Ellison Institute of Technology, Los Angeles, CA, USA.
Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA; Department of Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Ellison Institute of Technology, Los Angeles, CA, USA.
SLAS Discov. 2024 Jun;29(4):100163. doi: 10.1016/j.slasd.2024.100163. Epub 2024 May 23.
Organ-on-chip (OOC) models can be useful tools for cancer drug discovery. Advances in OOC technology have led to the development of more complex assays, yet analysis of these systems does not always account for these advancements, resulting in technical challenges. A challenging task in the analysis of these two-channel microfluidic models is to define the boundary between the channels so objects moving within and between channels can be quantified. We propose a novel imaging-based application of a thin plate spline method - a generalized cubic spline that can be used to model coordinate transformations - to model a tissue boundary and define compartments for quantification of invaded objects, representing the early steps in cancer metastasis. To evaluate its performance, we applied our analytical approach to an adapted OOC developed by Emulate, Inc., utilizing a two-channel system with endothelial cells in the bottom channel and colorectal cancer (CRC) patient-derived organoids (PDOs) in the top channel. Initial application and visualization of this method revealed boundary variations due to microscope stage tilt and ridge and valley-like contours in the endothelial tissue surface. The method was functionalized into a reproducible analytical process and web tool - the Chip Invasion and Contour Analysis (ChICA) - to model the endothelial surface and quantify invading tumor cells across multiple chips. To illustrate applicability of the analytical method, we applied the tool to CRC organoid-chips seeded with two different endothelial cell types and measured distinct variations in endothelial surfaces and tumor cell invasion dynamics. Since ChICA utilizes only positional data output from imaging software, the method is applicable to and agnostic of the imaging tool and image analysis system used. The novel thin plate spline method developed in ChICA can account for variation introduced in OOC manufacturing or during the experimental workflow, can quickly and accurately measure tumor cell invasion, and can be used to explore biological mechanisms in drug discovery.
器官芯片 (OOC) 模型可作为癌症药物发现的有用工具。OOC 技术的进步推动了更复杂检测方法的发展，但这些系统的分析并未始终考虑到这些进步，导致了技术挑战。在分析这些双通道微流控模型时，一个具有挑战性的任务是定义通道之间的边界，以便可以对在通道内和通道之间移动的物体进行定量分析。我们提出了一种基于成像的薄板样条方法的新应用 - 广义三次样条，可以用于对坐标变换进行建模 - 来对组织边界建模并定义用于量化入侵物体的隔室，代表癌症转移的早期步骤。为了评估其性能，我们将我们的分析方法应用于 Emulate, Inc. 开发的经过改进的 OOC，该 OOC 利用具有底通道中的内皮细胞和顶通道中的结直肠癌 (CRC) 患者衍生类器官 (PDO) 的双通道系统。该方法的初始应用和可视化揭示了由于显微镜载物台倾斜以及内皮组织表面的脊和谷状轮廓导致的边界变化。该方法被功能化为可重复的分析过程和网络工具 - 芯片入侵和轮廓分析 (ChICA) - 用于对内皮表面进行建模并对多个芯片上的入侵肿瘤细胞进行定量分析。为了说明分析方法的适用性，我们将该工具应用于用两种不同的内皮细胞类型接种的 CRC 类器官芯片，并测量了内皮表面和肿瘤细胞入侵动力学的明显变化。由于 ChICA 仅使用成像软件输出的位置数据，因此该方法适用于且与使用的成像工具和图像分析系统无关。在 ChICA 中开发的新型薄板样条方法可以解释在 OOC 制造或实验工作流程中引入的变化，可以快速准确地测量肿瘤细胞的入侵，并且可以用于探索药物发现中的生物学机制。
