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. 2016 Oct 10;215(1):37–46. doi: 10.1083/jcb.201603108

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© 2016 Albrecht et al.

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Figure 5. — The periodic confinement areas are separated by diffusion barriers. (A) Overlay of a reconstructed image of SPT localizations (black) on the AIS of a DIV5 neuron with trajectories (blue). The area selected for Markov model analysis is emphasized (box), and the model describes the diffusion from the left end to the right end of the area. (B) Plot of trajectories in area used for analysis and boundaries detected by the Markov model. x axis indicates distance, and tick marks are 200 nm. From the left, a ∼200-nm spacing of detected boundaries is apparent. As the pattern becomes more complex and is not perpendicular to axon propagation, boundaries are not as easily identified by the algorithm. (C) Plot of the commitment probability from the left to the right. The value corresponds to the local probability that a molecule will move to the right boundary rather than the left boundary next. Thus, the commitment probability indicates the progress of the transport process from left to right. In the case of pure random motion, a proportional increase in commitment would be detected along the axon. The slope of the graph is color-coded to emphasize local barriers detected as sudden changes in commitment between areas of proportional increase. (D) Plot of local change of the commitment probability. Peaks indicates positions where the commitment probability makes a step change and thus positions of dynamical boundaries (color-coded in C, corresponding to low-density regions in A and B).