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.

  • Review Article
  • Published:

Tutorial: methods for three-dimensional visualization of archival tissue material

Nature Protocols volume 16, pages 4945–4962 (2021)Cite this article

Subjects

Abstract

Analysis of three-dimensional patient specimens is gaining increasing relevance for understanding the principles of tissue structure as well as the biology and mechanisms underlying disease. New technologies are improving our ability to visualize large volume of tissues with subcellular resolution. One resource often overlooked is archival tissue maintained for decades in hospitals and research archives around the world. Accessing the wealth of information stored within these samples requires the use of appropriate methods. This tutorial introduces the range of sample preparation and microscopy approaches available for three-dimensional visualization of archival tissue. We summarize key aspects of the relevant techniques and common issues encountered when using archival tissue, including registration and antibody penetration. We also discuss analysis pipelines required to process, visualize and analyze the data and criteria to guide decision-making. The methods outlined in this tutorial provide an important and sustainable avenue for validating three-dimensional tissue organization and mechanisms of disease.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Serial 3D visualization techniques for archival tissue and their key features.
Fig. 2: Three-dimensional histology.
Fig. 3: Three-dimensional colorectal carcinoma.
Fig. 4: Nonserial 3D visualization techniques for archival tissue and their key features.
Fig. 5: Identification of glioma cell networks and collective invasion in patient samples.
Fig. 6: Generalized analysis pipelines for 3D visualization techniques of archival tissue.
Fig. 7: Resolution and visualization depth of 3D visualization approaches.

Similar content being viewed by others

Data availability

The KESM data shown in Fig. 3 and Supplementary Video 2 have been deposited in the Figshare repository (https://doi.org/10.6084/m9.figshare.14822508) (ref. 160).

References

  1. Gomez-Gaviro, M. V., Sanderson, D., Ripoll, J. & Desco, M. Biomedical applications of tissue clearing and three-dimensional imaging in health and disease. iScience 23, 101432 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Tian, T., Yang, Z. & Li, X. Tissue clearing technique: recent progress and biomedical applications. J. Anat. 238, 489–507 (2021).

    Article  PubMed  Google Scholar 

  3. Tomer, R., Khairy, K. & Keller, P. J. Light sheet microscopy in cell biology. Methods Mol. Biol. 931, 123–137 (2013).

    Article  CAS  PubMed  Google Scholar 

  4. Dodt, H. U. et al. Ultramicroscopy: three-dimensional visualization of neuronal networks in the whole mouse brain. Nat. Methods 4, 331–336 (2007).

    Article  CAS  PubMed  Google Scholar 

  5. 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 

  6. Gritsenko, P. G. et al. p120-catenin-dependent collective brain infiltration by glioma cell networks. Nat. Cell Biol. 22, 97–107 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Ilina, O. et al. Cell–cell adhesion and 3D matrix confinement determine jamming transitions in breast cancer invasion. Nat. Cell Biol. 22, 1103–1115 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Huang, B., Wang, W., Bates, M. & Zhuang, X. Three-dimensional super-resolution imaging by stochastic optical reconstruction microscopy. Science 319, 810–813 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Huang, B., Jones, S. A., Brandenburg, B. & Zhuang, X. Whole-cell 3D STORM reveals interactions between cellular structures with nanometer-scale resolution. Nat. Methods 5, 1047–1052 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Talbot, M. J. & White, R. G. Cell surface and cell outline imaging in plant tissues using the backscattered electron detector in a variable pressure scanning electron microscope. Plant Methods 9, 40 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Pan, C. et al. Shrinkage-mediated imaging of entire organs and organisms using uDISCO. Nat. Methods 13, 859–867 (2016).

    Article  CAS  PubMed  Google Scholar 

  12. Matsumoto, K. et al. Advanced CUBIC tissue clearing for whole-organ cell profiling. Nat. Protoc. 14, 3506–3537 (2019).

    Article  CAS  PubMed  Google Scholar 

  13. Chung, K. et al. Structural and molecular interrogation of intact biological systems. Nature 497, 332–337 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. 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 

  15. Puelles, V. G. et al. Novel 3D analysis using optical tissue clearing documents the evolution of murine rapidly progressive glomerulonephritis. Kidney Int. 96, 505–516 (2019).

