Objective: 3D optical coherence tomography (OCT) is used for analyses of human placenta organoids in situ without sample preparation. Methods: The trophoblast organoids analyzed were derived from primary human trophoblast. In this study a custom made ultra-high-resolution spectral domain OCT system with uniform spatial and axial resolution of 2.48 m in organoid tissue was used. The obtained OCT results align to differentiation status tested via quantitative polymerase chain reaction, Western blot analyses, immunohistochemistry, and immunofluorescence of histological sections. Results: 3D OCT enables a more detailed placenta organoid monitoring compared to brightfield microscopy. Inner architecture with light scattering bridges surrounding cavities were visualized and quantified in situ for the first time. The formation of these bridges and cavities is congruent to differentiated trophoblast organoids having developed syncytiotrophoblasts. Conclusion: Using 3D OCT in living placenta organoids is a fast tool to assess the differentiation status and resolve internal structures in situ, which is not possible with standard live cell imaging modality. Significance: Only recently human placenta-derived organoids were established, allowing to have a highly reproducible and stable in vitro model to investigate not only developmental but also physiological and pathophysiological processes during early pregnancy. To our knowledge, this work is the first to analyze living human placenta organoids using 3D OCT. Thereby, the rapid and especially non- endpoint OCT qualitative analyses align to the differentiation stage of organoids, which will aid future advancement in this field.

In this work, a novel fiber optic sensor based on Fabry–Pérot interferometry is adopted in an optical coherence photoacoustic microscopy (OC-PAM) system to enable high-resolution in vivo imaging. The complete OC-PAM system is characterized using the fiber optic sensor for photoacoustic measurement. After characterization, the performance of the system is evaluated by imaging zebrafish larvae in vivo. With a lateral resolution of 3.4 μm and an axial resolution of 3.7 μm in air, the optical coherence microscopy subsystem visualizes the anatomy of the zebrafish larvae. The photoacoustic microscopy subsystem reveals the vasculature of the zebrafish larvae with a lateral resolution of 1.9 μm and an axial resolution of 37.3 μm. As the two modalities share the same sample arm, we obtain inherently co-registered morphological and vascular images. This OC-PAM system provides comprehensive information on the anatomy and vasculature of the zebrafish larvae. Featuring compactness, broad detection bandwidth, and wide detection angle, the fiber optic sensor enables a large field of view with a static sensor position. We verified the feasibility of the fiber optic sensor for dual-modality in vivo imaging. The OC-PAM system, as a non-invasive imaging method, demonstrates its superiority in the investigation of zebrafish larvae, an animal model with increasing significance in developmental biology and disease research. This technique can also be applied for functional as well as longitudinal studies in the future.

Recent years have ushered in a transformative era in in vitro modeling with the advent of organoids, three-dimensional structures derived from stem cells or patient tumor cells. Still, fully harnessing the potential of organoids requires advanced imaging technologies and analytical tools to quantitatively monitor organoid growth. Optical coherence tomography (OCT) is a promising imaging modality for organoid analysis due to its high-resolution, label-free, non-destructive, and real-time 3D imaging capabilities, but accurately identifying and quantifying organoids in OCT images remain challenging due to various factors. Here, we propose an automatic deep learning-based pipeline with convolutional neural networks that synergistically includes optimized preprocessing steps, the implementation of a state-of-the-art deep learning model, and ad-hoc postprocessing methods, showcasing good generalizability and tracking capabilities over an extended period of 13 days. The proposed tracking algorithm thoroughly documents organoid evolution, utilizing reference volumes, a dual branch analysis, key attribute evaluation, and probability scoring for match identification. The proposed comprehensive approach enables the accurate tracking of organoid growth and morphological changes over time, advancing organoid analysis and serving as a solid foundation for future studies for drug screening and tumor drug sensitivity detection based on organoids.

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.

Despite chemotherapy, residual tumors often rely on so-called drug tolerant persister (DTP) cells, which evade treatment to give rise to therapy-resistant relapse and refractory disease. Detection of residual tumor cells proves to be challenging because of the rarity and heterogeneity of DTP cells. In the framework of a H2020 project, REAP will gather researchers and engineers from six countries, who will combine their expertise in biology, chemistry, oncology, material sciences, photonics, and electrical and biomedical engineering in the hope of revealing DTPs in cancer using contrast enhanced multimodal optical imaging. Laser sources for photoacoustic microscopy, photoacoustic tomography, and optical coherence tomography will be developed to enable the design of a two-photon laser scanning optical coherence photoacoustic microscopy system and an optical coherence photoacoustic tomography system. Furthermore, novel photoacoustic detectors using micro-ring resonator will be designed and fabricated, granting improved sensitivity and easier integration of multiple optical imaging modalities into a single system. Innovative algorithms will be developed to reconstruct and analyze the images quickly and automatically. With successful implementation of this four-year project, we can not only gain insight into the mechanisms governing DTPs, but also significantly advance the technology readiness level of contrast agents, lasers, sensors, and image analysis software through joint efforts.

Small cell lung cancer (SCLC) is an aggressive thoracic disease characterized by rapid proliferation and early metastatic spread. The survival outcomes for patients with SCLC remain notoriously poor, underlining that only modest improvements have been achieved in clinical settings to date. However, insights gained from human tumors and preclinical models in recent years have shed light on the heterogeneous molecular profile of SCLC. Numerous research groups have, therefore, begun to stratify SCLC into subgroups based on differential transcription factor expression, the tumor immune microenvironment, and other criteria. As SCLC subtypes show major differences in their molecular landscape and biological behavior, they may offer unique therapeutic vulnerabilities and serve as a framework for future personalized clinical trials. Here, we summarize impactful classification attempts from the past 10 years, highlighting discrepancies and connections between the nomenclature of each study, and expound upon relevant factors of SCLC biology influencing subtype composition and plasticity. This review delves into the implications of subgrouping for understanding and treating SCLC, as well as potential future directions for SCLC research.