Cell geometry distinguishes migration‐associated heterogeneity in two‐dimensional systems
Abstract In vitro migration assays are a cornerstone of cell biology and have found extensive utility in research. Over the past decade, several variations of the two‐dimensional (2D) migration assay have improved our understanding of this fundamental process. However, the ability of these approache...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-09-01
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| Series: | Computational and Systems Oncology |
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| Online Access: | https://doi.org/10.1002/cso2.1041 |
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| author | Sagar S Varankar Kishore Hari Sharon Kartika Sharmila A Bapat Mohit Kumar Jolly |
| author_facet | Sagar S Varankar Kishore Hari Sharon Kartika Sharmila A Bapat Mohit Kumar Jolly |
| author_sort | Sagar S Varankar |
| collection | DOAJ |
| description | Abstract In vitro migration assays are a cornerstone of cell biology and have found extensive utility in research. Over the past decade, several variations of the two‐dimensional (2D) migration assay have improved our understanding of this fundamental process. However, the ability of these approaches to capture the functional heterogeneity during migration and their accessibility to inexperienced users has been limited. We downloaded published time‐lapse 2D cell migration data sets and subjected them to feature extraction with the Fiji software. We used the “Analyze Particles” tool to extract 10 cell geometry features (CGFs), which were grouped into “shape,” “size,” and “position” descriptors. Next, we defined the migratory status of cells using the “MTrack2” plugin. All data obtained from Fiji were further subjected to rigorous statistical analysis with R version 4.0.2. We observed consistent associative trends between size and shape descriptors and validated our observations across four independent data sets. We used these descriptors to identify and characterize “nonmigrator (NM)” and “migrator (M)” subsets. Statistical analysis allowed us to identify considerable heterogeneity in the NM subset. Interestingly, differences in 2D‐packing appeared to affect CGF trends and heterogeneity within the migratory subsets. We developed an analytical pipeline using open source tools, to identify and morphologically characterize functional migratory subsets from label‐free, time‐lapse imaging data. Our quantitative approach identified heterogeneity between nonmigratory cells and predicted the influence of 2D‐packing on migration. |
| first_indexed | 2024-12-10T06:15:50Z |
| format | Article |
| id | doaj.art-54fed7aa22fa465dba1e488edd2d8c38 |
| institution | Directory Open Access Journal |
| issn | 2689-9655 |
| language | English |
| last_indexed | 2024-12-10T06:15:50Z |
| publishDate | 2022-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Computational and Systems Oncology |
| spelling | doaj.art-54fed7aa22fa465dba1e488edd2d8c382022-12-22T01:59:26ZengWileyComputational and Systems Oncology2689-96552022-09-0123n/an/a10.1002/cso2.1041Cell geometry distinguishes migration‐associated heterogeneity in two‐dimensional systemsSagar S Varankar0Kishore Hari1Sharon Kartika2Sharmila A Bapat3Mohit Kumar Jolly4Centre for BioSystems Science and Engineering Indian Institute of Science Bangalore IndiaCentre for BioSystems Science and Engineering Indian Institute of Science Bangalore IndiaDepartment of Biological Sciences Indian Institute of Science Education and Research Kolkata Mohanpur Nadia West Bengal IndiaNational Centre for Cell Science Savitribai Phule Pune University Ganeshkhind Pune IndiaCentre for BioSystems Science and Engineering Indian Institute of Science Bangalore IndiaAbstract In vitro migration assays are a cornerstone of cell biology and have found extensive utility in research. Over the past decade, several variations of the two‐dimensional (2D) migration assay have improved our understanding of this fundamental process. However, the ability of these approaches to capture the functional heterogeneity during migration and their accessibility to inexperienced users has been limited. We downloaded published time‐lapse 2D cell migration data sets and subjected them to feature extraction with the Fiji software. We used the “Analyze Particles” tool to extract 10 cell geometry features (CGFs), which were grouped into “shape,” “size,” and “position” descriptors. Next, we defined the migratory status of cells using the “MTrack2” plugin. All data obtained from Fiji were further subjected to rigorous statistical analysis with R version 4.0.2. We observed consistent associative trends between size and shape descriptors and validated our observations across four independent data sets. We used these descriptors to identify and characterize “nonmigrator (NM)” and “migrator (M)” subsets. Statistical analysis allowed us to identify considerable heterogeneity in the NM subset. Interestingly, differences in 2D‐packing appeared to affect CGF trends and heterogeneity within the migratory subsets. We developed an analytical pipeline using open source tools, to identify and morphologically characterize functional migratory subsets from label‐free, time‐lapse imaging data. Our quantitative approach identified heterogeneity between nonmigratory cells and predicted the influence of 2D‐packing on migration.https://doi.org/10.1002/cso2.10412D‐packingcell geometryfunctional heterogeneityphase contrast time‐lapse imaging |
| spellingShingle | Sagar S Varankar Kishore Hari Sharon Kartika Sharmila A Bapat Mohit Kumar Jolly Cell geometry distinguishes migration‐associated heterogeneity in two‐dimensional systems Computational and Systems Oncology 2D‐packing cell geometry functional heterogeneity phase contrast time‐lapse imaging |
| title | Cell geometry distinguishes migration‐associated heterogeneity in two‐dimensional systems |
| title_full | Cell geometry distinguishes migration‐associated heterogeneity in two‐dimensional systems |
| title_fullStr | Cell geometry distinguishes migration‐associated heterogeneity in two‐dimensional systems |
| title_full_unstemmed | Cell geometry distinguishes migration‐associated heterogeneity in two‐dimensional systems |
| title_short | Cell geometry distinguishes migration‐associated heterogeneity in two‐dimensional systems |
| title_sort | cell geometry distinguishes migration associated heterogeneity in two dimensional systems |
| topic | 2D‐packing cell geometry functional heterogeneity phase contrast time‐lapse imaging |
| url | https://doi.org/10.1002/cso2.1041 |
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