Beyond slides: The era of 3D histology

Beyond slides: The era of 3D histology

Zihang He
1
,
Yingying Li
1
,
Lixue Shi
1,2,*
*Correspondence to: Lixue Shi, Shanghai Xuhui Central Hospital, Zhongshan-Xuhui Hospital, Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai 200032, China. E-mail: shilixue@fudan.edu.cn
EXO. 2026;1:202622. 10.70401/EXO.2026.0021
Received: June 03, 2026Accepted: September 30, 2026Published: September 30, 2026
Tips Icon
This manuscript is made available in its unedited form to allow early access to the reported findings. Further editing will be completed before final publication. As such, the content may include errors, and standard legal disclaimers are applicable.

Abstract

Histology has long served as the foundation of tissue diagnosis, yet its reliance on thin 2D sections incompletely captures the spatial continuity, volumetric heterogeneity, and multicellular architecture of biological tissues. Recent advances in volumetric imaging, spatial omics, label-free optical pathology, and artificial intelligence are enabling tissue analysis beyond the conventional glass slide. In this Perspective, we discuss the emergence of 3D histology and the technological developments driving the transition from planar sampling to volumetric tissue analysis. We highlight how the added spatial dimension can reveal diagnostically relevant features that are only partially represented in conventional sections, and discuss efforts to extend molecular profiling into intact tissues through multiplexed imaging and volumetric spatial omics. We further examine the growing role of label-free modalities, particularly stimulated Raman scattering microscopy, for rapid and minimally destructive pathology. Finally, we consider the computational foundations required to reconstruct, align and interpret volumetric data, including emerging applications in virtual histology and molecular prediction. Together, these developments suggest that 3D histology is evolving into a broader framework for integrating tissue morphology, molecular state, and function within intact spatial contexts, with emerging opportunities for biological discovery and precision medicine.

Keywords

3D histopathology, non-destructive histology, molecular histology, label-free imaging, virtual staining

References

  • 1. Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology: New tools for diagnosis and precision oncology. Nat Rev Clin Oncol. 2019;16(11):703-715.
    [DOI]
  • 2. Liu Y, Dai Y, Wang L. Spatial omics at the forefront: Emerging technologies, analytical innovations, and clinical applications. Cancer Cell. 2026;44(1):24-49.
    [DOI] [PubMed] [PMC]
  • 3. Liu JT, Chow SS, Colling R, Downes MR, Farré X, Humphrey P, et al. Engineering the future of 3D pathology. J Pathol Clin Res. 2024;10(1):e347.
    [DOI] [PubMed] [PMC]
  • 4. Liu JTC, Glaser AK, Poudel C, Vaughan JC. Nondestructive 3D pathology with light-sheet fluorescence microscopy for translational research and clinical assays. Annu Rev Anal Chem. 2023;16(1):231-252.
    [DOI] [PubMed] [PMC]
  • 5. Almagro J, Messal HA, Zaw Thin M, van Rheenen J, Behrens A. Tissue clearing to examine tumour complexity in three dimensions. Nat Rev Cancer. 2021;21(11):718-730.
    [DOI]
  • 6. Liu JTC, Glaser AK, Bera K, True LD, Reder NP, Eliceiri KW, et al. Harnessing non-destructive 3D pathology. Nat Biomed Eng. 2021;5(3):203-218.
    [DOI] [PubMed] [PMC]
  • 7. Ertürk A. Deep 3D histology powered by tissue clearing, omics and AI. Nat Methods. 2024;21(7):1153-1165.
    [DOI]
  • 8. Kiemen AL, Braxton AM, Grahn MP, Han KS, Babu JM, Reichel R, et al. CODA: Quantitative 3D reconstruction of large tissues at cellular resolution. Nat Methods. 2022;19(11):1490-1499.
    [DOI] [PubMed] [PMC]
  • 9. Yoshizawa T, Hong SM, Jung D, Noë M, Kiemen A, Wu PH, et al. Three-dimensional analysis of extrahepatic cholangiocarcinoma and tumor budding. J Pathol. 2020;251(4):400-410.
    [DOI] [PubMed] [PMC]
  • 10. Wang X, Allen WE, Wright MA, Sylwestrak EL, Samusik N, Vesuna S, et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science. 2018;361(6400):eaat5691.
