Optical coherence photoacoustic microscopy for 3D cancer model imaging with AI-assisted organoid analysis

Abigail J. Deloria; Agnes Csiszar; Shiyu Deng; Mohammad Ali Sabbaghi; Francesco Branciforti; Lukasz Bugyi; Giulia Rotunno; Richard Haindl; Rainer Leitgeb; Massimo Salvi; Manojit Pramanik; Yi Yuan; Leopold Schmetterer; Gergely Szakacs; Wolfgang Drexler; Kristen M. Meiburger; Mengyang Liu

doi:10.1038/s41377-025-02177-2

Your Location：

Home >

Browse articles >

Optical coherence photoacoustic microscopy for 3D cancer model imaging with AI-assisted organoid analysis

Original Articles

- Optical coherence photoacoustic microscopy for 3D cancer model imaging with AI-assisted organoid analysis
- Optical coherence photoacoustic microscopy for 3D cancer model imaging with AI-assisted organoid analysis
- Light: Science & Applications 2026年15卷第4期页码：1215-1229
- Affiliations：
  
  1.Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
  2.Center for Cancer Research, Medical University of Vienna, Vienna, Austria
  3.PolitoBIOMed Lab, Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
  4.Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, USA
  5.School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei, China
  6.Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria
  7.Singapore Eye Research Institute, Singapore, Singapore
  8.SERI-NTU Advanced Ocular Engineering (STANCE) Laboratory, Singapore, Singapore
  9.Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore, Singapore
  10.Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
  11.School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore, Singapore
  12.Fondation Ophthalmologique Adolphe De Rothschild, Paris, France
- Author bio：
  
  Mengyang Liu (mengyang.liu@meduniwien.ac.at)
- Funds：
- DOI：10.1038/s41377-025-02177-2
  中图分类号：
- 收稿：2025-04-11，
  
  修回：2025-12-04，
  
  录用：2025-12-20，
  
  网络首发：2026-02-05，
  
  纸质出版：2026-04-30
- Accepted：
Scan QR Code
Abigail J. Deloria, Agnes Csiszar, Shiyu Deng, 等. Optical coherence photoacoustic microscopy for 3D cancer model imaging with AI-assisted organoid analysis[J]. Light: Science & Applications, 2026,15(4):1215-1229.

Deloria, A. J. et al. Optical coherence photoacoustic microscopy for 3D cancer model imaging with AI-assisted organoid analysis. Light: Science & Applications, 15, 1215-1229 (2026).
Abigail J. Deloria, Agnes Csiszar, Shiyu Deng, 等. Optical coherence photoacoustic microscopy for 3D cancer model imaging with AI-assisted organoid analysis[J]. Light: Science & Applications, 2026,15(4):1215-1229. DOI： 10.1038/s41377-025-02177-2.

Deloria, A. J. et al. Optical coherence photoacoustic microscopy for 3D cancer model imaging with AI-assisted organoid analysis. Light: Science & Applications, 15, 1215-1229 (2026). DOI： 10.1038/s41377-025-02177-2.

摘要

Abstract

Cancer organoids and cancer spheroids are 3D cell culture models with distinct yet overlapping purposes in cancer research. Various commercially available optical imaging techniques have been employed to study these cell cultures

but these methods suffer from various limitations such as the requirement of fluorescence labeling

complicated sample handling

and limited image volume size. In this work

we demonstrate a multimodal optical coherence photoacoustic microscopy (OC-PAM) system for the study of these models

overcoming these limitations. We first performed a longitudinal study using optical coherence microscopy (OCM) for breast cancer organoids. Using the OCM imaging results

artificial intelligence (AI)-based algorithms were developed to automatically segment individual organoids and classify their viability over time using a radiomics texture feature approach

enabling robust

quantitative tracking and classification at the single-organoid level. To supplement OCM's contrast

we then performed OC-PAM imaging of spheroid models with both melanin positive and melanin negative cells. In the second study

the OC-PAM images clearly mapped the distribution of melanin positive cells hidden amongst melanin negative cells. These results suggest that OC-PAM coupled with AI techniques can be a powerful tool to study cancer organoids and cancer spheroids.

