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Annual Review of Analytical Chemistry

Volume 16, 2023

Nondestructive 3D Pathology with Light-Sheet Fluorescence Microscopy for Translational Research and Clinical Assays

Abstract

In recent years, there has been a revived appreciation for the importance of spatial context and morphological phenotypes for both understanding disease progression and guiding treatment decisions. Compared with conventional 2D histopathology, which is the current gold standard of medical diagnostics, nondestructive 3D pathology offers researchers and clinicians the ability to visualize orders of magnitude more tissue within their natural volumetric context. This has been enabled by rapid advances in tissue-preparation methods, high-throughput 3D microscopy instrumentation, and computational tools for processing these massive feature-rich data sets. Here, we provide a brief overview of many of these technical advances along with remaining challenges to be overcome. We also speculate on the future of 3D pathology as applied in translational investigations, preclinical drug development, and clinical decision-support assays.

Keyword(s): computational pathology, digital pathology, optical-sectioning microscopy, precision medicine, spatial biology
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/content/journals/10.1146/annurev-anchem-091222-092734
2023-06-14
2024-04-29
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Literature Cited

  1. 1.
    Abeytunge S, Li Y, Larson B, Peterson G, Seltzer E et al. 2013. Confocal microscopy with strip mosaicing for rapid imaging over large areas of excised tissue. J. Biomed. Opt. 18:61227
     [Google Scholar]
  2. 2.
    Helmchen F, Denk W. 2005. Deep tissue two-photon microscopy. Nat. Methods 2:932–40
     [Google Scholar]
  3. 3.
    Tao YK, Shen D, Sheikine Y, Ahsen OO, Wang HH et al. 2014. Assessment of breast pathologies using nonlinear microscopy. PNAS 111:15304–9
     [Google Scholar]
  4. 4.
    Tu H, Liu Y, Turchinovich D, Marjanovic M, Lyngsø J et al. 2016. Stain-free histopathology by programmable supercontinuum pulses. Nat. Photon. 10:534–40
     [Google Scholar]
  5. 5.
    Yoshitake T, Giacomelli MG, Cahill LC, Schmolze DB, Vardeh H et al. 2016. Direct comparison between confocal and multiphoton microscopy for rapid histopathological evaluation of unfixed human breast tissue. J. Biomed. Opt. 21:126021
     [Google Scholar]
  6. 6.
    Mayerich D, Abbott L, McCormick B 2008. Knife-edge scanning microscopy for imaging and reconstruction of three-dimensional anatomical structures of the mouse brain. J. Microsc. 231:134–43
     [Google Scholar]
  7. 7.
    Humphrey PA. 1993. Complete histologic serial sectioning of a prostate gland with adenocarcinoma. Am. J. Surg. Pathol. 17:468–72
     [Google Scholar]
  8. 8.
    Li A, Gong H, Zhang B, Wang Q, Yan C et al. 2010. Micro-optical sectioning tomography to obtain a high-resolution atlas of the mouse brain. Science 330:1404–8
     [Google Scholar]
  9. 9.
    Shimozawa T, Yamagata K, Kondo T, Hayashi S, Shitamukai A et al. 2013. Improving spinning disk confocal microscopy by preventing pinhole cross-talk for intravital imaging. PNAS 110:3399–404
     [Google Scholar]
  10. 10.
    Nakano A. 2002. Spinning-disk confocal microscopy—a cutting-edge tool for imaging of membrane traffic. Cell Struct. Funct. 27:349–55
     [Google Scholar]
  11. 11.
    Bewersdorf J, Pick R, Hell SW. 1998. Multifocal multiphoton microscopy. Opt. Lett. 23:655–57
     [Google Scholar]
  12. 12.
    Bahlmann K, So PT, Kirber M, Reich R, Kosicki B et al. 2007. Multifocal multiphoton microscopy (MMM) at a frame rate beyond 600 Hz. Opt. Express 15:10991–98
     [Google Scholar]
  13. 13.
    Oron D, Tal E, Silberberg Y. 2005. Scanningless depth-resolved microscopy. Opt. Express 13:1468–76
     [Google Scholar]
  14. 14.
    Liu JTC, Mandella MJ, Loewke NO, Haeberle H, Ra H et al. 2010. Micromirror-scanned dual-axis confocal microscope utilizing a gradient-index relay lens for image guidance during brain surgery. J. Biomed. Opt. 15:026029
     [Google Scholar]
  15. 15.
