Instituto Gulbenkian de Ciência, Oeiras, Portugal.
Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, Turku, Finland.
PLoS Biol. 2024 Aug 8;22(8):e3002740. doi: 10.1371/journal.pbio.3002740. eCollection 2024 Aug.
In life sciences, tracking objects from movies enables researchers to quantify the behavior of single particles, organelles, bacteria, cells, and even whole animals. While numerous tools now allow automated tracking from video, a significant challenge persists in compiling, analyzing, and exploring the large datasets generated by these approaches. Here, we introduce CellTracksColab, a platform tailored to simplify the exploration and analysis of cell tracking data. CellTracksColab facilitates the compiling and analysis of results across multiple fields of view, conditions, and repeats, ensuring a holistic dataset overview. CellTracksColab also harnesses the power of high-dimensional data reduction and clustering, enabling researchers to identify distinct behavioral patterns and trends without bias. Finally, CellTracksColab also includes specialized analysis modules enabling spatial analyses (clustering, proximity to specific regions of interest). We demonstrate CellTracksColab capabilities with 3 use cases, including T cells and cancer cell migration, as well as filopodia dynamics. CellTracksColab is available for the broader scientific community at https://github.com/CellMigrationLab/CellTracksColab.
在生命科学领域，从电影中跟踪物体可以使研究人员能够量化单个粒子、细胞器、细菌、细胞甚至整个动物的行为。虽然现在有许多工具可以自动从视频中进行跟踪，但在编译、分析和探索这些方法生成的大型数据集方面仍然存在重大挑战。在这里，我们介绍了 CellTracksColab，这是一个专门为简化细胞跟踪数据的探索和分析而设计的平台。CellTracksColab 可以方便地对多个视野、条件和重复的结果进行编译和分析，确保了数据集的整体概述。CellTracksColab 还利用了高维数据降维和聚类的功能，使研究人员能够在没有偏见的情况下识别出独特的行为模式和趋势。最后，CellTracksColab 还包括专门的分析模块，可进行空间分析（聚类、与特定感兴趣区域的接近度）。我们通过 3 个用例展示了 CellTracksColab 的功能，包括 T 细胞和癌细胞的迁移以及丝状伪足动力学。CellTracksColab 可在 https://github.com/CellMigrationLab/CellTracksColab 上供更广泛的科学界使用。
