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.
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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]
-
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]
-
7. Ertürk A. Deep 3D histology powered by tissue clearing, omics and AI. Nat Methods. 2024;21(7):1153-1165.[DOI]
-
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]
-
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]
-
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]
-
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]
-
21. Tissue histology in 3D. Nat Methods. 2024;21(7):1133.[DOI]
-
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]
-
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]
-
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]
-
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]
-
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]
-
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]
-
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]
-
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]
-
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]
-
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]
-
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]
-
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]
-
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]
-
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]
-
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]
-
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]
-
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]
-
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]
-
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]
-
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]
-
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]
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© 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.
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