Correspondence to W. James Nelson: wjnelson{at}stanford.edu
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A. Reilein's current address is Margaret M. Dyson Vision Research Institute, Weill Medical College of Cornell University, New York, NY 10021.
Abbreviations used in this paper: APC, adenomatous polyposis coli protein; EB1, end-binding protein 1; td, tandem dimer.
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Introduction |
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Microtubules in epithelial cells have properties that distinguish them from microtubules in fibroblastic-like cells (Pepperkok et al., 1990; Shelden and Wadsworth, 1993; Waterman-Storer et al., 2000). The rates of growth and shortening are slower, the length of growth and shortening events is shorter, microtubule dynamicity (the total length grown and shortened divided by the life span of the microtubule) is lower (Shelden and Wadsworth, 1993), and cadherin-mediated cellcell contact leads to microtubule stabilization (Chausovsky et al., 2000). Acentrosomal microtubules can arise spontaneously in the peripheral cytoplasm and persist for many minutes; the minus ends of these microtubules are quite stable (Vorobjev et al., 1997; Yvon and Wadsworth, 1997). Microtubules in cytoplasts (acentrosomal enucleated cells) derived from epithelial cells display dynamic instability but not the treadmilling behavior observed in fibroblast-derived cytoplasts, supporting the idea that in epithelial cells, the minus end of microtubules is stable to depolymerization (Rodionov et al., 1999). It is not known whether these or other microtubule parameters contribute to the unique acentrosomal organization of microtubule networks in polarized epithelial cells.
To examine this problem, we studied microtubules on basal plasma membranes isolated from fully polarized MDCK epithelial cells. These basal membranes allow us to observe microtubule networks that are associated with the plasma membrane in considerably more detail than is possible in an intact cell. We have found that microtubules in basal membrane cytoplasts have an organization and dynamics that are similar to microtubules in intact MDCK cells and transport endocytic vesicles at rates consistent with motor-driven transport. Previously, we showed that the +Tip proteins APC (adenomatous polyposis coli protein), EB1 (end-binding protein 1), and p150Glued are distributed along these microtubules and that APC bound to the basal cortex acts as a template for microtubule binding and polymerization (Reilein and Nelson, 2005). In this study, we examine microtubule dynamics in detail and show how microtubule interactions with one another and the underlying cortex can generate a steady-state network in situ and in silico that has characteristics of a self-organized structure.
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Results |
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Immunofluorescence and scanning electron microscopy revealed that the basal microtubule network is organized by microtubules intersecting one another at a range of angles and often involves the convergence of multiple microtubules (Fig. 2). This organization is common to polarized MDCK, Caco-2, and EpH4 epithelial cells (Fig. 2, CE). Microtubule ends contacting the sides of other microtubules have different conformations (Fig. 2, FI), sometimes curving at the tip and sometimes terminating in a bulge or knob. Microtubules are often curved or looped (Fig. 2, C, D, G, and K). Regularly spaced discontinuities of reduced fluorescence are observed along microtubules in tubulin antibody-stained as well as GFP-tubulinlabeled microtubules (Fig. 2, C and D). These discontinuities are observed in deconvolved and nondeconvolved images, by confocal microscopy (unpublished data), and in unfixed microtubules containing GFP-tubulin (Fig. 2 J). Note that fixed GFP-tubulin specimens stained with tubulin antibody showed GFP fluorescence in gaps of antibody staining (Fig. 2 K) and vice versa, indicating that the gaps are caused by stearic inhibition of antibody binding or GFP-tubulin fluorescence, which is probably a result of +Tip proteins bound along the length of the microtubules (Reilein and Nelson, 2005).