    Article  PubMed  Google Scholar 

  16. Rios, A. C. et al. Intraclonal plasticity in mammary tumors revealed through large-scale single-cell resolution 3D imaging. Cancer Cell 35, 618–632 e616 (2019).

    Article  CAS  PubMed  Google Scholar 

  17. Baker, M. Biorepositories: building better biobanks. Nature 486, 141–146 (2012).

    Article  CAS  PubMed  Google Scholar 

  18. 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 

  19. Cacciatore, S. et al. Metabolic profiling in formalin-fixed and paraffin-embedded prostate cancer tissues. Mol. Cancer Res. 15, 439–447 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Cabrita, R. et al. Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature 577, 561–565 (2020).

    Article  CAS  PubMed  Google Scholar 

  21. Gao, X. H. et al. Comparison of fresh frozen tissue with formalin-fixed paraffin-embedded tissue for mutation analysis using a multi-gene panel in patients with colorectal cancer. Front. Oncol. 10, 310 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Saalfeld, S., Fetter, R., Cardona, A. & Tomancak, P. Elastic volume reconstruction from series of ultra-thin microscopy sections. Nat. Methods 9, 717–720 (2012).

    Article  CAS  PubMed  Google Scholar 

  23. Roberts, N. et al. Toward routine use of 3D histopathology as a research tool. Am. J. Pathol. 180, 1835–1842 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Booth, M. E. et al. Three-dimensional reconstruction of ductal carcinoma in situ with virtual slides. Histopathology 66, 966–973 (2015).

    Article  PubMed  Google Scholar 

  25. Tolkach, Y., Thomann, S. & Kristiansen, G. Three-dimensional reconstruction of prostate cancer architecture with serial immunohistochemical sections: hallmarks of tumour growth, tumour compartmentalisation, and implications for grading and heterogeneity. Histopathology 72, 1051–1059 (2018).

    Article  PubMed  Google Scholar 

  26. Korehisa, S. et al. A novel histological examination with dynamic three-dimensional reconstruction from multiple immunohistochemically stained sections of a PD-L1-positive colon cancer. Histopathology 72, 697–703 (2018).

    Article  PubMed  Google Scholar 

  27. Sy, J. & Ang, L. C. Microtomy: cutting formalin-fixed, paraffin-embedded sections. Methods Mol. Biol. 1897, 269–278 (2019).

    Article  CAS  PubMed  Google Scholar 

  28. Xu, B. et al. Detection and assessment of capsular invasion, vascular invasion and lymph node metastasis volume in thyroid carcinoma using microCT scanning of paraffin tissue blocks (3D whole block imaging): a proof of concept. Mod. Pathol. 33, 2449–2457 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Yagi, Y. et al. Three-dimensional histologic, immunohistochemical, and multiplex immunofluorescence analyses of dynamic vessel co-option of spread through air spaces in lung adenocarcinoma. J. Thorac. Oncol. 15, 589–600 (2020).

    Article  CAS  PubMed  Google Scholar 

  30. Song, Y., Treanor, D., Bulpitt, A. J. & Magee, D. R. 3D reconstruction of multiple stained histology images. J. Pathol. Inform. 4, S7 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Magee, D. et al. Histopathology in 3D: from three-dimensional reconstruction to multi-stain and multi-modal analysis. J. Pathol. Inform. 6, 6 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Adler, D. H. et al. Histology-derived volumetric annotation of the human hippocampal subfields in postmortem MRI. Neuroimage 84, 505–523 (2014).

    Article  PubMed  Google Scholar 

  33. Gavgiotaki, E. et al. Third Harmonic Generation microscopy distinguishes malignant cell grade in human breast tissue biopsies. Sci. Rep. 10, 11055 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Rivenson, Y. et al. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning. Nat. Biomed. Eng. 3, 466–477 (2019).

    Article  CAS  PubMed  Google Scholar 

  35. van den Brand, M. et al. Sequential immunohistochemistry: a promising new tool for the pathology laboratory. Histopathology 65, 651–657 (2014).

    Article  PubMed  Google Scholar 

  36. Wentzensen, N. et al. Combined serial section-based 3D reconstruction of cervical carcinoma invasion using H&E/p16INK4a/CD3 alternate staining. Cytom. A 71, 327–333 (2007).

