Fundamental life science and pharmaceutical research are continually striving to provide physiologically relevant context for their biological studies. Zebrafish present an opportunity for high-content screening (HCS) to bring a true in vivo model system to screening studies. Zebrafish embryos and young larvae are an economical, human-relevant model organism that are amenable to both genetic engineering and modification, and direct inspection via microscopy. The use of these organisms entails unique challenges that new technologies are overcoming, including artificial intelligence (AI).

In this perspective article, we describe the state-of-the-art in terms of automated sample handling, imaging, and data analysis with zebrafish during early developmental stages. We highlight advances in orienting the embryos, including the use of robots, microfluidics, and creative multi-well plate solutions. Analyzing the micrographs in a fast, reliable fashion that maintains the anatomical context of the fluorescently labeled cells is a crucial step. Existing software solutions range from AI-driven commercial solutions to bespoke analysis algorithms. Deep learning appears to be a critical tool that researchers are only beginning to apply, but already facilitates many automated steps in the experimental workflow. Currently, such work has permitted the cellular quantification of multiple cell types in vivo, including stem cell responses to stress and drugs, neuronal myelination and macrophage behavior during inflammation and infection. We evaluate pro and cons of proprietary versus open-source methodologies for combining technologies into fully automated workflows of zebrafish studies. Zebrafish are poised to charge into HCS with ever-greater presence, bringing a new level of physiological context.

基础生命科学和药物研究不断努力为其生物学研究提供与生理相关的背景。斑马鱼为高内涵筛选 (HCS) 提供了一个机会，将真正的体内模型系统带入筛选研究。斑马鱼胚胎和幼虫是一种经济的、与人类相关的模式生物，可以进行基因工程和修饰，并通过显微镜直接检查。这些生物的使用带来了新技术正在克服的独特挑战，包括人工智能 (AI)。

在这篇透视文章中，我们描述了斑马鱼在早期发育阶段的自动化样本处理、成像和数据分析方面的最新技术。我们重点介绍了胚胎定向方面的进展，包括使用机器人、微流体和创造性的多孔板解决方案。以快速、可靠的方式分析显微照片，以保持荧光标记细胞的解剖学背景是至关重要的一步。现有的软件解决方案范围从人工智能驱动的商业解决方案到定制分析算法。深度学习似乎是研究人员刚刚开始应用的关键工具，但已经促进了实验工作流程中的许多自动化步骤。目前，此类工作已允许在体内对多种细胞类型进行细胞定量，包括干细胞对压力和药物的反应、炎症和感染期间的神经元髓鞘形成和巨噬细胞行为。我们评估将技术结合到斑马鱼研究的全自动工作流程中的专有与开源方法的优缺点。斑马鱼准备以越来越多的存在冲入 HCS，带来新的生理环境。