Organoids are powerful experimental models for studying the ontogeny and progression of various diseases including cancer. Organoids are conventionally cultured in bulk using an extracellular matrix mimic. However, bulk-cultured organoids physically overlap, making it impossible to track the growth of individual organoids over time in high throughput. Moreover, local spatial variations in bulk matrix properties make it difficult to assess whether observed phenotypic heterogeneity between organoids results from intrinsic cell differences or differences in the microenvironment. Here, we developed a microwell-based method that enables high-throughput quantification of image-based parameters for organoids grown from single cells, which can further be retrieved from their microwells for molecular profiling. Coupled with a deep learning image-processing pipeline, we characterized phenotypic traits including growth rates, cellular movement, and apical-basal polarity in two CRISPR-engineered human gastric organoid models, identifying genomic changes associated with increased growth rate and changes in accessibility and expression correlated with apical-basal polarity.

A record of this paper’s transparent peer review process is included in the supplemental information.

类器官是研究包括癌症在内的各种疾病的个体发育和进展的强大实验模型。类器官通常使用细胞外基质模拟物进行批量培养。然而，大量培养的类器官在物理上重叠，使得不可能以高通量跟踪单个类器官随时间的生长。此外，本体基质特性的局部空间变化使得很难评估观察到的类器官之间的表型异质性是否是由内在细胞差异或微环境差异引起的。在这里，我们开发了一种基于微孔的方法，能够对单细胞生长的类器官的基于图像的参数进行高通量量化，并可以进一步从其微孔中检索以进行分子分析。结合深度学习图像处理流程，我们在两个 CRISPR 工程设计的人胃类器官模型中描述了表型特征，包括生长速率、细胞运动和顶端-基底极性，识别与生长速率增加以及可及性和表达变化相关的基因组变化与顶底极性相关。

补充信息中包含了本文透明同行评审过程的记录。