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The Effect of Substrate Density on the Rate of Migration of NIH-3T3 Fibroblasts Elizabeth Tsui ABSTRACT Previous studies have suggested connections between the migration structures in normal cells known as podosomes and the migration machinery of cancer cells. Furthermore, recent studies contain evidence supporting a relationship between tissue density and metastatic cancer risk. Given that an increase in the risk for metastatic cancer is directly related to the rate of cell migration, this experiment explored the possible relationships between metastatic cancer risk (determined by cell migration rate) and collagen concentration (tissue density’s determining factor) through the use of NIH-3T3 ibroblasts. Fibroblast cells were seeded on top of hydrogels of collagen concentrations corresponding to the elastic moduli of normal and cancerous tissue (1.0mg/mL and 4.0mg/mL, respectively). hey were subsequently observed migrating into the hydrogels over a 5 to 6 hour time period, and average cell counts from the surface of each gel were noted at three time points separated by 1 or 2 hour incubation intervals. ANOVA revealed that: 1) collagen concentration does induce a signiicant diference on the number of surface cells present over time; but 2) the slopes of the linear its (i.e. rate of migration) were not shown to signiicantly difer between collagen concentrations. hese results suggest that the density of a substrate may have some efect on cell migration, without afecting migration rate.
Introduction Certain cells in the body have a natural ability to form specialized structures that enable cellular migration. For example, white blood cells (leukocytes) must degrade extracellular matrices (ECMs) and migrate through multiple tissue barriers in order to ight of infections and foreign pathogens. Another example is seen in a newly proposed model for cell invasion, C. elegans; in order for the organism to complete normal development, a cell must migrate through an extracellular matrix known as the basement membrane [1,2]. Originally named rosettes because of their appearances in interference reference microscopy, these migratory structures are now commonly known as podosomes . Podosomes typically consist of an F-actin core surrounded by various adhesion proteins such as talin, vinculin, and paxilin, as well as integrins that allow the structures to bind to the underlying substrate [4,5]. As is commonly known, actin is a major class of microilaments, the components of the cytoskeleton responsible for cell motility. Podosomes are classiied according to their associated integrins, molecules that bind elements of the ECM and regulate ECM attachment . hey allow adhesive structures to form a bridge between the ECM and the cell cytoskeleton . β1 and β2 integrins are associated with podosomes and play critical roles in macrophage fusion . However, when podosome-like protrusions were examined in a 3 dimensional environment, only β1 integrins were shown to associate with the protrusions . Similar results noting diferences in podosome structure or morphology due to environmental factors have led to speculations suggesting that podosomes may play roles in helping cells sense their surrounding environments [9,10].
In order to migrate, cancer cells must detach from their original tumor sites and degrade the surrounding ECM. Interestingly, cancer cells form and extend similar F-actin rich protrusions known as invadopodia as the irst step in metastasis . Invadopodia found in cancer cells are analogous to podosomes and are implicated in cancer’s deadly ability to metastasize. In contrast to the shallow extension of podosomes, however, invadopodia are usually found clustered together as large actin and cortactin dots burrowing deep into the ECM. hey tend to be larger than podosomes, reaching measurements of 40µm2 (as compared to 0.4µm2) . Invadopodia tend to penetrate their surrounding substrates very deeply, and thus are associated with a signiicantly more focused and higher rate of degradation than podosomes are. Like podosomes, they recruit metalloproteinases to degrade matrices; however, the invadopodia’s more aggressive migration tendencies have been attributed to its additional recruitment of serine proteinases . Invadopodia have also been thought to have a role in helping the cell sense its environment. Studies have previously shown that tissue density may be related to the likelihood of developing cancer. For instance, a study in 2003 comparing a metastatic and nonmetastatic cancer found that an increase in collagen content was associated with tumor development . Furthermore, research done by Provenzano et al. (2009) showed that an increase in collagen concentration caused an increase in matrix density, a condition which promoted a malignant phenotype. Changes in microenvironment corresponded with changes in density, i.e. an increase in density created a more ibrous microenvironment with fewer matrix pores, as well as an increase in matrix density and rigidity which, inally, proVolume 2 | 2012-2013 | 27