Basal microtubules serve to transport endocytic vesicles
The basolateral membrane of polarized epithelial cells is a site of endocytosis and transcytosis of internalized vesicles (Perret et al., 2005). To determine whether basal microtubules are functional in endocytic vesicle transport, we prepared basal cytoplasts from polarized cells that had been incubated with LysoTracker red to label acidified vesicles or from cells that had been transfected with clathrin-DsRed to label endocytic vesicles. Both LysoTracker red (Fig. 3 A and Video 1, available at http://www.jcb.org/cgi/content/full/jcb.200505071/DC1) and clathrin-DsRedlabeled vesicles (Fig. 3 B and Video 2) translocated along basal microtubules at rates ranging from 0.2 to 0.5 µm/s, which is consistent with microtubule motor-driven activities. To quantify the percentage of vesicles that moved, we considered only those patches in which some vesicles moved to be sure that they were active and had sealed over to form cytoplasts. We counted vesicles that moved at least 1.3 µm in directed lateral movements over a period of 1 min and found that 21% of LysoTracker redlabeled vesicles (24/114 vesicles in 11 basal cytoplasts) exhibited directed lateral movement. The percentage of clathrin vesicles moving was more difficult to determine because of the lower intensity of some clathrin-DsRed spots. However, an approximate estimate showed that 720% of clathrin spots moved per minute. Note that it has been reported that in whole cells imaged by total internal reflection fluorescence microscopy, only 2% of clathrin spots moved laterally per minute at rates of microtubule motors in CHO cells (Rappoport et al., 2003), and 7% moved in CV-1 cells (Keyel et al., 2004). We conclude that the microtubule network in basal cytoplasts from polarized MDCK cells is functional in the transport of acidified and clathrin-containing endocytic vesicles.
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Whereas one end of the microtubules underwent periods of growth and shrinkage, which is indicative of the plus end, most of the newly arisen microtubules (19/26 observed in nine cytoplasts) showed shrinkage from the other end, which is designated the minus end (Fig. 4, AC and E); minus endshortening rates varied from 3 to 15 µm/min (Fig. 5). Our designation of the microtubule plus and minus ends based on their respective dynamics was independently confirmed by analyzing microtubule dynamics in basal cytoplasts from MDCK cells expressing the microtubule plus end marker protein EB1 tandem dimer (td) DsRed (Fig. 5 and Video 6, available at http://www.jcb.org/cgi/content/full/jcb.200505071/DC1). Note that EB1 bound along the entire length of microtubules but was enriched on the growing plus ends of microtubules (Reilein and Nelson, 2005). The bimodal distribution of EB1 movements (at the plus end of microtubules) is similar to that of unmarked microtubules, in which we identified the plus end as the rapidly growing end of the microtubule (Fig. 5, A and C). EB1 is also visible on microtubules undergoing dynamic instability at the edge of the cytoplast (Video 6). The behavior of the minus end of microtubules in these basal cytoplasts is different from that of acentrosomal microtubules in other epithelial cell types in which the minus end was reported to be stable (Vorobjev et al., 1997; Yvon and Wadsworth, 1997). Note, however, that depolymerizing minus ends often paused or became stabilized upon reaching another microtubule or area of stabilization on the cortex (Fig. 4).
In summary, we observed three major types of microtubule activities in the network: (1) stably localized microtubules, (2) microtubules undergoing dynamic instability at one end and, in some cases, shrinking from the other end, and (3) microtubules arising de novo and integrating into the network. These parameters generate a network that appears to have an overall steady-state pattern and yet has intrinsic dynamic properties that cause some remodeling of the pattern over time.
Computational modeling of microtubule parameters required for the formation of a steady-state microtubule network
The apparent random organization of microtubules in the basal network might suggest that the network could arise by the simple overlap of microtubules undergoing dynamic instability. However, our analysis of the network in situ revealed that additional parameters were involved, including increased microtubule stabilization as a result of microtubulecortex and microtubulemicrotubule interactions. To test the requirement of different microtubule parameters on the formation of the network, we developed a computational model that simulated dynamic microtubules in a two-dimensional space (see Materials and methods). Our goal was to define a minimal set of parameters that are necessary and sufficient to form a steady-state microtubule network.
We first tested whether the simulated assembly of 30 microtubules undergoing dynamic instability as the only parameter could lead to the formation of a steady-state structure. In this test, one end of the microtubule was modeled to undergo dynamic instability (designated the plus end), whereas the other end was kept inert (designated the minus end). This simulation resulted in a distribution of microtubules (mean length 2.3 ± 0.4 µm) in a 5 x 5µm space that exhibited continuous remodeling as a result of the disappearance of microtubules by depolymerization and spontaneous nucleation of microtubules elsewhere at random in the space (Fig. 7 A).
