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An end-to-end workflow for nondestructive 3D pathology

Nature Protocols volume 19, pages 1122–1148 (2024)Cite this article

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Abstract

Recent advances in 3D pathology offer the ability to image orders of magnitude more tissue than conventional pathology methods while also providing a volumetric context that is not achievable with 2D tissue sections, and all without requiring destructive tissue sectioning. Generating high-quality 3D pathology datasets on a consistent basis, however, is not trivial and requires careful attention to a series of details during tissue preparation, imaging and initial data processing, as well as iterative optimization of the entire process. Here, we provide an end-to-end procedure covering all aspects of a 3D pathology workflow (using light-sheet microscopy as an illustrative imaging platform) with sufficient detail to perform well-controlled preclinical and clinical studies. Although 3D pathology is compatible with diverse staining protocols and computationally generated color palettes for visual analysis, this protocol focuses on the use of a fluorescent analog of hematoxylin and eosin, which remains the most common stain used for gold-standard pathological reports. We present our guidelines for a broad range of end users (e.g., biologists, clinical researchers and engineers) in a simple format. The end-to-end workflow requires 3–6 d to complete, bearing in mind that data analysis may take longer.

Key points

  • The protocol details tissue preparation and staining using TO-PRO-3, a fluorescent analog of hematoxylin, and eosin. Tissue imaging using light-sheet fluorescence microscopy is described, including strategies for quality control in tissue preparation and 3D microscopy.

  • A fully open-source workflow requiring basic programming skills in Python is explained for initial 3D data processing, such as stitching, intensity leveling and digital staining to mimic the appearance of standard H&E histology.

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Fig. 1: Protocol overview.
Fig. 2: Image atlas showing Path3D datasets of archived human pathology specimens.
Fig. 3: Photos showing key tissue-processing steps.
Fig. 4: Example bead phantom and point spread function data.
Fig. 5: Examples of high- and low-quality tissue datasets.

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Data availability

Because of the large size of the imaging datasets shown in this paper, the datasets are not available in a public repository. They are available from the authors upon request.

Code availability

All code used in this protocol is provided on GitHub. For license information, please see the corresponding GitHub page. PSF computation: https://github.com/LiuBiophotonicsLab/Bead_PSF_computation; FalseColor-Python: https://github.com/serrob23/falsecolor; normalizing signal levels: https://github.com/LiuBiophotonicsLab/FixImage3D.

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Acknowledgements

This work was supported by funding from the National Institutes of Health (NIH): R01EB031002 and R01CA268207 (J.T.C.L.); R00CA240681 (A.K.G.); U01CA239055, 1R01LM013864, 1U01DK133090, U01CA248226 and 2R01DK118431 (A.J.); the Pacific Northwest Prostate Cancer SPORE P50CA097186 (L.D.T.); and the Indiana Center for Advanced Renal Microscopy and Molecular Imaging U54DK137328 (J.C.V. and J.T.C.L.); the National Science Foundation (NSF): 1934292 HDR: I-DIRSE-FW (J.T.C.L.) and NSF Graduate Research Fellowships DGE-1762114 (K.W.B. and L.A.E.B.); the Department of Defense (DoD) Prostate Cancer Research Program: W81XWH-20-1-0851 (J.T.C.L.) and W81XWH-18-10358 (L.D.T. and J.T.C.L.); Prostate Cancer UK: MA-ETNA19-005 (J.T.C.L.); and a Washington Research Foundation Postdoctoral Fellowship (C.P.). The authors also thank the following individuals for assistance with specimen collection and preparation for data presented in this manuscript: Suzanne M. Dintzis, Michael C. Haffner, Priti Lal, Chenyi Mao, Michelle M. Martinez Irizarry, Nick P. Reder, Deepti M. Reddi, Ruben M. Sandoval, Etsuo A. Susaki and Inti Zlobec. Finally, the authors acknowledge the Canary Foundation and the University of Washington Center for Laboratory Animal Training Resources for providing some specimens used in this study.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, University of Washington, Seattle, WA, USA

