运动性
免疫系统
生物
细胞迁移
质心
计算机科学
细胞
神经科学
人工智能
细胞生物学
免疫学
遗传学
作者
Diego Ulisse Pizzagalli,Pau Carrillo-Barberà,Elisa Palladino,Kevin Ceni,Benedikt Thelen,Alain Pulfer,Enrico Moscatello,Raffaella Fiamma Cabini,Johannes Textor,Inge M. N. Wortel,Rolf Krause,Santiago González
标识
DOI:10.1101/2024.12.02.626343
摘要
Studying the spatiotemporal dynamics of cells in living organisms is a current frontier in bioimaging. Intravital Microscopy (IVM) provides direct, long-term observation of cell behavior in living animals, from tissue to sub-cellular resolution. Hence, IVM has become crucial for studying complex biological processes in motion and across scales, such as the immune response to pathogens and cancer. However, IVM data are typically kept in private repositories inaccessible to the scientific community, hampering large-scale analysis that aggregates data from multiple laboratories. To solve this issue, we introduce Immunemap, an atlas of immune cell motility based on an Open Data platform that provides access to over 58'000 single-cell tracks and 1'049'000 cell-centroid annotations from 360 videos in murine models. Leveraging Immunemap and unsupervised learning, we systematically analyzed cell trajectories, identifying four main patterns of cell migration in immune cells. Two patterns correspond to behaviors previously characterized: directed movement and arresting. However, we identified two other patterns, characterized by low directionality and twisted paths, often considered random migration. We show that the newly defined patterns can be subdivided into two distinct types: within small areas, suggesting a focused patrolling around one or a few cells, and over larger areas, indicative of a more extended tissue patrolling. Furthermore, we show that the percentage of cells displaying these motility patterns changes in response to immune stimuli. Altogether, Immunemap embraces the FAIR principles, promoting data reuse to extract novel insights from immune cell dynamics through an image-based systems biology approach.
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