    [DOI] [PubMed] [PMC]
  • 11. Sui X, Lo JA, Luo S, He Y, Tang Z, Lin Z, et al. Scalable spatial single-cell transcriptomics and translatomics in 3D thick tissue blocks. Nat Methods. 2025;22(12):2574-2584.
    [DOI]
  • 12. Gandin V, Kim J, Yang LZ, Lian Y, Kawase T, Hu A, et al. Deep-tissue transcriptomics and subcellular imaging at high spatial resolution. Science. 2025;388(6744):eadq2084.
    [DOI] [PubMed] [PMC]
  • 13. Bhatia HS, Brunner AD, Öztürk F, Kapoor S, Rong Z, Mai H, et al. Spatial proteomics in three-dimensional intact specimens. Cell. 2022;185(26):5040-5058.e19.
    [DOI]
  • 14. Lu FK, Calligaris D, Olubiyi OI, Norton I, Yang W, Santagata S, et al. Label-free neurosurgical pathology with stimulated Raman imaging. Cancer Res. 2016;76(12):3451-3462.
    [DOI] [PubMed] [PMC]
  • 15. Winetraub Y, van Vleck A, Yuan E, Terem I, Zhao J, Yu C, et al. Noninvasive virtual biopsy using micro-registered optical coherence tomography (OCT) in human subjects. Sci Adv. 2024;10(15):eadi5794.
    [DOI] [PubMed] [PMC]
  • 16. Cao R, Nelson SD, Davis S, Liang Y, Luo Y, Zhang Y, et al. Label-free intraoperative histology of bone tissue via deep-learning-assisted ultraviolet photoacoustic microscopy. Nat Biomed Eng. 2023;7(2):124-134.
    [DOI]
  • 17. Li Y, Pillar N, Li J, Liu T, Wu D, Sun S, et al. Virtual histological staining of unlabeled autopsy tissue. Nat Commun. 2024;15(1):1684.
    [DOI] [PubMed] [PMC]
  • 18. Fu X, Cao Y, Bian B, Wang C, Graham D, Pathmanathan N, et al. Spatial gene expression at single-cell resolution from histology using deep learning with GHIST. Nat Methods. 2025;22(9):1900-1910.
    [DOI]
  • 19. Andani S, Chen B, Ficek-Pascual J, Heinke S, Casanova R, Hild BF, et al. Histopathology-based protein multiplex generation using deep learning. Nat Mach Intell. 2025;7(8):1292-1307.
    [DOI]
  • 20. Liu M, Villazon J, Forjaz A, Tian X, Fan R, Wang S, et al. 3D multi-omics tumour atlases: From technology to biology and clinical translation. Nat Rev Cancer. 2026;26(10):762-789.
    [DOI] [PubMed] [PMC]
  • 21. Tissue histology in 3D. Nat Methods. 2024;21(7):1133.
    [DOI]
  • 22. Yun YH, Chung KY, Lee Y, Sung TS, Ko D, Ryoo SB, et al. AI-powered 3D pathology protocol enhances enteric nervous system visualization and quantification for clinical diagnostics. Theranostics. 2025;15(15):7440-7453.
    [DOI] [PubMed] [PMC]
  • 23. Xie W, Reder NP, Koyuncu C, Leo P, Hawley S, Huang H, et al. Prostate cancer risk stratification via nondestructive 3D pathology with deep learning-assisted gland analysis. Cancer Res. 2022;82(2):334-345.
    [DOI] [PubMed] [PMC]
  • 24. Mai H, Luo J, Hoeher L, Al-Maskari R, Horvath I, Chen Y, et al. Whole-body cellular mapping in mouse using standard IgG antibodies. Nat Biotechnol. 2024;42(4):617-627.
    [DOI] [PubMed] [PMC]
  • 25. Mathur R, Wang Q, Schupp PG, Nikolic A, Hilz S, Hong C, et al. Glioblastoma evolution and heterogeneity from a 3D whole-tumor perspective. Cell. 2024;187(2):446-463.e16.
    [DOI]
  • 26. Roberts N, Magee D, Song Y, Brabazon K, Shires M, Crellin D, et al. Toward routine use of 3D histopathology as a research tool. Am J Pathol. 2012;180(5):1835-1842.