关键词

Keywords

references

Gunti, S. et al. Organoid and spheroid tumor models: techniques and applications. Cancers 13 , 874, https://doi.org/10.3390/cancers13040874 (2021)..

Zhao, Z. X. et al. Organoids. Nat. Rev. Methods Prim. 2 , 94, https://doi.org/10.1038/s43586-022-00174-y (2022)..

Pinto, B. et al. Three-dimensional spheroids as in vitro preclinical models for cancer research. Pharmaceutics 12 , 1186, https://doi.org/10.3390/pharmaceutics12121186 (2020)..

Quereda, V. et al. A cytotoxic three-dimensional-spheroid, high-throughput assay using patient-derived glioma stem cells. SLAS Discov. 23 , 842–849, https://doi.org/10.1177/2472555218775055 (2018)..

Lancaster, M. A. et al. Cerebral organoids model human brain development and microcephaly. Nature 501 , 373–379, https://doi.org/10.1038/nature12517 (2013)..

Halfter, K. et al. Testing chemotherapy efficacy in HER2 negative breast cancer using patient-derived spheroids. J. Transl. Med. 14 , 112. https://doi.org/10.1186/s12967-016-0855-3 (2016)..

Sachs, N. et al. A living biobank of breast cancer organoids captures disease heterogeneity. Cell 172 , 373–386.e10, https://doi.org/10.1016/j.cell.2017.11.010 (2018)..

Della Corte, C. M. et al. Antitumor activity of dual blockade o f PD-L1 and MEK in NSCLC patients derived three-dimensional spheroid cultures. J. Exp. Clin. Cancer Res. 38 , 253, https://doi.org/10.1186/s13046-019-1257-1 (2019)..

Barkauskas, C. E. et al. Lung organoids: current uses and future promise. Development 144 , 986–997, https://doi.org/10.1242/dev.140103 (2017)..

Jeppesen, M. et al. Short-term spheroid culture of primary colorectal cancer cells as an in vitro model for personalizing cancer medicine. PLOS ONE 12 , e0183074, https://doi.org/10.1371/journal.pone.0183074 (2017)..

van de Wetering, M. et al. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 161 , 933–945, https://doi.org/10.1016/j.cell.2015.03.053 (2015)..

Tomás-Bort, E. et al. 3D approaches to model the tumor microenvironment of pancreatic cancer. Theranostics 10 , 5074–5089, https://doi.org/10.7150/thno.42441 (2020)..

Li, X. N. et al. Oncogenic transformation of diverse gastrointestinal tissues in primary organoid culture. Nat. Med. 20 , 769–777, https://doi.org/10.1038/nm.3585 (2014)..

Hradecká, L. et al. Segmentation and tracking of mammary epithelial organoids in brightfield microscopy. IEEE Trans. Med. Imaging 42 , 281–290, https://doi.org/10.1109/TMI.2022.3210714 (2023)..

Kok, R. N. U. et al. Label-free cell imaging and tracking in 3D organoids. Cell Rep. Phys. Sci. 6 , 102522. https://doi.org/10.1016/j.xcrp.2025.102522 (2025)..

Steinbauer, S. et al. Enhanced bioluminescence imaging of tumor cells surviving chemotherapy in a murine model of triple-negative breast cancer. npj Breast Cancer 11 , 80. https://doi.org/10.1038/s41523-025-00795-y (2025)..

Drakhlis, L. et al. Human heart-forming organoids recapitulate early heart and foregut development. Nat. Biotechnol. 39 , 737–746, https://doi.org/10.1038/s41587-021-00815-9 (2021)..

Deben, C. et al. Development and validation of the Normalized Organoid Growth Rate (NOGR) metric in brightfield imaging-based assays. Commun. Biol. 7 , 1612, https://doi.org/10.1038/s42003-024-07329-5 (2024)..

Maharjan, S. et al. Advanced 3D imaging and organoid bioprinting for biomedical research and therapeutic applications. Adv. Drug Deliv. Rev. 208 , 115237. https://doi.org/10 .1016/j.addr.2024.115237 (2024)..

