Correlation of cell membrane dynamics and cell motility

BMC Bioinformatics

Proceedings

Alvin Ng¹  Merlin Veronika¹  Jagath C Rajapakse²  Paul Matsudaira³  Roy Welsch⁴ 

[1] Computation and Systems Biology, Singapore-MIT Alliance, Nanyang Technological University, 637460, Singapore;BioInformatics Research Centre, Nanyang Technological University, 637553, Singapore;Computation and Systems Biology, Singapore-MIT Alliance, Nanyang Technological University, 637460, Singapore;BioInformatics Research Centre, Nanyang Technological University, 637553, Singapore;Department of Biological Engineering, Massachusetts Institute of Technology, 02142, Cambridge, MA, USA;Computation and Systems Biology, Singapore-MIT Alliance, Nanyang Technological University, 637460, Singapore;Department of Biological Sciences, National University of Singapore, 117543, Singapore;Centre for BioImaging Sciences, National University of Singapore, 117543, Singapore;Mechanobiology Institute, National University of Singapore, 117411, Singapore;Computation and Systems Biology, Singapore-MIT Alliance, Nanyang Technological University, 637460, Singapore;Sloan School of Management, Massachusetts Institute of Technology, 02142, Cambridge, MA, USA;

关键词: Gaussian Mixture Model;    Minimum Description Length;    Edge Feature;    Edge Activity;    Cell Class;   

DOI  :  10.1186/1471-2105-12-S13-S19

来源: Springer

【 摘 要 】

BackgroundEssential events of cell development and homeostasis are revealed by the associated changes of cell morphology and therefore have been widely used as a key indicator of physiological states and molecular pathways affecting various cellular functions via cytoskeleton. Cell motility is a complex phenomenon primarily driven by the actin network, which plays an important role in shaping the morphology of the cells. Most of the morphology based features are approximated from cell periphery but its dynamics have received none to scant attention. We aim to bridge the gap between membrane dynamics and cell states from the perspective of whole cell movement by identifying cell edge patterns and its correlation with cell dynamics.ResultsWe present a systematic study to extract, classify, and compare cell dynamics in terms of cell motility and edge activity. Cell motility features extracted by fitting a persistent random walk were used to identify the initial set of cell subpopulations. We propose algorithms to extract edge features along the entire cell periphery such as protrusion and retraction velocity. These constitute a unique set of multivariate time-lapse edge features that are then used to profile subclasses of cell dynamics by unsupervised clustering.ConclusionsBy comparing membrane dynamic patterns exhibited by each subclass of cells, correlated trends of edge and cell movements were identified. Our findings are consistent with published literature and we also identified that motility patterns are influenced by edge features from initial time points compared to later sampling intervals.

【 授权许可】

Unknown   
© Veronika et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.