    [DOI] [PubMed] [PMC]
  • 27. Pichat J, Iglesias JE, Yousry T, Ourselin S, Modat M. A survey of methods for 3D histology reconstruction. Med Image Anal. 2018;46:73-105.
    [DOI] [PubMed]
  • 28. Gatenbee CD, Baker AM, Prabhakaran S, Swinyard O, Slebos RJC, Mandal G, et al. Virtual alignment of pathology image series for multi-gigapixel whole slide images. Nat Commun. 2023;14(1):4502.
    [DOI] [PubMed] [PMC]
  • 29. Ueda HR, Ertürk A, Chung K, Gradinaru V, Chédotal A, Tomancak P, et al. Tissue clearing and its applications in neuroscience. Nat Rev Neurosci. 2020;21(2):61-79.
    [DOI] [PubMed] [PMC]
  • 30. Weiss KR, Voigt FF, Shepherd DP, Huisken J. Tutorial: Practical considerations for tissue clearing and imaging. Nat Protoc. 2021;16(6):2732-2748.
    [DOI] [PubMed] [PMC]
  • 31. Glaser AK, Reder NP, Chen Y, McCarty EF, Yin C, Wei L, et al. Light-sheet microscopy for slide-free non-destructive pathology of large clinical specimens. Nat Biomed Eng. 2017;1(7):0084.
    [DOI] [PubMed] [PMC]
  • 32. Tanaka N, Kanatani S, Tomer R, Sahlgren C, Kronqvist P, Kaczynska D, et al. Whole-tissue biopsy phenotyping of three-dimensional tumours reveals patterns of cancer heterogeneity. Nat Biomed Eng. 2017;1(10):796-806.
    [DOI] [PubMed]
  • 33. Glaser AK, Reder NP, Chen Y, Yin C, Wei L, Kang S, et al. Multi-immersion open-top light-sheet microscope for high-throughput imaging of cleared tissues. Nat Commun. 2019;10(1):2781.
    [DOI] [PubMed] [PMC]
  • 34. Bishop KW, Erion Barner LA, Han Q, Baraznenok E, Lan L, Poudel C, et al. An end-to-end workflow for nondestructive 3D pathology. Nat Protoc. 2024;19(4):1122-1148.
    [DOI]
  • 35. Glaser AK, Bishop KW, Barner LA, Susaki EA, Kubota SI, Gao G, et al. A hybrid open-top light-sheet microscope for versatile multi-scale imaging of cleared tissues. Nat Methods. 2022;19(5):613-619.
    [DOI] [PubMed] [PMC]
  • 36. Barner LA, Glaser AK, Mao C, Susaki EA, Vaughan JC, Dintzis SM, et al. Multiresolution nondestructive 3D pathology of whole lymph nodes for breast cancer staging. J Biomed Opt. 2022;27(3):036501.
    [DOI] [PubMed] [PMC]
  • 37. Bhatia HS, Simons LH, Kuemmerle LB, McCabe C, Jansen S, He Z, et al. DISCO-seq: 3D single-cell transcriptomics of intact biological systems. bioRxiv [Preprint]. 2025.
    [DOI]
  • 38. Verhoef EI, van Cappellen WA, Slotman JA, Kremers GJ, Ewing-Graham PC, Houtsmuller AB, et al. Three-dimensional analysis reveals two major architectural subgroups of prostate cancer growth patterns. Mod Pathol. 2019;32(7):1032-1041.
    [DOI] [PubMed] [PMC]
  • 39. Lee SS, Bindokas VP, Lingen MW, Kron SJ. Nondestructive, multiplex three-dimensional mapping of immune infiltrates in core needle biopsy. Lab Invest. 2019;99(9):1400-1413.
    [DOI] [PubMed] [PMC]
  • 40. Koyuncu C, Janowczyk A, Farre X, Pathak T, Mirtti T, Fernandez PL, et al. Visual assessment of 2-dimensional levels within 3-dimensional pathology data sets of prostate needle biopsies reveals substantial spatial heterogeneity. Lab Invest. 2023;103(12):100265.
