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Supplemental Information Measurementofcancercellnucleidispersion

Segmentation of the cancer cell nuclei was accomplished by first 3D

interpolation since the x, y and z resolutions are not equal. After interpolation,

one voxel in our image stack represents a volume of 30.90 µm3. In order to

reduce noise, a 3D Gaussian kernel of 5 pixel radius with ( is the

standard deviation of the Gaussian function) is convolved to the original

image stack, estimating that the radius of a cancer cell nucleus is ~5-7 pixels

(~10-14 microns). To enhance the edges between closely packed nuclei, a

Laplacian kernel of 5 pixel radius with is then convolved on the

denoised image stack. After edge enhancement, a 3D binary image is created

based on its histogram. An ance transformation is applied to the binary image

and then the scale invariant seed finding approach45 is applied to detect the

center of the cell nuclei. Finally, the Evolving Generalized Voronoi Diagram46

was used to segment the nuclei based on the detected seeds. Once the

nuclei are segmented, the number of nuclei (N) and their locations (xi) can be

determined.

In our computation, numbers of spheroids are first determined before

calculating the dispersion. After the nuclei are segmented, we produce the 3D

histogram of nuclei distribution in the 3D spatial domain. The binning is done

by a volume of 17x17x5 voxel grids. Then we apply a Gaussian kernel of 5

pixel radius with *. We find the local maximum in the 3D histogram and

decide how many spheroids in a given images. The situation of one spheroid

and two spheroids (Fig. S3). A mask is created based on the convolved

image, shown by the blue contours (Fig. S3). Any dots outside of the 3D mask

will be considered as outlier. The grouping of nuclei to different clusters in 3D

is also illustrated (Fig. S4).

Once the nuclei are segmented, we need to measure the dispersion of

a given spheroid. First, we need to determine the center of spheroid. If there

are N nuclei in a given spheroid, the geometric centers of nuclei are

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Electronic Supplementary Material (ESI) for Integrative BiologyThis journal is © The Royal Society of Chemistry 2013

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Supplemental figure legends Fig. S1 A549 cells were grown in microwells on 2D from a sparse density of

800 cells/cm2 and cultured in the presence of concentration of control

(DMSO), 0.04μM, 0.16μM, 0.63μM, 2.5μM, 10μM. Cells were imaged after 72

h incubation using 10x objective (Olympus IX51). A. AZD 0530 B. A83-01.

Fig. S2 A549 cells were grown on 2D from a sparse density of 800 cells/cm2

and cultured in the presence of 2.5 M test compounds mixed in culture

medium. Cells were imaged after 72 h incubation using 10x objective

(Olympus IX51). A. DMSO control. B. MK-2206. C. A83-01. D. LY 364947. E.

SB 431542. F. SD-208. G. BMS-599626, H. CI-1033. I. Gefitinib. J. BMS-

536924. K. PD 0325901. L. Masitinib. M. AZD 0530. N. PP1.

Fig. S3 Projection of 3D histogram into X-Y plane. Purple dots in the left

images are detected local maxima. A. One spheroid is detected. B. Two

spheroids are detected. The blue contours in the right images show the

boundary of the mask used to determine if a nucleus is an outlier.

Fig. S4 Detection of two spheroids in image analysis. A. Two clusters are

detected at 0 h and marked with red and blue to represent the nuclei from two

different spheroids. The outliers are represented by black nuclei. B.

Segmentation of two spheroids at 12 h.

Fig. S5 Evaluation of drug effects on HUVEC monolayer growth. A and B.

Representative fluorescence images of HUVEC monolayers in the presence

of drug LY 364947 at 0 h and 36 h, respectively. C. Plots of normalized

HUVEC cell number for each compound at the highest dose used in this

study. Normalized cell number is the ratio of cell number at 36 h to that at 0 h.


Supplemental figures

Fig. S1


Fig. S2


Fig. S3


Fig. S4


Fig. S5


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