器官样块（organoids）是研究包括癌症在内的各种疾病的强大实验模型。传统上，器官样块是以大批量的方式通过模仿胞外基质进行培养。然而，大批量培养的器官样块在物理上重叠在一起，使得无法高通量地跟踪单个器官样块随时间的生长。此外，大批量基质的局部空间变化使得很难确定器官样块之间观察到的表型异质性是由细胞内在差异还是微环境差异引起的。在这里，我们开发了一种基于微阱的方法，可以高通量地量化基于图像的器官样块参数，这些器官样块是由单个细胞培养而来，可以从它们的微阱中取出进行分子谱分析。结合深度学习图像处理流程，我们表征了两个CRISPR改造的人类胃器官样块模型中的表型特征，包括生长速度、细胞运动和顶基极性，识别与增加生长速率相关的基因组变化以及与顶基极性相关的可及性和表达变化。关于本文的透明同行评审过程的记录包含在补充信息中。版权所有 © 2023 Elsevier Inc.

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.Copyright © 2023. Published by Elsevier Inc.