    [DOI] [PubMed] [PMC]
  • 41. Tanaka N, Kaczynska D, Kanatani S, Sahlgren C, Mitura P, Stepulak A, et al. Mapping of the three-dimensional lymphatic microvasculature in bladder tumours using light-sheet microscopy. Br J Cancer. 2018;118(7):995-999.
    [DOI] [PubMed] [PMC]
  • 42. Braxton AM, Kiemen AL, Grahn MP, Forjaz A, Parksong J, Mahesh Babu J, et al. 3D genomic mapping reveals multifocality of human pancreatic precancers. Nature. 2024;629(8012):679-687.
    [DOI]
  • 43. Fujii M, Sekine S, Sato T. Decoding the basis of histological variation in human cancer. Nat Rev Cancer. 2024;24(2):141-158.
    [DOI] [PubMed] [PMC]
  • 44. Capper D, Jones DTW, Sill M, Hovestadt V, Schrimpf D, Sturm D, et al. DNA methylation-based classification of central nervous system tumours. Nature. 2018;555(7697):469-474.
    [DOI]
  • 45. Cancer Genome Atlas Research Network, Kandoth C, Schultz N, Cherniack AD, Akbani R, Liu Y, et al. Integrated genomic characterization of endometrial carcinoma. Nature. 2013;497(7447):67-73.
    [DOI] [PubMed] [PMC]
  • 46. Guinney J, Dienstmann R, Wang X, de Reyniès A, Schlicker A, Soneson C, et al. The consensus molecular subtypes of colorectal cancer. Nat Med. 2015;21(11):1350-1356.
    [DOI] [PubMed] [PMC]
  • 47. Hoadley KA, Yau C, Hinoue T, Wolf DM, Lazar AJ, Drill E, et al. Cell-of-origin patterns dominate the molecular classification of 10, 000 tumors from 33 types of cancer. Cell. 2018;173(2):291-304.e6.
    [DOI] [PubMed] [PMC]
  • 48. Berglund E, Maaskola J, Schultz N, Friedrich S, Marklund M, Bergenstråhle J, et al. Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity. Nat Commun. 2018;9:2419.
    [DOI]
  • 49. Chung K, Wallace J, Kim SY, Kalyanasundaram S, Andalman AS, Davidson TJ, et al. Structural and molecular interrogation of intact biological systems. Nature. 2013;497(7449):332-337.
    [DOI]
  • 50. Kuett L, Catena R, Özcan A, Plüss A, Cancer Grand Challenges IMAXT Consortium, Schraml P, et al. Three-dimensional imaging mass cytometry for highly multiplexed molecular and cellular mapping of tissues and the tumor microenvironment. Nat Cancer. 2022;3(1):122-133.
    [DOI] [PubMed] [PMC]
  • 51. Zhao Y, Bucur O, Irshad H, Chen F, Weins A, Stancu AL, et al. Nanoscale imaging of clinical specimens using pathology-optimized expansion microscopy. Nat Biotechnol. 2017;35(8):757-764.
    [DOI]
  • 52. Ku T, Swaney J, Park JY, Albanese A, Murray E, Cho JH, et al. Multiplexed and scalable super-resolution imaging of three-dimensional protein localization in size-adjustable tissues. Nat Biotechnol. 2016;34(9):973-981.
    [DOI]
  • 53. Shi L, Wei M, Miao Y, Qian N, Shi L, Singer RA, et al. Highly-multiplexed volumetric mapping with Raman dye imaging and tissue clearing. Nat Biotechnol. 2022;40(3):364-373.
    [DOI] [PubMed] [PMC]
  • 54. Murakami TC, Xia M, Maeda Y, Yin Y, Barbano PE, Lin Z, et al. Artificial intelligence-driven whole-brain cell mapping with highly multiplexed in situ hybridization. Neuron. 2026;114(8):1380-1398.e9.
    [DOI]
  • 55. Sylwestrak EL, Rajasethupathy P, Wright MA, Jaffe A, Deisseroth K. Multiplexed intact-tissue transcriptional analysis at cellular resolution. Cell. 2016;164(4):792-804.
    [DOI] [PubMed] [PMC]
  • 56. Tanaka N, Kanatani S, Kaczynska D, Fukumoto K, Louhivuori L, Mizutani T, et al. Three-dimensional single-cell imaging for the analysis of RNA and protein expression in intact tumour biopsies. Nat Biomed Eng. 2020;4(9):875-888.
