4D Tumorigenesis Model for Quantitating Coalescence, Directed Cell Motility and Chemotaxis, Identifying Unique Cell Behaviors, and Testing Anticancer Drugs

Abstract

A 4D high-resolution computer-assisted reconstruction and motion analysis system has been developed and applied to the long-term (14-30 days) analysis of cancer cells migrating and aggregating within a 3D matrix. 4D tumorigenesis models more closely approximate the tumor microenvironment than 2D substrates and, therefore, are improved tools for elucidating the interactions within the tumor microenvironment that promote growth and metastasis. The model we describe here can be used to analyze the growth of tumor cells, aggregate coalescence, directed cell motility and chemotaxis, matrix degradation, the effects of anticancer drugs, and the behavior of immune and endothelial cells mixed with cancer cells. The information given in this chapter is also intended to acquaint the reader with computer-assisted methods and algorithms that can be used for high-resolution 3D reconstruction and quantitative motion analysis.

Directed movement toward, translocation along, penetration into and exit from vascular networks by breast cancer cells in 3D

“…J3D-DIAS4.2 [25,29,32] was used for accurate and precise quantitative analysis of cell movements and shapes. To reduce the size of files and increase the speed of analysis, JPEG images were imported into J3D-DIAS4.2 [25] as described above (Figure 1(d)) and saved as movies in the DIAS format.…”

“…J3D-DIAS4.2 [25,29,32] was used for accurate and precise quantitative analysis of cell movements and shapes. To reduce the size of files and increase the speed of analysis, JPEG images were imported into J3D-DIAS4.2 [25] as described above (Figure 1(d)) and saved as movies in the DIAS format. Image segmentation of HUVEC networks was performed automatically using a bitmap algorithm (Figure 2(a)) that retains gaps and spaces in the network [26].…”

“…Overlapping bitmap and edge traces of the optical sections were stacked in the z-axis (Figure 2(c,d)), respectively). Bitmap pixels were expanded into voxels and the voxel blocks wrapped into a continuous surface [26], while the pixel-based outlines of objects detected by thresholding were replaced with beta-spline models [25]. J3D-DIAS4.2 then built a faceted surface from the beta-splines using the 'marching cubes' algorithm [40].…”

“…J3D-DIAS4.2 then built a faceted surface from the beta-splines using the 'marching cubes' algorithm [40]. Path files in both cases were constructed from the position of the 3D centroid at 10 minute intervals [25] and velocity data calculated from the centroid track [32,41]. The surface area of HUVEC networks was calculated from the faceted reconstructions [25].…”

“…To this end, we have begun to develop a computerassisted 4D tumorigenesis model to facilitate the study of cell interactions related to tumorigenesis and metastasis [25,26]. Using this model, we previously observed by computer-assisted 3D reconstruction that cells from a variety of tumorigenic cancer cell lines, as well as cancer cells from fresh tumor tissues, actively aggregate and coalesce when seeded in a 3D Matrigel matrix, a behavior not observed in preparations of nontumorigenic cells [26][27][28].…”