Nanfang Hospital, Southern Medical University, Guangzhou, China.
Guangdong Research Center of Organoid Engineering and Technology, Guangzhou, China.
Physiol Rep. 2024 Jun;12(11):e16057. doi: 10.14814/phy2.16057.
The bronchoalveolar organoid (BAO) model is increasingly acknowledged as an ex-vivo platform that accurately emulates the structural and functional attributes of proximal airway tissue. The transition from bronchoalveolar progenitor cells to alveolar organoids is a common event during the generation of BAOs. However, there is a pressing need for comprehensive analysis to elucidate the molecular distinctions characterizing the pre-differentiated and post-differentiated states within BAO models. This study established a murine BAO model and subsequently triggered its differentiation. Thereafter, a suite of multidimensional analytical procedures was employed, including the morphological recognition and examination of organoids utilizing an established artificial intelligence (AI) image tracking system, quantification of cellular composition, proteomic profiling and immunoblots of selected proteins. Our investigation yielded a detailed evaluation of the morphologic, cellular, and molecular variances demarcating the pre- and post-differentiation phases of the BAO model. We also identified of a potential molecular signature reflective of the observed morphological transformations. The integration of cutting-edge AI-driven image analysis with traditional cellular and molecular investigative methods has illuminated key features of this nascent model.
支气管肺泡类器官 (BAO) 模型越来越被认为是一种能够准确模拟近端气道组织结构和功能特性的体外平台。从支气管肺泡祖细胞向肺泡类器官的转变是生成 BAO 的常见事件。然而，迫切需要全面分析来阐明 BAO 模型中预分化和后分化状态的分子特征。本研究建立了一个小鼠 BAO 模型，并随后触发其分化。此后，采用了一系列多维分析程序，包括使用成熟的人工智能 (AI) 图像跟踪系统对类器官进行形态识别和检查、细胞组成的定量分析、蛋白质组学分析和选定蛋白质的免疫印迹。我们的研究详细评估了 BAO 模型的预分化和分化后阶段的形态、细胞和分子差异。我们还确定了一个潜在的分子特征，反映了观察到的形态转化。将最先进的 AI 驱动的图像分析与传统的细胞和分子研究方法相结合，揭示了这个新兴模型的关键特征。