    Article  CAS  Google Scholar 

  37. Jansen, I. et al. Three-dimensional histopathological reconstruction of bladder tumours. Diagn. Pathol. 14, 25 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Vasaturo, A. & Galon, J. Multiplexed immunohistochemistry for immune cell phenotyping, quantification and spatial distribution in situ. Methods Enzymol. 635, 51–66 (2020).

    Article  CAS  PubMed  Google Scholar 

  39. Viratham Pulsawatdi, A. et al. A robust multiplex immunofluorescence and digital pathology workflow for the characterisation of the tumour immune microenvironment. Mol. Oncol. 14, 2384–2402 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Hong, S. M. et al. Three-dimensional visualization of cleared human pancreas cancer reveals that sustained epithelial-to-mesenchymal transition is not required for venous invasion. Mod. Pathol. 33, 639–647 (2019).

    Article  PubMed  CAS  Google Scholar 

  41. Li, A. et al. Micro-optical sectioning tomography to obtain a high-resolution atlas of the mouse brain. Science 330, 1404–1408 (2010).

    Article  CAS  PubMed  Google Scholar 

  42. Ragan, T. et al. Serial two-photon tomography for automated ex vivo mouse brain imaging. Nat. Methods 9, 255–258 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Seiriki, K. et al. Whole-brain block-face serial microscopy tomography at subcellular resolution using FAST. Nat. Protoc. 14, 1509–1529 (2019).

    Article  CAS  PubMed  Google Scholar 

  44. Mayerich, D., Abbott, L. & McCormick, B. Knife-edge scanning microscopy for imaging and reconstruction of three-dimensional anatomical structures of the mouse brain. J. Microsc. 231, 134–143 (2008).

    Article  CAS  PubMed  Google Scholar 

  45. Achanta, S. et al. A comprehensive integrated anatomical and molecular atlas of rat intrinsic cardiac nervous system. iScience 23, 101140 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Weiss, S. Shattering the diffraction limit of light: a revolution in fluorescence microscopy? Proc. Natl Acad. Sci. USA 97, 8747–8749 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Jones, C. G. Scanning electron microscopy: preparation and imaging for SEM. Methods Mol. Biol. 915, 1–20 (2012).

    Article  PubMed  Google Scholar 

  48. Rust, M. J., Bates, M. & Zhuang, X. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat. Methods 3, 793–795 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Miyazaki, H. et al. Application of low-vacuum scanning electron microscopy for renal biopsy specimens. Pathol. Res. Pract. 208, 503–509 (2012).

    Article  PubMed  Google Scholar 

  50. Inaga, S. et al. Low vacuum scanning electron microscopy for paraffin sections utilizing the differential stainability of cells and tissues with platinum blue. Arch. Histol. Cytol. 72, 101–106 (2009).

    Article  PubMed  Google Scholar 

  51. Inaga, S. et al. Rapid three-dimensional analysis of renal biopsy sections by low vacuum scanning electron microscopy. Arch. Histol. Cytol. 73, 113–125 (2010).

    Article  PubMed  Google Scholar 

  52. Sawaguchi, A. et al. Informative three-dimensional survey of cell/tissue architectures in thick paraffin sections by simple low-vacuum scanning electron microscopy. Sci. Rep. 8, 7479 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Bates, M., Huang, B., Dempsey, G. T. & Zhuang, X. Multicolor super-resolution imaging with photo-switchable fluorescent probes. Science 317, 1749–1753 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Creech, M. K., Wang, J., Nan, X. & Gibbs, S. L. Superresolution imaging of clinical formalin fixed paraffin embedded breast cancer with single molecule localization microscopy. Sci. Rep. 7, 40766 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Henriques, R. et al. QuickPALM: 3D real-time photoactivation nanoscopy image processing in ImageJ. Nat. Methods 7, 339–340 (2010).

    Article  CAS  PubMed  Google Scholar 

  56. Ouyang, W., Aristov, A., Lelek, M., Hao, X. & Zimmer, C. Deep learning massively accelerates super-resolution localization microscopy. Nat. Biotechnol. 36, 460–468 (2018).

    Article  CAS  PubMed  Google Scholar 

  57. Xu, J., Ma, H. & Liu, Y. Stochastic optical reconstruction microscopy (STORM). Curr. Protoc. Cytom. 81, 1–27 (2017).

    CAS  Google Scholar 

  58. Andresen, V. et al. Infrared multiphoton microscopy: subcellular-resolved deep tissue imaging. Curr. Opin. Biotechnol. 20, 54–62 (2009).

