Full metadata record
DC Field | Value | Language |
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dc.citation.startPage | 1129600 | - |
dc.citation.title | FRONTIERS IN IMMUNOLOGY | - |
dc.citation.volume | 14 | - |
dc.contributor.author | Song, Taegeun | - |
dc.contributor.author | Choi, Yongjun | - |
dc.contributor.author | Jeon, Jae-Hyung | - |
dc.contributor.author | Cho, Yoon-Kyoung | - |
dc.date.accessioned | 2023-12-21T12:41:55Z | - |
dc.date.available | 2023-12-21T12:41:55Z | - |
dc.date.created | 2023-05-18 | - |
dc.date.issued | 2023-04 | - |
dc.description.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. | - |
dc.identifier.bibliographicCitation | FRONTIERS IN IMMUNOLOGY, v.14, pp.1129600 | - |
dc.identifier.doi | 10.3389/fimmu.2023.1129600 | - |
dc.identifier.issn | 1664-3224 | - |
dc.identifier.scopusid | 2-s2.0-85153443048 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/64310 | - |
dc.identifier.url | http://dx.doi.org/10.3389/fimmu.2023.1129600 | - |
dc.identifier.wosid | 000970729400001 | - |
dc.language | 영어 | - |
dc.publisher | FRONTIERS MEDIA SA | - |
dc.title | A machine learning approach to discover migration modes and transition dynamics of heterogeneous dendritic cells | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
dc.relation.journalWebOfScienceCategory | Immunology | - |
dc.relation.journalResearchArea | Immunology | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | dendritic cell | - |
dc.subject.keywordAuthor | cell migration | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | transition dynamics | - |
dc.subject.keywordAuthor | maturation | - |
dc.subject.keywordPlus | ANOMALOUS DIFFUSION | - |
dc.subject.keywordPlus | ACTIN FLOWS | - |
dc.subject.keywordPlus | GENERATION | - |
dc.subject.keywordPlus | PATTERNS | - |
dc.subject.keywordPlus | CD8(+) | - |
dc.subject.keywordPlus | WALKS | - |
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