ArticleFrontiers in immunology2023
A machine learning approach to discover migration modes and transition dynamics of heterogeneous dendritic cells.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 11 citations in OpenAlex.
- Diversity in emergent cell locomotion from the coupling cytosolic and cortical Marangoni flows with reaction-diffusion dynamics.PLoS computational biology · 2026Article
- Deriving the A/B Cells Policy as a Robust Multi-Object Cell Pipeline for Time-Lapse Microscopy.International journal of molecular sciences · 2025Article
- Ovarian cancer cells exhibit diverse migration strategies on stiff collagenous substrata.Biophysical journal · 2024Article
- Cancer-on-a-chip model shows that the adenomatous polyposis coli mutation impairs T cell engagement and killing of cancer spheroids.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Learning how to find targets in the micro-world: the case of intermittent active Brownian particles.Soft matter · 2024Article
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Authors and funding
4 authors at 4 institutions in 1 country.
Funding
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Abstract
Dendritic cell (DC) migration is crucial for mounting immune responses. Immature DCs (imDCs) reportedly sense infections, while mature DCs (mDCs) move quickly to lymph nodes to deliver antigens to T cells. However, their highly heterogeneous and complex innate motility remains elusive. Here, we used an unsupervised machine learning (ML) approach to analyze long-term, two-dimensional migration trajectories of Granulocyte-macrophage colony-stimulating factor (GMCSF)-derived bone marrow-derived DCs (BMDCs). We discovered three migratory modes independent of the cell state: slow-diffusive (SD), slow-persistent (SP), and fast-persistent (FP). Remarkably, imDCs more frequently changed their modes, predominantly following a unicyclic SD→FP→SP→SD transition, whereas mDCs showed no transition directionality. We report that DC migration exhibits a history-dependent mode transition and maturation-dependent motility changes are emergent properties of the dynamic switching of the three migratory modes. Our ML-based investigation provides new insights into studying complex cellular migratory behavior.
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