This article is part of the supplement: Tenth International Conference on Bioinformatics. First ISCB Asia Joint Conference 2011 (InCoB/ISCB-Asia 2011): Bioinformatics
Correlation of cell membrane dynamics and cell motility
1 Computation and Systems Biology, Singapore-MIT Alliance, Nanyang Technological University, Singapore 637460
2 BioInformatics Research Centre, Nanyang Technological University, Singapore 637553
3 Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
4 Department of Biological Sciences, National University of Singapore, Singapore 117543
5 Centre for BioImaging Sciences, National University of Singapore, Singapore 117543
6 Mechanobiology Institute, National University of Singapore, Singapore 117411
7 Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
BMC Bioinformatics 2011, 12(Suppl 13):S19 doi:10.1186/1471-2105-12-S13-S19Published: 30 November 2011
Essential 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.
We 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.
By 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.