Time-Series Clustering of Single-Cell Trajectories in Collective Cell Migration
Collective invasion drives multicellular cancer cells to spread to surrounding normal tissues. To fully comprehend metastasis, the methodology of analysis of individual cell migration in tissue should be well developed. Extracting and classifying cells with similar migratory characteristics in a col...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Cancers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-6694/14/19/4587 |
| _version_ | 1797480366055882752 |
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| author | Zhuohan Xin Masashi K. Kajita Keiko Deguchi Shin-ichiro Suye Satoshi Fujita |
| author_facet | Zhuohan Xin Masashi K. Kajita Keiko Deguchi Shin-ichiro Suye Satoshi Fujita |
| author_sort | Zhuohan Xin |
| collection | DOAJ |
| description | Collective invasion drives multicellular cancer cells to spread to surrounding normal tissues. To fully comprehend metastasis, the methodology of analysis of individual cell migration in tissue should be well developed. Extracting and classifying cells with similar migratory characteristics in a colony would facilitate an understanding of complex cell migration patterns. Here, we used electrospun fibers as the extracellular matrix for the in vitro modeling of collective cell migration, clustering of mesenchymal and epithelial cells based on trajectories, and analysis of collective migration patterns based on trajectory similarity. We normalized the trajectories to eliminate the effect of cell location on clustering and used uniform manifold approximation and projection to perform dimensionality reduction on the time-series data before clustering. When the clustering results were superimposed on the trajectories before normalization, the results still exhibited positional similarity, thereby demonstrating that this method can identify cells with similar migration patterns. The same cluster contained both mesenchymal and epithelial cells, and this result was related to cell location and cell division. These data highlight the reliability of this method in identifying consistent migration patterns during collective cell migration. This provides new insights into the epithelial–mesenchymal interactions that affect migration patterns. |
| first_indexed | 2024-03-09T21:58:47Z |
| format | Article |
| id | doaj.art-8a86c3f0df324ecc92a05f6e92f8d11b |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-09T21:58:47Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-8a86c3f0df324ecc92a05f6e92f8d11b2023-11-23T19:53:15ZengMDPI AGCancers2072-66942022-09-011419458710.3390/cancers14194587Time-Series Clustering of Single-Cell Trajectories in Collective Cell MigrationZhuohan Xin0Masashi K. Kajita1Keiko Deguchi2Shin-ichiro Suye3Satoshi Fujita4Department of Advanced Interdisciplinary Science and Technology, University of Fukui, Fukui 910-8507, JapanDepartment of Applied Chemistry and Biotechnology, University of Fukui, Fukui 910-8507, JapanDepartment of Frontier Fiber Technology and Science, University of Fukui, Fukui 910-8507, JapanDepartment of Advanced Interdisciplinary Science and Technology, University of Fukui, Fukui 910-8507, JapanDepartment of Advanced Interdisciplinary Science and Technology, University of Fukui, Fukui 910-8507, JapanCollective invasion drives multicellular cancer cells to spread to surrounding normal tissues. To fully comprehend metastasis, the methodology of analysis of individual cell migration in tissue should be well developed. Extracting and classifying cells with similar migratory characteristics in a colony would facilitate an understanding of complex cell migration patterns. Here, we used electrospun fibers as the extracellular matrix for the in vitro modeling of collective cell migration, clustering of mesenchymal and epithelial cells based on trajectories, and analysis of collective migration patterns based on trajectory similarity. We normalized the trajectories to eliminate the effect of cell location on clustering and used uniform manifold approximation and projection to perform dimensionality reduction on the time-series data before clustering. When the clustering results were superimposed on the trajectories before normalization, the results still exhibited positional similarity, thereby demonstrating that this method can identify cells with similar migration patterns. The same cluster contained both mesenchymal and epithelial cells, and this result was related to cell location and cell division. These data highlight the reliability of this method in identifying consistent migration patterns during collective cell migration. This provides new insights into the epithelial–mesenchymal interactions that affect migration patterns.https://www.mdpi.com/2072-6694/14/19/4587collective cell migrationelectrospinningdimensionality reductionclusteringmigration pattern |
| spellingShingle | Zhuohan Xin Masashi K. Kajita Keiko Deguchi Shin-ichiro Suye Satoshi Fujita Time-Series Clustering of Single-Cell Trajectories in Collective Cell Migration Cancers collective cell migration electrospinning dimensionality reduction clustering migration pattern |
| title | Time-Series Clustering of Single-Cell Trajectories in Collective Cell Migration |
| title_full | Time-Series Clustering of Single-Cell Trajectories in Collective Cell Migration |
| title_fullStr | Time-Series Clustering of Single-Cell Trajectories in Collective Cell Migration |
| title_full_unstemmed | Time-Series Clustering of Single-Cell Trajectories in Collective Cell Migration |
| title_short | Time-Series Clustering of Single-Cell Trajectories in Collective Cell Migration |
| title_sort | time series clustering of single cell trajectories in collective cell migration |
| topic | collective cell migration electrospinning dimensionality reduction clustering migration pattern |
| url | https://www.mdpi.com/2072-6694/14/19/4587 |
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