    Article  CAS  PubMed  Google Scholar 

  59. Spalteholz, W. Über das Durchsichtigmachen von menschlichen und tierischen Präparaten und seine theoretischen Bedingungen, 2nd edn (n.p.; 1914).

  60. 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 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. 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 

  63. 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 

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

    Article  CAS  PubMed  Google Scholar 

  65. Becker, K., Jährling, N., Saghafi, S., Weiler, R. & Dodt, H.-U. Chemical clearing and dehydration of GFP expressing mouse brains. PLoS One 7, e33916 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Hama, H. et al. Scale: a chemical approach for fluorescence imaging and reconstruction of transparent mouse brain. Nat. Neurosci. 14, 1481–1488 (2011).

    Article  CAS  PubMed  Google Scholar 

  67. Ke, M.-T., Fujimoto, S. & Imai, T. SeeDB: a simple and morphology-preserving optical clearing agent for neuronal circuit reconstruction. Nat. Neurosci. 16, 1154–1161 (2013).

    Article  CAS  PubMed  Google Scholar 

  68. 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 

  69. 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 

  70. Ueda, H. R. et al. Tissue clearing and its applications in neuroscience. Nat. Rev. Neurosci. 21, 61–79 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Ueda, H. R. et al. Whole-brain profiling of cells and circuits in mammals by tissue clearing and light-sheet microscopy. Neuron 106, 369–387 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Gradinaru, V., Treweek, J., Overton, K. & Deisseroth, K. Hydrogel-tissue chemistry: principles and applications. Annu. Rev. Biophys. 20, 355–376 (2018).

    Article  CAS  Google Scholar 

  73. van Royen, M. E. et al. Three-dimensional microscopic analysis of clinical prostate specimens. Histopathology 69, 985–992 (2016).

    Article  PubMed  Google Scholar 

  74. Noe, M. et al. Immunolabeling of cleared human pancreata provides insights into three-dimensional pancreatic anatomy and pathology. Am. J. Pathol. 188, 1530–1535 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Tanaka, N. et al. Whole-tissue biopsy phenotyping of three-dimensional tumours reveals patterns of cancer heterogeneity. Nat. Biomed. Eng. 1, 796–806 (2017).

    Article  CAS  PubMed  Google Scholar 

  76. Nojima, S. et al. CUBIC pathology: three-dimensional imaging for pathological diagnosis. Sci. Rep. 7, 9269 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  77. Lai, H. M. et al. Next generation histology methods for three-dimensional imaging of fresh and archival human brain tissues. Nat. Commun. 9, 1066 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. Chen, Y. et al. Three-dimensional imaging and quantitative analysis in CLARITY processed breast cancer tissues. Sci. Rep. 9, 5624 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Verhoef, E. I. et al. Three-dimensional analysis reveals two major architectural subgroups of prostate cancer growth patterns. Mod. Pathol. 32, 1032–1041 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Tanaka, N. et al. Mapping of the three-dimensional lymphatic microvasculature in bladder tumours using light-sheet microscopy. Br. J. Cancer 118, 995–999 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Yoshizawa, T. et al. Three-dimensional analysis of extrahepatic cholangiocarcinoma and tumor budding. J. Pathol. 251, 400–410 (2020).

    Article  CAS  PubMed  Google Scholar 

  82. 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 

  83. Renier, N. et al. Mapping of brain activity by automated volume analysis of immediate early genes. Cell 165, 1789–1802 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Lee, E. et al. ACT-PRESTO: rapid and consistent tissue clearing and labeling method for 3-dimensional (3D) imaging. Sci. Rep. 6, 18631 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Tainaka, K. et al. Chemical landscape for tissue clearing based on hydrophilic reagents. Cell Rep. 24, 2196–2210 e2199 (2018).

    Article  CAS  PubMed  Google Scholar 

  86. Uhlen, P. & Tanaka, N. Improved pathological examination of tumors with 3D light-sheet microscopy. Trends Cancer 4, 337–341 (2018).