Street Broad Scientific moted an invasive phenotype [15,16]. Matrix rigidity is measured through the Young’s modulus, also known as the elastic modulus . he Young’s modulus measures the stifness of an object by measuring a substance’s resistance to deformation when a force is applied, e.g. objects with high stifness such as glass and diamonds have high elastic moduli. A higher elastic modulus is also associated with an increased density due to the more ibrous microenvironment. Normal mammary tissue has an elastic modulus of 167 ± 31 Pa, while the tumor itself has a much higher elastic modulus of about 4049 ± 938 Pa . However, the relationships of podosomes and invadopodia with their environments remain poorly characterized. As was shown by Van Goethem et al. (2011), the structure of podosomes varies widely with micro-environmental shifts. An example of this is the phenomenon of podosome group arrangement. In src-transformed cells, where the src tyrosine kinase is used to change the expression of a gene that codes for a component of podosomes, the podosomes form ring shaped structures known as rosettes. By comparison, in other cells, such as osteoplasts, podosomes tend to be arranged in clusters, showing the diversity of arrangement that podosomes possess in response to cell environment. Another study highlighting this phenomenon was done by Van Geoethem et al. (2011) using Matrigel, a gel that mimics a migratory cell’s typical environment. his study found that multiple podosomes are produced during migration, perhaps indicating that podosomes not only degrade matrices, but also seek out areas of lesser density in order to perform the most eicient matrix degradation. Expanding on this inding, Carman et al. (2007) found that during lateral migration of leukocytes, dozens of podosomes formed quickly along the endothelium to probe the surrounding environment. Over nuclei, the podosomes were quickly retracted without fully migrating into the substrate, leaving shallow “podoprints.” hus, a commonly purported hypothesis is that leukocytes use podosomes to locate areas of relatively low surface resistance in order to complete migration . Regardless of recent progress, there are a number of remaining questions about podosomes that need to be addressed. For instance, what is the efect of substrate density on the migration behavior and structure of podosomes? Do cells tend to migrate faster or slower on substrates of difering densities? he answers to these questions could have important implications for our understanding of cancer metastasis, because if invadosomes do migrate preferentially because of density, variations in substrate density between tissues could be used to determine likely sites of metastasis . To answer these questions, I observed NIH-3T3 Mouse Fibroblast cells seeded onto substrates of two collagen concentrations (4.0mg/mL and 1.0mg/mL) representing two substrate densities over a ive to six hour period to determine if substrate density has any efect on the rate of migration of the ibroblasts. Since previous papers 28 | 2012-2013 | Volume 2
REsEaRch have suggested a change in migratory response based on the cellular microenvironment, I expected that a diference in substrate density would: 1) create a diference in the mean number of cells present on the surface of the hydrogels over time; and 2) afect the rate of migration of cells from the surface into the hydrogels for both treatments, causing low density hydrogels to have rates of migration signiicantly diferent from high density hydrogels.
Materials and Methods My experiment consisted of two diferent treatments: the two collagen concentrations, 4.0mg/mL and 1.0mg/ mL, corresponding to high and low substrate densities, or cancerous and normal tissues, respectively. he concentrations were chosen based on previous literature from Paszek et al., 2005 and Provenzano et al., 2009. In order to ensure consistency with data collection, glass slides were uniform grids the size of 18mm x 18mm coverslips and labeled by hydrogel number and collagen concentration. To ensure uniformity in hydrogel size and shape, gel molds were made by wrapping glass coverslips with Carolina Observation Gel. Molds were then mounted onto the gridded glass slides and placed in Nunc cell culture dishes for gel formation (Figure 1).