    Bishop KW, Maitland KC, Rajadhyaksha M, Liu JTC. 2022. In vivo microscopy as an adjunctive tool to guide detection, diagnosis, and treatment. J. Biomed. Opt. 27:040601
     [Google Scholar]
  16. 16.
    Flusberg BA, Jung JC, Cocker ED, Anderson EP, Schnitzer MJ 2005. In vivo brain imaging using a portable 3.9 gram two-photon fluorescence microendoscope. Opt. Lett. 30:2272–74
     [Google Scholar]
  17. 17.
    Piyawattanametha W, Cocker ED, Burns LD, Barretto RP, Jung JC et al. 2009. In vivo brain imaging using a portable 2.9 g two-photon microscope based on a microelectromechanical systems scanning mirror. Opt. Lett. 34:2309–11
     [Google Scholar]
  18. 18.
    Yin C, Glaser AK, Leigh SY, Chen Y, Wei L et al. 2016. Miniature in vivo MEMS-based line-scanned dual-axis confocal microscope for point-of-care pathology. Biomed. Opt. Express 7:251–63
     [Google Scholar]
  19. 19.
    Sanai N, Snyder LA, Honea NJ, Coons SW, Eschbacher JM et al. 2011. Intraoperative confocal microscopy in the visualization of 5-aminolevulinic acid fluorescence in low-grade gliomas. J. Neurosurg. 115:740–48
     [Google Scholar]
  20. 20.
    Moen E, Bannon D, Kudo T, Graf W, Covert M, Van Valen D. 2019. Deep learning for cellular image analysis. Nat. Methods 16:1233–46
     [Google Scholar]
  21. 21.
    Belthangady C, Royer LA. 2019. Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction. Nat. Methods 16:1215–25
     [Google Scholar]
  22. 22.
    Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. 2019. Artificial intelligence in digital pathology—new tools for diagnosis and precision oncology. Nat. Rev. Clin. Oncol. 16:703–15
     [Google Scholar]
  23. 23.
    Niazi MKK, Parwani AV, Gurcan MN. 2019. Digital pathology and artificial intelligence. Lancet Oncol 20:e253–61
     [Google Scholar]
  24. 24.
    Wang Z, Chen J, Hoi SC 2020. Deep learning for image super-resolution: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 43:3365–87
     [Google Scholar]
  25. 25.
    Dong C, Loy CC, He K, Tang X. 2014. Learning a deep convolutional network for image super-resolution. Comp. Vis. ECCV 2014:184–99
     [Google Scholar]
  26. 26.
    Wang H, Rivenson Y, Jin Y, Wei Z, Gao R et al. 2019. Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nat. Methods 16:103–10
     [Google Scholar]
  27. 27.
    Weigert M, Schmidt U, Boothe T, Müller A, Dibrov A et al. 2018. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nat. Methods 15:1090–97
     [Google Scholar]
  28. 28.
    Liu Z, Keller PJ. 2016. Emerging imaging and genomic tools for developmental systems biology. Dev. Cell 36:597–610
     [Google Scholar]
  29. 29.
    Huisken J, Stainier DYR. 2009. Selective plane illumination microscopy techniques in developmental biology. Development 136:1963–75
     [Google Scholar]
  30. 30.
    Siedentopf H, Zsigmondy R. 1902. Uber Sichtbarmachung und Größenbestimmung ultramikoskopischer Teilchen, mit besonderer Anwendung auf Goldrubingläser. Ann. Phys. 315:1–39
     [Google Scholar]
  31. 31.
    Siedentopf H. 1903. On the rendering visible of ultra-microscopic particles and of ultra-microscopic bacteria. J. R. Microsc. Soc. 23:573–78
     [Google Scholar]
  32. 32.
    Voie AH, Burns DH, Spelman FA. 1993. Orthogonal-plane fluorescence optical sectioning: three-dimensional imaging of macroscopic biological specimens. J. Microsc. 170:229–36
     [Google Scholar]
  33. 33.
    Huisken J, Swoger J, Del Bene F, Wittbrodt J, Stelzer EHK. 2004. Optical sectioning deep inside live embryos by selective plane illumination microscopy. Science 305:1007–9
     [Google Scholar]
  34. 34.
    Keller PJ, Schmidt AD, Wittbrodt J, Stelzer EHK. 2008. Reconstruction of zebrafish early embryonic development by scanned light sheet microscopy. Science 322:1065–69
     [Google Scholar]
  35. 35.
