermann, T., Marrison, J., Hogg, K. & O’Toole, P. Confocal microscopy, methods and protocols. Methods Mol. Biol. 1075, 129–148 (2013).

    Article  Google Scholar 

  33. Susaki, E. A. et al. Whole-brain imaging with single-cell resolution using chemical cocktails and computational analysis. Cell 157, 726–739 (2014).

    Article  CAS  PubMed  Google Scholar 

  34. Weiss, K. R., Voigt, F. F., Shepherd, D. P. & Huisken, J. Tutorial: practical considerations for tissue clearing and imaging. Nat. Protoc. 16, 2732–2748 (2021).

    Article  CAS  PubMed  Google Scholar 

  35. Gerner, M. Y., Kastenmuller, W., Ifrim, I., Kabat, J. & Germain, R. N. Histo-cytometry: a method for highly multiplex quantitative tissue imaging analysis applied to dendritic cell subset microanatomy in lymph nodes. Immunity 37, 364–376 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Gehart, H. et al. Identification of enteroendocrine regulators by real-time single-cell differentiation mapping. Cell 176, 1158–1173.e16 (2019).

    Article  CAS  PubMed  Google Scholar 

  37. van Ineveld, R. L., Ariese, H. C. R., Wehrens, E. J., Dekkers, J. F. & Rios, A. C. Single-cell resolution three-dimensional imaging of intact organoids. J. Vis. Exp. 2020, e60709 (2020).

    Google Scholar 

  38. Calandrini, C. et al. An organoid biobank for childhood kidney cancers that captures disease and tissue heterogeneity. Nat. Commun. 11, 1310 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Hu, H. et al. Long-term expansion of functional mouse and human hepatocytes as 3D organoids. Cell 175, 1591–1606.e19 (2018).

    Article  CAS  PubMed  Google Scholar 

  40. Post, Y. et al. Snake venom gland organoids. Cell 180, 233–247.e21 (2020).

    Article  CAS  PubMed  Google Scholar 

  41. Schutgens, F. et al. Tubuloids derived from human adult kidney and urine for personalized disease modeling. Nat. Biotechnol. 37, 303–313 (2019).

    Article  CAS  PubMed  Google Scholar 

  42. Lee, S. S.-Y., Bindokas, V. P. & Kron, S. J. Multiplex three-dimensional optical mapping of tumor immune microenvironment. Sci. Rep. 7, 17031 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Jahr, W., Schmid, B., Schmied, C., Fahrbach, F. O. & Huisken, J. Hyperspectral light sheet microscopy. Nat. Commun. 6, 7990 (2015).

    Article  PubMed  Google Scholar 

  44. Cutrale, F. et al. Hyperspectral phasor analysis enables multiplexed 5D in vivo imaging. Nat. Methods 14, 149–152 (2017).

    Article  CAS  PubMed  Google Scholar 

  45. Thomson, J. A. et al. Embryonic stem cell lines derived from human blastocysts. Science 282, 1145–1147 (1998).

    Article  CAS  PubMed  Google Scholar 

  46. Lancaster, M. A. & Knoblich, J. A. Generation of cerebral organoids from human pluripotent stem cells. Nat. Protoc. 9, 2329–2340 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Taqi, S. A., Sami, S. A., Sami, L. B. & Zaki, S. A. A review of artifacts in histopathology. J. Oral. Maxillofac. Pathol. Jomfp 22, 279–279 (2018).

    Article  PubMed  Google Scholar 

  48. Kokkat, T. J., Patel, M. S., McGarvey, D., LiVolsi, V. A. & Baloch, Z. W. Archived formalin-fixed paraffin-embedded (FFPE) blocks: a valuable underexploited resource for extraction of DNA, RNA, and protein. Biopreserv. Biobank. 11, 101–106 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Wong, E. et al. Cut‐point for Ki‐67 proliferation index as a prognostic marker for glioblastoma. Asia Pac. J. Clin. Oncol. 15, 5–9 (2019).

    Article  PubMed  Google Scholar 

  50. Tavares, C. B., Braga, F. D. C. S. A. G., Sousa, E. B. & de O. Brito, J. N. P. Expression of Ki-67 in low-grade and high-grade astrocytomas. –J. Bras. Neurocirur. 27, 225–230 (2018).

    Google Scholar 

Download references

Acknowledgements

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 804412). This work was also financially supported by the Princess Máxima Center for Pediatric Oncology and a St. Baldrick’s Robert J. Arceci International Innovation award to A.C.R.