    Kevin W. Bishop, Lindsey A. Erion Barner, Qinghua Han, Elena Baraznenok, Lydia Lan, Gan Gao, Robert B. Serafin, Sarah S. L. Chow, David Brenes, Hongyi Huang, Dominie Miyasato, Soyoung Kang & Jonathan T. C. Liu

  2. Department of Bioengineering, University of Washington, Seattle, WA, USA

    Kevin W. Bishop, Qinghua Han, Elena Baraznenok & Jonathan T. C. Liu

  3. Department of Biology, University of Washington, Seattle, WA, USA

    Lydia Lan

  4. Department of Chemistry, University of Washington, Seattle, WA, USA

    Chetan Poudel & Joshua C. Vaughan

  5. Allen Institute for Neural Dynamics, Seattle, WA, USA

    Adam K. Glaser

  6. Department of Biomedical Engineering, Emory University, Atlanta, GA, USA

    Andrew Janowczyk

  7. Department of Oncology, Division of Precision Oncology, University Hospital of Geneva, Geneva, Switzerland

    Andrew Janowczyk

  8. Department of Diagnostics, Division of Clinical Pathology, University Hospital of Geneva, Geneva, Switzerland

    Andrew Janowczyk

  9. Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA

    Lawrence D. True & Jonathan T. C. Liu

  10. Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA

    Joshua C. Vaughan

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  1. Kevin W. Bishop
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Contributions

L.A.E.B., Q.H., E.B., L.L., C.P., G.G., A.K.G., H.H., L.D.T., S.K., J.C.V. and J.T.C.L. developed the tissue preparation methods. K.W.B., L.A.E.B, A.K.G. and J.T.C.L. developed the 3D microscopy methods. Q.H., E.B., G.G., R.B.S., S.S.L.C., A.K.G., A.J. and J.T.C.L. developed the data-processing methods. K.W.B., L.A.E.B., E.B., L.L., G.G., R.B.S., L.D.T., S.K. and J.T.C.L. developed the quality control methods. K.W.B., L.A.E.B, Q.H., E.B., L.L., C.P., G.G., R.B.S., A.K.G., H.H. and D.M. performed experiments and analyzed data. K.W.B., L.A.E.B. and J.T.C.L. led writing of the manuscript with assistance from D.B. All authors contributed to the manuscript.

Corresponding author

Correspondence to Jonathan T. C. Liu.

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Competing interests

J.T.C.L. is a cofounder, equity holder and board member of Alpenglow Biosciences, Inc. A.K.G. and L.D.T. are cofounders and equity holders of Alpenglow Biosciences, Inc. A.J. provides consulting for Merck, Lunaphore and Roche and has a sponsored research agreement with Roche. The remaining authors declare no competing interests.

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Nature Protocols thanks Michael W. Jenkins and Terence Tsz Wai Wong for their contribution to the peer review of this work.

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Key references using this protocol

Erion Barner, L. A. et al. Mod. Pathol. 36, 100322 (2023): https://doi.org/10.1016/j.modpat.2023.100322

Serafin, R. et al. J. Pathol. 260, 390–401 (2023): https://doi.org/10.1002/path.6090

Glaser, A. K. et al. Nat. Methods 19, 613–619 (2022): https://doi.org/10.1038/s41592-022-01468-5

Xie, W. et al. Cancer Res. 82, 334–345 (2022): https://doi.org/10.1158/0008-5472.CAN-21-2843

Serafin, R. et al. PLoS One 15, e0233198 (2020): https://doi.org/10.1371/journal.pone.0233198

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Supplementary Notes 1–3 and Figs. 1–10

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Supplementary Video

Video abstract, including volumetric renderings and 2D cross sections of Path3D datasets false-colored to mimic the appearance of standard H&E staining (human prostate biopsy and Barrett’s esophagus)

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Bishop, K.W., Erion Barner, L.A., Han, Q. et al. An end-to-end workflow for nondestructive 3D pathology. Nat Protoc 19, 1122–1148 (2024). https://doi.org/10.1038/s41596-023-00934-4

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