    Mickoleit M, Schmid B, Weber M, Fahrbach FO, Hombach S et al. 2014. High-resolution reconstruction of the beating zebrafish heart. Nat. Methods 11:919–22
     [Google Scholar]
  36. 36.
    Keller PJ, Ahrens MB. 2015. Visualizing whole-brain activity and development at the single-cell level using light-sheet microscopy. Neuron 85:462–83
     [Google Scholar]
  37. 37.
    Power RM, Huisken J. 2017. A guide to light-sheet fluorescence microscopy for multiscale imaging. Nat. Methods 14:360–73
     [Google Scholar]
  38. 38.
    Kumar A, Wu Y, Christensen R, Chandris P, Gandler W et al. 2014. Dual-view plane illumination microscopy for rapid and spatially isotropic imaging. Nat. Protoc. 9:2555–73
     [Google Scholar]
  39. 39.
    Strnad P, Gunther S, Reichmann J, Krzic U, Balazs B et al. 2015. Inverted light-sheet microscope for imaging mouse pre-implantation development. Nat. Methods 13:139–42
     [Google Scholar]
  40. 40.
    McGorty R, Liu H, Kamiyama D, Dong Z, Guo S, Huang B. 2015. Open-top selective plane illumination microscope for conventionally mounted specimens. Opt. Express 23:16142–53
     [Google Scholar]
  41. 41.
    McGorty R, Xie D, Huang B. 2017. High-NA open-top selective-plane illumination microscopy for biological imaging. Opt. Express 25:17798–810
     [Google Scholar]
  42. 42.
    Glaser AK, Reder NP, Chen Y, McCarty EF, Yin C et al. 2017. Light-sheet microscopy for slide-free non-destructive pathology of large clinical specimens. Nat. Biomed. Eng. 1:0084
     [Google Scholar]
  43. 43.
    Barner LA, Glaser AK, True LD, Reder NP, Liu JTC. 2019. Solid immersion meniscus lens (SIMlens) for open-top light-sheet microscopy. Opt. Lett. 44:4451–54
     [Google Scholar]
  44. 44.
    Barner LA, Glaser AK, Huang H, True LD, Liu JTC. 2020. Multi-resolution open-top light-sheet microscopy to enable efficient 3D pathology workflows. Biomed. Opt. Express 11:6605–19
     [Google Scholar]
  45. 45.
    Glaser AK, Reder NP, Chen Y, Yin C, Wei L et al. 2019. Multi-immersion open-top light-sheet microscope for high-throughput imaging of cleared tissues. Nat. Commun. 10:2781
     [Google Scholar]
  46. 46.
    Glaser AK, Bishop KW, Barner LA, Susaki EA, Kubota SI et al. 2022. A hybrid open-top light-sheet microscope for versatile multi-scale imaging of cleared tissues. Nat. Methods 19:613–19
     [Google Scholar]
  47. 47.
    Botcherby EJ, Juškaitis R, Booth MJ, Wilson T 2008. An optical technique for remote focusing in microscopy. Opt. Commun. 281:880–87
     [Google Scholar]
  48. 48.
    Dunsby C. 2008. Optically sectioned imaging by oblique plane microscopy. Opt. Express 16:20306–16
     [Google Scholar]
  49. 49.
    Voleti V, Patel KB, Li W, Perez Campos C, Bharadwaj S et al. 2019. Real-time volumetric microscopy of in vivo dynamics and large-scale samples with SCAPE 2.0. Nat. Methods 16:1054–62
     [Google Scholar]
  50. 50.
    Bouchard MB, Voleti V, Mendes CS, Lacefield C, Grueber WB et al. 2015. Swept confocally-aligned planar excitation (SCAPE) microscopy for high-speed volumetric imaging of behaving organisms. Nat. Photon. 9:113–19
     [Google Scholar]
  51. 51.
    Yang B, Chen X, Wang Y, Feng S, Pessino V et al. 2019. Epi-illumination SPIM for volumetric imaging with high spatial-temporal resolution. Nat. Methods 16:501–4
     [Google Scholar]
  52. 52.
    Millett-Sikking A, York A. 2019. High NA single-objective light-sheet: work in progress Zenodo: version 0.0.2. https://doi.org/10.5281/zenodo.3244420
  53. 53.