    [DOI] [PubMed] [PMC]
  • 57. Kanatani S, Kreutzmann JC, Li Y, West Z, Larsen LL, Nikou DV, et al. Whole-brain spatial transcriptional analysis at cellular resolution. Science. 2024;386(6724):907-915.
    [DOI]
  • 58. Fang R, Halpern A, Rahman MM, Huang Z, Lei Z, Hell SJ, et al. Three-dimensional single-cell transcriptome imaging of thick tissues. Elife. 2024;12:RP90029.
    [DOI] [PubMed] [PMC]
  • 59. Schott M, León-Periñán D, Splendiani E, Strenger L, Licha JR, Pentimalli TM, et al. Open-ST: High-resolution spatial transcriptomics in 3D. Cell. 2024;187(15):3953-3972.e26.
    [DOI]
  • 60. Kolabas ZI, Kuemmerle LB, Perneczky R, Förstera B, Ulukaya S, Ali M, et al. Distinct molecular profiles of skull bone marrow in health and neurological disorders. Cell. 2023;186(17):3706-3725.e29.
    [DOI] [PubMed] [PMC]
  • 61. Yoshida SY, Matsumoto K, Takagi S, Kinoshita FL, Yamashita K, Shigeta D, et al. Whole-organ and whole-body 3D atlases enable cellome-wide profiling. Cell. 2026;189(6):1836-1853.e19.
    [DOI] [PubMed]
  • 62. Pang Z, Schafroth MA, Ogasawara D, Wang Y, Nudell V, Lal NK, et al. In situ identification of cellular drug targets in mammalian tissue. Cell. 2022;185(10):1793-1805.e17.
    [DOI] [PubMed] [PMC]
  • 63. Pang Z, Leung VH, Wang CC, Attarpour A, Rinaldi A, Shen H, et al. Mapping cellular targets of covalent cancer drugs in the entire mammalian body. Cell. 2026;189(3):725-738.e15.
    [DOI]
  • 64. Shaked NT, Boppart SA, Wang LV, Popp J. Label-free biomedical optical imaging. Nat Photonics. 2023;17(12):1031-1041.
    [DOI] [PubMed] [PMC]
  • 65. Nohman AI, Ivren M, Alhalabi OT, Sahm F, Dao Trong P, Krieg SM, et al. Intraoperative label-free tissue diagnostics using a stimulated Raman histology imaging system with artificial intelligence: An initial experience. Clin Neurol Neurosurg. 2024;247:108646.
    [DOI] [PubMed]
  • 66. Pham DL, Gillette AA, Riendeau J, Wiech K, Guzman EC, Datta R, et al. Perspectives on label-free microscopy of heterogeneous and dynamic biological systems. J Biomed Opt. 2025;29(Suppl 2):S22702.
    [DOI] [PubMed] [PMC]
  • 67. Hollon TC, Pandian B, Adapa AR, Urias E, Save AV, Khalsa SSS, et al. Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Nat Med. 2020;26(1):52-58.
    [DOI] [PubMed] [PMC]
  • 68. Movahed-Ezazi M, Nasir-Moin M, Fang C, Pizzillo I, Galbraith K, Drexler S, et al. Clinical validation of stimulated Raman histology for rapid intraoperative diagnosis of central nervous system tumors. Mod Pathol. 2023;36(9):100219.
    [DOI] [PubMed] [PMC]
  • 69. Liu Z, Chen L, Cheng H, Ao J, Xiong J, Liu X, et al. Virtual formalin-fixed and paraffin-embedded staining of fresh brain tissue via stimulated Raman CycleGAN model. Sci Adv. 2024;10(13):eadn3426.
    [DOI] [PubMed] [PMC]
  • 70. Liu Z, Li Y, Chen L, Wei M, Wei M, Sun Y, et al. Ultrarapid deep 3D histology enables intraoperative mapping of glioma infiltration. Cell. 2026;S0092-S8674(26)00824.
    [DOI] [PubMed]
  • 71. Park J, Bai B, Ryu D, Liu T, Lee C, Luo Y, et al. Artificial intelligence-enabled quantitative phase imaging methods for life sciences. Nat Methods. 2023;20(11):1645-1660.
