Although our understanding of individual processes underlying cell migration continues to increase, major gaps in information concerning how they are coordinated spatially and temporally still remain.2
New techniques need to be developed that can bring insight into how these individual processes interact by quantifying dynamic cell movements and analyzing single cells in an automated manner. Computer-assisted, quantitative analysis of migrating cells provides an objective means of comparing migration properties of cells and yields insight into the underlying mechanisms of cell motility. Here, we report a dynamic multivariate analysis of single-cell motility that includes a combination of both novel algorithms/image analysis methods (surface area; DECCA) and existing techniques (cell speed). The first measurement we present, cell speed, was captured using a standard manual cell-tracking technique (Metamorph) from movies generated by time-lapse, phase contrast microscopy. The second measurement utilized a custom-written MATLAB algorithm designed to threshold images to calculate cell surface area. Obtaining surface area measurements gives us insight into the shape and overall health of cells. For example, epithelial cells tend to decrease their surface area when unhealthy or stressed and many cell lines change their shape upon differentiation, which is often reflected by a change in surface area. The third measurement, DECCA, is a novel measurement of total cell activity. That is, DECCA captures the pixel intensity change from one frame to the next, and averages these changes over the length of the movie. Thus, a higher DECCA value represents a higher amount of cell activity. This measurement is not necessarily a measurement of cell migration, as membrane protrusion without translocation, can also lead to high DECCA values. These three values were then combined using a unique identifier system to obtain three measurements per cell (for 1000+ individual cells, from 2 cells lines, on 2 substrates).
Our experimental results demonstrate the repeatability and reliability of our technique. Our data indicate low to medium-high correlations between all three measurements, depending on the particular cell line and substrate combinations. This range of relationships was anticipated for these cells, due in part to their high migration rates, and various shape changes observed in culture, when plated on Ln-332 or Fn.44,45
However, given different experimental guidelines (i.e., cell lines, ECM components, or introduction of mutations), these trends may certainly change. For example, NRK49F cells with defects in Rho
or adducin have been shown to have active lamellipodial ruffling, while being unable to migrate.46
Based on these findings, we hypothesize that these mutant cells would have an unchanged cell surface area and DECCA (compared to wild type), but their cell speed would decrease drastically. Furthermore, inclusion of leukocytes, or various other immune cells, may also significantly alter results, as these cells are commonly very motile, but much smaller and produce less dynamic shape changes.
As we outline in the Introduction section at length, cell speed analysis is one important component of studying cell migration. While methods of automated cell tracking exist in commercial software programs, they are not widely used in the field because either they require labeling of cells, or their accuracy and reproducibility (compared to manual tracking) is lacking.47
Many researchers prefer to track unlabeled cells using phase contrast microscopy, both for ease of use and to eliminate added variables. A fully automated system, termed the 2D DIAS, has been developed to study the motility of Dictyostelium amoebae
but thus far it has been more difficult to develop such a system for epithelial cells, due to their complex behavior and irregular cell shape.39
Ultimately, one of our immediate goals is to update our current manual speed tracking method to include a similar automated system, but not at the expense of accuracy.
Surface area analysis is also an important component of cell migration studies because cell size can be linked to cell shape and health. In general, cells that have suffered mild insults shrink in size as one of the first steps in the apoptotic pathway.49,50
Differentiation of cell lines is also often associated with a change in cell size that may be reflected in our surface area measurements.51,52
There are currently a number of available methods to obtain surface area measurements through image analysis. However, as we previously discussed in the Introduction, many of these methods rely on either the use of fluorescent labeling of cells, differential interference contrast (DIC) microscopy images, and edge detection methods that require heavy computing power, and also often making assumptions about a general cell shape. For some applications, our method of surface area estimation will work well for eukaryotic cells. It is not as accurate as some methods referenced above, but it shows relative changes in surface area very well for phase contrast images, and with very little processing power needed for our algorithm. In addition, our method allows a researcher to follow surface area changes over time (results not shown).
