Weinberg, R. A. How cancer arises. Sci. Am. 275, 62–70 (1996).
Google Scholar
Hanahan, D. Hallmarks of cancer: new dimensions. Cancer Discov. 12, 31–46 (2022).
Google Scholar
Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).
Google Scholar
Hanahan, D. & Weinberg, R. A. The hallmarks of cancer. Cell 100, 57–70 (2000).
Google Scholar
Sotiriou, C. & Pusztai, L. Gene-expression signatures in breast cancer. N. Engl. J. Med. 360, 790–800 (2009).
Google Scholar
Forjaz, A. et al. Three-dimensional assessments are necessary to determine the true, spatially resolved composition of tissues. Cell Rep. Methods 5, 101075 (2025).
Google Scholar
Song, A. H. et al. Analysis of 3D pathology samples using weakly supervised AI. Cell 187, 2502–2520.e17 (2024). This work establishes that weakly supervised AI on volumetric 3D pathology can outperform 2D baselines for prognosis, thereby providing a computational framework for the analysis of 3D histopathology at scale.
Google Scholar
Glaser, A. K. et al. Light-sheet microscopy for slide-free non-destructive pathology of large clinical specimens. Nat. Biomed. Eng. 1, 0084 (2017). This work established slide-free, open-top light-sheet microscopy as a practical route to rapid, non-destructive volumetric pathology of large clinical specimens, thereby providing a technical foundation for 3D tumour atlas acquisition.
Google Scholar
Tanaka, N. et al. Whole-tissue biopsy phenotyping of three-dimensional tumours reveals patterns of cancer heterogeneity. Nat. Biomed. Eng. 1, 796–806 (2017).
Google Scholar
Rozenblatt-Rosen, O. et al. The Human Tumor Atlas Network: charting tumor transitions across space and time at single-cell resolution. Cell 181, 236–249 (2020).
Google Scholar
Börner, K. et al. Human BioMolecular Atlas Program (HuBMAP): 3D human reference atlas construction and usage. Nat. Methods 22, 845–860 (2025).
Google Scholar
Snyder, M. P. et al. The human body at cellular resolution: the NIH Human BioMolecular Atlas Program. Nature 574, 187–192 (2019).
Google Scholar
Regev, A. et al. The Human Cell Atlas. eLife 6, e27041 (2017).
Google Scholar
Hricak, H. et al. Medical imaging and nuclear medicine: a Lancet Oncology Commission. Lancet Oncol. 22, e136–e172 (2021).
Google Scholar
Filson, C. P. et al. Prostate cancer detection with magnetic resonance–ultrasound fusion biopsy: the role of systematic and targeted biopsies. Cancer 122, 884–892 (2016).
Google Scholar
Bill-Axelson, A. et al. Radical prostatectomy or watchful waiting in prostate cancer — 29-year follow-up. N. Engl. J. Med. 379, 2319–2329 (2018).
Google Scholar
Yousaf, M. N. et al. Endoscopic ultrasound (EUS) and the management of pancreatic cancer. BMJ Open Gastroenterol. 7, e000408 (2020).
Google Scholar
Smyth, E. C., Nilsson, M., Grabsch, H. I., van Grieken, N. C. T. & Lordick, F. Gastric cancer. Lancet 396, 635–648 (2020).
Google Scholar
Smith-Bindman, R. et al. Projected lifetime cancer risks from current computed tomography imaging. JAMA Intern. Med. https://doi.org/10.1001/jamainternmed.2025.0505 (2025).
Google Scholar
Huang, Y. et al. Longitudinal MRI-based fusion novel model predicts pathological complete response in breast cancer treated with neoadjuvant chemotherapy: a multicenter, retrospective study. eClinicalMedicine 58, 101899 (2023).
Google Scholar
Mckinney, S. M. et al. International evaluation of an AI system for breast cancer screening. Nature 577, 89–94 (2020).
Google Scholar
Goggins, M. et al. Management of patients with increased risk for familial pancreatic cancer: updated recommendations from the International Cancer of the Pancreas Screening (CAPS) Consortium. Gut 69, 7–17 (2020).
Google Scholar
Kanth, P. & Inadomi, J. M. Screening and prevention of colorectal cancer. BMJ https://doi.org/10.1136/bmj.n1855 (2021).
Google Scholar
Chu, L. C. et al. Utility of CT radiomics features in differentiation of pancreatic ductal adenocarcinoma from normal pancreatic tissue. Am. J. Roentgenol. 213, 349–357 (2019).
Google Scholar
Wang, Y. et al. Abdominal multi-organ segmentation with organ-attention networks and statistical fusion. Med. Image Anal. 55, 88–102 (2019).
Google Scholar
Bera, K., Braman, N., Gupta, A., Velcheti, V. & Madabhushi, A. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat. Rev. Clin. Oncol. 19, 132–146 (2022).
Google Scholar
Jiang, Y. et al. Predicting peritoneal recurrence and disease-free survival from CT images in gastric cancer with multitask deep learning: a retrospective study. Lancet Digit. Health 4, e340–e350 (2022).
Google Scholar
Han, Y. et al. Histologic subtype classification of non-small cell lung cancer using PET/CT images. Eur. J. Nucl. Med. Mol. Imaging 48, 350–360 (2021).
Google Scholar
Vanguri, R. S. et al. Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer. Nat. Cancer 3, 1151–1164 (2022).
Google Scholar
Borm, K. J. et al. FDG/PET-CT-based lymph node atlas in breast cancer patients. Int. J. Radiat. Oncol. Biol. Phys. 103, 574–582 (2019).
Google Scholar
Sjöholm, T. et al. A whole-body FDG PET/MR atlas for multiparametric voxel-based analysis. Sci. Rep. https://doi.org/10.1038/s41598-019-42613-z (2019).
Google Scholar
Diot, G. et al. Multispectral optoacoustic tomography (MSOT) of human breast cancer. Clin. Cancer Res. 23, 6912–6922 (2017).
Google Scholar
Leppert, J. et al. Multiphoton excitation of autofluorescence for microscopy of glioma tissue. Neurosurgery 58, 759–767 (2006).
Google Scholar
Bouma, B. E. et al. Optical coherence tomography. Nat. Rev. Methods Primers https://doi.org/10.1038/s43586-022-00162-2 (2022).
Google Scholar
Kiemen, A. L. et al. Tissue clearing and 3D reconstruction of digitized, serially sectioned slides provide novel insights into pancreatic cancer. Med 4, 75–91 (2023).
Google Scholar
Liu, J. T. C. et al. Harnessing non-destructive 3D pathology. Nat. Biomed. Eng. 5, 203–218 (2021).
Google Scholar
Walsh, L. A. & Quail, D. F. Decoding the tumor microenvironment with spatial technologies. Nat. Immunol. 24, 1982–1993 (2023).
Google Scholar
Haddad, T. S. et al. Tutorial: methods for three-dimensional visualization of archival tissue material. Nat. Protoc. 16, 4945–4962 (2021).