    [DOI]
  • 72. Park Y, Depeursinge C, Popescu G. Quantitative phase imaging in biomedicine. Nat Photonics. 2018;12(10):578-589.
    [DOI]
  • 73. Park J, Shin SJ, Kim G, Cho H, Ryu D, Ahn D, et al. Revealing 3D microanatomical structures of unlabeled thick cancer tissues using holotomography and virtual H&E staining. Nat Commun. 2025;16:4781.
    [DOI]
  • 74. Fujimoto J, Swanson E. The development, commercialization, and impact of optical coherence tomography. Invest Ophthalmol Vis Sci. 2016;57(9):OCT1-OCT13.
    [DOI] [PubMed] [PMC]
  • 75. Scholler J, Groux K, Goureau O, Sahel JA, Fink M, Reichman S, et al. Dynamic full-field optical coherence tomography: 3D live-imaging of retinal organoids. Light Sci Appl. 2020;9:140.
    [DOI] [PubMed] [PMC]
  • 76. Aghigh A, Bancelin S, Rivard M, Pinsard M, Ibrahim H, Légaré F. Second harmonic generation microscopy: A powerful tool for bio-imaging. Biophys Rev. 2023;15(1):43-70.
    [DOI]
  • 77. Chen X, Nadiarynkh O, Plotnikov S, Campagnola PJ. Second harmonic generation microscopy for quantitative analysis of collagen fibrillar structure. Nat Protoc. 2012;7(4):654-669.
    [DOI]
  • 78. Li X, Kot JCK, Tsang VTC, Lo CTK, Huang B, Tian Y, et al. Ultraviolet photoacoustic microscopy with tissue clearing for high-contrast histological imaging. Photoacoustics. 2022;25:100313.
    [DOI] [PubMed] [PMC]
  • 79. Martell MT, Haven NJM, Cikaluk BD, Restall BS, McAlister EA, Mittal R, et al. Deep learning-enabled realistic virtual histology with ultraviolet photoacoustic remote sensing microscopy. Nat Commun. 2023;14(1):5967.
    [DOI] [PubMed] [PMC]
  • 80. He H, Zhu W, Miao H, Wang S, Du Z, Zhang H, et al. Label-free tissue NIR-II autofluorescence imaging for visualization of human liver malignancy. Nat Biomed Eng. 2026;1-13.
    [DOI]
  • 81. Saccomano M, Albers J, Tromba G, Dobrivojević Radmilović M, Gajović S, Alves F, et al. Synchrotron inline phase contrast µCT enables detailed virtual histology of embedded soft-tissue samples with and without staining. J Synchrotron Rad. 2018;25(4):1153-1161.
    [DOI]
  • 82. Walsh CL, Tafforeau P, Wagner WL, Jafree DJ, Bellier A, Werlein C, et al. Imaging intact human organs with local resolution of cellular structures using hierarchical phase-contrast tomography. Nat Methods. 2021;18(12):1532-1541.
    [DOI] [PubMed] [PMC]
  • 83. Romano M, Bravin A, Wright MD, Jacques L, Miettinen A, Hlushchuk R, et al. X-ray phase contrast 3D virtual histology: Evaluation of lung alterations after microbeam irradiation. Int J Radiat Oncol Biol Phys. 2022;112(3):818-830.
    [DOI] [PubMed]
  • 84. Komorowski K, Reichmann J, Drakhlis L, Zweigerdt R, Salditt T. 3D histology of human heart-forming organoids by X-ray phase-contrast tomography. Commun Biol. 2025;8(1):1411.
    [DOI] [PubMed] [PMC]
  • 85. Dubey A, Yamashita E, Stangeland B, Abbas I, Fooksman D, Harris RA, et al. Brain tumors induce widespread disruption of calvarial bone and alteration of skull marrow immune landscape. Nat Neurosci. 2025;28(11):2231-2246.
    [DOI]
  • 86. Song AH, Williams M, Williamson DFK, Chow SSL, Jaume G, Gao G, et al. Analysis of 3D pathology samples using weakly supervised AI. Cell. 2024;187(10):2502-2520.e17.
    [DOI] [PubMed] [PMC]
  • 87. Joshi S, Forjaz A, Han KS, Shen Y, Queiroga V, Selaru FA, et al. InterpolAI: Deep learning-based optical flow interpolation and restoration of biomedical images for improved 3D tissue mapping. Nat Methods. 2025;22(7):1556-1567.