    Kumar M, Kishore S, Nasenbeny J, McLean DL, Kozorovitskiy Y. 2018. Integrated one- and two-photon scanned oblique plane illumination (SOPi) microscopy for rapid volumetric imaging. Opt. Express 26:13027–41
     [Google Scholar]
  54. 54.
    Hoffmann M, Judkewitz B. 2019. Diffractive oblique plane microscopy. Optica 6:5
     [Google Scholar]
  55. 55.
    Sapoznik E, Chang B-J, Huh J, Ju RJ, Azarova EV et al. 2020. A versatile oblique plane microscope for large-scale and high-resolution imaging of subcellular dynamics. eLife 9:e57681
     [Google Scholar]
  56. 56.
    Yang B, Lange M, Millett-Sikking A, Solak AC, Kumar SV et al. 2021. High-resolution, large imaging volume, and multi-view single objective light-sheet microscopy. bioRxiv 309229. https://doi.org/10.1101/2020.09.22.309229
    [Crossref]
  57. 57.
    Li T, Ota S, Kim J, Wong ZJ, Wang Y et al. 2014. Axial plane optical microscopy. Sci. Rep. 4:7253
     [Google Scholar]
  58. 58.
    Kim J, Wojcik M, Wang Y, Moon S, Zin EA et al. 2019. Oblique-plane single-molecule localization microscopy for tissues and small intact animals. Nat. Methods 16:853–57
     [Google Scholar]
  59. 59.
    Migliori B, Datta MS, Dupre C, Apak MC, Asano S et al. 2018. Light sheet theta microscopy for rapid high-resolution imaging of large biological samples. BMC Biol 16:57
     [Google Scholar]
  60. 60.
    Keller PJ, Schmidt AD, Santella A, Khairy K, Bao Z et al. 2010. Fast, high-contrast imaging of animal development with scanned light sheet-based structured-illumination microscopy. Nat. Methods 7:637–42
     [Google Scholar]
  61. 61.
    Hoyer P, de Medeiros G, Balázs B, Norlin N, Besir C et al. 2016. Breaking the diffraction limit of light-sheet fluorescence microscopy by RESOLFT. PNAS 113:3442–46
     [Google Scholar]
  62. 62.
    Friedrich M, Gan Q, Ermolayev V, Harms GS. 2011. STED-SPIM: Stimulated emission depletion improves sheet illumination microscopy resolution. Biophys. J. 100:L43–45
     [Google Scholar]
  63. 63.
    Zhao F, Zhu L, Fang C, Yu T, Zhu D, Fei P. 2020. Deep-learning super-resolution light-sheet add-on microscopy (Deep-SLAM) for easy isotropic volumetric imaging of large biological specimens. Biomed. Opt. Express 11:7273–85
     [Google Scholar]
  64. 64.
    Dean KM, Roudot P, Welf ES, Danuser G, Fiolka R. 2015. Deconvolution-free subcellular imaging with axially swept light sheet microscopy. Biophys. J. 108:2807–15
     [Google Scholar]
  65. 65.
    Fu Q, Martin BL, Matus DQ, Gao L. 2016. Imaging multicellular specimens with real-time optimized tiling light-sheet selective plane illumination microscopy. Nat. Commun. 7:11088
     [Google Scholar]
  66. 66.
    Rohrbach A, Fahrbach FO, Alessandri K, Nassoy P, Gurchenkov V. 2013. Self-reconstructing sectioned Bessel beams offer submicron optical sectioning for large fields of view in light-sheet microscopy. Opt. Express 21:11425–40
     [Google Scholar]
  67. 67.
    Fahrbach FO, Simon P, Rohrbach A. 2010. Microscopy with self-reconstructing beams. Nat. Photon. 4:780–85
     [Google Scholar]
  68. 68.
    Planchon TA, Gao L, Milkie DE, Davidson MW, Galbraith JA et al. 2011. Rapid three-dimensional isotropic imaging of living cells using Bessel beam plane illumination. Nat. Methods 8:417–23
     [Google Scholar]
  69. 69.
    Vettenburg T, Dalgarno HIC, Nylk J, Coll-Lladó C, Ferrier DEK et al. 2014. Light-sheet microscopy using an Airy beam. Nat. Methods 11:541–44
     [Google Scholar]
  70. 70.
