Geometric control of Myosin-II orientation during axis elongation

  1. Matthew F Lefebvre
  2. Nikolas H Claussen
  3. Noah P Mitchell
  4. Hannah J Gustafson
  5. Sebastian J Streichan  Is a corresponding author
  1. University of California, Santa Barbara, United States

Abstract

The actomyosin cytoskeleton is a crucial driver of morphogenesis. Yet how the behavior of largescale cytoskeletal patterns in deforming tissues emerges from the interplay of geometry, genetics, and mechanics remains incompletely understood. Convergent extension in D. melanogaster embryos provides the opportunity to establish a quantitative understanding of the dynamics of anisotropic non-muscle myosin II. Cell-scale analysis of protein localization in fixed embryos suggests that gene expression patterns govern myosin anisotropy via complex rules. However, technical limitations have impeded quantitative and dynamic studies of this process at the whole embryo level, leaving the role of geometry open. Here we combine in toto live imaging with quantitative analysis of molecular dynamics to characterize the distribution of myosin anisotropy and the corresponding genetic patterning. We found pair rule gene expression continuously deformed, flowing with the tissue frame. In contrast, myosin anisotropy orientation remained approximately static, and was only weakly deflected from the stationary dorsal-ventral axis of the embryo. We propose that myosin is recruited by a geometrically defined static source, potentially related to the embryoscale epithelial tension, and account for transient deflections by cytoskeletal turnover and junction reorientation by flow. With only one parameter, this model quantitatively accounts for the time course of myosin anisotropy orientation in wild-type, twist, and even-skipped embryos as well as embryos with perturbed egg geometry. Geometric patterning of the cytoskeleton suggests a simple physical strategy to ensure a robust flow and formation of shape.

Data availability

All data for this article is available publicly without any restrictions. In our article, we make use of two datasets: (1) confocal microscopy data of FRAP experiments, which is available on the Dryad data repository https://doi.org/10.25349/D94C8M. (2) Lightsheet microscopy data of entire embryos. The data we use in the current publication is a subset of a larger dataset, the 'Morphodynamic atlas of Drosophila development', which is publicly available on the Dryad data repository https://doi.org/10.25349/D9WW43. This collection is indexed by the fly genotype and fluorescent marker imaged, so that the movies and images used in the current publication can be found easily. Lightsheet microscopy integrates microscopy and computational processing and its computational pipeline creates intermediate, 'raw' data files, which are of very large size (TBs for a single movie). This raw data is available upon request from the corresponding author without restriction or need for a specific research proposal. The analysis code used is available on GitHub https://github.com/nikolas-claussen/Geometric-control-of-Myosin-II-orientation-during-axis-elongation.

The following data sets were generated

Article and author information

Author details

  1. Matthew F Lefebvre

    Department of Physics, University of California, Santa Barbara, Santa Barbara, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Nikolas H Claussen

    Department of Physics, University of California, Santa Barbara, Santa Barbara, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Noah P Mitchell

    Department of Physics, University of California, Santa Barbara, Santa Barbara, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Hannah J Gustafson

    Department of Physics, University of California, Santa Barbara, Santa Barbara, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Sebastian J Streichan

    Department of Physics, University of California, Santa Barbara, Santa Barbara, United States
    For correspondence
    streicha@ucsb.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6105-9087

Funding

National Institutes of Health (5 R35 GM138203)

  • Sebastian J Streichan

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2023, Lefebvre et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,684
    views
  • 214
    downloads
  • 16
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Matthew F Lefebvre
  2. Nikolas H Claussen
  3. Noah P Mitchell
  4. Hannah J Gustafson
  5. Sebastian J Streichan
(2023)
Geometric control of Myosin-II orientation during axis elongation
eLife 12:e78787.
https://doi.org/10.7554/eLife.78787

Share this article

https://doi.org/10.7554/eLife.78787

Further reading

    1. Cell Biology
    2. Physics of Living Systems
    Deb Sankar Banerjee, Shiladitya Banerjee
    Research Article

    Accurate regulation of centrosome size is essential for ensuring error-free cell division, and dysregulation of centrosome size has been linked to various pathologies, including developmental defects and cancer. While a universally accepted model for centrosome size regulation is lacking, prior theoretical and experimental works suggest a centrosome growth model involving autocatalytic assembly of the pericentriolar material. Here, we show that the autocatalytic assembly model fails to explain the attainment of equal centrosome sizes, which is crucial for error-free cell division. Incorporating latest experimental findings into the molecular mechanisms governing centrosome assembly, we introduce a new quantitative theory for centrosome growth involving catalytic assembly within a shared pool of enzymes. Our model successfully achieves robust size equality between maturing centrosome pairs, mirroring cooperative growth dynamics observed in experiments. To validate our theoretical predictions, we compare them with available experimental data and demonstrate the broad applicability of the catalytic growth model across different organisms, which exhibit distinct growth dynamics and size scaling characteristics.

    1. Cell Biology
    2. Physics of Living Systems
    Marta Urbanska, Yan Ge ... Jochen Guck
    Research Article

    Cell mechanical properties determine many physiological functions, such as cell fate specification, migration, or circulation through vasculature. Identifying factors that govern the mechanical properties is therefore a subject of great interest. Here, we present a mechanomics approach for establishing links between single-cell mechanical phenotype changes and the genes involved in driving them. We combine mechanical characterization of cells across a variety of mouse and human systems with machine learning-based discriminative network analysis of associated transcriptomic profiles to infer a conserved network module of five genes with putative roles in cell mechanics regulation. We validate in silico that the identified gene markers are universal, trustworthy, and specific to the mechanical phenotype across the studied mouse and human systems, and demonstrate experimentally that a selected target, CAV1, changes the mechanical phenotype of cells accordingly when silenced or overexpressed. Our data-driven approach paves the way toward engineering cell mechanical properties on demand to explore their impact on physiological and pathological cell functions.