TY - JOUR
T1 - Profiling Dynamic Patterns of Single-Cell Motility
AU - Maity, Debonil
AU - Sivakumar, Nikita
AU - Kamat, Pratik
AU - Zamponi, Nahuel
AU - Min, Chanhong
AU - Du, Wenxuan
AU - Jayatilaka, Hasini
AU - Johnston, Adrian
AU - Starich, Bartholomew
AU - Agrawal, Anshika
AU - Riley, Deanna
AU - Venturutti, Leandro
AU - Melnick, Ari
AU - Cerchietti, Leandro
AU - Walston, Jeremy
AU - Phillip, Jude M.
N1 - Publisher Copyright:
© 2024 The Author(s). Advanced Science published by Wiley-VCH GmbH.
PY - 2024/10/16
Y1 - 2024/10/16
N2 - 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.
AB - 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.
KW - cell motility
KW - high-throughput cell phenotyping
KW - single-cell behaviors
UR - http://www.scopus.com/inward/record.url?scp=85200975418&partnerID=8YFLogxK
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U2 - 10.1002/advs.202400918
DO - 10.1002/advs.202400918
M3 - Article
C2 - 39136147
AN - SCOPUS:85200975418
SN - 2198-3844
VL - 11
JO - Advanced Science
JF - Advanced Science
IS - 38
M1 - 2400918
ER -