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To measure the development of the network, individual time frames were correlated with future frames (t = 2 min; for a rationale for choosing this time interval, see Fig. S1, available at http://www.jcb.org/cgi/content/full/jcb.200505071/DC1). To determine to what degree a stable configuration had been generated, we used the correlation coefficient as an index to compare how well two images align with one another. Image correlation analysis is used frequently to compare two images for pattern similarity, such as in fingerprint matching (Russ, 1998). The correlation coefficient is zero when two images are dissimilar and is one when two images are identical. Therefore, we defined a network as being at steady state when it reached and maintained a high correlation coefficient. Such a network contains a microtubule pattern that does not undergo large changes but has some slow remodeling as a result of dynamic instability at the plus end of some microtubules and the de novo growth and depolymerization of a relatively minor fraction of microtubules. A self-organizing system must be continually dynamic yet maintain a stable pattern, and, therefore, the correlation coefficient should be high but should not reach one (Misteli, 2001; Anderson, 2002).
We simulated microtubule network formation for three conditions: no interactions, microtubulemicrotubule stabilization, and microtubulecortex stabilization. In all conditions, the correlation coefficient and microtubule length increased at similar rates and reached steady state (Fig. 7 B). In the case with no interactions, the microtubules reached a steady-state length but not a steady-state pattern; consequently, the correlation coefficient was low. However, the highest correlation coefficients occurred in networks formed with either microtubulemicrotubule or microtubulecortex stabilization points (Fig. 7 C). We also examined the effect of increasing the number of cortex stabilization points on the correlation coefficient for network formation (Fig. 7 D). Fig. 7 D shows that the correlation increases with an increasing number of cortical stabilization points. Thus, in the simulation, microtubulemicrotubule and microtubulecortex stabilization can cause the formation of a steady-state microtubule network. Together, these in situ and in silico analyses indicate that the stabilization of microtubules either by microtubulemicrotubule or microtubulecortex interactions can cause the formation of a steady-state network. In situ, the cortical interactions help to maintain the location of stable microtubules.
As a further test of the computational model, we sought to examine the formation of a microtubule network in silico using the locations of cortical stabilization spots and microtubule nucleation sites taken from an in situ example and the aforementioned microtubule stabilization criteria. Fortuitously, we imaged a basal cytoplast that spontaneously assembled a de novo microtubule network in 10 min after a lag of
9 min, which could be used for this direct comparison (Fig. 8). The reason for this lag is unknown, but it is inconsequential for the comparative analysis of in situ and in silico growth of the network. Note that this type of naked patch was a rare event; this patch had probably lost most of its microtubules during sonication and, therefore, had a large surface of APC spots over which a new microtubule network could form. Nevertheless, this patch, albeit rare, was very useful as it allowed us to test our simulation under more stringent conditions of almost complete de novo formation of a microtubule network. A similar analysis was also performed on a patch with a less dramatic reorganization of the microtubule network (Fig. S2, available at http://www.jcb.org/cgi/content/full/jcb.200505071/DC1). Microtubules grew from the sides or ends or other microtubules and integrated into the forming network through connections with other microtubules (Fig. 8 and Video 9, available at http://www.jcb.org/cgi/content/full/jcb.200505071/DC1). The network appeared to reach a steady state that persisted for the additional 20 min of imaging. Retrospective staining for APC showed that many of the microtubules had grown over and remained colocalized with APC spots on the cortex (Fig. 9 A) as shown previously (Reilein and Nelson, 2005). Fig. 9 B shows still images from Video 9 using the microtubules from t = 0 min as fiduciary marks. To generate an in silico model of this microtubule network, we first mapped the boundary of the basal cytoplast, the distributions of APC spots and the original microtubules (t = 0 min), and the relative positions of all microtubule nucleation events that occurred (Fig. 9 C). We then let the simulation run using the parameter of increased rescue frequency upon microtubulemicrotubule and microtubulecortex (APC spots) interactions (Fig. 9 C). The pattern of microtubules that arose appears to be similar to that of the in situ microtubule network at 20 min. Further analysis showed that the mean length of microtubules in silico increased rapidly to 3.9 ± 2.4 µm (Fig. 9 D), which is comparable with the in situ mean length of 3.6 ± 2.0 µm. Although there were some fluctuations in the correlation coefficient as a result of the irregular timing of nucleation events that added new microtubules to the pattern, it reached a plateau approaching a correlation coefficient of one (Fig. 9 D), which is demonstrative of a steady-state microtubule network.
