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A machine learning approach to discover migration modes and transition dynamics of heterogeneous dendritic cells

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Title
A machine learning approach to discover migration modes and transition dynamics of heterogeneous dendritic cells
Author(s)
Song, T.; Yongjun Choi; Jeon, J.-H.; Yoon-Kyoung Cho
Publication Date
2023-04
Journal
Frontiers in Immunology, v.14
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. Copyright © 2023 Song, Choi, Jeon and Cho.
URI
https://pr.ibs.re.kr/handle/8788114/13451
DOI
10.3389/fimmu.2023.1129600
ISSN
1664-3224
Appears in Collections:
Center for Soft and Living Matter(첨단연성물질 연구단) > 1. Journal Papers (저널논문)
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