    Article  PubMed  Google Scholar 

  87. Tomer, R. et al. SPED light sheet microscopy: fast mapping of biological system structure and function. Cell 163, 1796–1806 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Miltenyi BioTec. UltraMicroscope Blaze. Miltenyi BioTec https://www.miltenyibiotec.com/JP-en/products/ultramicroscope-blaze.html (2021).

  89. Chen, B. C. et al. Lattice light-sheet microscopy: imaging molecules to embryos at high spatiotemporal resolution. Science 346, 1257998 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  90. Chakraborty, T. et al. Light-sheet microscopy of cleared tissues with isotropic, subcellular resolution. Nat. Methods 16, 1109–1113 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Lu, C. H. et al. Lightsheet localization microscopy enables fast, large-scale, and three-dimensional super-resolution imaging. Commun. Biol. 2, 177 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  92. Glaser, A. K. et al. Light-sheet microscopy for slide-free non-destructive pathology of large clinical specimens. Nat. Biomed. Eng. 1, 0084 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  93. Glaser, A. K. et al. Multi-immersion open-top light-sheet microscope for high-throughput imaging of cleared tissues. Nat. Commun. 10, 2781 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  94. Royer, L. A. et al. Adaptive light-sheet microscopy for long-term, high-resolution imaging in living organisms. Nat. Biotechnol. 34, 1267–1278 (2016).

    Article  CAS  PubMed  Google Scholar 

  95. McDole, K. et al. In toto imaging and reconstruction of post-implantation mouse development at the single-cell level. Cell 175, 859–876 e833 (2018).

    Article  CAS  PubMed  Google Scholar 

  96. Verhoef, E. I. et al. Three-dimensional architecture of common benign and precancerous prostate epithelial lesions. Histopathology 74, 1036–1044 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  97. Korobchevskaya, K., Lagerholm, B., Colin-York, H. & Fritzsche, M. Exploring the potential of airyscan microscopy for live cell imaging. Photonics 4, 41 (2017).

    Article  CAS  Google Scholar 

  98. Wang, H. et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nat. Methods 16, 103–110 (2019).

    Article  CAS  PubMed  Google Scholar 

  99. Jonkman, J., Brown, C. M., Wright, G. D., Anderson, K. I. & North, A. J. Tutorial: guidance for quantitative confocal microscopy. Nat. Protoc. 15, 1585–1611 (2020).

    Article  CAS  PubMed  Google Scholar 

  100. 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 

  101. Weigelin, B., Bakker, G.-J. & Friedl, P. Third harmonic generation microscopy of cells and tissue organization. J. Cell Sci. 129, 245–255 (2016).

    CAS  PubMed  Google Scholar 

  102. Kalendar, W.A. Computed Tomography: Fundamentals, System Technology, Image Quality, Applications (John Wiley & Sons, 2011).

  103. Katsamenis, O. L. et al. X-ray micro-computed tomography for nondestructive three-dimensional (3D) X-ray histology. Am. J. Pathol. 189, 1608–1620 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  104. Virta, J. et al. X-ray microtomography is a novel method for accurate evaluation of small-bowel mucosal morphology and surface area. Sci. Rep. 10, 13164 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Ortega-Gil, A., Vaquero, J. J., Gonzalez-Arjona, M., Rullas, J. & Munoz-Barrutia, A. X-ray-based virtual slicing of TB-infected lungs. Sci. Rep. 9, 19404 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Robinson, S. K., Ramsden, J. J., Warner, J., Lackie, P. M. & Roose, T. Correlative 3D imaging and microfluidic modelling of human pulmonary lymphatics using immunohistochemistry and high-resolution muCT. Sci. Rep. 9, 6415 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  107. Andreev, A. & Koo, D. E. S. Practical guide to storage of large amounts of microscopy data. Microsc. Today 28, 42–45 (2020).

    Article  Google Scholar 

  108. Amat, F. et al. Efficient processing and analysis of large-scale light-sheet microscopy data. Nat. Protoc. 10, 1679–1696 (2015).

    Article  CAS  PubMed  Google Scholar 

  109. Balázs, B., Deschamps, J., Albert, M., Ries, J. & Hufnagel, L. A real-time compression library for microscopy images. Preprint at bioRxiv https://doi.org/10.1101/164624 (2017).

