1Institut National de la Santé et de la Recherche Médicale Unité Mixte de Recherche 514, Reims, France; and 2Laboratoire dImmuno-Oncologie Moléculaire, Faculté de Médecine, Monastir, Tunisia
Submitted 1 July 2005 ; accepted in final form 13 July 2005
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ABSTRACT |
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tumor invasion; metastasis; image analysis; kinetic migration; epidermal growth factor
The process by which cancer cells leave the primary tumor and enter adjacent tissue is known as tumor invasion, whereas metastasis refers to secondary tumor colonization of tissue at a distance from the primary lesion. Tumor progression requires the dispersion of epithelial cells from neoplastic clusters and cell invasion of adjacent stromal connective tissue. The transformation of a tumor cell to a metastatic phenotype necessitates changes in cell-cell adhesion (29). The stability of these cell interactions is accomplished through a variety of proteins and structures (11). The E-cadherin-catenin complex, by mediating intercellular adhesion, regulates the architectural integrity of epithelia. Downregulation of its expression contributes to the acquisition by cells of an invasive phenotype (20).
In recent years, three-dimensional (3D) cell culture techniques have been developed to most closely simulate tissues. They were applied to the quantification of human neutrophil motility in 3D collagen gels (22), the measurement of adhesion proteins (23), proliferation and locomotion of ras oncogenic cells in the presence of flavonoid-structured molecules (16), T-cell migration (10), migration of dendritic cells (13), the adhesion-mediated cell migration (5), or the determination of motility phenotypes of highly invasive cells (8). However, to date, no specific study has been devoted to the comparative 3D motion analysis of noninvasive and invasive cells.
In previous studies (18), we were interested in studying the sociological behavior of invasive and noninvasive cells in a 2D model. This model did not mimic the in vivo conditions by which the invasive cells migrated through a 3D environment. Some studies aimed to study cell migration and locomotion by tracking cells in 3D collagen gels, but in these studies, cells were tracked only according to x- and y-coordinates and their results consisted of 2D trajectories rather than 3D trajectories (10, 11, 13).
The development of computational analysis allows the study of cell behavior in 3D models and therefore impelled us to develop a better approach of the in vivo conditions and a better comprehension of biological specimens (3), particularly, the invasiveness of cells. To make 3D cell study possible, we have developed an appropriate microenvironment consisting of a two-layer collagen I gel, which permitted the registration of many images at different z positions to be performed. The cells were embedded within the collagen gel, and images were recorded using time-lapse videomicroscopy, and this gave us the different cell positions according to time among x, y, and z series.
To validate our 3D model, we investigated the effects of epidermal growth factor (EGF), which is known to induce caveolin-independent endocytosis of E-cadherin, to phosphorylate catenin- leading to the dissociation of the complex cadherin-catenin (14), to disrupt cell-cell adhesion, and to cause epithelial-to-mesenchymal transition in human cells overexpressing EGF receptor (17), thus promoting tumor cell motility and invasion (25). Finally, to reinforce the hypothesis that 3D models are more appropriate for studying the invasive capacity of cells, we compared the migratory behavior of invasive and noninvasive cells in 2D and 3D culture models.
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MATERIALS AND METHODS |
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2D cell migration quantitation. Cells were incubated with a fluorescent dye (Hoechst-33258; Molecular Probes, Eugene, OR) for 30 min at 0.1 mg/ml in the culture medium to fluorescently stain living cell nuclei. Cells were then washed with culture medium to remove excess fluorescent dye. Cell at a density of 2·105 cells/ml were plated into each culture dish (diameter 3.5 cm). Cell motility analysis was performed using an inverted microscope (Axiovert 200; Zeiss, Oberkochen, Germany) equipped with a small transparent environmental chamber (Climabox; Zeiss) with 5% CO2 in air at 37°C. The microscope was driven by the Metamorph software (Roper Scientific), and images were recorded with a charge-coupled device camera (Coolsnap; Roper Scientific). The microscope was equipped with an epifluorescence illumination source (Hg lamp), together with a 360-nm excitation filter and a 510-nm emission filter. Fluorescent images were collected every 15 min for 1 h, 24 h after being seeded at x32 magnification. Cell migration was characterized and quantified using a software previously described (37) that measures nuclei trajectories as well as cell migration speed.
Two-layer type I collagen gel preparation. The type I collagen gel was extracted from rat tails according to the method described by Chambard et al. (6). In our study, this gel was used at 2 mg/ml because a higher concentration reduces the light transmission through an optical system (8). To visualize cells in a 3D culture model, we have developed a microenvironment assay, which consists of a two-layer type I collagen gel. The first collagen gel layer was prepared by mixing 400 µl of collagen I at 2 mg/ml, 150 µl of RPMI 5x, 15 µl of 1 N NaOH, and 100 µl of DMEM with 10% fetal calf serum. To form the first layer of the microenvironment, 150 µl of this mixture was deposited on the membrane of a double-compartment chamber (Transwell) and polymerized for 30 min at 37°C. A second collagen gel layer was formed by mixing 400 µl of collagen I at 2 mg/ml, 150 µl of RPMI 5x, 15 µl of 1N NaOH, and 100 µl of DMEM with 10% fetal calf serum with cell suspension at 6.5 x 104 cells/ml for the 16HBE cells and 13 x 104 cells/ml for the BZR cells. This mixture (150 µl) was added over the first layer, and 1.5 ml of DMEM was placed in the basal compartment of the chamber, which was thereafter maintained at 37°C for 24 h. To test the effect of EGF, the DMEM medium in the basal compartment was complemented with EGF at 200 ng/ml.