Google Scholar
Kiemen, A. L. et al. CODA: quantitative 3D reconstruction of large tissues at cellular resolution. Nat. Methods 19, 1490–1499 (2022). This work introduces a scalable framework for reconstructing large tissues from hundreds to thousands of serial H&E sections at cellular resolution, enabling quantitative 3D reconstruction from routine pathology material.
Google Scholar
Rios, A. C. et al. Intraclonal plasticity in mammary tumors revealed through large-scale single-cell resolution 3D imaging. Cancer Cell 35, 618–632.e6 (2019).
Google Scholar
Matsumoto, K. et al. Advanced CUBIC tissue clearing for whole-organ cell profiling. Nat. Protoc. 14, 3506–3537 (2019).
Google Scholar
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).
Google Scholar
Ertürk, A. et al. Three-dimensional imaging of solvent-cleared organs using 3DISCO. Nat. Protoc. 7, 1983–1995 (2012).
Google Scholar
Renier, N. et al. iDISCO: a simple, rapid method to immunolabel large tissue samples for volume imaging. Cell 159, 896–910 (2014).
Google Scholar
Almagro, J., Messal, H. A., Zaw Thin, M., Van Rheenen, J. & Behrens, A. Tissue clearing to examine tumour complexity in three dimensions. Nat. Rev. Cancer 21, 718–730 (2021).
Google Scholar
Tanaka, N. et al. Three-dimensional single-cell imaging for the analysis of RNA and protein expression in intact tumour biopsies. Nat. Biomed. Eng. 4, 875–888 (2020).
Google Scholar
Olarte, O. E., Andilla, J., Gualda, E. J. & Loza-Alvarez, P. Light-sheet microscopy: a tutorial. Adv. Opt. Photon. 10, 111 (2018).
Google Scholar
Ruscitti, F. et al. Longitudinal assessment of bleomycin-induced lung fibrosis by micro-CT correlates with histological evaluation in mice. Multidiscip. Respir. Med. https://doi.org/10.1186/s40248-017-0089-0 (2017).
Google Scholar
Zangooei, M. H., Margolis, R. & Hoyt, K. Multiscale computational modeling of cancer growth using features derived from microCT images. Sci. Rep. https://doi.org/10.1038/s41598-021-97966-1 (2021).
Google Scholar
Ecclestone, B. R. et al. Three-dimensional virtual histology in unprocessed resected tissues with photoacoustic remote sensing (PARS) microscopy and optical coherence tomography (OCT). Sci. Rep. https://doi.org/10.1038/s41598-021-93222-8 (2021).
Google Scholar
Sui, X. et al. Scalable spatial single-cell transcriptomics and translatomics in 3D thick tissue blocks. Nat. Methods 22, 2574–2584 (2025). This work extends imaging-based spatial molecular profiling from thin sections to thick tissue blocks, enabling scalable 3D single-cell transcriptomic and translatomic (mRNA translation) mapping in intact tissues.
Google Scholar
Fang, R. et al. Three-dimensional single-cell transcriptome imaging of thick tissues. eLife https://doi.org/10.7554/elife.90029.2 (2024).
Google Scholar
Gandin, V. et al. Deep-tissue transcriptomics and subcellular imaging at high spatial resolution. Science https://doi.org/10.1126/science.adq2084 (2025). This work has advanced imaging-based deep-tissue spatial transcriptomics by coupling high-resolution transcript detection with subcellular imaging in thick specimens, substantially expanding the depth and resolution accessible to 3D molecular atlas studies.
Google Scholar
Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).
Google Scholar
Kanatani, S. et al. Whole-brain spatial transcriptional analysis at cellular resolution. Science 386, 907–915 (2024).
Google Scholar
Wang, Y. et al. EASI-FISH for thick tissue defines lateral hypothalamus spatio-molecular organization. Cell 184, 6361–6377.e24 (2021).
Google Scholar
Denk, W. & Horstmann, H. Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure. PLoS Biol. 2, e329 (2004).
Google Scholar
Gage, S. H. Serial sections. In Proc. American Society of Microscopists Seventh Annual Meeting 202–208 (Wiley, 1884).
Junker, J. P. et al. Genome-wide RNA tomography in the zebrafish embryo. Cell 159, 662–675 (2014).
Google Scholar
Sampath Kumar, A. et al. Spatiotemporal transcriptomic maps of whole mouse embryos at the onset of organogenesis. Nat. Genet. 55, 1176–1185 (2023).
Google Scholar
Mo, C.-K. et al. Tumour evolution and microenvironment interactions in 2D and 3D space. Nature 634, 1178–1186 (2024). This work demonstrates that 3D reconstruction refines inference in tumour evolution and microenvironmental interactions beyond what is possible in 2D analyses.
Google Scholar
Xiao, Z. et al. 3D reconstruction of a gastrulating human embryo. Cell 187, 2855–2874.e19 (2024).
Google Scholar
Song, A. H. et al. Artificial intelligence for digital and computational pathology. Nat. Rev. Bioeng. 1, 930–949 (2023).
Google Scholar
Rashid, R. et al. Narrative online guides for the interpretation of digital-pathology images and tissue-atlas data. Nat. Biomed. Eng. 6, 515–526 (2021).
Google Scholar
Lin, J.-R. et al. Multiplexed 3D atlas of state transitions and immune interaction in colorectal cancer. Cell 186, 363–381.e19 (2023). This work established a landmark 3D colorectal cancer atlas showing how tumour cell state transitions and immune interactions are organized across intact tumour volumes.
Google Scholar
Pentimalli, T. M. et al. Combining spatial transcriptomics and ECM imaging in 3D for mapping cellular interactions in the tumor microenvironment. Cell Syst. https://doi.org/10.1016/j.cels.2025.101261 (2025).
Google Scholar
Braxton, A. M. et al. 3D genomic mapping reveals multifocality of human pancreatic precancers. Nature 629, 679–687 (2024). This work has revealed the multifocal architecture of human pancreatic precancers through 3D genomic mapping, illustrating how volumetric analysis can reshape the understanding of tumour initiation.
Google Scholar
Kiemen, A. L. et al. Intraparenchymal metastases as a cause for local recurrence of pancreatic cancer. Histopathology 82, 504–506 (2023).
Google Scholar
Kiemen, A. L. et al. PanIN or IPMN? Redefining lesion size in 3 dimensions. Am. J. Surg. Pathol. 48, 839–845 (2024).
Google Scholar
Forjaz, A. et al. 3D multi-omic mapping of whole nondiseased human fallopian tubes at cellular resolution reveals a large incidence of ovarian cancer precursors. Preprint at bioRxiv https://doi.org/10.1101/2025.09.21.677628 (2025).
Morrison, S. J. & Spradling, A. C. Stem cells and niches: mechanisms that promote stem cell maintenance throughout life. Cell 132, 598–611 (2008).
Google Scholar
Briscoe, J. & Small, S. Morphogen rules: design principles of gradient-mediated embryo patterning. Development 142, 3996–4009 (2015).
Google Scholar
Dezem, F. S. et al. Spatially resolved single-cell omics: methods, challenges, and future perspectives. Annu. Rev. Biomed. Data Sci. https://doi.org/10.1146/annurev-biodatasci-102523-103640 (2024).