  110. Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  111. Ellenberg, J. et al. A call for public archives for biological image data. Nat. Methods 15, 849–854 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Allan, C. et al. OMERO: flexible, model-driven data management for experimental biology. Nat. Methods 9, 245–253 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  PubMed  Google Scholar 

  114. Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Rueden, C. T. et al. ImageJ2: ImageJ for the next generation of scientific image data. BMC Bioinformatics 18, 529 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  116. Arena, E. T. et al. Quantitating the cell: turning images into numbers with ImageJ. Wiley Interdiscip. Rev. Dev. Biol. https://doi.org/10.1002/wdev.260 (2017).

  117. Kvilekval, K., Fedorov, D., Obara, B., Singh, A. & Manjunath, B. S. Bisque: a platform for bioimage analysis and management. Bioinformatics 26, 544–552 (2010).

    Article  CAS  PubMed  Google Scholar 

  118. Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  119. McQuin, C. et al. CellProfiler 3.0: next-generation image processing for biology. PloS Biol. 16, e2005970 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  120. Sage, D. et al. Super-resolution fight club: assessment of 2D and 3D single-molecule localization microscopy software. Nat. Methods 16, 387–395 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Horl, D. et al. BigStitcher: reconstructing high-resolution image datasets of cleared and expanded samples. Nat. Methods 16, 870–874 (2019).

    Article  PubMed  CAS  Google Scholar 

  122. Bria, A. & Iannello, G. TeraStitcher—a tool for fast automatic 3D-stitching of teravoxel-sized microscopy images. BMC Bioinformatics 13, 316 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  123. Pichat, J., Iglesias, J. E., Yousry, T., Ourselin, S. & Modat, M. A survey of methods for 3D histology reconstruction. Med. Image Anal. 46, 73–105 (2018).

    Article  PubMed  Google Scholar 

  124. Kartasalo, K. et al. Comparative analysis of tissue reconstruction algorithms for 3D histology. Bioinformatics 34, 3013–3021 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Swaney, J. et al. Scalable image processing technius for quantitative analysis of volumetric biological images from light-sheet microscopy. Preprint at bioRxiv https://doi.org/10.1101/576595 (2019).

  126. Reinhard, E., Mashikhmin, M., Gooch, B. & Shirley, P. Color transfer between images. IEEE Comput. Graph. Appl. 21, 34–41 (2001).

    Article  Google Scholar 

  127. Bautista, P. A., Hashimoto, N. & Yagi, Y. Color standardization in whole slide imaging using a color calibration slide. J. Pathol. Inform. 5, 4 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  128. Bautista, P. A. & Yagi, Y. Staining correction in digital pathology by utilizing a dye amount table. J. Digit. Imaging 28, 283–294 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  129. Preibisch, S. et al. Efficient Bayesian-based multiview deconvolution. Nat. Methods 11, 645–648 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Becker, K. et al. Deconvolution of light sheet microscopy recordings. Sci. Rep. 9, 17625 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  131. Khan, A. M., Rajpoot, N., Treanor, D. & Magee, D. A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution. IEEE Trans. Biomed. Eng. 61, 1729–1738 (2014).

    Article  PubMed  Google Scholar 

  132. Vahadane, A. et al. Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans. Med. Imaging 35, 1962–1971 (2016).

    Article  PubMed  Google Scholar 

  133. Bejnordi, B. E. et al. Stain specific standardization of whole-slide histopathological images. IEEE Trans. Med. Imaging 35, 404–415 (2016).

    Article  PubMed  Google Scholar 

  134. Belthangady, C. & Royer, L. A. Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction. Nat. Methods 16, 1215–1225 (2019).

    Article  CAS  PubMed  Google Scholar 

  135. Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  136. Borland, D. et al. Segmentor: a tool for manual refinement of 3D microscopy annotations. BMC Bioinformatics 22, 260 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  137. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    Article  CAS  PubMed  Google Scholar 

