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Self-organization of an acentrosomal microtubule network at the basal cortex of polarized epithelial cells.

Reilein A, Yamada S, Nelson WJ - J. Cell Biol. (2005)

Bottom Line: Microtubules undergoing dynamic instability without any stabilization points continuously remodel their organization without reaching a steady-state network.However, the addition of increased microtubule stabilization at microtubule-microtubule and microtubule-cortex interactions results in the rapid assembly of a steady-state microtubule network in silico that is remarkably similar to networks formed in situ.These results define minimal parameters for the self-organization of an acentrosomal microtubule network.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, Beckman Center for Molecular and Genetic Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.

ABSTRACT
Mechanisms underlying the organization of centrosome-derived microtubule arrays are well understood, but less is known about how acentrosomal microtubule networks are formed. The basal cortex of polarized epithelial cells contains a microtubule network of mixed polarity. We examined how this network is organized by imaging microtubule dynamics in acentrosomal basal cytoplasts derived from these cells. We show that the steady-state microtubule network appears to form by a combination of microtubule-microtubule and microtubule-cortex interactions, both of which increase microtubule stability. We used computational modeling to determine whether these microtubule parameters are sufficient to generate a steady-state acentrosomal microtubule network. Microtubules undergoing dynamic instability without any stabilization points continuously remodel their organization without reaching a steady-state network. However, the addition of increased microtubule stabilization at microtubule-microtubule and microtubule-cortex interactions results in the rapid assembly of a steady-state microtubule network in silico that is remarkably similar to networks formed in situ. These results define minimal parameters for the self-organization of an acentrosomal microtubule network.

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Stochastic simulation based on an in situ microtubule network. (A) Retrospective staining of APC for the microtubule network shown in Fig. 8. The location of the APC spots are used in the simulation below. Bar, 5 μm. (B) Although new microtubules added to the network, the original microtubules remained in place (marked in yellow). Time is given in minutes. (C) Stochastic simulation based on the in situ patch. Yellow filaments are preexisting microtubules; red dots are APC spots derived from the retrospective image; blue circles are nucleation sites derived from the in situ patch; blue lines represent microtubules. (D) The mean lengths of newly added microtubules and correlation coefficients of the simulated microtubule network. The curve levels off at a high correlation at 15 min, indicating that the network has reached a steady state. Error bars represent SD.
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fig9: Stochastic simulation based on an in situ microtubule network. (A) Retrospective staining of APC for the microtubule network shown in Fig. 8. The location of the APC spots are used in the simulation below. Bar, 5 μm. (B) Although new microtubules added to the network, the original microtubules remained in place (marked in yellow). Time is given in minutes. (C) Stochastic simulation based on the in situ patch. Yellow filaments are preexisting microtubules; red dots are APC spots derived from the retrospective image; blue circles are nucleation sites derived from the in situ patch; blue lines represent microtubules. (D) The mean lengths of newly added microtubules and correlation coefficients of the simulated microtubule network. The curve levels off at a high correlation at 15 min, indicating that the network has reached a steady state. Error bars represent SD.

Mentions: 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 microtubule–microtubule and microtubule–cortex (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.


Self-organization of an acentrosomal microtubule network at the basal cortex of polarized epithelial cells.

Reilein A, Yamada S, Nelson WJ - J. Cell Biol. (2005)

Stochastic simulation based on an in situ microtubule network. (A) Retrospective staining of APC for the microtubule network shown in Fig. 8. The location of the APC spots are used in the simulation below. Bar, 5 μm. (B) Although new microtubules added to the network, the original microtubules remained in place (marked in yellow). Time is given in minutes. (C) Stochastic simulation based on the in situ patch. Yellow filaments are preexisting microtubules; red dots are APC spots derived from the retrospective image; blue circles are nucleation sites derived from the in situ patch; blue lines represent microtubules. (D) The mean lengths of newly added microtubules and correlation coefficients of the simulated microtubule network. The curve levels off at a high correlation at 15 min, indicating that the network has reached a steady state. Error bars represent SD.
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fig9: Stochastic simulation based on an in situ microtubule network. (A) Retrospective staining of APC for the microtubule network shown in Fig. 8. The location of the APC spots are used in the simulation below. Bar, 5 μm. (B) Although new microtubules added to the network, the original microtubules remained in place (marked in yellow). Time is given in minutes. (C) Stochastic simulation based on the in situ patch. Yellow filaments are preexisting microtubules; red dots are APC spots derived from the retrospective image; blue circles are nucleation sites derived from the in situ patch; blue lines represent microtubules. (D) The mean lengths of newly added microtubules and correlation coefficients of the simulated microtubule network. The curve levels off at a high correlation at 15 min, indicating that the network has reached a steady state. Error bars represent SD.
Mentions: 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 microtubule–microtubule and microtubule–cortex (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.

Bottom Line: Microtubules undergoing dynamic instability without any stabilization points continuously remodel their organization without reaching a steady-state network.However, the addition of increased microtubule stabilization at microtubule-microtubule and microtubule-cortex interactions results in the rapid assembly of a steady-state microtubule network in silico that is remarkably similar to networks formed in situ.These results define minimal parameters for the self-organization of an acentrosomal microtubule network.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, Beckman Center for Molecular and Genetic Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.

ABSTRACT
Mechanisms underlying the organization of centrosome-derived microtubule arrays are well understood, but less is known about how acentrosomal microtubule networks are formed. The basal cortex of polarized epithelial cells contains a microtubule network of mixed polarity. We examined how this network is organized by imaging microtubule dynamics in acentrosomal basal cytoplasts derived from these cells. We show that the steady-state microtubule network appears to form by a combination of microtubule-microtubule and microtubule-cortex interactions, both of which increase microtubule stability. We used computational modeling to determine whether these microtubule parameters are sufficient to generate a steady-state acentrosomal microtubule network. Microtubules undergoing dynamic instability without any stabilization points continuously remodel their organization without reaching a steady-state network. However, the addition of increased microtubule stabilization at microtubule-microtubule and microtubule-cortex interactions results in the rapid assembly of a steady-state microtubule network in silico that is remarkably similar to networks formed in situ. These results define minimal parameters for the self-organization of an acentrosomal microtubule network.

Show MeSH
Related in: MedlinePlus