Automated characterization of amoeboid motility

Abstract

Cells use a variety of methods to migrate. One of these, referred to as amoeboid motion, involves alternating cycles of morphological expansion and retraction [1]. Employed by numerous mammalian cell lines including neutrophils and lymphocytes [2], [3], it is also seen in the metastatic migration of some tumor cells [4]. Amoeboid motility is best understood in the social amoeba Dictyostelium discoideum. It is a complex cellular process driven by highly-organized cytoskeletal dynamics containing numerous molecular species and regulated by an intricate signaling network. An important aspect of studying is to understand how the roles played by these molecules and signaling pathways are integrated. And a widely used approach is to characterize phenotype-specific morphological dynamics among various types of cells.Traditionally, amoeboid motility has been characterized by a number of different parameters [5]. Some, such as velocity, directional persistence, and chemotactic index, are determined by the position of the cell's centroid as it migrates in response to external chemical cues, a process known as chemotaxis. Characterizations which address cellular morphology are based on cell shape parameters such as perimeter, area, roundness, and body orientation. Though these parameters can be used to identify some differences between strains of chemotactic cells, they primarily provide global information about chemotaxis and chemoattractant-induced cell shape changes, hence are insufficient to distinguish cell strains based on local morphological information, such as pseudopodial protrusions, that typify amoeboid motility. When the activities of pseudopodia are described in some studies, the protrusions have been identified and outlined manually [6], [7]. In addition to being highly time consuming, these manual methods have the drawback that they are based on subjective judgements. Thus, an efficient and systematic approach is necessary for cell shape characterization.Here, we develop a series of automated methods to characterize amoeboid locomotion based on the skeleton of a planar shape [8]. Skeletonization, also known as the medial axis transform, is a technique in morphological image processing used to reduce a binary shape into a series of connected lines -the skeleton -which roughly maintain the form of the shape. Though it has long been used to analyze static shapes 5 25 50 70 95 Fig. 1. Five snapshots at different time points from a chemotaxing cell moving towards a needle on the left side. They show the cell outline (white), the skeleton (green) and the tips (protrusions in red, retractions in blue) in each image. The times (in seconds) when each image was taken from the beginning of the movie are labeled at the lower-left corners. The scale bar at the lower-right corner in the last image represents the length of 5 μm.in cell biology [9]-[12], skeletonization has not been used to track dynamic shape information during amoeboid motility. To achieve this, three imaging techniques were used to acquire movies of moti...