State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China.
State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China; Institute of Biomaterials and Medical Devices, Southeast University, Suzhou, Jiangsu, 215163, China.
Comput Biol Med. 2024 May;173:108322. doi: 10.1016/j.compbiomed.2024.108322. Epub 2024 Mar 26.
Patient-derived organoids have proven to be a highly relevant model for evaluating of disease mechanisms and drug efficacies, as they closely recapitulate in vivo physiology. Colorectal cancer organoids, specifically, exhibit a diverse range of morphologies, which have been analyzed with image-based profiling. However, the relationship between morphological subtypes and functional parameters of the organoids remains underexplored. Here, we identified two distinct morphological subtypes ("cystic" and "solid") across 31360 bright field images using image-based profiling, which correlated differently with viability and apoptosis level of colorectal cancer organoids. Leveraging object detection neural networks, we were able to categorize single organoids achieving higher viability scores as "cystic" than "solid" subtype. Furthermore, a deep generative model was proposed to predict apoptosis intensity based on a apoptosis-featured dataset encompassing over 17000 bright field and matched fluorescent images. Notably, a significant correlation of 0.91 between the predicted value and ground truth was achived, underscoring the feasibility of this generative model as a potential means for assessing organoid functional parameters. The underlying cellular heterogeneity of the organoids, i.e., conserved colonic cell types and rare immune components, was also verified with scRNA sequencing, implying a compromised tumor microenvironment. Additionally, the "cystic" subtype was identified as a relapse phenotype featuring intestinal stem cell signatures, suggesting that this visually discernible relapse phenotype shows potential as a novel biomarker for colorectal cancer diagnosis and prognosis. In summary, our findings demonstrate that the morphological heterogeneity of colorectal cancer organoids explicitly recapitulate the association of phenotypic features and exogenous perturbations through the image-based profiling, providing new insights into disease mechanisms.
患者来源的类器官已被证明是评估疾病机制和药物疗效的高度相关模型，因为它们密切重现体内生理学。具体来说，结直肠癌细胞类器官表现出多种多样的形态，这些形态已通过基于图像的分析进行了分析。然而，形态亚型与类器官的功能参数之间的关系仍未得到充分探索。在这里，我们使用基于图像的分析在 31360 个明场图像中鉴定了两种不同的形态亚型（“囊性”和“实体”），它们与结直肠癌细胞类器官的活力和凋亡水平的相关性不同。利用目标检测神经网络，我们能够对单个类器官进行分类，实现更高活力评分的类器官被归类为“囊性”，而不是“实体”亚型。此外，提出了一种深度生成模型，基于包含超过 17000 个明场和匹配荧光图像的凋亡特征数据集来预测凋亡强度。值得注意的是，预测值和真实值之间实现了 0.91 的显著相关性，突出了该生成模型作为评估类器官功能参数的潜在手段的可行性。类器官的细胞异质性，即保守的结肠细胞类型和罕见的免疫成分，也通过 scRNA 测序得到了验证，暗示肿瘤微环境受损。此外，“囊性”亚型被鉴定为具有肠干细胞特征的复发表型，这表明这种可视觉识别的复发表型有望成为结直肠癌诊断和预后的新型生物标志物。总之，我们的研究结果表明，结直肠癌细胞类器官的形态异质性通过基于图像的分析明确重现了表型特征和外源性扰动之间的关联，为疾病机制提供了新的见解。
