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Active nematic order and dynamic lane formation of microtubules driven by membrane-bound diffusing motors

Citation

Hirst, Linda (2022), Active nematic order and dynamic lane formation of microtubules driven by membrane-bound diffusing motors, Dryad, Dataset, https://doi.org/10.6071/M37088

Abstract

Dynamic lane formation and long-range active nematic alignment is reported using a geometry in which kinesin motors are directly coupled to a lipid bilayer, allowing for in-plane motor diffusion during microtubule gliding. We use fluorescence microscopy to image protein distributions in and below the dense two-dimensional microtubule layer, revealing evidence of diffusion-enabled kinesin restructuring within the fluid membrane substrate as microtubules collectively glide above. We find that the lipid membrane acts to promote filament-filament alignment within the gliding layer, enhancing the formation of a globally aligned active nematic state. We also report the emergence of an intermediate, locally ordered state in which apolar dynamic lanes of nematically-aligned microtubules migrate across the substrate. To understand this emergent behavior, we implement a continuum model obtained from coarse graining a collection of self-propelled rods, with propulsion set by the local motor kinetics. Tuning the microtubule and kinesin concentrations as well as active propulsion in these simulations reveals that increasing motor activity promotes dynamic nematic lane formation. Simulations and experiments show that, following fluid bilayer substrate mediated spatial motor restructuring, the total motor concentration becomes enriched below the microtubule lanes that they drive, with the feedback leading to more dynamic lanes. Our results have implications for membrane coupled active nematics in vivo as well as for engineering dynamic and reconfigurable materials where the structural elements and power sources can dynamically co-localize, enabling efficient mechanical work.

Methods

The data includes

1) Measurements of microtubule length. 

2) Calculations of order parameter in different phases.

For experimental imaging we used fluorescence microscopy (Leica Microsystems Inc. DM 2500P fluorescence Microscope, (Buffalo Grove, IL, USA)) and confocal fluorescence microscopy (Zeiss LSM 889 with AiryScan + FAST and a Gallium arsenide phosphide (GaAsP) photon counting photodetector.). A QImaging Retigia Exi camera (Surrey, BC, Canada) and an ORCA - Flash4.0 LT+ Digital CMOS camera, (Hamamatsu, Shizuoka, Japan) were used to record fluorescence movies under low light conditions. The images were recorded at 10 second time intervals with either 20x, 40x, or 63x objectives. For the phase diagram in Figure 3a we used a region of interest (ROI) of 500px x 500px (where 2.1 px = 1 µm) and the following order parameter thresholds to determine the phase behavior: Nematic S > 0.25, Coexistence 0.20 < S < 0.25, Lane formation 0.10 < S < 0.20 and Isotropic 0 < S < 0.10. In Figure 3 (c-d) we calculated the nematic order parameter and the ROI box size was varied to compare local and global order parameters. Image analysis was performed using ImageJ (http://imagej.nih.gov/ij/) including the ImageJ plugin, OrientationJ to calculate angular distributions of microtubules (23). Order parameters in each image were calculated with the ImageJ plugin, OrientationJ and a bin size of 5° to calculate angular distributions of the microtubules using the published method from (23). To calculate microtubule surface density on the bilayer, five different regions of area, A are randomly selected for each flow cell. The microtubules were counted manually for those selected regions and the fraction of fluorescently labeled microtubules for each flow cell used to find the total microtubules per unit area.