Google Scholar
Bressan, D., Battistoni, G. & Hannon, G. J. The dawn of spatial omics. Science https://doi.org/10.1126/science.abq4964 (2023).
Google Scholar
Moffitt, J. R., Lundberg, E. & Heyn, H. The emerging landscape of spatial profiling technologies. Nat. Rev. Genet. 23, 741–759 (2022).
Google Scholar
Lin, J.-R., Fallahi-Sichani, M. & Sorger, P. K. Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method. Nat. Commun. 6, 8390 (2015).
Google Scholar
Rivest, F. et al. Fully automated sequential immunofluorescence (seqIF) for hyperplex spatial proteomics. Sci. Rep. https://doi.org/10.1038/s41598-023-43435-w (2023).
Google Scholar
Schwarz, J. S. et al. Approachable, agile, and precise spatial biology with the cellscape platform. Microsc. Today 32, 37–49 (2024).
Google Scholar
Kinkhabwala, A. et al. MACSima imaging cyclic staining (MICS) technology reveals combinatorial target pairs for CAR T cell treatment of solid tumors. Sci. Rep. https://doi.org/10.1038/s41598-022-05841-4 (2022).
Google Scholar
Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968–981.e15 (2018).
Google Scholar
Black, S. et al. CODEX multiplexed tissue imaging with DNA-conjugated antibodies. Nat. Protoc. 16, 3802–3835 (2021).
Google Scholar
He, S. et al. High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging. Nat. Biotechnol. 40, 1794–1806 (2022).
Google Scholar
Cheng, Y., Burrack, R. K. & Li, Q. Spatially resolved and highly multiplexed protein and RNA in situ detection by combining CODEX with RNAscope in situ hybridization. J. Histochem. Cytochem. 70, 571–581 (2022).
Google Scholar
Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417–422 (2014).
Google Scholar
Schulz, D. et al. Simultaneous multiplexed imaging of mRNA and proteins with subcellular resolution in breast cancer tissue samples by mass cytometry. Cell Syst. 6, 25–36 (2018).
Google Scholar
Kuett, L. et al. Three-dimensional imaging mass cytometry for highly multiplexed molecular and cellular mapping of tissues and the tumor microenvironment. Nat. Cancer 3, 122–133 (2021). This work extends IMC into three dimensions, enabling highly multiplexed single-cell mapping of tumour and microenvironmental architecture across tissue volumes.
Google Scholar
Angelo, M. et al. Multiplexed ion beam imaging of human breast tumors. Nat. Med. 20, 436–442 (2014).
Google Scholar
Keren, L. et al. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 174, 1373–1387.e19 (2018).
Google Scholar
Liu, Y. et al. High-plex protein and whole transcriptome co-mapping at cellular resolution with spatial CITE-seq. Nat. Biotechnol. 41, 1405–1409 (2023).
Google Scholar
Liu, Y. et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 183, 1665–1681.e18 (2020).
Google Scholar
Buchberger, A. R., DeLaney, K., Johnson, J. & Li, L. Mass spectrometry imaging: a review of emerging advancements and future insights. Anal. Chem. 90, 240–265 (2018).
Google Scholar
Mund, A. et al. Deep visual proteomics defines single-cell identity and heterogeneity. Nat. Biotechnol. 40, 1231–1240 (2022).
Google Scholar
Rosenberger, F. A. et al. Deep visual proteomics maps proteotoxicity in a genetic liver disease. Nature https://doi.org/10.1038/s41586-025-08885-4 (2025). This work shows that deep visual proteomics can resolve spatial proteotoxicity programmes in situ, highlighting the power of image-guided proteomics for mechanistic tissue analysis.
Hu, B. et al. High-resolution spatially resolved proteomics of complex tissues based on microfluidics and transfer learning. Cell 188, 734–748.e22 (2025). This work introduces a high-resolution spatial proteomics framework that combines microfluidics with transfer learning, markedly improving the scalability and anatomical precision of tissue proteome mapping.
Google Scholar
Kim, S. et al. PHLI-seq: constructing and visualizing cancer genomic maps in 3D by phenotype-based high-throughput laser-aided isolation and sequencing. Genome Biol. https://doi.org/10.1186/s13059-018-1543-9 (2018).
Google Scholar
Carraro, C. et al. Chromatin accessibility profiling of targeted cell populations with laser capture microdissection coupled to ATAC-seq. Cell Rep. Methods 3, 100598 (2023).
Google Scholar
Schillebeeckx, M. et al. Laser capture microdissection–reduced representation bisulfite sequencing (LCM-RRBS) maps changes in DNA methylation associated with gonadectomy-induced adrenocortical neoplasia in the mouse. Nucleic Acids Res. 41, e116 (2013).
Google Scholar
Zhao, T. et al. Spatial genomics enables multi-modal study of clonal heterogeneity in tissues. Nature 601, 85–91 (2022).
Google Scholar
Chang, L. et al. Droplet Hi-C enables scalable, single-cell profiling of chromatin architecture in heterogeneous tissues. Nat. Biotechnol. 43, 1694–1707 (2025). This work established a scalable single-cell chromatin conformation capture technology for heterogeneous tissues, providing a feasible technology for 3D cancer genome atlas construction.
Google Scholar
Zhou, J. et al. Human body single-cell atlas of 3D genome organization and DNA methylation. Preprint at bioRxiv https://doi.org/10.1101/2025.03.23.644697 (2025).
Deng, Y. et al. Spatial profiling of chromatin accessibility in mouse and human tissues. Nature 609, 375–383 (2022).
Google Scholar
Deng, Y. et al. Spatial-CUT&Tag: spatially resolved chromatin modification profiling at the cellular level. Science 375, 681–686 (2022).
Google Scholar
Wang, S. et al. Spatial organization of chromatin domains and compartments in single chromosomes. Science 353, 598–602 (2016).
Google Scholar
Su, J.-H., Zheng, P., Kinrot, S. S., Bintu, B. & Zhuang, X. Genome-scale imaging of the 3D organization and transcriptional activity of chromatin. Cell 182, 1641–1659.e26 (2020).
Google Scholar
Takei, Y. et al. Spatial multi-omics reveals cell-type-specific nuclear compartments. Nature 641, 1037–1047 (2025).
Google Scholar
Payne, A. C. et al. In situ genome sequencing resolves DNA sequence and structure in intact biological samples. Science 371, eaay3446 (2021).
Google Scholar
Liu, M. et al. Tracing the evolution of single-cell 3D genomes in Kras-driven cancers. Nat. Genet. 57, 3075–3087 (2025). This work generated single-cell 3D genome atlases of mutant KRAS-driven cancers, linking chromatin architecture to tumour evolution in the native tissue context across disease progression.
Google Scholar
Lu, T., Ang, C. E. & Zhuang, X. Spatially resolved epigenomic profiling of single cells in complex tissues. Cell 185, 4448–4464.e17 (2022).
Google Scholar
Chen, Y. et al. Non-disruptive 3D profiling of combinations of epigenetic marks in single cells. Preprint at bioRxiv https://doi.org/10.1101/2025.06.13.659535 (2025).