    [DOI]
  • 88. Hörl D, Rojas Rusak F, Preusser F, Tillberg P, Randel N, Chhetri RK, et al. BigStitcher: Reconstructing high-resolution image datasets of cleared and expanded samples. Nat Methods. 2019;16(9):870-874.
    [DOI]
  • 89. Preibisch S, Amat F, Stamataki E, Sarov M, Singer RH, Myers E, et al. Efficient Bayesian-based multiview deconvolution. Nat Methods. 2014;11(6):645-648.
    [DOI]
  • 90. Mayer J, Robert-Moreno A, Sharpe J, Swoger J. Attenuation artifacts in light sheet fluorescence microscopy corrected by OPTiSPIM. Light Sci Appl. 2018;7:70.
    [DOI] [PubMed] [PMC]
  • 91. Rivenson Y, Wang H, Wei Z, de Haan K, Zhang Y, Wu Y, et al. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning. Nat Biomed Eng. 2019;3(6):466-477.
    [DOI] [PubMed]
  • 92. Liu A, Wang X, Cai J, Li C. Score-based diffusion model for Unpaired virtual histology staining. In: Li C, Qin W, Wu J, Zaki N. editors. Computational mathematics modeling in cancer analysis. Cham: Springer; 2026. p. 30-39.
    [DOI]
  • 93. Wu E, Bieniosek M, Wu Z, Thakkar N, Charville GW, Makky A, et al. ROSIE: AI generation of multiplex immunofluorescence staining from histopathology images. Nat Commun. 2025;16:7633.
    [DOI]
  • 94. Wang C, Chan AS, Fu X, Ghazanfar S, Kim J, Patrick E, et al. Benchmarking the translational potential of spatial gene expression prediction from histology. Nat Commun. 2025;16:1544.
    [DOI]
  • 95. Almagro-Pérez C, Song AH, Weishaupt L, Kim A, Jaume G, Williamson DFK, et al. AI-driven 3D spatial transcriptomics. 2502.17761 [Preprint]. 2025.
    [DOI]
  • 96. Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18(2):203-211.
    [DOI] [PubMed]
  • 97. Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O. 3D U-Net: Learning dense volumetric segmentation from sparse annotation. In: Ourselin S, Joskowicz L, Sabuncu MR, Unal G, Wells W. editors. Medical image computing and computer-assisted intervention–MICCAI 2016. Cham: Springer; 2016. p. 424-432.
    [DOI]
  • 98. Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, et al. Segment anything. In: 2023 IEEE/CVF international conference on computer vision (ICCV). IEEE; 2023. p. 3992-4003.
    [DOI]
  • 99. Wang H, Guo S, Ye J, Deng Z, Cheng J, Li T, et al. SAM-Med3D: A vision foundation model for general-purpose segmentation on volumetric medical images. IEEE Trans Neural Netw Learn Syst. 2025;36(10):17599-17612.
    [DOI]
  • 100. Erion Barner LA, Gao G, Reddi DM, Lan L, Burke W, Mahmood F, et al. Artificial intelligence-triaged 3-dimensional pathology to improve detection of esophageal neoplasia while reducing pathologist workloads. Mod Pathol. 2023;36(12):100322.
    [DOI] [PubMed]
  • 101. Gao G, Yan R, Song AH, Hsieh HC, Barner LAE, Wang F, et al. Deep-learning triage of three-dimensional pathology datasets for comprehensive and efficient pathologist assessments. Nat Biomed Eng. 2026.
    [DOI] [PubMed] [PMC]
  • 102. Chen RJ, Ding T, Lu MY, Williamson DFK, Jaume G, Song AH, et al. Towards a general-purpose foundation model for computational pathology. Nat Med. 2024;30(3):850-862.
    [DOI] [PubMed] [PMC]
  • 103. Lu MY, Chen B, Williamson DFK, Chen RJ, Liang I, Ding T, et al. A visual-language foundation model for computational pathology. Nat Med. 2024;30(3):863-874.