Figure 1. Hydrogel molds in Nunc cell culture dishes mounted on top of gridded microscope slides. Gels for replicates 1 and 2 were made using Hystem cell culture scafold kits (Sigma-Aldrich) according to manufacturer’s instructions. Collagen concentrations for these replicates were prepared by adding 110µL of 4.0mg/mL or 1.0mg/mL collagen solution to 15mL centrifuge tubes. 250µL of each gel solution were then pipetted into the appropriate gel molds. For replicate three, Hystem-C cell scafolds obtained from Glycosan Biosystems were formed from a 7.5mL kit according to manufacturer’s instructions. To form the hydrogels with the low collagen concentration of 1.0mg/mL, 250µL of Gelin-S (reconstituted collagen concentration of 4mg/mL) were added to 15mL centrifuge tubes (Gelin-S is simply a powdered version of the collagen solution that was used in previous trials, suggested by the manufacturer as an alternative collagen source). 750µL of DG water was then added to the centrifuge tube
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REsEaRch to achieve a inal concentration of 1.0mg/mL; the tube was then mixed until a slightly viscuous, clear solution was obtained, and 1 mL of the solution was then added to a ready centrifuge tube. 500µL of the completed hydrogel was added to prepared gel molds. To form the hydrogels with the high collagen concentration (4.0mg/mL), 1 mL of Gelin-S (collagen concentration of 4.0mg/mL) was mixed with 1 mL of Hystem and 500µL of Extralink. 500µL of the complete hydrogel solution were pipetted into the appropriate gel molds and allowed to solidify. NIH-3T3 ibroblasts obtained from the Soderling Lab at Duke University were cultured in a 37°C CO2 incubator until the cells reached about 80% conluence. Cell density was then determined with a hemocytometer, while 500µL of cell slurry at a density of approximately 5000 cells/mL were added to the solidiied hydrogels; cells were allowed to attach for an hour (replicates 1 and 2) or two hours (replicate three) before taking the initial cell count. Starting cell counts for hydrogels at both concentrations were not statistically diferent from each other, as expected. Observation times were changed from 1 hour after initial incubation to 2 hours after initial incubation because cells from replicates one and two seemed to require more time to acclimate and attach to the hydrogels before the initial count, as is shown in Figure 2 below.
Figure 2. Cells on surface of hydrogels at 1 hour after initial incubation (left) and two hours after incubation (right).
licate (times are given in hours after initial incubation). Clumps of cells were counted as single cells to prevent bias towards higher cell counts. he mean cell counts of four randomly chosen boxes were used in calculating the means for each type of substrate, which was then plotted against time to determine rate of migration as represented by slope of linear it. he mean cell number and slopes of the linear its were then compared using JMP Student Edition 8 to determine statistical signiicance by ANOVA.
Results ANOVAs of mean surface cell counts from each hydrogel were performed to determine statistical signiicance. he mean number of surface cells in replicate one did differ signiicantly over time, indicating viability of ibroblast migration (i.e. the cells were able to form the structures necessary to initiate and continue migration into the hydrogels). his result was demonstrated in all replicates. Collagen concentration (representing substrate density) was shown in replicate one to have no signiicant inluence on mean cell counts over time (p= 0.367). his indicates that the mean number of surface cells is not afected by substrate density, which is inconsistent with the original hypothesis. However, the replicates two and three ofer opposing results. In replicate two, the ANOVA indicated that there was a signiicant diference in mean number of surface cells between high and low collagen concentrations (p = 0.0175), which supports my original hypothesis that manipulation of density would manifest a change between high and low collagen concentration substrates. Another interesting result lies in the shape of the graphed data. For both low and high collagen concentrations, the number of surface cells present over time seemed to decrease linearly, with RMSE values of 0.9958 (low) and 0.9941 (high). his suggests that while substrate density afects the overall rate of migration, it does not afect the linear trend in migration rate.