Schmidt, D. R. et al. Metabolomics in cancer research and emerging applications in clinical oncology. CA Cancer J. Clin. 71, 333–358 (2021).
Google Scholar
Rowles, J. L. & Patti, G. J. Decoding cancer across scales with metabolomics. Nat. Rev. Cancer https://doi.org/10.1038/s41568-026-00908-0 (2026).
Google Scholar
Zhang, Z. et al. When cancer drug resistance meets metabolomics (bulk, single-cell and/or spatial): progress, potential, and perspective. Front. Oncol. https://doi.org/10.3389/fonc.2022.1054233 (2023).
Google Scholar
Min, X. et al. Spatially resolved metabolomics: from metabolite mapping to function visualising. Clin. Transl. Med. https://doi.org/10.1002/ctm2.70031 (2024).
Google Scholar
Caprioli, R. M., Farmer, T. B. & Gile, J. Molecular imaging of biological samples: localization of peptides and proteins using MALDI-TOF MS. Anal. Chem. 69, 4751–4760 (1997).
Google Scholar
Wiseman, J. M., Ifa, D. R., Song, Q. & Cooks, R. G. Tissue imaging at atmospheric pressure using desorption electrospray ionization (DESI) mass spectrometry. Angew. Chem. Int. Ed. 45, 7188–7192 (2006).
Google Scholar
Schwamborn, K. & Caprioli, R. M. Molecular imaging by mass spectrometry — looking beyond classical histology. Nat. Rev. Cancer 10, 639–646 (2010).
Google Scholar
Castro, D. C., Xie, Y. R., Rubakhin, S. S., Romanova, E. V. & Sweedler, J. V. Image-guided MALDI mass spectrometry for high-throughput single-organelle characterization. Nat. Methods 18, 1233–1238 (2021).
Google Scholar
Ly, A. et al. High-mass-resolution MALDI mass spectrometry imaging of metabolites from formalin-fixed paraffin-embedded tissue. Nat. Protoc. 11, 1428–1443 (2016).
Google Scholar
He, Q., Sun, C., Liu, J. & Pan, Y. MALDI-MSI analysis of cancer drugs: significance, advances, and applications. Trends Anal. Chem. 136, 116183 (2021).
Google Scholar
Planque, M., Igelmann, S., Ferreira Campos, A. M. & Fendt, S.-M. Spatial metabolomics principles and application to cancer research. Curr. Opin. Chem. Biol. 76, 102362 (2023).
Google Scholar
He, M. J. et al. Comparing DESI-MSI and MALDI-MSI mediated spatial metabolomics and their applications in cancer studies. Front. Oncol. https://doi.org/10.3389/fonc.2022.891018 (2022).
Google Scholar
Unsihuay, D. et al. High-resolution imaging and identification of biomolecules using nano-DESI coupled to ion mobility spectrometry. Anal. Chim. Acta 1186, 339085 (2021).
Google Scholar
Laskin, J., Heath, B. S., Roach, P. J., Cazares, L. & Semmes, O. J. Tissue imaging using nanospray desorption electrospray ionization mass spectrometry. Anal. Chem. 84, 141–148 (2012).
Google Scholar
Bergman, H.-M. & Lanekoff, I. Profiling and quantifying endogenous molecules in single cells using nano-DESI MS. Analyst 142, 3639–3647 (2017).
Google Scholar
Marques, C. et al. Global and spatial metabolomics of individual cells using a tapered pneumatically assisted nano-DESI probe. J. Am. Soc. Mass Spectrom. 34, 2518–2524 (2023).
Google Scholar
Morse, N. et al. Reliable identification of prostate cancer using mass spectrometry metabolomic imaging in needle core biopsies. Lab. Investig. 99, 1561–1571 (2019).
Google Scholar
Seeley, E. H. & Caprioli, R. M. 3D imaging by mass spectrometry: a new frontier. Anal. Chem. 84, 2105–2110 (2012).
Google Scholar
Randall, E. C. et al. Integrated mapping of pharmacokinetics and pharmacodynamics in a patient-derived xenograft model of glioblastoma. Nat. Commun. 9, 4904 (2018).
Google Scholar
Freudiger, C. W. et al. Label-free biomedical imaging with high sensitivity by stimulated Raman scattering microscopy. Science 322, 1857–1861 (2008).
Google Scholar
Zumbusch, A., Holtom, G. R. & Xie, X. S. Three-dimensional vibrational imaging by coherent anti-Stokes Raman scattering. Phys. Rev. Lett. 82, 4142–4145 (1999).
Google Scholar
Hamada, K. et al. Raman microscopy for dynamic molecular imaging of living cells. J. Biomed. Opt. 13, 044027 (2008).
Google Scholar
Jang, H. et al. Super-resolution SRS microscopy with A-PoD. Nat. Methods 20, 448–458 (2023). This work provides a super-resolution SRS framework for label-free chemical imaging at subcellular resolution, supporting nanoscale studies of cellular metabolism.
Google Scholar
Klar, T. A., Jakobs, S., Dyba, M., Egner, A. & Hell, S. W. Fluorescence microscopy with diffraction resolution barrier broken by stimulated emission. Proc. Natl Acad. Sci. USA 97, 8206–8210 (2000).
Google Scholar
Ilgen, P. et al. STED super-resolution microscopy of clinical paraffin-embedded human rectal cancer tissue. PLoS ONE 9, e101563 (2014).
Google Scholar
Rust, M. J., Bates, M. & Zhuang, X. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat. Methods 3, 793–796 (2006).
Google Scholar
Betzig, E. et al. Imaging intracellular fluorescent proteins at nanometer resolution. Science 313, 1642–1645 (2006).
Google Scholar
Hess, S. T., Girirajan, T. P. K. & Mason, M. D. Ultra-high resolution imaging by fluorescence photoactivation localization microscopy. Biophys. J. 91, 4258–4272 (2006).
Google Scholar
Xu, J. et al. Super-resolution imaging reveals the evolution of higher-order chromatin folding in early carcinogenesis. Nat. Commun. 11, 1899 (2020).
Google Scholar
Paidi, S. K. et al. Label-free Raman spectroscopy reveals signatures of radiation resistance in the tumor microenvironment. Cancer Res. 79, 2054–2064 (2019).
Google Scholar
Elumalai, S., Managó, S. & De Luca, A. C. Raman microscopy: progress in research on cancer cell sensing. Sensors 20, 5525 (2020).
Google Scholar
Du, J. et al. Raman-guided subcellular pharmaco-metabolomics for metastatic melanoma cells. Nat. Commun. 11, 4830 (2020).
Google Scholar
Becker, L. et al. Raman imaging and fluorescence lifetime imaging microscopy for diagnosis of cancer state and metabolic monitoring. Cancers 13, 5682 (2021).
Google Scholar
Lita, A. et al. Toward single-organelle lipidomics in live cells. Anal. Chem. 91, 11380–11387 (2019).
Google Scholar
Karuna, A. et al. Label-free volumetric quantitative imaging of the human somatic cell division by hyperspectral coherent anti-Stokes Raman scattering. Anal. Chem. 91, 2813–2821 (2019).
