BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.

Biological imaging collects data over time and length scales that range from atoms and molecules to cells and tissues with a frequent goal of molecular detection in live tissues and organs. There are two generic approaches employed in microscopy to study the structure and function of molecules, cells, and organisms. Both collect large numbers of images and generate TB-size datasets but for different purposes. In high-content screening applications drugs are applied to live cells grown in wells and imaged in automated microscopes. A thorough study will examine the effects of a pharmacological agent at several dilutions by collecting images of cells at different time-points and generate TB datasets from millions of cells. After image processing and analysis, critical concentrations of drug activity are identified and mechanism of action is extracted from downstream information. The experiment is convoluted by marking various structures or molecules with a signature color, thus allowing for specific objects to be localized in space and tracked in time. The second approach has the goal of a 3D structure. Datasets consist of either serial slices or projections through the object, a cell, tissue, or organism and are acquired by light or electron microscopy. These methods are also linked in a time series to generate the dynamics of the structure from which mechanical and kinetic parameters are extracted. A significant problem is that these approaches can easily generate TBs of image data in minutes to hour periods. Thus, in biological imaging it is easy to generate data but the pressing problems are how to manage and organize image datasets, relate image datasets obtained by different microscopy methods, extract information from images, and to generate dynamic and 3D models of biological structures. These downstream steps contribute to the total dataset for the experiment.

BACKGROUND: High content screening techniques are increasingly used to understand the regulation and progression of cell motility. The demand of new platforms, coupled with availability of terabytes of data has challenged the traditional technique of identifying cell populations by manual methods and resulted in development of high-dimensional analytical methods. RESULTS: In this paper, we present sub-populations analysis of cells at the tissue level by using dynamic features of the cells. We used active contour without edges for segmentation of cells, which preserves the cell morphology, and autoregressive modeling to model cell trajectories. The sub-populations were obtained by clustering static, dynamic and a combination of both features. We were able to identify three unique sub-populations in combined clustering. CONCLUSION: We report a novel method to identify sub-populations using kinetic features and demonstrate that these features improve sub-population analysis at the tissue level. These advances will facilitate the application of high content screening data analysis to new and complex biological problems.