Kandula Arun Kumar Reddy, Phamornratanakun Tanakit, Gomez Angello Huerta, El-Mokahal Marcel, Ma Zhen, Feng Yunhe, Yang Huaxiao
Department of Biomedical Engineering, University of North Texas, Denton, Texas, USA.
Department of Computer Science & Engineering, University of North Texas, Denton, Texas, USA.
Adv Intell Discov. 2025 Aug;1(2). doi: 10.1002/aidi.202400041. Epub 2025 Apr 24.
Human pluripotent stem cell (hPSC)-derived cardiac organoids (COs) are the most recent three-dimensional tissue structure that mimics the human heart's structure and functionality for modeling heart development and disease. Fluorescent labeling and imaging are commonly utilized to characterize the cellular information in COs. However, the additional step of fluorescence labeling and imaging is time-consuming, inefficient, and typically for end-timepoint characterization. Meanwhile, the COs are routinely examined by brightfield/phase contrast microscope to track live-cell organoid formation in structure and morphology. Although the brightfield microscope provides essential information about COs, such as morphology and overall structure, it limits our understanding of cardiovascular cells (e.g., cardiomyocytes, CMs and endothelial cells, ECs) and corresponding quantifications in COs. Is it possible to overcome these limitations of bright-field microscopic imaging and provide cardiovascular cell type-specific information similar to the fluorescence-labeled imaging acquisition in COs? This research addresses this limitation by proposing a generative AI system for colorizing phase contrast images of COs from bright-field microscopic imaging using conditional generative adversarial networks (cGANs) to generate cardiovascular cell type-specific fluorescence images of COs. By giving these phase contrast images with multichannel fluorescence colorization, this intelligence system unlocks cell type and quantifications of COs in high efficiency and accuracy.
人多能干细胞（hPSC）来源的心脏类器官（COs）是最新的三维组织结构，可模拟人类心脏的结构和功能，用于心脏发育和疾病建模。荧光标记和成像通常用于表征COs中的细胞信息。然而，荧光标记和成像的额外步骤既耗时又低效，且通常用于终点表征。同时，常规通过明场/相差显微镜检查COs，以跟踪活细胞类器官在结构和形态上的形成。尽管明场显微镜提供了有关COs的基本信息，如形态和整体结构，但它限制了我们对心血管细胞（如心肌细胞、CMs和内皮细胞、ECs）以及COs中相应定量的理解。是否有可能克服明场显微成像的这些局限性，并提供与COs中荧光标记成像采集类似的心血管细胞类型特异性信息？本研究通过提出一种生成式人工智能系统来解决这一局限性，该系统使用条件生成对抗网络（cGANs）对明场显微成像的COs相差图像进行上色，以生成COs的心血管细胞类型特异性荧光图像。通过为这些相差图像赋予多通道荧光上色，该智能系统高效且准确地解锁了COs的细胞类型和定量信息。