Google Scholar
Le, T. T., Huff, T. B. & Cheng, J.-X. Coherent anti-Stokes Raman scattering imaging of lipids in cancer metastasis. BMC Cancer 9, 42 (2009).
Google Scholar
Guerenne-Del Ben, T. et al. Multiplex coherent anti-Stokes Raman scattering microspectroscopy detection of lipid droplets in cancer cells expressing TrkB. Sci. Rep. https://doi.org/10.1038/s41598-020-74021-z (2020).
Google Scholar
de Albuquerque, C. D. L. & Schultz, Z. D. Super-resolution surface-enhanced Raman scattering imaging of single particles in cells. Anal. Chem. 92, 9389–9398 (2020).
Google Scholar
Zhang, W. et al. Multi-molecular hyperspectral PRM-SRS microscopy. Nat. Commun. 15, 1599 (2024).
Google Scholar
Huang, K.-C., Li, J., Zhang, C., Tan, Y. & Cheng, J.-X. Multiplex stimulated Raman scattering imaging cytometry reveals lipid-rich protrusions in cancer cells under stress condition. iScience 23, 100953 (2020).
Google Scholar
Zhang, L. et al. Spectral tracing of deuterium for imaging glucose metabolism. Nat. Biomed. Eng. 3, 402–413 (2019).
Google Scholar
Shi, L. et al. Optical imaging of metabolic dynamics in animals. Nat. Commun. 9, 2995 (2018).
Google Scholar
Li, Y. et al. Imaging cellular metabolic rewiring with SuMMIT-SRS. Preprint at bioRxiv https://doi.org/10.1101/2025.10.10.681769 (2025).
Bagheri, P., Hoang, K., Fung, A. A., Hussain, S. & Shi, L. Visualizing cancer cell metabolic dynamics regulated with aromatic amino acids using DO-SRS and 2PEF microscopy. Front. Mol. Biosci. https://doi.org/10.3389/fmolb.2021.779702 (2021).
Google Scholar
Fung, A. A. et al. Imaging sub-cellular methionine and insulin interplay in triple negative breast cancer lipid droplet metabolism. Front. Oncol. https://doi.org/10.3389/fonc.2022.858017 (2022).
Google Scholar
Tan, Y., Lin, H. & Cheng, J.-X. Profiling single cancer cell metabolism via high-content SRS imaging with chemical sparsity. Sci. Adv. https://doi.org/10.1126/sciadv.adg6061 (2023).
Google Scholar
Wei, M. et al. Volumetric chemical imaging by clearing-enhanced stimulated Raman scattering microscopy. Proc. Natl Acad. Sci. USA 116, 6608–6617 (2019).
Google Scholar
Fung, A. A. et al. Label-free multimodal optical biopsy reveals biomolecular and morphological features of diabetic kidney tissue in 2D and 3D. Nat. Commun. 16, 4509 (2025).
Google Scholar
Vickovic, S. et al. SM-Omics is an automated platform for high-throughput spatial multi-omics. Nat. Commun. 13, 795 (2022).
Google Scholar
Ben-Chetrit, N. et al. Integration of whole transcriptome spatial profiling with protein markers. Nat. Biotechnol. 41, 788–793 (2023).
Google Scholar
Zhang, D. et al. Spatial dynamics of brain development and neuroinflammation. Nature 647, 213–227 (2025). This work combines spatial tri-omic profiling with large-scale reconstruction to resolve developmental and inflammatory dynamics in the brain.
Google Scholar
Guo, P. et al. Multiplexed spatial mapping of chromatin features, transcriptome and proteins in tissues. Nat. Methods 22, 520–529 (2025).
Google Scholar
Enninful, A. et al. Integration of imaging-based and sequencing-based spatial omics mapping on the same tissue section via DBiTplus. Nat. Methods https://doi.org/10.1038/s41592-025-02948-0 (2026).
Google Scholar
Lee, C. N., Fu, H., Cardilla, A., Zhou, W. & Deng, Y. Spatial joint profiling of DNA methylome and transcriptome in tissues. Nature 646, 1261–1271 (2025).
Google Scholar
Mateo, L. J. et al. Visualizing DNA folding and RNA in embryos at single-cell resolution. Nature 568, 49–54 (2019).
Google Scholar
Liu, M. et al. Chromatin tracing and multiplexed imaging of nucleome architectures (MINA) and RNAs in single mammalian cells and tissue. Nat. Protoc. 16, 2667–2697 (2021).
Google Scholar
Liu, M. et al. Multiplexed imaging of nucleome architectures in single cells of mammalian tissue. Nat. Commun. 11, 2907 (2020).
Google Scholar
Mlecnik, B. et al. Integrative analyses of colorectal cancer show immunoscore is a stronger predictor of patient survival than microsatellite instability. Immunity 44, 698–711 (2016).
Google Scholar
Jia, G. et al. Spatial immune scoring system predicts hepatocellular carcinoma recurrence. Nature 640, 1031–1041 (2025).
Google Scholar
Hwang, W. L. et al. Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment. Nat. Genet. 54, 1178–1191 (2022).
Google Scholar
Khaliq, A. M. et al. Spatial transcriptomic analysis of primary and metastatic pancreatic cancers highlights tumor microenvironmental heterogeneity. Nat. Genet. 56, 2455–2465 (2024).
Google Scholar
Danenberg, E. et al. Breast tumor microenvironment structures are associated with genomic features and clinical outcome. Nat. Genet. 54, 660–669 (2022).
Google Scholar
Greenwald, A. C. et al. Integrative spatial analysis reveals a multi-layered organization of glioblastoma. Cell 187, 2485–2501.e26 (2024).
Google Scholar
Klughammer, J. et al. A multi-modal single-cell and spatial expression map of metastatic breast cancer biopsies across clinicopathological features. Nat. Med. 30, 3236–3249 (2024).
Google Scholar
Islam, M. et al. Temporal recording of mammalian development and precancer. Nature 634, 1187–1195 (2024).
Google Scholar
Zhou, X., Shi, B., Huang, G., Liu, J. & Wei, W. Trends in cancer imaging. Trends Cancer 10, 1023–1037 (2024).
Google Scholar
Acosta, J. N., Falcone, G. J., Rajpurkar, P. & Topol, E. J. Multimodal biomedical AI. Nat. Med. 28, 1773–1784 (2022).
Google Scholar
Elhanani, O., Ben-Uri, R. & Keren, L. Spatial profiling technologies illuminate the tumor microenvironment. Cancer Cell 41, 404–420 (2023).
Google Scholar
Baker, G. J. et al. Quality control for single-cell analysis of high-plex tissue profiles using CyLinter. Nat. Methods 21, 2248–2259 (2024).
Google Scholar
Wernersson, E. et al. Deconwolf enables high-performance deconvolution of widefield fluorescence microscopy images. Nat. Methods 21, 1245–1256 (2024).
Google Scholar
Pang, M., Roy, T. K., Wu, X. & Tan, K. Cellotype: a unified model for segmentation and classification of tissue images. Nat. Methods 22, 348–357 (2025).
Google Scholar
Greenwald, N. F. et al. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nat. Biotechnol. 40, 555–565 (2022).