Author information

Authors and Affiliations

Authors

Contributions

R.L.v.I. conceived the protocol. R.L.v.I., R.C., M.B.R. and H.C.R.A. performed experiments and analyzed data. A.P. performed FFPE tissue experiments. N.B. and H.C.R.A. performed brain organoid culturing and imaging. M. Kool provided hESC. M. Kleinnijenhuis designed STAPL-3D and performed data analyses. S.M.C.d.S.L. provided human fetal kidney material. M.B.R. made the video. A.C.R. helped design the protocol. R.L.v.I. and R.C. wrote the manuscript. M.B.R., M.A., E.J.W. and A.C.R. co-wrote the manuscript. A.C.R. supervised the project.

Corresponding author

Correspondence to Anne C. Rios.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Protocols thanks Irene Costantini and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Key reference using this protocol

van Ineveld, R. L. et al. Nat. Biotechnol. 39, 1239–1245 (2021): https://doi.org/10.1038/s41587-021-00926-3

Extended data

Extended Data Fig. 1 Workflow for spectral eight-color mix design with commercially available antibodies.

Emission slot tables serve as an example for marker mix design and present one possible combination of dyes and antibodies specific to brain tissue.

Extended Data Fig. 2 Clearing comparison.

a, Widefield microscopic images of human embryonic kidney tissues uncleared or cleared with FUnGI, Ce3D, CUBIC or iDISCO. Squares represent 1 mm. b, Optical sections at two depths (surface and 100 μm) for all eight channels per condition. Red asterisks indicate lack of specific signal in this channel. Scale bars, 50 μm. c, Quantification of the 10% highest intensity pixels per z-plane per channel for every condition. Channels without sufficient specific signal indicated with a red asterisk were not quantified.

Extended Data Fig. 3 Sequential 32-channel lambda-scan performed on a Leica SP8.

a, Optical section of unmixed eight-color labeled human embryonic kidney. DAPI (not shown), KI67 (aqua), PAX8 (yellow), NCAM1 (magenta), SIX2 (green), CDH1 (red), CDH6 blue) and F-ACTIN (gray). Scale bar, 50 μm. b, Magnification of white inset of a depicting optical sections of combinations of overlay channels for visual inspection. DAPI (gray), F-ACTIN (signal intensity gradient: red-yellow-white) (left), CDH1 (blue), SIX2 (green), NCAM1 (red) (middle), CDH6 (blue), KI67 (green), PAX8 (red) (right). Scale bars, 50 μm. c, Single-channel optical sections of the white inset in a for visual inspection. Scale bars, 50 μm.

Extended Data Fig. 4 Two-photon six-color spectral imaging.

a, 3D rendering of a human pediatric Wilms tumor sample labeled with DAPI (not shown), KI67 (cyan), NCAM1 (blue), SIX2 (green), CDH1 (red) and F-ACTIN (signal intensity gradient: black-red-orange-yellow). b, Wilms tumor dataset labeled with DAPI (not shown), KI67 (cyan), NCAM1 (blue), SIX2 (green), CDH1 (red) and F-ACTIN (gray). Scale bar, 100 μm. c, Single-channel optical sections of the same Wilms tumor dataset. Scale bars, 100 μm.

Supplementary information

Supplementary Information

Supplementary Methods and Supplementary Tables 1 and 2.

Supplementary Movie 1

Summary movie of the mLSR-3D protocol. 0:40–0:50, Optical section z-stack fly-through animation of human embryonic kidney labeled with DAPI (not shown), KI67 (cyan), PAX8 (yellow), NCAM1 (blue), SIX2 (green), CDH1 (red), CDH6 (orange) and F ACTIN (black-red-yellow-white). This movie has been modified from ref. 26. 1:00–1:20, Animation depicting the 3D rendering of breast cancer organoids from Fig. 3d labeled with DAPI (gray), KI67 (green), B-CAT (yellow), CDH1 (red) and K8/18 (blue). 0:00–0:25, Animation depicting the 3D rendering of low-grade glioma from Fig. 6b labeled with DAPI (gray), F ACTIN (black-red-yellow-white), OLIG2 (magenta), TUBB3 (yellow), KI67 (green), GFAP (red), IBA1 (blue) and MAP2 (cyan).

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

van Ineveld, R.L., Collot, R., Román, M.B. et al. Multispectral confocal 3D imaging of intact healthy and tumor tissue using mLSR-3D. Nat Protoc 17, 3028–3055 (2022). https://doi.org/10.1038/s41596-022-00739-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-022-00739-x

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer