Differences in adhesion and protrusion properties correlate with differences in migration speed under EGF stimulation
© Hou et al.; licensee BioMed Central Ltd 2012
Received: 23 December 2011
Accepted: 11 May 2012
Published: 11 May 2012
Cell migration plays an essential role in many biological processes, such as cancer metastasis, wound healing and immune response. Cell migration is mediated through protrusion and focal adhesion (FA) assembly, maturation and disassembly. Epidermal growth factor (EGF) is known to enhance migration rate in many cell types; however it is not known how FA maturation, FA dynamics and protrusion dynamics are regulated during EGF-induced migration. Here we use total internal reflection fluorescence (TIRF) microscopy and image analysis to quantify FA properties and protrusion dynamics under different doses of EGF stimulation.
EGF was found to broaden the distribution of cell migration rates, generating more fast and slow cells. Furthermore, groups based on EGF stimulation condition or cell migration speed were marked by characteristic signatures. When data was binned based on EGF stimulation conditions, FA intensity and FA number per cell showed the largest difference among stimulation groups. FA intensity decreased with increasing EGF concentration and FA number per cell was highest under intermediate stimulation conditions. No difference in protrusion behavior was observed. However, when data was binned based on cell migration speed, FA intensity and not FA number per cell showed the largest difference among groups. FA intensity was lower for fast migrating cells. Additionally, waves of protrusion tended to correlate with fast migrating cells.
Only a portion of the FA properties and protrusion dynamics that correlate with migration speed, correlate with EGF stimulation condition. Those that do not correlate with EGF stimulation condition constitute the most sensitive output for identifying why cells respond differently to EGF. The idea that EGF can both increase and decrease the migration speed of individual cells in a population has particular relevance to cancer metastasis where the microenvironment can select subpopulations based on some adhesion and protrusion characteristics, leading to a more invasive phenotype as would be seen if all cells responded like an “average” cell.
Cell migration plays an important role in tumor progression . During invasion and metastasis, migration is driven by soluble extracellular cues like epidermal growth factor (EGF). EGF is a well-known chemoattractant [2–4]; however, uniform doses also stimulate chemokinetic responses. EGF’s control of cell motility originates from its regulation of adhesion and protrusion [5–7]. This occurs through altering adhesive attachments called focal adhesions (FAs) [8–10] as well as actin cytoskeleton organization.[8, 11, 12] The response to EGF at the level of cell migration is dose dependent, but there exists a range of maximal stimulation concentrations. Often migration saturates at 2–10 nM EGF [2, 13, 14], but some of the studies showed an inhibition of migration at EGF concentrations >2-10 nM [7, 15]. This is in agreement with other work demonstrating that in certain contexts, EGF can inhibit migration [16, 17]. Within each study there is wide diversity in migration behavior, even among cells observed during the same experiment . Interestingly, the distribution in migration speed and persistence time appears to be dependent on EGF stimulation , suggesting that EGF controls not only the mean response, but also the amount of cell-to-cell variability. Cell-to-cell variability has been widely observed, and has drawn much attention due to its influence on physiology , pathology and pharmacology . Consequently, mathematical models [19, 20] have been used to show that even small changes in the distribution of protein concentrations yield enhanced wound healing or metastasis due to the selection of an optimal subpopulation. When the subpopulation is defined based on migration speed, it will not only be beneficial to examine the distribution of protein concentrations, but also higher level characteristics like focal adhesion (FA) properties and cytoskeletal dynamics.
FAs are dynamic, macromolecular structures that serve as both mechanical linkages and centers of intracellular signal transduction [22–24]. They assemble as nascent adhesions, mature into focal complexes, focal adhesions and fibrillar adhesions and disassemble . Consequently, FAs exhibit different morphological maturation states throughout their lifetime and this is thought to regulate their behavior. For example, small, nascent FAs, transmit strong forces and serve as traction points for propulsive forces to move the cell body forward [25, 26]. They also generate signals for protrusion by activating actin accessory proteins [27–31]. Under tension, these small FAs can mature into larger focal complexes, focal adhesions and fibrillar complexes with different force transmission characteristics and propensities for protrusion signaling [32–34]. Several morphological characteristics have been used to predict traction force and cell migration speed including FA protein density, number per cell, sliding speed, lifetime, size and elongation [22, 23, 25, 33–35]. These morphological characteristics have begun to be quantitatively measured [36, 37] and the distributions properly quantified . However, their direct correlation to migratory states as well as their response to extracellular cues like EGF is unknown.
Protrusion is mediated by actin polymerization, whereas retraction is driven through myosin II activity and actin depolymerization [39, 40]. Protrusion and retraction can either occur continuously in spatially confined regions as in keratocyte migration or it can occur in cycles or waves of protrusion that move laterally along the edge [41–43]. This has been characterized in several cell types when cells are either spreading  or migrating [41–43]. In fact a recent paper has shown that slower migrating keratocytes employ lateral protrusion waves . While the timing of the cycles and the propagation of the waves is dependent on intracellular pathways, very little work has been done to examine how protrusion is quantitatively altered in response to extracellular stimuli like EGF.
To understand the relationship between EGF-stimulated cell migration, FA properties and protrusion dynamics, we imaged metastatic (MTLn3) and non-metastatic (MTC) cell lines. We analyzed the cell migration speed and persistence under various EGF stimulation conditions and found that EGF moderately increased the median migration rate and persistence of MTLn3 cells, whereas it had no significant effect on the speed and persistence of MTC cells. Interestingly, higher concentrations of EGF broadened the distributions and increased the coefficient of variation of both the migration rate and persistence of MTLn3 cells, but not MTC cells. When data was binned based on EGF stimulation conditions, FA intensity and FA number per cell showed the largest difference among stimulation groups. FA intensity decreased with increasing EGF concentration and FA number per cell was highest under intermediate stimulation conditions. No difference in protrusion behavior was observed. However, when data was binned based on cell migration speed, FA intensity and not FA number per cell showed the largest difference among groups. FA intensity was lower for fast migrating cells. Additionally, waves of protrusion tended to correlate with fast migrating cells. Consequently, low FA intensity and waves of protrusion are markers for fast migrating cells, but these characteristics are only partially predictive of EGF stimulation conditions because of the large cell-to-cell variability in response to EGF.
EGF stimulation broadens the distributions of migration speed and persistence of MTLn3 cells
Given that EGF did not dramatically affect the median migration, we grouped cells according to no (0 nM EGF), low (0.01 and 0.1 nM) and high (1, 10 and 100 nM) EGF stimulation. Additionally, since the variability in cell migration speed seems to be an additional feature of the data, we also grouped cells based on cell migration speed. A k-means clustering algorithm for a cluster number equal to two was applied to the migration speeds of all cells under different EGF concentrations. The cutoff speed between slow and fast migration cells was found to be 42 μm/hr. Thus we assigned cells with speeds of greater than 42 μm/hr to the fast migrating group and cells with speeds of less than 42 μm/hr to the slow migrating group. Having grouped cells in two different ways, we wanted to examine FA characteristics and protrusion dynamics to see if certain signatures were exhibited by EGF stimulated cells or fast moving cells.
The distribution of FA characteristics differ under different EGF stimulation conditions and between fast and slow migrating cells
Qualitative assessment of tracking results
Summary of FA characteristics in fast migrating cells and those stimulated with low and high EGF concentrations
Fast Migrating Cells
FA Size (μm2)
0.30 - 3.0
0.18 - 3.0
0.24 – 3.0
FA Sliding Speed (μm/hr)
FA Lifetime (s)
0 - 440
160 - 530
160 - 620
FA Intensity (grayscale)
1.3 – 2.1
1.3 – 2.2
Unique spatial organization of protrusion and retraction is exhibited in fast migrating cells
Variability in cell response to environmental cues is becoming a more appreciated phenomenon that can drive how populations of cells respond to their environment. Cell-to-cell variability can arise from heterogeneity in protein level [47, 48] or organization of cellular structures such as the membrane  or the cytoskeleton . Interestingly, this variability can be enhanced by extracellular stimuli . The idea that variability can be enhanced under certain conditions sets up the interesting possibility that the mean response is a relatively poor statistical metric. Rather, the distribution itself or the standard deviation or another parameter that characterizes the distribution may be more appropriate. The obvious result of this dependence on the distribution is a sensitizing of a subpopulation of cells to particular environments. This is acutely evident in pathologies such as cancer metastasis, where subpopulations of cells are selected based on differing responses to the tumor microenvironment. Therefore, the fastest cells most likely drive metastasis, whereas the average cell migration rate might be less important. We showed that the distribution of cell migration speed and persistence is very much regulated under EGF stimulation, even though the average response differs marginally. Indeed, this has been demonstrated previously.  Ware et al. generated distributions of migration rate in response to no EGF or high EGF concentration. However, the focus of that paper was primarily on the changes in the average migration response and the widening of the distribution in response to EGF was evident, but not discussed. What causes this widening? Heterogeneity in the local ECM concentration might play a role. We have examined collagen coverage and it tends to be fairly homogeneous at the resolution of the light microscope (~100 nm) and we observed cells in close proximity that varied greatly with respect to their migration speed. However, ECM inhomogeneity cannot be fully dismissed as a possible cause for the cell-to-cell variability. Another cause of the cell-to-cell variability might be autocrine or paracrine signaling. MTLn3 cells are known to secrete other EGF receptor ligands, namely TGF-α . However, we did not observe clustering of migration speeds around sources. Often cells in the same clusters showed distinct behavior. A third possibility is that concentrations of signaling, adhesion or cytoskeletal regulatory proteins might contribute to the heterogeneity. This might be the most probable cause of the cell-to-cell variability; however determining which specific components might contribute to this is the subject of further investigation.
Given that local protrusion is linked to FA intensity and that FA intensity was lowest in fast migrating cells we were encouraged to examine the protrusion dynamics under different stimulation conditions. These cells are known to respond acutely to EGF stimulation with two peaks of barbed end formation resulting in a robust protrusion response . However, cells are often not exposed to these acute signals in vivo and so we asked how protrusion changes under chronic EGF stimulation. We found that while EGF stimulation condition correlated poorly with lateral waves generated in cells, fast migrating cells usually generated lateral waves of protrusion as has been seen elsewhere [41–43, 57].The existence of lateral protrusion waves suggests locally activated feedback loops that travel laterally along the edge of the cell [43, 58]. This positive feedback loop operates through adhesion signaling for protrusion and protrusion resulting in more adhesions . How does this behavior relate to migration rate? Barnhart et al. noticed that keratocytes migrating on more adhesive substrates generated these lateral waves and migrated with a slower speed . We see an opposite relationship, where high speeds result in lateral waves of protrusion. This difference may be related to the differences in cytoskeletal organization and in fact morphology between these cells. Keratocytes adopt highly regular persistent cytoskeletal structure and cellular morphology resulting in extremely fast migration speeds (500–600 μm/hr). MTLn3 cells on the other hand have a varied cytoskeletal structure and cellular morphology and are much slower (<100 μm/hr). Consequently, highly organized, persistent protrusion that is seen in keratocytes results in the fastest migrating cells. Less efficient, but somewhat organized lateral protrusion seen in both keratocytes and MTLn3 cells results in intermediate speeds. Poorly organized protrusion seen in MTLn3 cells results in slow speeds. Local differences in ECM in our system might explain why EGF is not a primary driver for fast migrating cells or lateral protrusion waves, leading high cell-to-cell variability (Figure 7).
EGF was found to broaden the distribution of cell migration rates, generating both faster and slower cells, but not dramatically affecting the average response. Several different adhesion and protrusion characteristics correlated with EGF stimulation and cell migration speed, however there is a hierarchy of these correlations. FA intensity and number per cell correlate with EGF stimulation conditions. FA intensity decreases with increasing EGF stimulation and FA number per cell is highest at low EGF stimulation conditions. In contrast, FA intensity and not number per cell as well as protrusion waves correlate with cell speed. Fast cells are marked by low FA intensity and protrusion waves. Consequently, while EGF stimulation could regulate FA intensity to modulate cell speed directly or by partially activating protrusion waves, other environmental factors most likely lead to protrusion waves. Adhesion and protrusion characteristics that do not correlate with EGF stimulation condition but do correlate with cell migration speed constitute the most sensitive outputs for identifying why cells respond differently to EGF. The idea that EGF can both increase and decrease the migration speed of individual cells in a population has particular relevance to cancer metastasis where the microenvironment can select subpopulations based on some adhesion and protrusion characteristics, leading to a more invasive phenotype as would be seen if all cells responded like an “average” cell.
Cell culture media was α-MEM medium with L-glutamine (Invitrogen) containing 5 % fetal bovine serum (Invitrogen) and 1 % penicillin-streptomycin (Invitrogen). Collagen and poly-L-lysine (PLL) solution contained 3 μg/ml of rat tail collagen I (Invitrogen) and 2 μg/ml of poly-L-lysine hydrochloride (Sigma), dissolved in 0.5 M acetic acid (Fisher) and sterilized under ultraviolet light for 30 minutes. Serum free imaging media was α-MEM medium without phenol red (Invitrogen) containing 1 mg/ml bovine serum albumin (Sigma), 12 mM HEPES (Fisher), and 1 % penicillin-streptomycin (Invitrogen), adjusted to pH 7.4 and filtered through 0.22 μm pore size filter (Millipore, Fisher).
Rat mammary adenocarcinoma cell lines (metastatic MTLn3 and non-metastatic MTC) were obtained from Dr. Jeffrey E. Segall (Albert Einstein college of Medicine). Cell lines were derived from the 13762NF rat mammary adenocarcinoma tumor. Cells were maintained in cell culture media at 37 °C in 5 % CO2 and were passed every 2 or 3 days. Collagen and PLL solution was incubated on 22 × 22 mm squeaky cleaned coverslips (Corning, Fisher) at room temperature for 1 hour. Cells were seeded on coverslips with collagen and PLL and incubated for 24 ~ 48 hours at 37 °C in 5 % CO2 (50,000 ~ 100,000 cells/coverslip).
Cell migration assay
using a non-linear least squares regression analysis. The sampling time is every two minutes for 6–8 hours. The mean-squared displacement was constructed using non-overlapping time intervals. Consequently, the model was fitted to data up to a 30 min time lag due to the small number of displacements (<12-16) at time lags greater than 30 min. To quantify protrusion rate we used a constrained optimization program to measure the protrusion and retraction rates from masked images as done previously . The cell edge was segmented into 100 sectors. The average protrusion rate in these sectors was calculated over time.
MTLn3 cells were incubated on coverslips with collagen and PLL for 24 hours and transfected with paxillin-EGFP and Fugene 6 (Roche) according to the manufacturer’s protocol (6 μl of Fugene 6 and 3 μg of EGFP-paxillin). After one hour transfection, the media was changed to cell culture media and the transfected cells were maintained at 37 °C in 5 % CO2 for 23 hours. Then the cells were switched to serum free imaging media for 2 hours. Coverslips were mounted onto glass slide chambers in serum free imaging media with different concentrations of EGF (0, 0.01, 0.1, 1, 10 and 100 nM EGF). Chambers were maintained at 37 °C for 2 hours and then imaged on a heated stage every 10 seconds for 40 ~ 60 minutes. TIRF images were captured at 60× oil objective (NA 1.49, Nikon) equipped with a TIRF illuminator and fiber optic-coupled laser illumination. The 488 nm laser line of an air-cooled tunable Argon laser (Omnichrome Model 543-AP-A01, Melles Griot) was reflected off a dichroic mirror (89000 ET-QUAD, Chroma). Camera and shutter were controlled by μManager 1.3. An automated segmentation and tracking algorithm was utilized for large-scale analysis of FA dynamics . FAs smaller than 0.05 μm2 and larger than 10 μm2 were excluded from our analysis because they represent either FAs consisting of less than three pixels or several FAs clustered together. FA fluorescence intensities were calibrated to the standard condition of 1 mW laser power with a 300 ms exposure time, so FA intensity should be directly proportional to protein level across all samples.
All graphs and statistical analyses were done using JMP and Matlab software. Distributions of FA properties were constructed in the following ways. FA number as described in the results section is more precisely a FA number per cell and consequently the distribution was generated by using the calculated FA number per cell at each time point during the experiment for each cell. Consequently, the number of measurements of FA number per cell is the product of the average number of frames and the cell number. All the other distributions of FA properties were generated by using the time-averaged FA property for each FA in each cell. Consequently, the number of measurements of FA properties is the product of the average FA number and the cell number. Differences between conditions under various EGF concentrations and cell migration speeds were quantified by calculating the Kolmogorov-Smirnov statistic using the Matlab function . Model distributions were fitted by minimizing the Kolmogorov-Smirnov statistic using the Matlab function between the experimental distribution and the model distribution. To determine the statistical differences of the mean values between conditions under various EGF concentrations and cell migration speeds, a student’s t-test was utilized and a p < 0.01 was deemed significant.
We thank the Roy J. Carver Charitable Trust for funding ICS and YH. We also thank Dr. Jeffrey Segall for cell lines, Dr. Antonio Sechi and Thomas Wurflinger for help with the FA analysis; Dr. Gaudenz Danuser and Shann-Ching Chen for help with the protrusion analysis and Nick Romsey for help with the cell migration analysis.
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