Google Scholar
Stringer, C. & Pachitariu, M. Cellpose3: one-click image restoration for improved cellular segmentation. Nat. Methods 22, 592–599 (2025).
Google Scholar
Pachitariu, M. & Stringer, C. Cellpose 2.0: how to train your own model. Nat. Methods 19, 1634–1641 (2022).
Google Scholar
Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100–106 (2021).
Google Scholar
Schapiro, D. et al. MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging. Nat. Methods 19, 311–315 (2022).
Google Scholar
Navarro, J. F., Sjöstrand, J., Salmén, F., Lundeberg, J. & Ståhl, P. L. ST Pipeline: an automated pipeline for spatial mapping of unique transcripts. Bioinformatics 33, 2591–2593 (2017).
Google Scholar
Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
Google Scholar
Satpathy, A. T. et al. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat. Biotechnol. 37, 925–936 (2019).
Google Scholar
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).
Google Scholar
Hao, Y. et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat. Biotechnol. 42, 293–304 (2024).
Google Scholar
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).
Google Scholar
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
Google Scholar
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).
Google Scholar
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. https://doi.org/10.1186/s13059-017-1382-0 (2018).
Google Scholar
Granja, J. M. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 53, 403–411 (2021).
Google Scholar
Biancalani, T. et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat. Methods 18, 1352–1362 (2021).
Google Scholar
Welch, J. D. et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177, 1873–1887.e17 (2019).
Google Scholar
Kleshchevnikov, V. et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat. Biotechnol. 40, 661–671 (2022).
Google Scholar
Chen, S. et al. Integration of spatial and single-cell data across modalities with weakly linked features. Nat. Biotechnol. 42, 1096–1106 (2024).
Google Scholar
Yang, P. et al. Spatial integration of multi-omics single-cell data with SIMO. Nat. Commun. 16, 1265 (2025).
Google Scholar
Tang, Z. et al. Search and match across spatial omics samples at single-cell resolution. Nat. Methods 21, 1818–1829 (2024).
Google Scholar
Bao, F. et al. Integrative spatial analysis of cell morphologies and transcriptional states with MUSE. Nat. Biotechnol. 40, 1200–1209 (2022).
Google Scholar
Long, Y. et al. Deciphering spatial domains from spatial multi-omics with SpatialGlue. Nat. Methods 21, 1658–1667 (2024).
Google Scholar
Coleman, K. et al. Resolving tissue complexity by multimodal spatial omics modeling with MISO. Nat. Methods 22, 530–538 (2025).
Google Scholar
Ertürk, A. Deep 3D histology powered by tissue clearing, omics and AI. Nat. Methods 21, 1153–1165 (2024).
Google Scholar
Lyman, M. R. et al. Spatial proteomics and transcriptomics reveal early immune cell organization in pancreatic intraepithelial neoplasia. JCI Insight https://doi.org/10.1172/jci.insight.191595 (2025).
Google Scholar
Bell, A. T. F. et al. PanIN and CAF transitions in pancreatic carcinogenesis revealed with spatial data integration. Cell Syst. 15, 753–769.e5 (2024).
Google Scholar
Forjaz, A. et al. PIVOT: an open-source tool for multi-omic spatial data registration. Preprint at bioRxiv https://doi.org/10.1101/2025.06.08.658506 (2025).
Zhou, X., Dong, K. & Zhang, S. Integrating spatial transcriptomics data across different conditions, technologies and developmental stages. Nat. Comput. Sci. 3, 894–906 (2023).
Google Scholar
Xia, C.-R., Cao, Z.-J., Tu, X.-M. & Gao, G. Spatial-linked alignment tool (SLAT) for aligning heterogenous slices. Nat. Commun. 14, 7236 (2023).
Google Scholar
Zeira, R., Land, M., Strzalkowski, A. & Raphael, B. J. Alignment and integration of spatial transcriptomics data. Nat. Methods 19, 567–575 (2022).
Google Scholar
Qiu, X. et al. Spatiotemporal modeling of molecular holograms. Cell 187, 7351–7373.e61 (2024). This work introduces a spatiotemporal modelling framework for molecular holograms that enables large-scale reconstruction, alignment and interrogation of whole-organism 3D cellular atlases.
Google Scholar
Klein, D. et al. Mapping cells through time and space with moscot. Nature 638, 1065–1075 (2025). This work established a scalable optimal-transport framework for mapping cells through time and space, offering a powerful computational foundation for reconstructing cellular trajectories in atlas-scale datasets.
Google Scholar
Joshi, S. et al. InterpolAI: deep learning-based optical flow interpolation and restoration of biomedical images for improved 3D tissue mapping. Nat. Methods 22, 1556–1567 (2025).
Google Scholar
Reda, F. et al. FILM: frame interpolation for large motion. In 17th European Conference on Computer Vision (ECCV 2022) 250–266 (Springer Nature, 2022).
Sim, H., Oh, J. & Kim, M. XVFI: eXtreme video frame interpolation. In Proc. IEEE/CVF International Conference on Computer Vision (ICCV) 14489–14498 (IEEE, 2021).
Isola, P., Zhu, J.-Y., Zhou, T. & Efros, A. A. Image-to-image translation with conditional adversarial networks. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 1125–1134 (IEEE, 2017).
Liu, M.-Y., Breuel, T. & Kautz, J. Unsupervised image-to-image translation networks. In 31st Conference on Neural Information Processing Systems (NIPS 2017) (NeurIPS Foundation, 2017).
Zhu, J.-Y. et al. Toward multimodal image-to-image translation. In 31st Conference on Neural Information Processing Systems (NIPS 2017) (NeurIPS Foundation, 2017).
Qiu, F., Zhang, Y., Huang, Z.-L., Zhu, X. & Wang, Z. PASB: pathology-aware Schrödinger bridge for virtual immunohistochemical staining. Med. Image Anal. 108, 103869 (2026).
Google Scholar
Bai, B. et al. Deep learning-enabled virtual histological staining of biological samples. Light Sci. Appl. https://doi.org/10.1038/s41377-023-01104-7 (2023).
Google Scholar
Serafin, R., Xie, W., Gaser, A. K. & Liu, J. T. C. FalseColor-Python: a rapid intensity-leveling and digital-staining package for fluorescence-based slide-free digital pathology. PLoS ONE 15, e0233198 (2020).
Google Scholar
Giacomelli, M. G. et al. Virtual hematoxylin and eosin transillumination microscopy using epi-fluorescence imaging. PLoS ONE 11, e0159337 (2016).
Google Scholar
Zhang, D. et al. Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology. Nat. Biotechnol. 42, 1372–1377 (2024).
Google Scholar
Almagro-Pérez, C. et al. AI-driven 3D spatial transcriptomics. Preprint at https://doi.org/10.48550/arxiv.2502.17761 (2025).
Moen, E. et al. Deep learning for cellular image analysis. Nat. Methods 16, 1233–1246 (2019).
Google Scholar
Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J. & Maier-Hein, K. H. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18, 203–211 (2021).
Google Scholar
Hollon, T. C. et al. Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Nat. Med. 26, 52–58 (2020).