Dietrich, B. et al. NOTCH3 signalling controls human trophoblast stem cell expansion and differentiation. Development 150 , dev202152. https://doi.org/10.1242/dev.202152 (2023)..

Noh, S. et al. Structural analysis of cerebral organoids using confocal microscopy and transmission/scanning electron microscopy. Microsc. Microanalysis 31 , ozae119. https://doi.org/10.1093/mam/ozae119 (2025)..

Yildirim, M. et al. Label-free three-photon imaging of intact human cerebral organoids for tracking early events in brain development and deficits in Rett syndrome. eLife 11 , e78079. https://doi.org/10.7554/eLife.78079 (2022)..

Jain, A. et al. Morphodynamics of human early brain organoid development. Nature 644 , 1010–1019, https://doi.org/10.1038/s41586-025-09151-3 (2025)..

Dekkers, J. F. et al. High-resolution 3D imaging of fixed and cleared organoids. Nat. Protoc. 14 , 1756–1771, https://doi.org/10.1038/s41596-019-0160-8 (2019)..

Hofbauer, P. et al. Cardioids reveal self-organizing principles of human cardiogenesis. Cell 184 , 3299–3317.e22, https://doi.org/10.1016/j.cell.2021.04.034 (2021)..

Drexler, W. et al. Optical coherence tomography today: speed, contrast, and multimodality. J. Biomed. Opt. 19 , 071412, https://doi.org/10.1117/1.JBO.19.7.071412 (2014)..

Leitgeb, R. et al. Enhanced medical diagnosis for dOCTors: a perspective of optical coherence tomography. J. Biomed. Opt. 26 , 100601, https://doi.org/10.1117/1.JBO.26.10.100601 (2021)..

Zhang, L. et al. Oblique scanning laser microscopy for simultaneously volumetric structural and molecular imaging using only one raster scan. Sci. Rep. 7 , 8591. https://doi.org/10.1038/s41598-017-08822-0 (2017)..

Huang, Y. Y. et al. Optical coherence tomography detects necrotic regions and volumetrically quantifies multicellular tumor spheroids. Cancer Res. 77 , 6011–6020, https://doi.org/10.1158/0008-5472.Can-17-0821 (2017)..

Gil, D. A., Deming, D. A. & Skala, M. C. Volumetric growth tracking of patient-derived cancer organoids using optical coherence tomography. Biomed. Opt. Express 12 , 3789–3805, https://doi.org/10.1364/BOE.428197 (2021)..

Deloria, A. J. et al. Ultra-high-resolution 3D optical coherence tomography reveals inner structures of human placenta-derived trophoblast organoids. IEEE Trans. Biomed. Eng. 68 , 2368–2376, https://doi.org/10.1109/TBME.2020.3038466 (2021)..

Ming, Y. X. et al. Longitudinal morphological and functional characterization of human heart organoids using optical coherence tomography. Biosens. Bioelectron. 207 , 114136. https://doi.org/10.1016/j.bios.2022.114136 (2022)..

Hao, S. Y. et al. Dual-modality imaging system for monitoring human heart organoids beating in vitro. Opt. Lett. 48 , 3929–3932, https://doi.org/10.1364/OL.493824 (2023)..

Abd El-Sadek, I. et al. Label-free visualization and quantification of the drug-type-dependent response of tumor spheroids by dynamic optical coherence tomography. Sci. Rep. 14 , 3366. https://doi.org/10.1038/s41598-024-53171-4 (2024)..

Abd El-Sadek, I. et al. Label-free drug response evaluation of human derived tumor spheroids using three-dimensional dynamic optical coherence tomography. Sci. Rep. 13 , 15377. https://doi.org/10.1038/s41598-023-41846-3 (2023)..

Hosseinaee, Z., Tummon Sim mons, J. A. & Reza, P. H. Dual-modal photoacoustic imaging and optical coherence tomography [Review ] . Front. Phys. 8 , 616618. https://doi.org/10.3389/fphy.2020.616618 (2021)..

Wang, L. V. & Yao, J. J. A practical guide to photoacoustic tomography in the life sciences. Nat. Methods 13 , 627–638, https://doi.org/10.1038/nmeth.3925 (2016)..