After one to two hours, the gels were observed and photographed at 100x with a Nikon inverted scope and attached Nikon D5100 DSLR. Surface cells were counted in four randomly chosen 20.25mm2 boxes over three two hour time intervals, given in the table below. Each hydrogel had four subsamples within the treatment, and all data analysis was performed with JMP Student Edition 8 (SAS). Time Point 1 2 3
Replicate 1 1 hour 3 hours 5 hours
Replicate 2 1 hour 3 hours 5 hours
Replicate 3 2 hours 4 hours 6 hours
Table 1. Description of observation time points by rep-
Figure 3. Time (hrs after initial incubation) was plotted against the average of surface cell counts obtained from each hydrogel by collagen concentration. A linear equation was plotted for hydrogels with a collagen concentration of 1.0mg/mL (low), yielding the model: y=6.6667x +44 (R2 = 0.9958). A linear it was also obtained Volume 2 | 2012-2013 | 29
Street Broad Scientific for hydrogels with a 4.0mg/mL collagen concentration (high). his analysis yielded the model: y = -8.4583x + 57.375 (R2 = 0.9941). Error bars represent Âą1 standard error. Similar relationships were seen in replicate three of the experiment. ANOVA showed signiicant diferences in surface cells present in regards to both time and collagen concentration with p<0.0001 and p<0.0081, respectively. his shows that not only did mean cell number decrease over time, the cell counts of the low hydrogel were signiicantly less than the number of surface cells present on the high collagen substrates, again supporting the original hypothesis that substrate density would have an efect on the mean number of cells present on the surface of the gels over time. Linear regression lines were again shown to it well with the decrease in mean surface cells over time, supporting the conclusion that the mean number of surface cells decreases linearly over time.
Figure 4. Time (hrs after initial incubation) was plotted against the average of surface cell counts obtained from each hydrogel by collagen concentration. A linear regresssion was plotted for hydrogels with a collagen concentration of 1.0mg/mL (low), yielding the model: y= -6.4125x + 45.067 (R2 = 0.9977) . A linear it was also obtained for hydrogels with a 4.0mg/mL collagen concentration (high). his analysis yielded the model: y = -7.65x + 54.75 (R2 = 0.9897). Error bars shown represent Âą1 standard error. he slopes of the linear regression lines for high and low collagen concentrations for replicates two and three were compared in JMP using a t-test. However, for both replicates two and three, the slopes were not shown to be signiicantly diferent between treatments. Previous experiments speculated that a change in substrate density would correspond to some change in migration; however, this report showed that while mean surface cell counts changed between collagen concentrations, the rate of decrease between treatments was not signiicantly diferent, thereby leading to the conclusion that mean surface cell count, but not rate of migration, was afected by substrate density. 30 | 2012-2013 | Volume 2
Conclusion his experiment sought to address two questions: 1) what is the efect of substrate density on the rate of migration of NIH-3T3 ibroblasts; and 2) does manipulating the density of the cellâ€™s environment (shown to have an efect on morphology) also afect how quickly cells move into a substrate? hree main conclusions can be drawn from this experiment. First, the mean surface cell number over time seemed to decrease linearly for both high and low collagen concentrations, meaning that substrate density had no obvious efect on the shape of the graph of the decrease in cell number. Second, analysis showed that substrate density afected the mean number of cells found on the surface of the hydrogel in two out of three replicates, largely supporting my original expectation. Interestingly, the data do not support the hypothesis that a change in substrate density would create a change in the rate of decrease of surface cells (i.e. the slope of the linear its compared between both concentrations), meaning that rate of decrease was not afected by substrate density. However, three possible circumstances may have led to error within this result. First, the low sample size within each replicate, decreased further within replicate two, may have increased variance overall. Moreover, while collagen is the largest component of the ECM, collagen is supported by a number of other substances, such as laminin, that may play a larger role in determining efect on migration. Furthermore, it is possible that the collagen concentration tested in this experiment did not present enough of a contrast to detectably alter the rates of cellular migration. As was seen in Provenzano et. al (2009), collagen concentration and elastic moduli of a substrate vary widely with an uncertainties ranging from 31Pa to 938Pa