Google Scholar
Cui, H. et al. Towards multimodal foundation models in molecular cell biology. Nature 640, 623–633 (2025).
Google Scholar
Heimberg, G. et al. A cell atlas foundation model for scalable search of similar human cells. Nature 638, 1085–1094 (2025).
Google Scholar
Hao, M. et al. Large-scale foundation model on single-cell transcriptomics. Nat. Methods 21, 1481–1491 (2024).
Google Scholar
Tejada-Lapuerta, A. et al. Nicheformer: a foundation model for single-cell and spatial omics. Nat. Methods 22, 2525–2538 (2025).
Google Scholar
Cui, H. et al. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat. Methods 21, 1470–1480 (2024).
Google Scholar
Yu, T. et al. Tissue reassembly with generative AI. Preprint at bioRxiv https://doi.org/10.1101/2025.02.13.638045 (2025).
Lin, Y. et al. ST-Align: a multimodal foundation model for image-gene alignment in spatial transcriptomics. Preprint at https://doi.org/10.48550/arxiv.2411.16793 (2024).
Chen, W. et al. A visual-omics foundation model to bridge histopathology with spatial transcriptomics. Nat. Methods 22, 1568–1582 (2025).
Google Scholar
Schapiro, D. et al. histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data. Nat. Methods 14, 873–876 (2017).
Google Scholar
Zhu, Q., Shah, S., Dries, R., Cai, L. & Yuan, G.-C. Identification of spatially associated subpopulations by combining scRNAseq and sequential fluorescence in situ hybridization data. Nat. Biotechnol. 36, 1183–1190 (2018).
Google Scholar
Hu, J. et al. SpaGCN: integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nat. Methods 18, 1342–1351 (2021).
Google Scholar
Jerby-Arnon, L. & Regev, A. DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data. Nat. Biotechnol. 40, 1467–1477 (2022).
Google Scholar
Zhao, E. et al. Spatial transcriptomics at subspot resolution with BayesSpace. Nat. Biotechnol. 39, 1375–1384 (2021).
Google Scholar
Chidester, B., Zhou, T., Alam, S. & Ma, J. SpiceMix enables integrative single-cell spatial modeling of cell identity. Nat. Genet. 55, 78–88 (2023).
Google Scholar
Svensson, V., Teichmann, S. A. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat. Methods 15, 343–346 (2018).
Google Scholar
Nitzan, M., Karaiskos, N., Friedman, N. & Rajewsky, N. Gene expression cartography. Nature 576, 132–137 (2019).
Google Scholar
Sun, S., Zhu, J. & Zhou, X. Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies. Nat. Methods 17, 193–200 (2020).
Google Scholar
Armingol, E., Officer, A., Harismendy, O. & Lewis, N. E. Deciphering cell–cell interactions and communication from gene expression. Nat. Rev. Genet. 22, 71–88 (2021).
Google Scholar
Aivazidis, A. et al. Cell2fate infers RNA velocity modules to improve cell fate prediction. Nat. Methods 22, 698–707 (2025).
Google Scholar
Palla, G. et al. Squidpy: a scalable framework for spatial omics analysis. Nat. Methods 19, 171–178 (2022).
Google Scholar
Dries, R. et al. Giotto: a toolbox for integrative analysis and visualization of spatial expression data. Genome Biol. https://doi.org/10.1186/s13059-021-02286-2 (2021).
Google Scholar
Gayoso, A. et al. A Python library for probabilistic analysis of single-cell omics data. Nat. Biotechnol. 40, 163–166 (2022).
Google Scholar
Ma, C. et al. Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics. Nat. Methods 21, 2239–2247 (2024).
Google Scholar
Lee, S., Min, H. & Yoon, S. Will solid-state drives accelerate your bioinformatics? In-depth profiling, performance analysis and beyond. Brief. Bioinform. 17, 713–727 (2016).
Google Scholar
Fraga-González, G. et al. Affording reusable data: recommendations for researchers from a data-intensive project. Sci. Data https://doi.org/10.1038/s41597-025-04565-0 (2025).
Google Scholar
Suryadevara, V. et al. SenNet recommendations for detecting senescent cells in different tissues. Nat. Rev. Mol. Cell Biol. 25, 1001–1023 (2024).
Google Scholar
Keller, M. S. et al. Vitessce: integrative visualization of multimodal and spatially resolved single-cell data. Nat. Methods 22, 63–67 (2025).
Google Scholar
Berry, S. et al. Analysis of multispectral imaging with the AstroPath platform informs efficacy of PD-1 blockade. Science 372, eaba2609 (2021).
Google Scholar
Nichterwitz, S. et al. Laser capture microscopy coupled with Smart-seq2 for precise spatial transcriptomic profiling. Nat. Commun. 7, 12139 (2016).
Google Scholar
Chen, J. et al. Spatial transcriptomic analysis of cryosectioned tissue samples with Geo-seq. Nat. Protoc. 12, 566–580 (2017).
Google Scholar
Peng, G. et al. Spatial transcriptome for the molecular annotation of lineage fates and cell identity in mid-gastrula mouse embryo. Dev. Cell 36, 681–697 (2016).
Google Scholar
Lovatt, D. et al. Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue. Nat. Methods 11, 190–196 (2014).
Google Scholar
Honda, M. et al. High-depth spatial transcriptome analysis by photo-isolation chemistry. Nat. Commun. 12, 4416 (2021).
Google Scholar
Kishi, J. Y. et al. Light-Seq: light-directed in situ barcoding of biomolecules in fixed cells and tissues for spatially indexed sequencing. Nat. Methods 19, 1393–1402 (2022).
Google Scholar
Hu, K. H. et al. ZipSeq: barcoding for real-time mapping of single cell transcriptomes. Nat. Methods 17, 833–843 (2020).
Google Scholar
Lee, A. C. et al. Spatial epitranscriptomics reveals A-to-I editome specific to cancer stem cell microniches. Nat. Commun. 13, 2540 (2022).
Google Scholar
Venkatramani, A. et al. Multiplexed optical barcoding and sequencing for spatial omics. Sci. Rep. https://doi.org/10.1038/s41598-026-41186-y (2026).
Google Scholar
Merritt, C. R. et al. Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat. Biotechnol. 38, 586–599 (2020).
Google Scholar
Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).
Google Scholar
Vickovic, S. et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987–990 (2019).
Google Scholar
Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).
Google Scholar
Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313–319 (2021).
Google Scholar
Russell, A. J. C. et al. Slide-tags enables single-nucleus barcoding for multimodal spatial genomics. Nature 625, 101–109 (2024).
Google Scholar
Cho, C.-S. et al. Microscopic examination of spatial transcriptome using Seq-Scope. Cell 184, 3559–3572.e22 (2021).
Google Scholar
Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777–1792.e21 (2022).
Google Scholar
Schott, M. et al. Open-ST: high-resolution spatial transcriptomics in 3D. Cell 187, 3953–3972.e26 (2024).
Google Scholar
Fu, X. et al. Polony gels enable amplifiable DNA stamping and spatial transcriptomics of chronic pain. Cell 185, 4621–4633.e17 (2022).
