Forceful patterning: theoretical principles of mechanochemical pattern formation

Rombouts, Jan; Elliott, Jenna; Erzberger, Anna

doi:10.15252/embr.202357739

Cited by 4 publications

(1 citation statement)

References 244 publications

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“…Patterns are widespread in nature and can be observed across scales from subcellular to tissue and organism level. The complex interactions and mechanisms that underlie pattern formation processes are a topic of great interest in various fields (Rombouts et al, 2023). On a tissue-or cellular-scale, pattern formation is captured through 2D or 3D microscopy.…”

Section: Introductionmentioning

confidence: 99%

Spherical harmonics texture extraction for versatile analysis of biological objects

Gros,

Passmore,

Borst

et al. 2024

Preprint

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The characterization of phenotypes in cells or organisms from microscopy data largely depends on differences in the spatial distribution of image intensity. Multiple methods exist for quantifying the intensity distribution - or image texture - across objects in natural images. However, many of these texture extraction methods do not directly adapt to 3D microscopy data. Here, we present Spherical Texture extraction, which measures the variance in intensity per angular wavelength by calculating the Spherical Harmonics or Fourier power spectrum of a spherical or circular projection of the angular mean intensity of the object. This method provides a 20-value characterization that quantifies the scale of features in the spherical projection of the intensity distribution, giving a different signal if the intensity is, for example, clustered in parts of the volume or spread across the entire volume. We apply this method to different systems and demonstrate its ability to describe various biological problems through feature extraction. The Spherical Texture extraction characterizes biologically defined gene expression patterns in Drosophila melanogaster embryos, giving a quantitative read-out for pattern formation. Our method can also quantify morphological differences in Caenorhabditis elegans germline nuclei, which lack a predefined pattern. We show that the classification of germline nuclei using their Spherical Texture outperforms a convolutional neural net when training data is limited. Additionally, we use a similar pipeline on 2D cell migration data to extract polarization direction, quantifying the alignment of fluorescent markers to the migration direction. We implemented the Spherical Texture method as a plugin in ilastik, making it easy to install and apply to any segmented 3D or 2D dataset. Additionally, this technique can also easily be applied through a Python package to provide extra feature extraction for any object classification pipeline or downstream analysis.

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Section: Introductionmentioning

confidence: 99%

Spherical harmonics texture extraction for versatile analysis of biological objects

Gros,

Passmore,

Borst

et al. 2024

Preprint

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Mechanochemical patterning and wave propagation in multicellular tubes

Yu,

2024

Journal of the Mechanics and Physics of Solids

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Information content and optimization of self-organized developmental systems

Brückner,

Tkačik

2024

Proc. Natl. Acad. Sci. U.S.A.

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A key feature of many developmental systems is their ability to self-organize spatial patterns of functionally distinct cell fates. To ensure proper biological function, such patterns must be established reproducibly, by controlling and even harnessing intrinsic and extrinsic fluctuations. While the relevant molecular processes are increasingly well understood, we lack a principled framework to quantify the performance of such stochastic self-organizing systems. To that end, we introduce an information-theoretic measure for self-organized fate specification during embryonic development. We show that the proposed measure assesses the total information content of fate patterns and decomposes it into interpretable contributions corresponding to the positional and correlational information. By optimizing the proposed measure, our framework provides a normative theory for developmental circuits, which we demonstrate on lateral inhibition, cell type proportioning, and reaction–diffusion models of self-organization. This paves a way toward a classification of developmental systems based on a common information-theoretic language, thereby organizing the zoo of implicated chemical and mechanical signaling processes.

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Forceful patterning: theoretical principles of mechanochemical pattern formation

Cited by 4 publications

References 244 publications

Spherical harmonics texture extraction for versatile analysis of biological objects

Spherical harmonics texture extraction for versatile analysis of biological objects

Mechanochemical patterning and wave propagation in multicellular tubes

Information content and optimization of self-organized developmental systems

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