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Discussion |
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In this study, we report the organization of microtubule networks bound to the basal cortex of polarized epithelial cells. These microtubules form the base of microtubule bundles that span the apicobasal axis of these columnar cells and, as we showed, are active in the transport of endocytic vesicles on the basal membrane. The organizational cues for this cortical microtubule network do not appear to involve treadmilling or motor proteins. Treadmilling gives the appearance of microtubule translocation as a result of the simultaneous addition of tubulin subunits to the plus end and subtraction from the minus end; this behavior was not observed in the basal cytoplasts. We also found little evidence for the involvement of microtubule motors in network organization on the basal membrane. It is noteworthy that the network does contain both plus and minus enddirected motor activity as evidenced by the bidirectional movement of endocytic vesicles over the microtubules at rates comparable to those of kinesins and dynein. However, we did not observe microtubules gliding on the surface of the basal cortex. We occasionally observed a microtubule tip sliding along another microtubule, but in those few cases, the rates of movement were slower than that expected of kinesins and dynein and were more consistent with microtubule polymerization.
Instead, our results indicate that the basal microtubule network is generated by increased microtubule stabilization at sites of microtubulemicrotubule and microtubulecortex intersections. Which proteins contribute to these stabilization points? We have shown previously that +Tip proteins APC, EB1, and p150Glued in the basal network are distributed not just at the plus ends of microtubules but along the length of microtubules and that they localize to the end of one microtubule binding to the side of another (Reilein and Nelson, 2005). We also showed that -tubulin was localized along microtubules, at intersections between two microtubules, and, possibly, between microtubules and the cortex; these observations are similar to recent results on the distribution of
-tubulin along microtubules and in the cytoplasm of other cells (Janson et al., 2005; Murata et al., 2005). In addition, APC and perhaps other proteins associated with the basal cortex form a template for organization and polymerization of microtubules. We showed that microtubules pause and shrink to small stubs at distinct points on the membrane cortex without completely depolymerizing, which is indicative of points of microtubule stabilization on the basal cortex (Reilein and Nelson, 2005). Moreover, purified tubulin dimers assembled into microtubule networks over these APC spots on the cortex that were similar in organization and complexity to that in intact cells and on isolated basal membrane patches. Significantly, antibodies to APC inhibited the formation of this network (Reilein and Nelson, 2005), indicating a direct role of APC in binding and stabilizing polymerizing microtubules.
We tested whether a stable microtubule network can be formed in silico by superimposing dynamic instability with only the additional parameters of increased microtubule rescue frequency associated with microtubulemicrotubule and microtubulecortex binding, as we observed in situ. By direct comparison with a microtubule network formed in situ, we found that our in silico model of microtubule network assembly using a minimum of microtubule parameters faithfully reproduces the assembly and dynamic organization of a bone fide basal microtubule network. Both the in silico and in situ microtubule networks assembled from microtubules that continued to display dynamic instability and add and subtract microtubules from the network and yet maintained the same overall pattern, indicating that they had reached a steady-state organization. The simulation illustrates the minimal parameters that are involved in remodeling the network in situ. However, it is likely that other parameters play a role: for example, depolymerization of the minus ends contributes to the ability of the microtubule network to remodel itself after reaching an overall steady-state pattern. This parameter was not included in the computational model, as the timing of minus end depolymerization could not be predicted.
A self-organizing structure is defined as a dynamic structure in which material must be continuously exchanged but has an overall stable configuration (Misteli, 2001), and the emergence of a structure that arises from multiple local interactions in which the global level organization must arise solely from within the system and must not be generated from external guiding forces (Anderson, 2002). Thus, our analysis of microtubule networks on the basal cortex of polarized cells has uncovered a new mechanism for the self-organization of microtubules undergoing dynamic instability that requires increased microtubule stabilization associated with microtubulemicrotubule and microtubulecortex binding.
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Materials and methods |
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Basal membrane isolation
Cells were plated at confluent density on 12-mm transwell polycarbonate filter membranes of 0.4-µm pore size (Corning) and were grown for 35 d (MDCK and EpH4 cells) or 2 wk (Caco-2 cells) to allow for cell polarization. Cells were rinsed and incubated for 10 min in hypotonic buffer (15 mM Hepes, pH 7.3, 15 mM KCl, 1 mM MgCl2, and 1 mM EGTA) and sonicated at 4°C with a brief (<1 s) pulse using a sonifier (model 250; Branson Ultrasonics) set at duty cycle 20 and an output 1922% with a 1/8-inch microprobe held 57 mm above the surface of the cells (Drees et al., 2005). Membrane patches were rinsed briefly in buffer before imaging or fixation.