Time-lapse videomicroscopy recordings of the 3D culture model. Cell motility analysis was performed using an inverted microscope (Zeiss Axiovert 200) equipped with a small transparent environmental chamber (Climabox; Zeiss) with 5% CO2 in air at 37°C. The microscope was driven by the Metamorph software (Roper Scientific), and images were recorded with a charge-coupled device camera (Coolsnap; Roper Scientific). Image sequences of the cells within the collagen gel were recorded every hour at at least 100 successive Z levels (3 µm between each Z level) at x20 magnification.
Interactive tracking of cell positions in a four-dimensional data set. Quantifying cell migration from a 3D assay requires the four-dimensional (4D) coordinates (xij,yij,zij,tj) of each cell (i) to be determined at each time step (tj). This has to be done from the series of 3D volumetric images recorded as a function of time, which constitutes a 4D data set.
We decided to concentrate our efforts on interactive tracking rather than on fully automatic tracking. Even interactive tracking in a 4D space (3D + time) is not completely trivial if some computer tools are not available. (This probably explains why the quantification of 3D migration is often performed in 2D projections only.) We describe below several tools we have developed for this purpose.
First, one (3D) volume is displayed as a set of three orthographic planes, representing the (x-y), (x-z), and (y-z) orthogonal planes passing through one selected voxel. The selected voxel can be modified at any time by clicking (with the graphic mouse) within any of the three graphic windows or by moving one of the three cursors associated with the x, y, and z directions of the 3D space. When the selected voxel is assumed to correspond to the center of a cell, the 4D coordinates of this cell (xij,yij,zij,tj) can be saved in a data file. A graphic marker is then inserted within the current volume to avoid processing this cell twice.
Second, when all of the cells of a volume have been selected, the user can replace the current volume with the following one in the series, corresponding to time tj+1 = tj + t. If the user chooses it as an option, all of the positions of cells processed in the previous volume may appear marked in the present volume. It thus becomes relatively easy to find the cells of the present volume in the vicinity of their (marked) position in the previous volume.
Third, finding small objects (as cells) in a volume on the basis of only the three orthographic planes is not completely simple. Thus additional tools prove necessary. We developed such a tool that we call "search" images. These images (one or several per volume of the time series) concentrate the information contained in each whole volume into one (or several) 2D images. They are built as 2D projections of the 3D volume. Although (x-y), (x-z), and (y-z) projections can be built and visualized simultaneously, we explain only the principle for (x-y) projections. Starting from an (x-y-z) volume, the (x-y) projection is built by integrating the whole information along the z direction. By doing so, all cells contained in the volume become visible in the (x-y) projection, which was not the case for the three orthographic planes. The (x-y) projection can be obtained by several different methods (offered as different options to the user). 1) In the maximum projection method, the gray level value at each (x,y) position of the (x-y) projection is the maximum gray level value existing along the z direction at this (x,y) position; 2) for the average projection method, instead of the maximum value, the average gray level value along the z direction is computed; and 3) for the extended depth-of-focus method, at each (x,y) position of the (x-y) projection, the position (x,y,z) of the volume that contains the maximum information is searched and the corresponding information (i.e., gray level) is inserted into the projection image. As a result, all the cells in the volume appear focused in the projection image. This is realized using a wavelet-based approach as already described in another context (15, 31).
With the last option, the "search" image can be used in the following way: by clicking on any cell displayed in the "search" image, a set of three crossing planes is positioned in the volume under study, and the three orthographic planes (see above) are displayed. This is possible because, when the "search" image was built, the z-coordinate associated with each (x,y) pixel was saved. This allows the user to move the cursor a little bit to find and mark the exact position of the cell. Because markers of the cells selected in the previous and in the current volume are also displayed in these "search" images, there is no risk of missing any cell or of processing it twice.
Finally, additional tools are also available to display the data set dynamically, thus allowing the user to better interpret the 4D data set. To obtain this result, animations can be performed, at chosen speed, on any of the 2D images described above, i.e., the three orthographic planes and the three "search" images.
Displaying 3D trajectories of cells.
