Sub-population analysis based on temporal features of high content images

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.Computation and Systems Biology Programme of Singapore--Massachusetts Institute of Technology Allianc

Cell profiling with dynamic features for high-throughput images

Subpopulation heterogeneity has been spawning intense studies at genetic and molecular level due to its occurrence at all biological levels from cells to tissues. We envisioned studying this biological phenomenon through image based profiling methods incorporating motility based features. We developed population profiling methods for analysing subpopulations arising in single-cell lines by introducing motility based dynamic features. Combination of these features with morphological features improved the accuracy of classification of cell states. We introduced unsupervised methods so that prior training data is not required. Also the use of motility features for identifying membrane dynamics and its correlation with whole cell dynamics were investigated. We were able to identify subpopulations of cells with similar dynamic profiles but having different membrane patterns. The profiling pipeline using dynamic features were demonstrated by identifying mitotic phases in cells undergoing cell-cycle. Cells passing through mitotic division exhibit motility characteristics unique to each phase which were utilized for phase recognition. The methods were validated with real image data and the results compared well with ground truth.Doctor of Philosophy (SCE

Correlation of cell membrane dynamics and cell motility

Abstract 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