Published in the leading journal Light: Science & Applications, the study introduces a powerful optical coherence photoacoustic microscopy (OC-PAM) system for high-resolution, three-dimensional imaging of cancer organoids.
Three-dimensional cancer models such as organoids and spheroids have become indispensable tools for studying tumour biology and treatment response. However, conventional imaging techniques often require labeling, invasive procedures, or lack the ability to monitor samples over time or to provide 3D information. The REAP consortium addressed these limitations by developing an AI-enhanced OC-PAM system that enables label-free, non-invasive, and longitudinal 3D live-cell imaging of cancer models.
The newly established platform allows researchers to track the growth of individual organoids in a culture over time, evaluate therapy response by assessing individual organoid viability, and identify proxies of drug-tolerant persister cells. Importantly, all of these capabilities are achieved without fluorescent markers or destructive sample preparation, preserving biological integrity while delivering quantitative, volumetric, high-resolution data.
Combining optical coherence and photoacoustic microscopy with advanced AI-based analysis, the system provides detailed structural and functional information from complex 3D cancer models. The results establish OC-PAM as a versatile and powerful imaging technology for investigating drug resistance mechanisms, rare cell populations, and heterogeneous treatment responses.
By enabling comprehensive, non-invasive monitoring of cancer organoids, the technology holds strong potential for accelerating cancer research, improving drug development strategies, and advancing precision oncology.
The research was performed in frame of REAP, a European consortium funded by the EU Horizon 2020 framework (Call: Information and Communication Technologies, Disruptive Photonics Technology).
Publikation: Light: Science & Applications
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, Richard Leitgeb, Massimo Salvi, Manojit Pramanik, Yi Yuan, Leopold Schmetterer, Gergely Szakacs, Wolfgang Drexler, Kristen M. Meiburger, Mengyang Liu*.
Light Sci Appl. 2026 Feb 5;15(1):106. doi: 10.1038/s41377-025-02177-2.
https://www.nature.com/articles/s41377-025-02177-2