for normal and cancerous tissues, respectively. he collagen concentrations used in this experiment may have fallen within that error range, resulting in an inefective discrepancy in tissue densities and a concomitantly undetectable change in cellular migration rates. Future experimentation would include a larger sample size in order to decrease the amount of variance in data. Experimental systems for cell culture suspension could also be improved. For instance, as is shown in the left half of igure 2, there were certain areas in which cells may have been transferred in clumps which would lead to a decrease in cell count, since each clump was counted as one cell. Furthermore, since the boxes chosen for cell counts were random, there was no way of excluding those regions from data collection. To see if another substance plays a larger role in determining migration rate, a diferent ECM component could be tested in place of the collagen.. If collagen was used again, however, more collagen concentrations could be tested to see if there is a threshold collagen concentration that needs to be reached before achieving a change in migration behavior. Time interval observed could also be extended in order to see if the efects of sub-
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REsEaRch strate density require a longer time before they produce changes in migration behavior. Future experiments sandwiching the cells between instead of on top of substrates could assess the cell’s density preferences. By giving the cells a choice, regions more conducive to metastasis can be determined. If rate of migration is afected by a change in substrate density, then a literature search can be conducted to determine tissue densities throughout the body, and metastasis rates could subsequently be compared to see if the invadopodia of cancer cells are similarly afected. his could be the potential link tying podosomes, the migration machinery of normal cells, to invadopodia, the structures that equipcancer with the ability to metastasize. While this explanation may not encompass the whole picture, identiication of factors determining the direction of cell migration would represent a key advancement in the ight against cancer. Ideally, these podosomes could be the key to detecting potential sites of metastasis. However, applications of this knowledge can only be made possible with further research. As of now, the steps leading to fully developed invadopodia are unclear. For instance, does the formation of an F-actin core spark the development of the invasive protrusion by gathering surrounding metalloproteinases, or does the gathering of the proteins lead to the development of an F-actin core ? Other questions could address the implications of diferences between podosomes in diferent cell types, i.e. are the same properties universally affected in all cell types? Also, it is still unknown whether invadopodia are truly related to podosomes. As a concrete deinition of both is lacking, it is diicult to say whether the relationship between these two cellular components is truly homologous, or only supericial. Even with all of the remaining questions, possible experimental models for learning more about podosomes, invadopodia, and their roles in tissue invasion have already been proposed. he Invadosome Consortium, a group of scientists committed to learning more about these structures, is making great strides in the elucidation of these organelles. Together, these and other initiatives are slowly but surely increasing what is known about the structures that have the potential to be incredible targets in the ight against cancer.
Acknowledgements I would like to thank Soderling and Blobe Labs at Duke University for providing NIH-3T3 Fibroblasts. Additionally, Dr. Amy Sheck and Korah Wiley, North Carolina School of Science and Mathematics, for invaluable assistance and advice. Research in Biology Peers: Ian Maynor, William Ge, Jordan Harrison, Chelsey Lin, Ashwin Monian, Jackson Mower, Aakash Gandhi, Hun Wong, Mark Kirollos, and Natalia Von Windheim for their advice and suggestions. I would also like to thank Nathaniel Doty, Glycosan Biosystems, for assistance with hydrogels. Fi-
nally, I give my thanks to the Glaxo Endowment to NCSSM for research funding.
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Street Broad Scientific  Paszek, M. J., N. Zahir, K. R. Johnson, J. N. Lakins, G. I. Rozenberg, A. Gefen, C. A. Reinhart-King, S. S. Margulies, M. Dembo, D. Boettiger, D. A. Hammer, and V. M. Weaver. 2005. Tensional homeostasis and the malignant phenotype. Cancer Cell 8: 241-253.  Gillette, B.M., N. S. Rossen, N. Das, D. Leong, M. Wang, A. Dugar, S. K. Sia. 2011. Engineering extracellular matrix structures in 3D multiphase tissues. Biomaterials: doi:10.1016/j.biomaterials.2011.05.043. Accessed: April 19, 2012.
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