Google Scholar
Bai, Z. et al. Spatially exploring RNA biology in archival formalin-fixed paraffin-embedded tissues. Cell 187, 6760–6779.e24 (2024). This work extends spatial transcriptomics in FFPE tissue from gene-expression mapping to broader RNA biology, including RNA processing and variation, thereby increasing the clinical utility of archival specimens for spatial oncology.
Google Scholar
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).
Google Scholar
Moffitt, J. R. et al. Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362, eaau5324 (2018).
Google Scholar
Cheng, Y. et al. Sequencing-free whole-genome spatial transcriptomics at single-molecule resolution. Cell https://doi.org/10.1016/j.cell.2025.09.006 (2025). This work introduces a sequencing-free, single-molecule spatial transcriptomics strategy with whole-genome scope, representing a notable advance in throughput and spatial precision for imaging-based atlas construction.
Google Scholar
Eng, C.-H. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+. Nature 568, 235–239 (2019).
Google Scholar
Codeluppi, S. et al. Spatial organization of the somatosensory cortex revealed by osmFISH. Nat. Methods 15, 932–935 (2018).
Google Scholar
Kishi, J. Y. et al. SABER amplifies FISH: enhanced multiplexed imaging of RNA and DNA in cells and tissues. Nat. Methods 16, 533–544 (2019).
Google Scholar
Dardani, I. et al. ClampFISH 2.0 enables rapid, scalable amplified RNA detection in situ. Nat. Methods 19, 1403–1410 (2022).
Google Scholar
Rouhanifard, S. H. et al. ClampFISH detects individual nucleic acid molecules using click chemistry-based amplification. Nat. Biotechnol. 37, 84–89 (2019).
Google Scholar
Sountoulidis, A. et al. SCRINSHOT enables spatial mapping of cell states in tissue sections with single-cell resolution. PLoS Biol. 18, e3000675 (2020).
Google Scholar
Lee, J. H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014).
Google Scholar
Chen, X., Sun, Y.-C., Church, G. M., Lee, J. H. & Zador, A. M. Efficient in situ barcode sequencing using padlock probe-based BaristaSeq. Nucleic Acids Res. 46, e22 (2018).
Google Scholar
Liu, S. et al. Barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel in situ analyses. Nucleic Acids Res. 49, e58 (2021).
Google Scholar
Alon, S. et al. Expansion sequencing: spatially precise in situ transcriptomics in intact biological systems. Science 371, eaax2656 (2021).
Google Scholar
Wu, X. et al. Spatial multi-omics at subcellular resolution via high-throughput in situ pairwise sequencing. Nat. Biomed. Eng. 8, 872–889 (2024).
Google Scholar
Saka, S. K. et al. Immuno-SABER enables highly multiplexed and amplified protein imaging in tissues. Nat. Biotechnol. 37, 1080–1090 (2019).
Google Scholar
Radtke, A. J. et al. IBEX: a versatile multiplex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues. Proc. Natl Acad. Sci. USA 117, 33455–33465 (2020).
Google Scholar
Gut, G., Herrmann, M. D. & Pelkmans, L. Multiplexed protein maps link subcellular organization to cellular states. Science 361, eaar7042 (2018).
Google Scholar
Hu, T. et al. Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology. Nat. Commun. 14, 8260 (2023).
Google Scholar
Ribeiro Da Cunha, B., Fonseca, L. P. & Calado, C. R. C. Metabolic fingerprinting with Fourier-transform infrared (FTIR) spectroscopy: towards a high-throughput screening assay for antibiotic discovery and mechanism-of-action elucidation. Metabolites 10, 145 (2020).
Google Scholar
Baker, M. J. et al. Using Fourier transform IR spectroscopy to analyze biological materials. Nat. Protoc. 9, 1771–1791 (2014).
Google Scholar
Shuster, S. O., Curtis, A. E. & Davis, C. M. Optical photothermal infrared imaging using metabolic probes in biological systems. Preprint at bioRxiv https://doi.org/10.1101/2024.09.19.613881 (2024).
Zhang, D. et al. Spatial epigenome–transcriptome co-profiling of mammalian tissues. Nature 616, 113–122 (2023).
Google Scholar
Li, H. et al. Spatially resolved genome-wide joint profiling of epigenome and transcriptome with spatial-ATAC-RNA-seq and spatial-CUT&Tag-RNA-seq. Nat. Protoc. 20, 2383–2417 (2025).
Google Scholar
Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat. Protoc. 15, 1484–1506 (2020).
Google Scholar
Dahlberg, S. K., Bonet, D. F., Franzén, L., Ståhl, P. L. & Hoffecker, I. T. Hidden network preserved in Slide-tags data allows reference-free spatial reconstruction. Nat. Commun. 16, 9652 (2025).
Google Scholar
Femino, A. M., Fay, F. S., Fogarty, K. & Singer, R. H. Visualization of single RNA transcripts in situ. Science 280, 585–590 (1998).
Google Scholar
Xia, C., Babcock, H. P., Moffitt, J. R. & Zhuang, X. Multiplexed detection of RNA using MERFISH and branched DNA amplification. Sci. Rep. https://doi.org/10.1038/s41598-019-43943-8 (2019).
Google Scholar
Moffitt, J. R. et al. High-performance multiplexed fluorescence in situ hybridization in culture and tissue with matrix imprinting and clearing. Proc. Natl Acad. Sci. USA 113, 14456–14461 (2016).
Google Scholar
Chen, F., Tillberg, P. W. & Boyden, E. S. Expansion microscopy. Science 347, 543–548 (2015).
Google Scholar
Xia, C., Fan, J., Emanuel, G., Hao, J. & Zhuang, X. Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression. Proc. Natl Acad. Sci. USA 116, 19490–19499 (2019).
Google Scholar
Wang, G., Moffitt, J. R. & Zhuang, X. Multiplexed imaging of high-density libraries of RNAs with MERFISH and expansion microscopy. Sci. Rep. https://doi.org/10.1038/s41598-018-22297-7 (2018).
Google Scholar
Shah, S., Lubeck, E., Zhou, W. & Cai, L. In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus. Neuron 92, 342–357 (2016).
Google Scholar
Cohen, L. et al. Whole-transcriptome-scale and isoform-resolved spatial imaging of single cells in complex tissues. Preprint at bioRxiv https://doi.org/10.1101/2025.08.27.672533 (2025).
Vu, T. et al. Spatial transcriptomics using combinatorial fluorescence spectral and lifetime encoding, imaging and analysis. Nat. Commun. 13, 169 (2022).
Google Scholar
Lee, J. Y. et al. Absolute quantitation of individual SARS-CoV-2 RNA molecules provides a new paradigm for infection dynamics and variant differences. eLife 11, e74153 (2022).
Google Scholar
Borm, L. E. et al. Scalable in situ single-cell profiling by electrophoretic capture of mRNA using EEL FISH. Nat. Biotechnol. 41, 222–231 (2022).
Google Scholar
Ke, R. et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nat. Methods 10, 857–860 (2013).
Google Scholar
Lee, J. H. et al. Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues. Nat. Protoc. 10, 442–458 (2015).
Google Scholar
