A machine learning approach to discover migration modes and transition dynamics of heterogeneous dendritic cells
SCIE
SCOPUS
- Title
- A machine learning approach to discover migration modes and transition dynamics of heterogeneous dendritic cells
- Authors
- Song, Taegeun; Choi, Yongjun; Jeon, Jae-Hyung; Cho, Yoon-Kyoung
- Date Issued
- 2023-04
- Publisher
- Frontiers Media S.A.
- 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.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/123659
- DOI
- 10.3389/fimmu.2023.1129600
- Article Type
- Article
- Citation
- Frontiers in Immunology, vol. 14, 2023-04
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