Fixation and antibodies
Fixation of microtubules in fully polarized cells on filters was performed with 0.3% glutaraldehyde and 0.1% Triton X-100 in 37°C BRB80 buffer (80 mM Pipes, pH 6.9, 1 mM EGTA, and 1 mM MgCl2) for 20 min with gentle agitation followed by three 5-min washes in 1 mg/ml NaBH4 in Ringers buffer to quench unreacted aldehyde groups. The fixation of microtubules on isolated membrane patches was performed with 0.3% glutaraldehyde except in the case of -tubulin staining, in which basal patches were fixed with 0.08% glutaraldehyde and 2% freshly prepared PFA in BRB80, as suggested by Murata et al. (2005). Microtubules were stained with DM1A mouse monoclonal
-tubulin antibody (Sigma-Aldrich),
-tubulin was stained with mouse monoclonal GTU-88 (Sigma-Aldrich), and APC was stained with an affinity-purified polyclonal antibody to a central APC2 domain (Munemitsu et al., 1994). Secondary antibodies conjugated to FITC or rhodamine were obtained from Jackson ImmunoResearch Laboratories. Specimens were mounted in Vectashield mounting medium (Vector Laboratories).
Fluorescence microscopy of fixed specimens
Image z-stacks were collected in 0.20-µm steps on an inverted microscope (IX-70; Olympus) with a 100x NA 1.35 oil immersion objective (Olympus) and were captured with a cooled CCD camera (Photometrics). Images were collected at room temperature and processed using DeltaVision deconvolution software (Applied Precision) on a workstation (Silicon Graphics, Inc.). For three-dimensional image reconstruction, deconvolved optical sections were combined using Volocity software (Improvision, Inc.).
Scanning electron microscopy
Basal membranes were prepared from MDCK cells polarized on transwell filters and were fixed for 30 min in 2% glutaraldehdye in BRB80 buffer. For gold labeling of microtubules, unreacted aldehydes were quenched with NaBH4, and microtubules were stained with the DM1A monoclonal antibody diluted 1:50 followed by 15 nm gold-labeled secondary antibody. Samples were fixed again after immunolabeling in 2% glutaraldehyde. Samples were processed according to the procedure of Svitkina et al. (1995). In brief, samples were changed from glutaraldehyde to 0.1% tannic acid for 20 min and 0.1% uranyl acetate for 20 min, dehydrated through an ethanol series, and critical point dried in ethanol. Filters were cut from the plastic holders only after critical point drying. Samples were rotary shadowed with platinum at a 45° angle. Samples were imaged with a scanning electron microscope (XL30 Sirion; FEI Company) at 5 kV and spot size 3 in ultrahigh resolution mode.
Live imaging of basal cytoplasts
MDCK cells were plated at confluent density on transwell filters and grown 35 d to allow for cell polarization. Basal membranes were prepared as described above. Filters were rinsed several times in BRB80 buffer, mounted on a glass slide between two strips of double-stick tape, and covered with BRB80 buffer with 2 mM ATP and 1 mM GTP. A 22-mm glass coverslip was placed on top and sealed in place with silicone vacuum grease. The inclusion of 2 mM ATP and 1 mM GTP in the sonication and imaging buffers resulted in similar parameters of microtubule dynamics and vesicle motility as those without these nucleotides added, indicating that cytoplasts sealed over very quickly after sonication. Basal patches that exhibited microtubule dynamics were presumed to be sealed-over cytoplasts. Fluorescence imaging was performed with a microscope (200M Axiovert; Carl Zeiss MicroImaging, Inc.) run by Slidebook software (Marianas System; Intelligent Imaging Innovation, Inc.) using a 100x NA 1.4 objective lens heated to 37°C. For imaging of EB1-td DsRed or of microtubule dynamics with GFP-tubulin, images were captured for 5 s each with no interval between frames. For imaging of vesicles moving along microtubules, LysoTracker red was imaged with a DsRed filter for 200 ms or clathrin-DsRed for 3 s, and GFP-tubulin was imaged with the GFP filter for 4 s. Retrospective staining of APC was performed as described previously (Reilein and Nelson, 2005).
