Nanoparticle-assisted optical tethering of endosomes reveals the cooperative function of dyneins in retrograde axonal transport

Abstract

Dynein-dependent transport of organelles from the axon terminals to the cell bodies is essential to the survival and function of neurons. However, quantitative knowledge of dyneins on axonal organelles and their collective function during this long-distance transport is lacking because current technologies to do such measurements are not applicable to neurons. Here, we report a new method termed nanoparticle-assisted optical tethering of endosomes (NOTE) that made it possible to study the cooperative mechanics of dyneins on retrograde axonal endosomes in live neurons. In this method, the opposing force from an elastic tether causes the endosomes to gradually stall under load and detach with a recoil velocity proportional to the dynein forces. These recoil velocities reveal that the axonal endosomes, despite their small size, can recruit up to 7 dyneins that function as independent mechanical units stochastically sharing load, which is vital for robust retrograde axonal transport. This study shows that NOTE, which relies on controlled generation of reactive oxygen species, is a viable method to manipulate small cellular cargos that are beyond the reach of current technology.

Figure 1. Nanoparticle assisted optical tethering of endosomes in axons.

(a) Schematic of the retrograde axonal transport of nanoparticle-WGA endosomes from axon terminals to the cell bodies in neurons. (b) Unperturbed retrograde transport trajectories of QD-endosomes (black, 45 W/cm², 32fps) and INP100-endosomes (red/gray, 1.4 W/cm², 10fps). (c) Retrograde INP50-endosomes (19 W/cm², 32fps) stochastically becoming stationary within the imaging field of view. (d) Gradual stalling and fast reversals (jumps) exhibited by an affected INP-endosome. Inset zooms in on a few jumps with the corresponding kymograph is also shown. (e) Laser-affected INP50-endosomes exhibiting jumps at different locations along the axon. (f) Percentage of laser-affected endosomes, for different nanoparticles at varying laser powers.

Figure 2. Stochastic load-sharing model and the nature of F_opp in NOTE.

(a) INP-endosome jumps of different sizes labeled with the approximate jump sizes (black arrows and dotted lines). The typical shoulders in stall profiles (red/gray arrows) indicate stochastic load-sharing among endosome-bound dyneins. (b) Stochastic load sharing model for tethered endosome motion. The endosome jumps are explained in terms of the stalling and detachment of the leading dyneins under the elastic opposing force (F_opp) of stretched tether. (c) Tracked positions of the INP-endosome in Fig. 1e reflecting the underlying microtubular track. Endosome positions just before dynein detachment (blue/triangles) and 125 ms after detachment (red/circles) show that the jumps are parallel to the microtubular track. (d) Distribution of INP-endosome displacement perpendicular to microtubule. (e) Occasional reversed motion seen after jumps (black arrows), resulted in a stall/jump in the opposite direction (red/gray arrows).

Figure 3. Detachment velocities from fitting the endosome jumps.

(a) Global fitting (red) of pre- and post-detachment curves to estimate the detachment time and position (q_s, t_d), detachment velocity V_detach, and damping constant k/γ, from an endosome jump. (b) Power law calibration (red) of position uncertainties (black, standard deviation over 10s of tracking of coverslip bound INPs as a function of fluorescence intensity) used in model fitting. (c) Retrograde INP100-endosome trajectory exhibiting a range of jump sizes and multiple-dynein stall profiles (dotted arrows) at 45 W/cm², 150fps. Experimental jump sizes (near multiples of 0.23 μm) are labeled in blue and the detachment velocities V_detach (near multiples of 13 μm/s) are labeled in red. (d) Approximate linearity of V_detach with experimental jump size. e) V_detach obtained with k/γ as a fitting parameter (black) or fixed at 65/s (red) plotted vs the computed jump size.

Figure 4. Parameter distributions for retrograde INP100-endosomes.

(a) Linear correlation of V_detach with the computed jump size for the retrograde INP100-endosomes (45 W/cm², 150fps). The dispersion from linearity results from the variation in k/γ among the endosomes and the standard errors (SE) in fitting. Only jumps with <15% SE in V_detach (306 out of 961 jumps) are considered here. (b) Distribution of V_detach exhibits discrete peaks that are near multiples of ~6 μm/s (c) Distribution of the computed jump size. (d) Distribution of k/γ. The mean ± sd are shown for the distributions in b, and d.

Figure 5. Stall duration of endosomes prior to detachment.

(a) T_stall duration shown for a few INP100-endosome jumps. (b) Distribution of the stall duration T_stall for INP100-endosomes (c) Mean T_stall for different nanoparticle/ligand endosomes.

Figure 6. Retrograde transport of silica-coated iron oxide nanoparticles and polymer nanoparticles.

(a) Robust retrograde transport trajectories of silica-coated INP endosomes (black, 45 W/cm², 10fps, 561 nm). (b) Retrograde trajectories of polymer nanoparticle endosomes, stalling within seconds of entering the field of view (red/gray, 15 W/cm², 10fps, 488 nm). Inset shows the endosome jumps before the endosome became stationary under the laser illumination.