    Remacha E, Friedrich L, Vermot J, Fahrbach FO. 2020. How to define and optimize axial resolution in light-sheet microscopy: a simulation-based approach. Biomed. Opt. Express 11:8–26
     [Google Scholar]
  71. 71.
    Shi Y, Daugird TA, Legant WR. 2022. A quantitative analysis of various patterns applied in lattice light sheet microscopy. Nat. Commun. 13:4607
     [Google Scholar]
  72. 72.
    Chang BJ, Dean KM, Fiolka R. 2020. Systematic and quantitative comparison of lattice and Gaussian light-sheets. Opt. Express 28:27052–77
     [Google Scholar]
  73. 73.
    Reynaud EG, Peychl J, Huisken J, Tomancak P. 2014. Guide to light-sheet microscopy for adventurous biologists. Nat. Methods 12:30–34
     [Google Scholar]
  74. 74.
    Battistella E, Quintana JF, McConnell G. 2022. Application of light-sheet mesoscopy to image host-pathogen interactions in intact organs. Front. Cell. Infect. Microbiol. 12:903957
     [Google Scholar]
  75. 75.
    Amat F, Hockendorf B, Wan Y, Lemon WC, McDole K, Keller PJ. 2015. Efficient processing and analysis of large-scale light-sheet microscopy data. Nat. Protoc. 10:1679–96
     [Google Scholar]
  76. 76.
    Gibbs HC, Mota SM, Hart NA, Min SW, Vernino AO et al. 2021. Navigating the light-sheet image analysis software landscape: concepts for driving cohesion from data acquisition to analysis. Front. Cell Dev. Biol. 9:739079
     [Google Scholar]
  77. 77.
    Andreev A, Koo DES. 2020. Practical guide to storage of large amounts of microscopy data. Microsc. Today 28:42–45
     [Google Scholar]
  78. 78.
    Balazs B, Deschamps J, Albert M, Ries J, Hufnagel L. 2017. A real-time compression library for microscopy images. bioRxiv 164624. https://doi.org/10.1101/164624
  79. 79.
    Beati I, Andreica E, Majer P. 2020. ImarisWriter: open source software for storage of large images in blockwise multi-resolution format. arXiv 10311. https://arxiv.org/abs/2008.10311
  80. 80.
    Hörl D, Rusak FR, Preusser F, Tillberg P, Randel N et al. 2019. BigStitcher: reconstructing high-resolution image datasets of cleared and expanded samples. Nat. Methods 16:870–74
     [Google Scholar]
  81. 81.
    Bria A, Iannello G. 2012. TeraStitcher—a tool for fast automatic 3D-stitching of teravoxel-sized microscopy images. BMC Bioinform. 13:316
     [Google Scholar]
  82. 82.
    Xie W, Reder NP, Koyuncu C, Leo P, Hawley S et al. 2022. Prostate cancer risk stratification via nondestructive 3D pathology with deep learning–assisted gland analysis. Cancer Res. 82:334–45
     [Google Scholar]
  83. 83.
    Richardson DS, Lichtman JW. 2015. Clarifying tissue clearing. Cell 162:246–57
     [Google Scholar]
  84. 84.
    Tainaka K, Kuno A, Kubota SI, Murakami T, Ueda HR. 2016. Chemical principles in tissue clearing and staining protocols for whole-body cell profiling. Annu. Rev. Cell Dev. Biol. 32:713–41
     [Google Scholar]
  85. 85.
    Almagro J, Messal HA, Zaw Thin M, van Rheenen J, Behrens A 2021. Tissue clearing to examine tumour complexity in three dimensions. Nat. Rev. Cancer 21:718–30
     [Google Scholar]
  86. 86.
    Molbay M, Kolabas ZI, Todorov MI, Ohn TL, Ertürk A. 2021. A guidebook for DISCO tissue clearing. Mol. Syst. Biol. 17:e9807
     [Google Scholar]
  87. 87.
    Choi SW, Guan W, Chung K 2021. Basic principles of hydrogel-based tissue transformation technologies and their applications. Cell 184:4115–36
     [Google Scholar]
  88. 88.
    Richardson DS, Guan W, Matsumoto K, Pan C, Chung K et al. 2021. Tissue clearing. Nat. Rev. Methods Primers 1:84
     [Google Scholar]
  89. 89.
    Ke M-T, Fujimoto S, Imai T. 2013. SeeDB: a simple and morphology-preserving optical clearing agent for neuronal circuit reconstruction. Nat. Neurosci. 16:1154–61
     [Google Scholar]
  90. 90.
