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

Taegeun Song; Yongjun Choi; Jae-Hyung Jeon; Yoon-Kyoung Cho

doi:10.3389/fimmu.2023.1129600

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

Front Immunol. 2023 Apr 4:14:1129600. doi: 10.3389/fimmu.2023.1129600. eCollection 2023.

Authors

Taegeun Song^{1

2}, Yongjun Choi^{3

4}, Jae-Hyung Jeon^{1

5}, Yoon-Kyoung Cho^{3

4}

Affiliations

¹ Department of Physics, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.
² Department of Data information and Physics, Kongju National University, Gongju, Republic of Korea.
³ Center for Soft and Living Matter, Institute for Basic Science (IBS), Ulsan, Republic of Korea.
⁴ Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea.
⁵ Asia Pacific Center for Theoretical Physics (APCTP), Pohang, Republic of Korea.

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.

Keywords: cell migration; dendritic cell; machine learning; maturation; transition dynamics.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Cell Differentiation
Dendritic Cells*
Machine Learning
T-Lymphocytes*

Grants and funding

Institute for Basic Science; project code IBS-R020-D1 (to YC and Y-KC). National Research Foundation of Korea; grant no.: 2022R1F1A1074045, 2017K1A1A2013241, and 2020R1A2C4002490 (to TS and J-HJ).