Analysis of microtubule dynamics
Images were sharpened in ImageJ (National Institutes of Health) with the Fourier transform bandpass filter to remove high and low spatial frequency signals using the limits of 0.1921.6 µm. The positions of plus or minus ends of individual microtubules with time were measured in ImageJ and exported to Microsoft Excel. The lengths of individual microtubules were graphed as a function of time (life histories). Only microtubules that were actively undergoing growth and shrinkage were followed, not the microtubules that were in an extended state of pause. The rates of growth and shortening events were determined by linear regression of events with changes in length 0.7 µm. Means and SDs were calculated per event. The catastrophe frequency was calculated by dividing the number of transitions from growth or pause to shortening by the total time for each individual microtubule. The rescue frequency was calculated by dividing the total number of transitions from shortening to pause or growth by the total time for each individual microtubule. Changes in length of <0.15 µm were considered to be pauses.
Stochastic simulation of microtubule dynamics
Computer-simulated microtubules diffusing and exhibiting dynamic instability at their plus ends were nucleated randomly in a 5 x 5µm space. If a microtubule depolymerized completely, a new nucleation site was assigned randomly. The dynamic instability was simulated with catastrophe (= 0.003 x L) and rescue (= 0.003 x [13-L]) frequency (1/s), where L is microtubule length (Nedelec, 2002). Experimentally measured growth (2.54 µm/min) and shrinkage (5.06 µm/min) rates were used for the simulation. Drag coefficients for translational and rotational diffusion are microtubule length dependent (Howard, 2001) and were calculated using a fluid viscosity of 0.05 pNs/µm2 (Nedelec, 2002). To prevent microtubules from growing beyond the boundary, the catastrophe frequency was increased when microtubule tips exceeded the boundary. A point of stabilization in the microtubule network was modeled as an increase in the rescue frequency to 0.5/s when the tip was within 50 nm of the stabilization spot. All calculations were performed using Matlab (Mathworks).
We compared the distribution of microtubules in two images of a simulated microtubule network separated by t. The image correlation analysis is used frequently to compare two images for pattern similarity (Russ, 1998). The correlation coefficient is zero when two images are dissimilar and is one when two images are identical. Correlation coefficients are calculated by performing conjugate multiplication in the Fourier domain using Matlab;
t = 2 min was chosen as the time interval based on the graph in Fig. S1. The correlation coefficient is defined as the maximum value of inverted Fourier transformed images. This image correlation does not recognize individual microtubules but rather recognizes the overall image pattern of the simulated microtubule network. Thus, the correlation depends on the differences in the pattern and density of the network from one time point to another. As a result, the correlation between two independent simulations will have a background correlation value (Fig. 7 C, squares). The correlation coefficient was calculated over the time course with a fixed time interval (
t = 2 min; Fig. 7 C).
For the simulation based on the in situ patch in Fig. 8, the coordinates of preexisting microtubules, nucleation sites, the direction of polymerization, and the cortex interaction sites were taken from the patch shown in Figs. 8 and 9 A. A similar analysis was performed for the patch shown in Fig. S2.
Online supplemental material
Fig. S1 shows the relationship of the correlation coefficient to t. Fig. S2 shows a second example of a computational analysis based on an in situ microtubule network. Fig. S3 shows centrosomal and noncentrosomal
-tubulin staining in whole MDCK cells. Video 1 shows LysoTracker redlabeled vesicles moving along microtubules in a basal cytoplast. Video 2 shows clathrin-DsRedlabeled vesicles moving on microtubules in a basal cytoplast. Video 3 shows microtubules arising de novo from the sides of other microtubules in an MDCK cell basal cytoplast. Video 4 shows microtubules in an MDCK cell basal cytoplast slowing and pausing at points on the cortex. Video 5 shows a de novo microtubule growing over a specific point where another microtubule end terminates in an MDCK cell basal cytoplast. Video 6 shows a basal cytoplast prepared from polarized cells expressing EB1-td DsRed. Video 7 shows computer-generated images of simulated microtubule networks with no microtubule interactions and microtubulemicrotubule interactions. Video 8 is a simulation of a microtubule network with microtubulecortex interactions. Video 9 shows the de novo formation of a microtubule network in a basal cytoplast. Video 10 is a computer simulation based on the in situ microtubule network shown in Video 9. Online supplemental material is available at http://www.jcb.org/cgi/content/full/jcb.200505071/DC1.
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Acknowledgments |
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This work was supported by a National Cancer Institute grant (PHS 5T32CA09151) to A. Reilein, the Department of Health and Human Services, an American Cancer Society Postdoctoral Fellowship grant (PF-03-016-01-CSM) to A. Reilein, and a National Institutes of Health grant (GM35527) to W.J. Nelson.
Submitted: 12 May 2005
Accepted: 27 October 2005
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