Intravital microscopy has revolutionized live-cell imaging by allowing the study of spatial–temporal cell dynamics in living animals. However, the complexity of the data generated by this technology has limited the development of effective computational tools to identify and quantify cell processes. Amongst them, apoptosis is a crucial form of regulated cell death involved in tissue homeostasis and host defense. Live-cell imaging enabled the study of apoptosis at the cellular level, enhancing our understanding of its spatial–temporal regulation. However, at present, no computational method can deliver robust detection of apoptosis in microscopy timelapses. To overcome this limitation, we developed ADeS, a deep learning-based apoptosis detection system that employs the principle of activity recognition. We trained ADeS on extensive datasets containing more than 10,000 apoptotic instances collected both in vitro and in vivo, achieving a classification accuracy above 98% and outperforming state-of-the-art solutions. ADeS is the first method capable of detecting the location and duration of multiple apoptotic events in full microscopy timelapses, surpassing human performance in the same task. We demonstrated the effectiveness and robustness of ADeS across various imaging modalities, cell types, and staining techniques. Finally, we employed ADeS to quantify cell survival in vitro and tissue damage in mice, demonstrating its potential application in toxicity assays, treatment evaluation, and inflammatory dynamics. Our findings suggest that ADeS is a valuable tool for the accurate detection and quantification of apoptosis in live-cell imaging and, in particular, intravital microscopy data, providing insights into the complex spatial–temporal regulation of this process.

中文翻译：

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基于变压器的活细胞成像中凋亡细胞死亡的时空检测

活体显微镜通过研究活体动物的时空细胞动力学，彻底改变了活细胞成像。然而，该技术生成的数据的复杂性限制了识别和量化细胞过程的有效计算工具的开发。其中，细胞凋亡是参与组织稳态和宿主防御的受调节细胞死亡的重要形式。活细胞成像能够在细胞水平上研究细胞凋亡，增强我们对其时空调节的理解。然而，目前还没有计算方法可以在显微镜延时摄影中提供对细胞凋亡的可靠检测。为了克服这一限制，我们开发了 ADeS，一种基于深度学习的细胞凋亡检测系统，采用活动识别原理。我们在包含 10,000 多个体外和体内收集的细胞凋亡实例的广泛数据集上训练 ADeS，实现了 98% 以上的分类准确率，并且优于最先进的解决方案。ADeS 是第一种能够在完整的显微镜延时中检测多个细胞凋亡事件的位置和持续时间的方法，在同一任务中超越了人类的表现。我们证明了 ADeS 在各种成像模式、细胞类型和染色技术中的有效性和稳健性。最后，我们利用 ADeS 来量化小鼠的体外细胞存活和组织损伤，展示了其在毒性测定、治疗评估和炎症动力学方面的潜在应用。我们的研究结果表明，ADeS 是准确检测和量化活细胞成像中细胞凋亡的宝贵工具，特别是活体显微镜数据，为该过程的复杂时空调节提供了见解。