  138. Falk, T. et al. U-Net: deep learning for cell counting, detection, and morphometry. Nat. Methods 16, 67–70 (2019).

    Article  CAS  PubMed  Google Scholar 

  139. Arganda-Carreras, I. et al. Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics 33, 2424–2426 (2017).

    Article  CAS  PubMed  Google Scholar 

  140. Berg, S. et al. ilastik: interactive machine learning for (bio)image analysis. Nat. Methods 16, 1226–1232 (2019).

    Article  CAS  PubMed  Google Scholar 

  141. Haberl, M. G. et al. CDeep3M-Plug-and-Play cloud-based deep learning for image segmentation. Nat. Methods 15, 677–680 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. Bannon, D. et al. DeepCell Kiosk: scaling deep learning-enabled cellular image analysis with Kubernetes. Nat. Methods 18, 43–45 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Kim, T., Moon, S. & Xu, K. Information-rich localization microscopy through machine learning. Nat. Commun. 10, 1996 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  144. Speiser, A., Turaga, S. C. & Macke, J. H. Teaching deep neural networks to localize sources in super-resolution microscopy by combining simulation-based learning and unsupervised learning. Preprint at https://arxiv.org/abs/1907.00770 (2019).

  145. van der Laak, J., Litjens, G. & Ciompi, F. Deep learning in histopathology: the path to the clinic. Nat. Med. 27, 775–784 (2021).

    Article  PubMed  CAS  Google Scholar 

  146. Yu, K. H., Beam, A. L. & Kohane, I. S. Artificial intelligence in healthcare. Nat. Biomed. Eng. 2, 719–731 (2018).

    Article  PubMed  Google Scholar 

  147. Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V. & Madabhushi, A. Artificial intelligence in digital pathology—new tools for diagnosis and precision oncology. Nat. Rev. Clin. Oncol. 16, 703–715 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  148. Liu, J. T. C. et al. Harnessing non-destructive 3D pathology. Nat. Biomed. Eng. 5, 203–218 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  149. Janowczyk, A., Zuo, R., Gilmore, H., Feldman, M. & Madabhushi, A. HistoQC: an open-source quality control tool for digital pathology slides. JCO Clin. Cancer Inform. 3, 1–7 (2019).

    Article  PubMed  Google Scholar 

  150. Pietzsch, T., Saalfeld, S., Preibisch, S. & Tomancak, P. BigDataViewer: visualization and processing for large image data sets. Nat. Methods 12, 481–483 (2015).

    Article  CAS  PubMed  Google Scholar 

  151. Yushkevich, P. A. & Gerig, G. ITK-SNAP: an intractive medical image segmentation tool to meet the need for expert-guided segmentation of complex medical images. IEEE Pulse 8, 54–57 (2017).

    Article  PubMed  Google Scholar 

  152. de Chaumont, F., Dallongeville, S. & Olivo-Marin, J.-C. ICY: a new open-source community image processing software. IEEE International Symposium on Biomedical Imaging https://doi.org/10.1109/ISBI.2011.5872395 (2011).

  153. Peng, H., Ruan, Z., Long, F., Simpson, J. H. & Myers, E. W. V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets. Nat. Biotechnol. 28, 348–353 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  154. Bria, A., Iannello, G. & Peng, H. An open-source VAA3D plugin for real-time 3D visualization of terabyte-sized volumetric images. IEEE International Symposium on Biomedical Imaging https://doi.org/10.1109/ISBI.2015.7163925 (2015).

  155. Bria, A., Iannello, G., Onofri, L. & Peng, H. TeraFly: real-time three-dimensional visualization and annotation of terabytes of multidimensional volumetric images. Nat. Methods 13, 192–194 (2016).

    Article  CAS  PubMed  Google Scholar 

  156. Peng, H., Bria, A., Zhou, Z., Iannello, G. & Long, F. Extensible visualization and analysis for multidimensional images using Vaa3D. Nat. Protoc. 9, 193–208 (2014).

    Article  CAS  PubMed  Google Scholar 

  157. Lytle, N. K., Barber, A. G. & Reya, T. Stem cell fate in cancer growth, progression and therapy resistance. Nat. Rev. Cancer 18, 669–680 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  158. Kingston, B. R., Syed, A. M., Ngai, J., Sindhwani, S. & Chan, W. C. W. Assessing micrometastases as a target for nanoparticles using 3D microscopy and machine learning. Proc. Natl Acad. Sci. USA 116, 14937–14946 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  159. Rohé, M.-M., Datar, M., Heimann, T., Sermesant, M. & Pennec, X. SVF-Net: learning deformable image registration using shape matching. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2017, 266–274 (Springer, 2017).

  160. Haddad, T.S. et al. Tutorial: methods for three-dimensional visualization of archival tissue material. Figshare. https://doi.org/10.6084/m9.figshare.14822508 (2021).