    Hou B, Zhang D, Zhao S, Wei M, Yang Z et al. 2015. Scalable and DiI-compatible optical clearance of the mammalian brain. Front. Neuroanat. 9:19
     [Google Scholar]
  91. 91.
    Klingberg A, Hasenberg A, Ludwig-Portugall I, Medyukhina A, Männ L et al. 2017. Fully automated evaluation of total glomerular number and capillary tuft size in nephritic kidneys using lightsheet microscopy. J. Am. Soc. Nephrol. 28:452–59
     [Google Scholar]
  92. 92.
    Nojima S, Susaki EA, Yoshida K, Takemoto H, Tsujimura N et al. 2017. CUBIC pathology: three-dimensional imaging for pathological diagnosis. Sci. Rep. 7:9269
     [Google Scholar]
  93. 93.
    Tanaka N, Kanatani S, Tomer R, Sahlgren C, Kronqvist P et al. 2017. Whole-tissue biopsy phenotyping of three-dimensional tumours reveals patterns of cancer heterogeneity. Nat. Biomed. Eng. 1:796–806
     [Google Scholar]
  94. 94.
    Barner LA, Glaser AK, Mao C, Susaki EA, Vaughan JC et al. 2022. Multiresolution nondestructive 3D pathology of whole lymph nodes for breast cancer staging. J. Biomed. Opt. 27:036501–13
     [Google Scholar]
  95. 95.
    Zhao S, Todorov MI, Cai R, Al-Maskari R, Steinke H et al. 2020. Cellular and molecular probing of intact human organs. Cell 180:796–812.e19
     [Google Scholar]
  96. 96.
    Chen F, Tillberg PW, Boyden ES. 2015. Expansion microscopy. Science 347:543–48
     [Google Scholar]
  97. 97.
    Chozinski TJ, Halpern AR, Okawa H, Kim H-J, Tremel GJ et al. 2016. Expansion microscopy with conventional antibodies and fluorescent proteins. Nat. Methods 13:485–88
     [Google Scholar]
  98. 98.
    Ku T, Swaney J, Park J-Y, Albanese A, Murray E et al. 2016. Multiplexed and scalable super-resolution imaging of three-dimensional protein localization in size-adjustable tissues. Nat. Biotechnol. 34:973–81
     [Google Scholar]
  99. 99.
    Tillberg PW, Chen F. 2019. Expansion microscopy: scalable and convenient super-resolution microscopy. Annu. Rev. Cell Dev. Biol. 35:683–701
     [Google Scholar]
  100. 100.
    Wassie AT, Zhao Y, Boyden ES. 2019. Expansion microscopy: principles and uses in biological research. Nat. Methods 16:33–41
     [Google Scholar]
  101. 101.
    Zhao Y, Bucur O, Irshad H, Chen F, Weins A et al. 2017. Nanoscale imaging of clinical specimens using pathology-optimized expansion microscopy. Nat. Biotechnol. 35:757–64
     [Google Scholar]
  102. 102.
    Valdes PA, Yu C-CJ, Aronson J, Zhao Y, Bernstock JD et al. 2021. Decrowding expansion pathology: unmasking previously invisible nanostructures and cells in intact human brain pathology specimens. bioRxiv 471271. https://doi.org/10.1101/2021.12.05.471271
  103. 103.
    Murray E, Cho HH, Goodwin D, Ku T, Swaney J et al. 2015. Simple, scalable proteomic imaging for high-dimensional profiling of intact systems. Cell 163:1500–14
     [Google Scholar]
  104. 104.
    Ku T, Guan W, Evans NB, Sohn CH, Albanese A et al. 2020. Elasticizing tissues for reversible shape transformation and accelerated molecular labeling. Nat. Methods 17:609–13
     [Google Scholar]
  105. 105.
    Kim S-Y, Cho JH, Murray E, Bakh N, Choi H et al. 2015. Stochastic electrotransport selectively enhances the transport of highly electromobile molecules. PNAS 112:E6274–83
     [Google Scholar]
  106. 106.
    Susaki EA, Shimizu C, Kuno A, Tainaka K, Li X et al. 2020. Versatile whole-organ/body staining and imaging based on electrolyte-gel properties of biological tissues. Nat. Commun. 11:1982
     [Google Scholar]
  107. 107.
