美国陈-扎克伯格生物Loïc A. Royer团队宣布他们开发出Ultrack：在生物尺度上突破细胞追踪的极限。相关论文发表在2025年8月25日出版的《自然—方法学》杂志上。

该团队提出了Ultrack，一种通用的、可扩展的细胞跟踪方法，通过考虑从多个算法和参数集派生的候选分割来解决这一挑战。Ultrack利用时间一致性来选择最佳分段，即使在分段不确定的情况下也能确保机器人的性能。该课题组人员在不同的数据集上验证了他们的方法，包括斑马鱼、果蝇和线虫胚胎的TB级发育延时记录，以及多色和无标签的细胞成像。小组证明，Ultrack在细胞跟踪挑战中取得了卓越或相当的性能，特别是在长时间跟踪密集排列的3D胚胎细胞时。

此外，课题组研究人员提出了一种通过双通道稀疏标记进行跟踪验证的方法，该方法能够生成高保真的地面真相，从而确定长期细胞跟踪评估的边界。他们的方法可以作为带有Fiji和Napari插件的Python包免费获得，并且可以部署在高性能计算环境中，促进研究社区的广泛采用。

研究人员表示，通过二维、三维（3D）和多通道延时记录跟踪活细胞对于理解组织尺度的生物过程至关重要。尽管成像技术取得了进步，但准确跟踪细胞仍然具有挑战性，特别是在复杂和拥挤的组织中，细胞分割往往是模糊的。

附：英文原文

Title: Ultrack: pushing the limits of cell tracking across biological scales

Author: Bragantini, Jordo, Theodoro, Ilan, Zhao, Xiang, Huijben, Teun A.P.M., Hirata-Miyasaki, Eduardo, VijayKumar, Shruthi, Balasubramanian, Akilandeswari, Lao, Tiger, Agrawal, Richa, Xiao, Sheng, Lammerding, Jan, Mehta, Shalin, X. Falco, Alexandre, Jacobo, Adrian, Lange, Merlin, Royer, Loc A.

Issue&Volume: 2025-08-25

Abstract: Tracking live cells across two-dimensional, three-dimensional (3D) and multichannel time-lapse recordings is crucial for understanding tissue-scale biological processes. Despite advancements in imaging technology, accurately tracking cells remains challenging, particularly in complex and crowded tissues where cell segmentation is often ambiguous. We present Ultrack, a versatile and scalable cell tracking method that tackles this challenge by considering candidate segmentations derived from multiple algorithms and parameter sets. Ultrack leverages temporal consistency to select optimal segments, ensuring robust performance even under segmentation uncertainty. We validate our method on diverse datasets, including terabyte-scale developmental time-lapse recordings of zebrafish, fruit fly and nematode embryos, as well as multicolor and label-free cellular imaging. We demonstrate that Ultrack achieves superior or comparable performance in the cell tracking challenge, particularly when tracking densely packed 3D embryonic cells over extended periods. Moreover, we propose an approach to tracking validation via dual-channel sparse labeling that enables high-fidelity ground-truth generation, pushing the boundaries of long-term cell tracking assessment. Our method is freely available as a Python package with Fiji and Napari plugins and can be deployed in a high-performance computing environment, facilitating widespread adoption by the research community.

DOI: 10.1038/s41592-025-02778-0

Source: https://www.nature.com/articles/s41592-025-02778-0