Published September 28, 2022 | Version v2

Microtubule retrograde flow retains neuronal polarization in a fluctuating state

  • 1. German Center for Neurodegenerative Diseases

Description

In developing vertebrate neurons, a neurite is formed by more than a hundred microtubules. While individual microtubules are dynamic, the microtubule array has been regarded as stationary. Using live-cell imaging of neurons in culture or in brain slices, combined with photoconversion techniques and pharmacological manipulations, we uncovered that the microtubule array flows retrogradely within neurites to the soma. This flow drives cycles of microtubule density, a hallmark of the fluctuating state before axon formation, thereby inhibiting neurite growth. The motor protein dynein fuels this process. Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension. Thus, keeping neurites short is an active process. Microtubule retrograde flow is a novel type of cytoskeletal dynamics, which changes the hitherto axon-centric view of neuronal polarization.

Notes

All scripts were written using Python 3.7 or 3.8, with environments build with conda (Anaconda Inc.; 2020. Available from: https://docs.anaconda.com/) and using the following packages: SciPy 1.6.2 (for FigureFlow) or 1.5.2 (https://pypi.org/project/scipy/) (67), sci-kit image 0.17.2 or 0.18.1 (https://pypi.org/project/scikit-image/;) (68), numpy 1.20.3 (https://pypi.org/project/numpy/) (69), seaborn 0.11.1 (for FigureFlow) or 0.11.0  (https://pypi.org/project/seaborn/) (70), scikit-posthocs 0.6.7 (https://pypi.org/project/scikit-posthocs/) (71), matplotlib 3.34 or 3.4.2 (https://pypi.org/project/matplotlib/) (72), pandas 1.3.0 (for FigureFlow) or 1.1.3 (https://pandas.pydata.org/) (73), and python-pptx 0.6.19 (https://pypi.org/project/python-pptx/). For viewing data and for some manual analysis detailed in the methods section of the publication (upon acceptance; currently in revision) ImageJ (NIH, RRID: SCR_002074; https://imagej.nih.gov/ij/) was used, including several ImageJ plugins detailed in the methods section of the publication.

Funding provided by: Wings for Life
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100008191
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Funding provided by: International Foundation for Research in Paraplegia
Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100001708
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Funding provided by: SFB 1158*
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Funding provided by: ImmunoSensation2*
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Funding provided by: Roger de Spoelberch Prize*
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Funding provided by: Deutsche Forschungsgesellschaft (DFG)*
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Funding provided by: SFB 1089 *
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Funding provided by: NRW network iBehave*
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Funding provided by: Joachim Herz Foundation
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100008662
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Funding provided by: International Max Planck Research School for Brain and Behavior*
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Additional details

Related works

Is cited by
10.1101/2021.09.01.458567 (DOI)
Is source of
10.5061/dryad.2fqz612s8 (DOI)