Profiling Dynamic Patterns of Single‐Cell Motility
Abstract Cell motility plays an essential role in many biological processes as cells move and interact within their local microenvironments. Current methods for quantifying cell motility typically involve tracking individual cells over time, but the results are often presented as averaged values acr...
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| Format: | Article |
| Language: | English |
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Wiley
2024-10-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202400918 |
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| author | Debonil Maity Nikita Sivakumar Pratik Kamat Nahuel Zamponi Chanhong Min Wenxuan Du Hasini Jayatilaka Adrian Johnston Bartholomew Starich Anshika Agrawal Deanna Riley Leandro Venturutti Ari Melnick Leandro Cerchietti Jeremy Walston Jude M. Phillip |
| author_facet | Debonil Maity Nikita Sivakumar Pratik Kamat Nahuel Zamponi Chanhong Min Wenxuan Du Hasini Jayatilaka Adrian Johnston Bartholomew Starich Anshika Agrawal Deanna Riley Leandro Venturutti Ari Melnick Leandro Cerchietti Jeremy Walston Jude M. Phillip |
| author_sort | Debonil Maity |
| collection | DOAJ |
| description | Abstract Cell motility plays an essential role in many biological processes as cells move and interact within their local microenvironments. Current methods for quantifying cell motility typically involve tracking individual cells over time, but the results are often presented as averaged values across cell populations. While informative, these ensemble approaches have limitations in assessing cellular heterogeneity and identifying generalizable patterns of single‐cell behaviors, at baseline and in response to perturbations. In this study, CaMI is introduced, a computational framework designed to leverage the single‐cell nature of motility data. CaMI identifies and classifies distinct spatio‐temporal behaviors of individual cells, enabling robust classification of single‐cell motility patterns in a large dataset (n = 74 253 cells). This framework allows quantification of spatial and temporal heterogeneities, determination of single‐cell motility behaviors across various biological conditions and provides a visualization scheme for direct interpretation of dynamic cell behaviors. Importantly, CaMI reveals insights that conventional cell motility analyses may overlook, showcasing its utility in uncovering robust biological insights. Together, a multivariate framework is presented to classify emergent patterns of single‐cell motility, emphasizing the critical role of cellular heterogeneity in shaping cell behaviors across populations. |
| format | Article |
| id | doaj-art-01868bf7623248bdabb6fc926f4e3dca |
| institution | OA Journals |
| issn | 2198-3844 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-01868bf7623248bdabb6fc926f4e3dca2025-08-20T02:17:06ZengWileyAdvanced Science2198-38442024-10-011138n/an/a10.1002/advs.202400918Profiling Dynamic Patterns of Single‐Cell MotilityDebonil Maity0Nikita Sivakumar1Pratik Kamat2Nahuel Zamponi3Chanhong Min4Wenxuan Du5Hasini Jayatilaka6Adrian Johnston7Bartholomew Starich8Anshika Agrawal9Deanna Riley10Leandro Venturutti11Ari Melnick12Leandro Cerchietti13Jeremy Walston14Jude M. Phillip15Department of Biomedical Engineering Johns Hopkins University Baltimore MD 21212 USADepartment of Biomedical Engineering Johns Hopkins University Baltimore MD 21212 USAInstitute for Nanobiotechnology Johns Hopkins University Baltimore MD 21212 USADepartment of Medicine Division of Hematology and Medical Oncology Weill Cornell Medicine New York 10065 USADepartment of Biomedical Engineering Johns Hopkins University Baltimore MD 21212 USAInstitute for Nanobiotechnology Johns Hopkins University Baltimore MD 21212 USAInstitute for Nanobiotechnology Johns Hopkins University Baltimore MD 21212 USAInstitute for Nanobiotechnology Johns Hopkins University Baltimore MD 21212 USAInstitute for Nanobiotechnology Johns Hopkins University Baltimore MD 21212 USADepartment of Chemical and Biomolecular Engineering Johns Hopkins University Baltimore MD 21212 USAInstitute for Nanobiotechnology Johns Hopkins University Baltimore MD 21212 USADepartment of Pathology and Laboratory Medicine University of British Columbia Centre for Lymphoid Cancer British Columbia Cancer Research Institute Vancouver British Columbia V6T 1Z4 CanadaDepartment of Medicine Division of Hematology and Medical Oncology Weill Cornell Medicine New York 10065 USADepartment of Medicine Division of Hematology and Medical Oncology Weill Cornell Medicine New York 10065 USAInstitute for Nanobiotechnology Johns Hopkins University Baltimore MD 21212 USADepartment of Biomedical Engineering Johns Hopkins University Baltimore MD 21212 USAAbstract Cell motility plays an essential role in many biological processes as cells move and interact within their local microenvironments. Current methods for quantifying cell motility typically involve tracking individual cells over time, but the results are often presented as averaged values across cell populations. While informative, these ensemble approaches have limitations in assessing cellular heterogeneity and identifying generalizable patterns of single‐cell behaviors, at baseline and in response to perturbations. In this study, CaMI is introduced, a computational framework designed to leverage the single‐cell nature of motility data. CaMI identifies and classifies distinct spatio‐temporal behaviors of individual cells, enabling robust classification of single‐cell motility patterns in a large dataset (n = 74 253 cells). This framework allows quantification of spatial and temporal heterogeneities, determination of single‐cell motility behaviors across various biological conditions and provides a visualization scheme for direct interpretation of dynamic cell behaviors. Importantly, CaMI reveals insights that conventional cell motility analyses may overlook, showcasing its utility in uncovering robust biological insights. Together, a multivariate framework is presented to classify emergent patterns of single‐cell motility, emphasizing the critical role of cellular heterogeneity in shaping cell behaviors across populations.https://doi.org/10.1002/advs.202400918cell motilityhigh‐throughput cell phenotypingsingle‐cell behaviors |
| spellingShingle | Debonil Maity Nikita Sivakumar Pratik Kamat Nahuel Zamponi Chanhong Min Wenxuan Du Hasini Jayatilaka Adrian Johnston Bartholomew Starich Anshika Agrawal Deanna Riley Leandro Venturutti Ari Melnick Leandro Cerchietti Jeremy Walston Jude M. Phillip Profiling Dynamic Patterns of Single‐Cell Motility Advanced Science cell motility high‐throughput cell phenotyping single‐cell behaviors |
| title | Profiling Dynamic Patterns of Single‐Cell Motility |
| title_full | Profiling Dynamic Patterns of Single‐Cell Motility |
| title_fullStr | Profiling Dynamic Patterns of Single‐Cell Motility |
| title_full_unstemmed | Profiling Dynamic Patterns of Single‐Cell Motility |
| title_short | Profiling Dynamic Patterns of Single‐Cell Motility |
| title_sort | profiling dynamic patterns of single cell motility |
| topic | cell motility high‐throughput cell phenotyping single‐cell behaviors |
| url | https://doi.org/10.1002/advs.202400918 |
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