Yao, J. J., Song, L. & Wang, L. V. Photoacoustic microscopy: superdepth, superresolution, and superb contrast. IEEE Pulse 6 , 34–37, https://doi.org/10.1109/MPUL.2015.2409100 (2015)..

Ntziachristos, V. Going deeper than microscopy: the optical imaging frontier in biology. Nat. Methods 7 , 603–614, https://doi.org/10.1038/nmeth.1483 (2010)..

Haindl, R. et al. Functional optical coherence tomography and photoacoustic microscopy imaging for zebrafish larvae. Biomed. Opt. Express 11 , 2137–2151, https://doi.org/10.1364/BOE.390410 (2020)..

Haindl, R. et al. Dual modality reflection mode optical coherence and photoacoustic microscopy using an akinetic sensor. Opt. Lett. 42 , 4319–4322, https://doi.org/10.1364/OL.42.004319 (2017)..

Zhu, X. Y. et al. Resolution-matched reflection mode photoacoustic microscopy and optical coherence tomography dual modality system. Photoacoustics 19 , 100188. https://doi.org/10.1016/j.pacs.2020.100188 (2020)..

Englert, L. et al. Fast 3D optoacoustic mesoscopy of neuromelanin through entire human midbrain organoids at single-cell resolution. Laser Photonics Rev. 17 , 2300443. https://doi.org/10.1002/lpor.202300443 (2023)..

Darrigues, E. et al. Tracking Gold Nanorods' interaction with large 3D pancreatic-stromal tumor spheroids by multimodal imaging: fluorescence, photoacoustic, and photothermal microscopies. Sci. Rep. 10 , 3362. https://doi.org/10.1038/s41598-020-59226-6 (2020)..

Branciforti, F. et al. Segmentation and multi-timepoint tracking of 3D cancer organoids from optical coherence tomography images using deep neural networks. Diagnostics 14 , 1217, https://doi.org/10.3390/diagnostics14121217 (2024)..

Faust, O. et al. Comparative assessment of texture features for the identification of cancer in ultrasound images: a review. Biocybern. Biomed. Eng. 38 , 275–296, https://doi.org/10.1016/j.bbe.2018.01.001 (2018)..

Chitalia, R. D. & Kontos, D. Role of texture analysis in breast MRI as a cancer biomarker: a review. J. Magn. Reson. Imaging 49 , 927–938, https://doi.org/10.1002/jmri.26556 (2019)..

Wang, B. J. et al. Deep learning based characterization of human organoids using optical coherence tomography. Biomed. Opt. Express 15 , 3112–3127, https://doi.org/10.1364/BOE.515781 (2024)..

Xu, M. Z. et al. Artificial intelligence-aided optical imaging for cancer theranostics. Semin. Cancer Biol. 94 , 62–80, https://doi.org/10.1016/j.semcancer.2023.06.003 (2023)..

Wu, J. C. et al. Learned end-to-end high-resolution lensless fiber imaging towards real-time cancer diagnosis. Sci. Rep. 12 , 18846. https://doi.org/10.1038/s41598-022-23490-5 (2022)..

Han, G. R. et al. Machine learning in point-of-care testing: innovations, challenges, and opportunities. Nat. Commun. 16 , 3165. https://doi.org/10.1038/s41467-025-58527-6 (2025)..

Wang, S. et al. Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy. Light Sci. Appl. 13 , 254, https://doi.org/10.1038/s41377-024-01597-w (2024)..

Du, Y. et al. Hybrid multimode - multicore fibre based holographic endoscope for deep-tissue neurophotonics. Light Adv. Manuf. 3 , 408–416, https://doi.org/10.37188/lam.2022.029 (2022)..

Duarte, A. A. et al. BRCA-deficient mouse mammary tumor organoids to study cancer-drug resistance. Nat. Methods 15 , 134–140, https://doi.org/10.1038/nmeth.4535 (2018)..

Szebényi, K. et al. Effective targeting of breast cancer by the inhibition of P-glycoprotein mediated removal of toxic lipid peroxidation byproducts from drug tolerant persister cells. Drug Resist. Updates 71 , 101007. https://doi.org/10.1016/j.drup.2023.101007 (2023)..