    Bancroft JD, Gamble M, eds. 2013. Theory and Practice of Histological Techniques Edinburgh, UK: Churchill Livingston
  108. 108.
    Giacomelli MG, Husvogt L, Vardeh H, Faulkner-Jones BE, Hornegger J et al. 2016. Virtual hematoxylin and eosin transillumination microscopy using epi-fluorescence imaging. PLOS ONE 11:e0159337
     [Google Scholar]
  109. 109.
    Elfer KN, Sholl AB, Wang M, Tulman DB, Mandava SH et al. 2016. DRAQ5 and eosin (‘D&E’) as an analog to hematoxylin and eosin for rapid fluorescence histology of fresh tissues. PLOS ONE 11:e0165530
     [Google Scholar]
  110. 110.
    Serafin R, Xie W, Glaser AK, Liu JTC. 2020. FalseColor-Python: a rapid intensity-leveling and digital-staining package for fluorescence-based slide-free digital pathology. PLOS ONE 15:e0233198
     [Google Scholar]
  111. 111.
    Chen Y, Xie W, Glaser AK, Reder NP, Mao C et al. 2019. Rapid pathology of lumpectomy margins with open-top light-sheet (OTLS) microscopy. Biomed. Opt. Express 10:1257–72
     [Google Scholar]
  112. 112.
    Mao C, Lee MY, Jhan J-R, Halpern AR, Woodworth MA et al. 2020. Feature-rich covalent stains for super-resolution and cleared tissue fluorescence microscopy. Sci. Adv. 6:eaba4542
     [Google Scholar]
  113. 113.
    Lee MY, Mao C, Glaser AK, Woodworth MA, Halpern AR et al. 2022. Fluorescent labeling of abundant reactive entities (FLARE) for cleared-tissue and super-resolution microscopy. Nat. Protoc. 17:819–46
     [Google Scholar]
  114. 114.
    Gerdes MJ, Sevinsky CJ, Sood A, Adak S, Bello MO et al. 2013. Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue. PNAS 110:11982–87
     [Google Scholar]
  115. 115.
    Lin J-R, Izar B, Wang S, Yapp C, Mei S et al. 2018. Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes. eLife 7:e31657
     [Google Scholar]
  116. 116.
    Goltsev Y, Samusik N, Kennedy-Darling J, Bhate S, Hale M et al. 2018. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174:968–81.e15
     [Google Scholar]
  117. 117.
    Radtke AJ, Kandov E, Lowekamp B, Speranza E, Chu CJ et al. 2020. IBEX: a versatile multiplex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues. PNAS 117:33455–65
     [Google Scholar]
  118. 118.
    Schwanhäusser B, Busse D, Li N, Dittmar G, Schuchhardt J et al. 2011. Global quantification of mammalian gene expression control. Nature 473:337–42
     [Google Scholar]
  119. 119.
    Payne AC, Chiang ZD, Reginato PL, Mangiameli SM, Murray EM et al. 2020. In situ genome sequencing resolves DNA sequence and structure in intact biological samples. Science 371: https://doi.org/10.1126/science.aay3446
    [Crossref] [Google Scholar]
  120. 120.
    Eng C-HL, Lawson M, Zhu Q, Dries R, Koulena N et al. 2019. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+. Nature 568:235–39
     [Google Scholar]
  121. 121.
    Lee JH, Daugharthy ER, Scheiman J, Kalhor R, Yang JL et al. 2014. Highly multiplexed subcellular RNA sequencing in situ. Science 343:1360–63
     [Google Scholar]
  122. 122.
    Wang X, Allen WE, Wright MA, Sylwestrak EL, Samusik N et al. 2018. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361:eaat5691
     [Google Scholar]
  123. 123.
    Stahl PL, Salmen F, Vickovic S, Lundmark A, Navarro JF et al. 2016. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353:78–82
     [Google Scholar]
  124. 124.
    Liu Y, Yang M, Deng Y, Su G, Enninful A et al. 2020. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 183:1665–81.e18
     [Google Scholar]
  125. 125.
    Lv J, Shi Q, Han Y, Li W, Liu H et al. 2021. Spatial transcriptomics reveals gene expression characteristics in invasive micropapillary carcinoma of the breast. Cell Death Dis. 12:1095
     [Google Scholar]
  126. 126.
    Merritt CR, Ong GT, Church SE, Barker K, Danaher P et al. 2020. Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat. Biotechnol. 38:586–99
     [Google Scholar]
  127. 127.