Once the coordinates (xij,yij,zij,tj) of every cell i are recorded in a data file, all the trajectories are implicitly known and parameters can be deduced, such as the simple ones used in this study or more sophisticated ones. The schematic representation of the parameters computed from the position of a cell at time t and the position of the same cell at time t+t is displayed in Fig. 1. We measured the cell trajectory length in the horizontal plane (x,y), in the vertical direction (z) and the total length of the trajectory. It was also useful to visualize these trajectories in the corresponding 3D space (x-y-z). For this, we developed another piece of software, called "trajectory," which offers the following possibilities: 1) different modes of visualization and rendering of the trajectories (for instance, one color per cell trajectory, or one color per time step, or one color per speed); 2) the possibility of interactively rotating and zooming the volume containing the trajectories; and 3) saving the visualization as a movie, which can be played elsewhere later on.
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Statistical analysis. Data are expressed as means ± SD. Students t-test was used to compare the migration speed of the cell lines. P < 0.05 was considered significant.
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RESULTS |
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2D analysis of 16HBE14o and BZR migration. To reinforce the hypothesis that 3D models are more appropriate for studying the invasive capacity of cells, we compared the migratory behavior of 16HBE14o and BZR cells in 2D and 3D culture models. As shown in Fig. 6, at 24 h of culture in 2D, the migration speed of the noninvasive 16HBE14o cells was not significantly different compared with the migration speed of the invasive BZR cells, contrary to the results obtained with the 3D model. In addition, we observed that the migration speed of both cell lines was significantly lower (P < 0.001) in 3D compared with 2D experiments.
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DISCUSSION |
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Infiltration of 3D gels provides a more realistic assay that may additionally involve different mechanisms or receptors compared with invasion in the classic Boyden chamber or in 2D migration models. The limitations of considering biology in effectively just two dimensions are now becoming clear (1). Bissells group has demonstrated important differences in the behavior of cells grown in 2D and 3D cultures (33, 34). More recently, 3D studies by Friedls group have revealed unexpected subtleties in the mechanisms that cancer cells use to break out from primary tumors (35). A fully automated 3D cell-tracking system has been developed by Demou and McIntire (8) for analyzing the infiltration and migration of invasive cells, both processes having been demonstrated to be dependent on the cell type and the microenvironment. However, in this later work, the distance between sequential z fields was 20 µm, compared with 3 µm in our study.
An interesting point to underline concerns the differences in the behavior of the noninvasive cells and invasive cells according to the culture model used. Our present data demonstrate that the 3D environment allowed us to differentiate the noninvasive cells from the invasive cells, whereas when cultured in 2D, the migration behavior of both cell lines was similar. However, we also observed that the cell migration speed in 3D was lower compared with the cell migration measured in a 2D model of cell dispersion. This difference in cell migration speed according to the culture model used could be related to the fact that to migrate through a collagen gel, the cells must degrade the collagen or infiltrate within the collagen network by modifying their shape. The invasive BZR cells have the capacity to secrete metalloproteinases able to degrade the collagen matrix, and this characteristic may likely explain their improved capacity to migrate within a collagen gel. To the contrary, the noninvasive 16Hbe14o cells do not secrete metalloproteinases, which is in favor of a lower migratory capacity (20).
An important point concerns the alterations of the dynamics of cells. For both cell lines that we studied, large intraindividual (for a given cell at different times) as well as large interindividual (for a given time in different cells) migration speeds were observed. These results emphasize those described by Niggemann et al. (21), who reported that individual tumor cells migrate neither continuously nor with constant velocity. These authors reported that cells have locomotion and relative stationary phases and that the analysis of video sequences reveals that the cells are constantly active, using the stationary phase to extend pseudopodia and reorient themselves in the 3D matrix.
Type I collagen was used in our 3D model because it represents, in the respiratory mucosa, the major component of the interstitial tissue. This 3D model represents a simplified model of invasion, which does not take into account the other extracellular matrix components involved in cell migration (26). However, this simple model will allow us to study the specific effect of other extracellular matrix constituents that could be added within the 3D gel.
Because EGF has been demonstrated to have a motogenic effect on airway epithelial cells (36), we analyzed the effect of EGF on the migratory pattern of the noninvasive 16Hbe14o cells. EGF has been shown to promote tumor cell motility and invasion (17, 25, 27, 28). Our data confirm that EGF could act as a motogenic agent by increasing the motility of noninvasive cells. This increased motility is likely related to the enhanced expression and activity of matrix metalloproteinases (MMPs). Bredin et al. (4) have shown that certain growth factors (particularly EGF) are able to promote invasiveness through their ability to trigger the expression and activity of matrix-degrading MMP-2 and MMP-9. With the use of human skin organ culture, Varani (32) showed that in the presence of exogenous EGF, the dermal-epidermal juncture is eroded and epithelial cells invade the dermis concomitantly with induction of MMPs and stimulation of fibronectin synthesis. EGF has also been demonstrated to be involved in the disruption of cell-cell adhesion and to cause epithelial-to-mesenchymal transition in relation to the endocytosis of E-cadherin (17).
In conclusion, the 3D model of cell migration and invasion allowed us to study the migration kinetics of cells, a primordial factor that allowed us to differentiate between invasive and noninvasive cells, such a differentiation being more unlikely in 2D systems. This model permitted us to compare two different cell lines and will help advance the molecular targeted therapy of cancer by approaching in vivo conditions.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
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