Profiling Dynamic Patterns of Single‐Cell Motility

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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Main Authors: 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
Format: Article
Language:English
Published: Wiley 2024-10-01
Series:Advanced Science
Subjects:
cell motility
high‐throughput cell phenotyping
single‐cell behaviors
Online Access:https://doi.org/10.1002/advs.202400918
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Summary: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.
ISSN:2198-3844

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