    Delorey TM, Ziegler CGK, Heimberg G, Normand R, Yang Y et al. 2021. COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets. Nature 595:107–13
     [Google Scholar]
  128. 128.
    Bourgenot C, Saunter CD, Taylor JM, Girkin JM, Love GD. 2012. 3D adaptive optics in a light sheet microscope. Opt. Express 20:13252–61
     [Google Scholar]
  129. 129.
    Booth MJ. 2014. Adaptive optical microscopy: the ongoing quest for a perfect image. Light Sci. Appl. 3:e165
     [Google Scholar]
  130. 130.
    Ji N 2017. Adaptive optical fluorescence microscopy. Nat. Methods 14:374–80
     [Google Scholar]
  131. 131.
    Royer L, Lemon W, Chhetri R, Wan Y, Coleman M et al. 2016. Adaptive light-sheet microscopy for long-term, high-resolution imaging in living organisms. Nat. Biotechnol. 34:1267–78
     [Google Scholar]
  132. 132.
    Krzic U, Gunther S, Saunders TE, Streichan SJ, Hufnagel L. 2012. Multiview light-sheet microscope for rapid in toto imaging. Nat. Methods 9:730–33
     [Google Scholar]
  133. 133.
    de Medeiros G, Norlin N, Gunther S, Albert M, Panavaite L et al. 2015. Confocal multiview light-sheet microscopy. Nat. Commun. 6:8881
     [Google Scholar]
  134. 134.
    Wu Y, Wawrzusin P, Senseney J, Fischer RS, Christensen R et al. 2013. Spatially isotropic four-dimensional imaging with dual-view plane illumination microscopy. Nat. Biotechnol. 31:1032–38
     [Google Scholar]
  135. 135.
    Singh SP, Wang L, Gupta S, Goli H, Padmanabhan P, Gulyas B. 2020. 3D deep learning on medical images: a review. Sensors 20:5097
     [Google Scholar]
  136. 136.
    Dou Q, Yu L, Chen H, Jin Y, Yang X et al. 2017. 3D deeply supervised network for automated segmentation of volumetric medical images. Med. Image Anal. 41:40–54
     [Google Scholar]
  137. 137.
    Lu MY, Williamson DFK, Chen TY, Chen RJ, Barbieri M, Mahmood F. 2021. Data-efficient and weakly supervised computational pathology on whole-slide images. Nat. Biomed. Eng. 5:555–70
     [Google Scholar]
  138. 138.
    Zhou Z-H. 2018. A brief introduction to weakly supervised learning. Natl. Sci. Rev. 5:44–53
     [Google Scholar]
  139. 139.
    Campanella G, Hanna MG, Geneslaw L, Miraflor A, Silva VWK et al. 2019. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25:1301–9
     [Google Scholar]
  140. 140.
    Lu MY, Chen TY, Williamson DFK, Zhao M, Shady M et al. 2021. AI-based pathology predicts origins for cancers of unknown primary. Nature 594:106–10
     [Google Scholar]
  141. 141.
    van der Laak J, Litjens G, Ciompi F. 2021. Deep learning in histopathology: the path to the clinic. Nat. Med. 27:775–84
     [Google Scholar]
  142. 142.
    Kiemen A, Braxton AM, Grahn MP, Han KS, Babu JM et al. 2020. In situ characterization of the 3D microanatomy of the pancreas and pancreatic cancer at single cell resolution. bioRxiv 416909. https://doi.org/10.1101/2020.12.08.416909
  143. 143.
    Agrawal V, Udupa J, Tong Y, Torigian D. 2020. BRR-Net: a tandem architectural CNN-RNN for automatic body region localization in CT images. Med. Phys. 47:5020–31
     [Google Scholar]
  144. 144.
    Liu JTC, Glaser AK, Bera K, True LD, Reder NP et al. 2021. Harnessing non-destructive 3D pathology. Nat. Biomed. Eng. 5:203–18
     [Google Scholar]
/content/journals/10.1146/annurev-anchem-091222-092734
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Nondestructive 3D Pathology with Light-Sheet Fluorescence Microscopy for Translational Research and Clinical Assays
Annual Review of Analytical Chemistry 16, 231 (2023); https://doi.org/10.1146/annurev-anchem-091222-092734
/content/journals/10.1146/annurev-anchem-091222-092734
/content/journals/10.1146/annurev-anchem-091222-092734
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