  161. Falk, M., Ynnerman, A., Treanor, D. & Lundstrom, C. Interactive visualization of 3D histopathology in native resolution. IEEE Trans. Vis. Comput. Graph. https://doi.org/10.1109/TVCG.2018.2864816 (2018).

Download references

Acknowledgements

This work received funding from the Dutch Cancer Society, project number 10602/2016-2. P.F. was supported by the European Research Council (617430-DEEPINSIGHT), NIH-U54 CA210184-01, and the Cancer Genomics Center (CGC.nl). The authors thank D. Jutt for the render in Fig. 3 and Supplementary Video 2, Prof. N. Shepherd for the image slides in Fig. 2, Supplementary Video 1 and M. Falk for the renders in Supplementary Video 1, and J.-M. Bokhorst for training the deep learning network applied to the KESM dataset illustrated in Fig. 3, Supplementary Video 2.

Author information

Authors and Affiliations

  1. Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, the Netherlands

    Tariq Sami Haddad & Iris Nagtegaal

  2. Department of Cell Biology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, the Netherlands

    Peter Friedl

  3. David H. Koch Center for Applied Research of Genitourinary Cancers, Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Peter Friedl

  4. Cancer GenomiCs.nl (CGC.nl), http://cancergenomics.nl, Utrecht, the Netherlands

    Peter Friedl

  5. Strateos, Menlo Park, CA, USA

    Navid Farahani

  6. Leeds Teaching Hospitals NHS Trust, Leeds, UK

    Darren Treanor

  7. University of Leeds, Leeds, UK

    Darren Treanor

  8. Department of Clinical Pathology, and Department of Clinical and Experimental Medicine, Linkoping University, Linköping, Sweden

    Darren Treanor

  9. Center for Medical Imaging Science and Visualization (CMIV), Linköping, Sweden

    Darren Treanor

  10. Institute of Pathology, University of Bern, Bern, Switzerland

    Inti Zlobec

Authors
  1. Tariq Sami Haddad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter Friedl
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Navid Farahani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Darren Treanor
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Inti Zlobec
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Iris Nagtegaal
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The authors contributed to the various sections of this tutorial as follows: T.H., I.N. conceptualization and draft writing; N.F., knife-edge scanning microscopy; P.F., thick sectioning without clearing.; D.T., 3D histology; I.Z., X-ray microcomputed tomography. All authors read, revised and approved the final manuscript.

Corresponding author

Correspondence to Tariq Sami Haddad.

Ethics declarations

Competing interests

Strateos is a commercial entity and the affiliated author (N.F.) is a company employee. The remaining authors declare no competing interests.

Additional information

Peer review information Nature Protocols thanks Hiroki R. Ueda and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

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

Supplementary information

Supplementary Video 1

3D malignant colorectal adenocarcinoma consisting of 50 slide images with an interslice distance of 20 μm digitized twice at 20x, once with an H&E stain and a second time with a Pan-cytokeratin immunohistochemical stain to reveal the epithelium in brown. Data related to Figure 2.

Supplementary Video 2

3D reconstruction of the isolated tumor class of a formalin-fixed paraffin-embedded colorectal carcinoma specimen processed using Knife-Edge Scanning Microscopy (KESM) technology and segmented using deep learning. Data related to Figure 3.

Supplementary Video 3

3D reconstruction of invasive ductal carcinoma lesion in a patient sample (30 µm Z-stack, 5 µm step). Collective strands of carcinoma cells were detected by positive E-cadherin staining; collagen bundles (SHG); nuclei (DAPI). The video is representative for 7 independent samples. Data related to Figure 5.

Supplementary Video 4

3D reconstruction of invasive lobular carcinoma lesion in a patient sample (60 µm Z-stack, 5 µm step). Collective strands of carcinoma cells were detected by positive staining for pan-cytokeratin; collagen bundles (SHG); nuclei (DAPI). The video is representative for 3 independent samples. Data related to Figure 5.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Haddad, T.S., Friedl, P., Farahani, N. et al. Tutorial: methods for three-dimensional visualization of archival tissue material. Nat Protoc 16, 4945–4962 (2021). https://doi.org/10.1038/s41596-021-00611-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-021-00611-4

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

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing