Department of Orthopaedic Surgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA.
Nat Commun. 2023 Jun 6;14(1):3168. doi: 10.1038/s41467-023-38832-8.
High throughput drug screening is an established approach to investigate tumor biology and identify therapeutic leads. Traditional platforms use two-dimensional cultures which do not accurately reflect the biology of human tumors. More clinically relevant model systems such as three-dimensional tumor organoids can be difficult to scale and screen. Manually seeded organoids coupled to destructive endpoint assays allow for the characterization of treatment response, but do not capture transitory changes and intra-sample heterogeneity underlying clinically observed resistance to therapy. We present a pipeline to generate bioprinted tumor organoids linked to label-free, time-resolved imaging via high-speed live cell interferometry (HSLCI) and machine learning-based quantitation of individual organoids. Bioprinting cells gives rise to 3D structures with unaltered tumor histology and gene expression profiles. HSLCI imaging in tandem with machine learning-based segmentation and classification tools enables accurate, label-free parallel mass measurements for thousands of organoids. We demonstrate that this strategy identifies organoids transiently or persistently sensitive or resistant to specific therapies, information that could be used to guide rapid therapy selection.
高通量药物筛选是一种研究肿瘤生物学和鉴定治疗靶点的成熟方法。传统的平台使用二维培养物，不能准确反映人类肿瘤的生物学特性。更具临床相关性的模型系统，如三维肿瘤类器官，可能难以规模化和筛选。人工接种的类器官与破坏性终点测定相结合，可以用于描述治疗反应，但无法捕捉到临床上观察到的治疗耐药性背后的瞬时变化和样本内异质性。我们提出了一种生成生物打印肿瘤类器官的方案，通过高速活细胞干涉测量（HSLCI）和基于机器学习的个体类器官定量与无标记、时间分辨成像相关联。细胞生物打印产生具有未改变的肿瘤组织学和基因表达谱的 3D 结构。HSLCI 成像与基于机器学习的分割和分类工具相结合，可实现数千个类器官的精确、无标记的平行质量测量。我们证明了这种策略可以识别对特定治疗方法敏感或耐药的类器官，这些信息可用于指导快速的治疗选择。