    [DOI] [PubMed] [PMC]
  • 104. Xu H, Usuyama N, Bagga J, Zhang S, Rao R, Naumann T, et al. A whole-slide foundation model for digital pathology from real-world data. Nature. 2024;630(8015):181-188.
    [DOI] [PubMed] [PMC]
  • 105. Moore J, Basurto-Lozada D, Besson S, Bogovic J, Bragantini J, Brown EM, et al. OME-Zarr: A cloud-optimized bioimaging file format with international community support. bioRxiv. 2023;2023.02.17.528834.
    [DOI] [PubMed] [PMC]
  • 106. Marconato L, Palla G, Yamauchi KA, Virshup I, Heidari E, Treis T, et al. SpatialData: An open and universal data framework for spatial omics. Nat Methods. 2025;22(1):58-62.
    [DOI] [PubMed] [PMC]
  • 107. Wilkinson MD, Dumontier M, Aalbersberg IJJ, Appleton G, Axton M, Baak A, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016;3:160018.
    [DOI] [PubMed] [PMC]
  • 108. Chen Y, Xie W, Glaser AK, Reder NP, Mao C, Dintzis SM, et al. Rapid pathology of lumpectomy margins with open-top light-sheet (OTLS) microscopy. Biomed Opt Express. 2019;10(3):1257-1272.
    [DOI] [PubMed] [PMC]
  • 109. Hollon T, Jiang C, Chowdury A, Nasir-Moin M, Kondepudi A, Aabedi A, et al. Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging. Nat Med. 2023;29(4):828-832.
    [DOI]
  • 110. Hu J, Li X, Coleman K, Schroeder A, Ma N, Irwin DJ, et al. SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nat Methods. 2021;18(11):1342-1351.
    [DOI] [PubMed]
  • 111. Bao F, Deng Y, Wan S, Shen SQ, Wang B, Dai Q, et al. Integrative spatial analysis of cell morphologies and transcriptional states with MUSE. Nat Biotechnol. 2022;40(8):1200-1209.
    [DOI]
  • 112. He S, Jin Y, Nazaret A, Shi L, Chen X, Rampersaud S, et al. Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor-immune hubs. Nat Biotechnol. 2025;43(2):223-235.
    [DOI] [PubMed] [PMC]
  • 113. Zeira R, Land M, Strzalkowski A, Raphael BJ. Alignment and integration of spatial transcriptomics data. Nat Methods. 2022;19(5):567-575.
    [DOI] [PubMed] [PMC]
  • 114. Argelaguet R, Cuomo ASE, Stegle O, Marioni JC. Computational principles and challenges in single-cell data integration. Nat Biotechnol. 2021;39(10):1202-1215.
    [DOI] [PubMed] [PMC]
  • 115. Chen W, Zhang P, Tran TN, Xiao Y, Li S, Shah VV, et al. A visual-omics foundation model to bridge histopathology with spatial transcriptomics. Nat Methods. 2025;22(7):1568-1582.
    [DOI] [PubMed] [PMC]
  • 116. Liu Y, Wang C, Wang Z, Chen L, Li Z, Song J, et al. High-parameter spatial multi-omics through histology-anchored integration. Nat Methods. 2026;23(2):373-386.
    [DOI]
  • 117. Voskuil FJ, Vonk J, van der Vegt B, Kruijff S, Ntziachristos V, van der Zaag PJ, et al. Intraoperative imaging in pathology-assisted surgery. Nat Biomed Eng. 2022;6(5):503-514.
    [DOI]
  • 118. van der Laak J, Litjens G, Ciompi F. Deep learning in histopathology: The path to the clinic. Nat Med. 2021;27(5):775-784.
    [DOI] [PubMed] [PMC]

© The Author(s) 2026. This is an Open Access article licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Publisher’s Note

Science Exploration remains a neutral stance on jurisdictional claims in published maps and institutional affiliations. The views expressed in this article are solely those of the author(s) and do not reflect the opinions of the Editors or the publisher.

Share And Cite

Science Exploration Style
He Z, Li Y, Shi L. Beyond slides: The era of 3D histology. EXO. 2026;1:202622. https://doi.org/10.70401/EXO.2026.0021

Submit a Manuscript
Author Instructions
Cite this Article
Export Citation
Article Metrics
0
View
0
Download
Cited
Article Updates
Citation Icon Get citation