Ramirez, M. et al. Diverse drug-resistance mechanisms can emerge from drug-tolerant cancer persister cells. Nat. Commun. 7 , 10690. https://doi.org/10.1038/ncomms10690 (2016)..

Liu, C. B. et al. Advances in imaging techniques and genetically encoded probes for photoacoustic imaging. Theranostics 6 , 2414–2430, https://doi.org/10.7150/thno.15878 (2016)..

Weber, J., Beard, P. C. & Bohndiek, S. E. Contrast agents for molecular photoacoustic imaging. Nat. Methods 13 , 639–650, https://doi.org/10.1038/nmeth.3929 (2016)..

Wang, L. V. Multiscale photoacoustic microscopy and computed tomography. Nat. Photonics 3 , 503–509, https://doi.org/10.1038/nphoton.2009.157 (2009)..

Ansari, R. et al. All-optical forward-viewing photoacoustic probe for high-resolution 3D endoscopy. Light Sci. Appl. 7 , 75, https://doi.org/10.1038/s41377-018-0070-5 (2018)..

Preisser, S. et al. All-optical highly sensitive akinetic sensor for ultrasound detection and photoacoustic imaging. Biomed. Opt. Express 7 , 4171–4186, https://doi.org/10.1364/BOE.7.004171 (2016)..

Liu, M. Y. et al. REAP: revealing drug tolerant persister cells in cancer using contrast enhanced optical coherence and photoacoustic tomography. J. Phys. Photonics 3 , 021001, https://doi.org/10.1088/2515-7647/abf02f (2021)..

Hoover, E. E. & Squier, J. A. Advances in multiphoton microscopy technology. Nat. Photonics 7 , 93–101, https://doi.org/10.1038/nphoton.2012.361 (2013)..

Elliott, A. D. Confocal microscopy: principles and modern practices. Curr. Protoc. Cytom. 92 , e68. https://doi.org/10.1002/cpcy.68 (2020)..

Stelzer, E. H. K. et al. Light sheet fluorescence microscopy. Nat. Rev. Methods Prim. 1 , 73, https://doi.org/10.1038/s43586-021-00069-4 (2021)..

Zhu, X. Y. et al. J. High speed innovations in photoacoustic microscopy. npj Imaging 2 , 46, https://doi.org/10.1038/s44303-024-00052-0 (2024)..

Glandorf, L. et al. Bessel beam optical coherence microscopy enables multiscale assessment of cerebrovascular network morphology and function. Light Sci. Appl. 13 , 307, https://doi.org/10.1038/s41377-024-01649-1 (2024)..

Kvåle Løvmo, M. et al. Ultrasound-induced reorientation for multi-angle optical coherence tomography. Nat. Commun. 15 , 2391. https://doi.org/10.1038/s41467-024-46506-2 (2024)..

Deng, S. Y. et al. An optical coherence photoacoustic microscopy system using a fiber optic sensor. APL Photonics 6 , 096103. https://doi.org/10.1063/5.0059351 (2021)..

Haindl, R. et al. Ultra-high-resolution SD-OCM imaging with a compact polarization-aligned 840 nm broad band combined-SLED source. Biomed. Opt. Express 11 , 3395–3406, https://doi.org/10.1364/BOE.394229 (2020)..

Zhang, W. W. et al. K-net: towards unified image segmentation. 35th International conference on neural information processing systems. 10326–10338, https://dl.acm.org/doi/10.5555/3540261.3541051 https://dl.acm.org/doi/10.5555/3540261.3541051 (2021).

Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9 , 676–682, https://doi.org/10.1038/nmeth.2019 (2012)..

van Griethuysen, J. J. M. etal. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77 , e104–e107, https://doi.org/10.1158/0008-5472.Can-17-0339 (2017)..

Chen, T. Q. & Guestrin, C. XGBoost: a scalable tree boosting system. 22nd ACM SIGKDD International Conference on knowledge discovery and data mining. 785–794, https://dl.acm.org/doi/10.1145/2939672.2939785 https://dl.acm.org/doi/10.1145/2939672.2939785 (2016).

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

暂无数据