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

Frontiers in Immunology

Immunology

Yoon-Kyoung Cho¹  Yongjun Choi¹  Jae-Hyung Jeon²  Taegeun Song³ 

[1] Center for Soft and Living Matter, Institute for Basic Science (IBS), Ulsan, Republic of Korea;Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea;Department of Physics, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea;Asia Pacific Center for Theoretical Physics (APCTP), Pohang, Republic of Korea;Department of Physics, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea;Department of Data information and Physics, Kongju National University, Gongju, Republic of Korea;

关键词: dendritic cell;    cell migration;    machine learning;    transition dynamics;    maturation;   

DOI  :  10.3389/fimmu.2023.1129600

 received in 2022-12-22, accepted in 2023-03-06,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 Song, Choi, Jeon and Cho