Nuclear Scaling Is Coordinated among Individual Nuclei in Multinucleated Muscle Fibers

Optimal cell performance depends on cell size and the appropriate relative size, i.e., scaling, of the nucleus. How nuclear scaling is regulated and contributes to cell function is poorly understood, especially in skeletal muscle fibers, which are among the largest cells, containing hundreds of nuclei. Here, we present a Drosophila in vivo system to analyze nuclear scaling in whole multinucleated muscle fibers, genetically manipulate individual components, and assess muscle function. Despite precise global coordination, we find that individual nuclei within a myofiber establish different local scaling relationships by adjusting their size and synthetic activity in correlation with positional or spatial cues. While myonuclei exhibit compensatory potential, even minor changes in global nuclear size scaling correlate with reduced muscle function. Our study provides the first comprehensive approach to unraveling the intrinsic regulation of size in multinucleated muscle fibers. These insights to muscle cell biology will accelerate the development of interventions for muscle diseases

Mechanical positioning of multiple nuclei in muscle cells.

Many types of large cells have multiple nuclei. In skeletal muscle fibers, the nuclei are distributed along the cell to maximize their internuclear distances. This myonuclear positioning is crucial for cell function. Although microtubules, microtubule associated proteins, and motors have been implicated, mechanisms responsible for myonuclear positioning remain unclear. We used a combination of rough interacting particle and detailed agent-based modeling to examine computationally the hypothesis that a force balance generated by microtubules positions the muscle nuclei. Rather than assuming the nature and identity of the forces, we simulated various types of forces between the pairs of nuclei and between the nuclei and cell boundary to position the myonuclei according to the laws of mechanics. We started with a large number of potential interacting particle models and computationally screened these models for their ability to fit biological data on nuclear positions in hundreds of Drosophila larval muscle cells. This reverse engineering approach resulted in a small number of feasible models, the one with the best fit suggests that the nuclei repel each other and the cell boundary with forces that decrease with distance. The model makes nontrivial predictions about the increased nuclear density near the cell poles, the zigzag patterns of the nuclear positions in wider cells, and about correlations between the cell width and elongated nuclear shapes, all of which we confirm by image analysis of the biological data. We support the predictions of the interacting particle model with simulations of an agent-based mechanical model. Taken together, our data suggest that microtubules growing from nuclear envelopes push on the neighboring nuclei and the cell boundaries, which is sufficient to establish the nearly-uniform nuclear spreading observed in muscle fibers

Elimination steps in Filter 2.

A: Average absolute x-position of the nuclei as a function of the cell width, predicted by 12 model classes that passed Filter 1. The calculations were made for the force ranges that ensured that the criteria of Filter 1 (not using the SF/DF criteria, see Methods for details) were satisfied and for the force magnitude that minimized the error of the fit of the calculations to the biological data. The light green rectangle marks those model classes for which the average absolute x-position increases with cell width as observed in the data (see B). B: Measured dependence of the average absolute x-position on the cell width. C: Effect of repulsive, increasing with distance side forces with a finite range for three different cell widths (a,b,c). Blue circles and red stars show the individual contributions from the left and right side respectively, the black lines in a,b,c show the resulting force felt by a nucleus at position x (positive/negative forces cause movement to the right/left). D: As C, but for repulsive side forces, decreasing with distance. E: For each model class and each combination of force ranges, the color represents how well the four criteria depicted in Fig 2B, Filter 2, are fulfilled. Dark Blue = all criteria fulfilled for all 14 cells, Red = all are violated. The dark green rectangle marks those model classes which were examined further. White and Black circles mark examples shown in F. F: Examples for sensitivity on the force range for one of the 14 cells. Shown are one example with valid positioning pattern (white circle) and one with invalid patterning (black circle), which the exception of C4, where both patterns are valid.</p

Microscopic simulation using Cytosim.

A: To study the force acting on a nucleus created by pushing microtubules, we placed a nucleus near a boundary (left) and near a second nucleus (right) and used the speed to calculate the force (details in Methods). B: Force calculation results from 5 realizations at 7 distances of A (dots) and power law fits of their average. Using the notation of Eq (1) this yielded αN = −7.6, αS = αP = −3.3, Ms = Mp = 12.4, vertical dashed lines mark the radius and diameter of a nucleus. C and D: Comparison between the microscopic simulation using Cytosim and the trajectories of positioning nuclei obtained from solving Eq (1) using the parameters obtained from B. Left: Final positions of nuclei (colored) and microtubuli (black) at t = 17h, right: Initial positions (transparent, marked with a, trajectories (solid and dotted lines) and final positions (marked with b, black/grey) of the agent-based and interacting particle simulations. Shown are simulations for a VL4 type cell (C) and a VL3 type cell (D). Movies comparing the simulations are shown in S1 and S2 Video. Parameters of the stochastic agent-based simulation can be found in Table 2, those of the interacting particle simulation are given by the fit in B.</p