We believe the introduction of the DECCA measurement is our most significant contribution, as this technique captures cell activity in a way that no other applications we are aware of have demonstrated previously. It is important to note that DECCA measures the protrusive activity of cells, whether or not they actually move in a processive manner (i.e., across a substrate). We mention a number of examples previously in this text that would display such behavior (e.g., mutant cells). In fact, a DECCA index need not correlate positively with movement; any positive correlation with cell speed is an indication of how efficient cell activity is towards actual migration. A simple example includes comparison of a motile cell that physically moves across a field, compared to a non-motile cell. The moving cell will always have a DECCA value, since its “footprint” changes from frame to frame. However, the non-moving cell may have a low or high DECCA, depending on the protrusion activity of particular cell. The cell may be completely inactive (if all images are the same, DECCA = 0), or it may change shape without moving its nucleus. by lamellapodial ruffling or creating numerous cell protrusions that lead to shape change. As a result, the image pixel intensity changes, even though their nucleus does not move. A manual version of differential imaging was previously shown in a publication by Fukui et al., and was referred to as producing “difference pictures.”53
However, we are unaware of any other algorithms/computational methods that reflect the same activity as DECCA. Originally, DECCA was developed with the intent to distinguish between two cell types that have the same migration speed, but very different membrane protrusion dynamics (to be included in future work). For example, our analysis demonstrated that although HT-1080 cell speed was significantly altered (p < 0.01) by changing the matrix, the surface area and DECCA of these same cells were essentially unaltered (p > 0.05). This data may indicate that HT-1080 cells have the ability to spread and become activated by both matrices, but for reasons yet to be determined, the cells have a significantly slower migration rate on Fn. Although we cannot explain these differences based on our preliminary analysis, our assay was able to provide additional insight, which would have been missed using population-based cell migration techniques or classical motility tracking assays. By understanding the interplay between cell speed, surface area and DECCA measurements, our method may lead to additional cell migration hypotheses, and thus additional findings.
Knowledge of the fundamental biological mechanisms of cell motility is currently spurring the development of novel pharmacological and genetic approaches that attempt to harness this process, in order to ultimately overcome pathological events such as cancer metastasis. Researchers have screened thousands of compounds for the ability to inhibit cell migration, in hopes of developing new drug targets.54,55
However, commonly used assays that study these interactions cannot distinguish off-target effects. For example, adding formaldehyde to cells would surely halt their cell migration, but will do so by fixing and killing the cells, not because it is a specific inhibitor of migration. In some instances, applying a DECCA measurement may be a useful control for cell health, because of its high resolution and focus on individual cell parameters. However, in order to use our method for large scale screens such as these, some parameters of our experiment will need to be improved upon. We are actively developing many other aspects of this method that can facilitate more efficient data collection and analysis. The most notable addition needed is fully-automated cell tracking software as we mention earlier. We are currently working on a new cell tracking system using our differential imaging method to develop a completely automated technique for epithelial cells similar to those for amoebae.
There are also two known additions needed to track cells using our algorithms: (1) a center of mass calculation on the surface area images, and (2) a center of mass calculation on the DECCA images. These calculations may then be used to predict center of motion and center of velocity, respectively. Accurate edge detection can then be combined with center calculations to identify individual cells and necessary regions of interest (ROIs). This method of auto-selection would also help to narrow the ROI to the minimum window size. With our current program, an operator must manually select the ROI. Future versions of the program will auto-select all ROIs by means of a fast, movie-spanning analysis of the SD of pixel intensity both spatially and temporally. By auto-selecting ROIs, we will decrease both image processing time and non-specific background by applying minimally sized ROIs. Other modifications to our image processing programs are also being adopted, including changes to noise reduction, and image normalization methods.
Another powerful addition to this method is the ability to look at speed, surface area and DECCA at all time points over the course of the movies. For our initial proof-of-principle analysis presented here, all data points were averaged over the course of the movie (e.g., one cell speed measurement per cell per movie). In fact, there were 49 individual measurements each for cell speed, surface area and DECCA. We are keenly aware that our method has thus far untapped data present in these measurements. For example, one could study the stop-and-go pattern of cell locomotion or the change of surface area and DECCA over the course of a movie after exposure to a ligand. We are currently developing statistical methods to analyze our data in this manner.
Here, we present a method that produces a multivariate profile for individual cells based on multiple measurements we have collected: cell speed, surface area and DECCA. In this regard, we can generate three dimensional plots, where each data point represents an individual cell (example in Suppl. Fig. 1). In the future, we plan to use this technique to separate interesting sub-populations within specific cell lines using similar statistical techniques that are used for statistical analysis of cell sorting data.56,57
In this manner, we can further dissect the complex mechanisms of cell migration.