Abstract
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 to provide a parameter-free and data-agnostic application to any segmented 3D or 2D dataset. Additionally, this technique can also be applied through a Python package to provide extra feature extraction for any object classification pipeline or downstream analysis.
| Original language | English |
|---|---|
| Article number | e1012349 |
| Number of pages | 20 |
| Journal | PLoS Computational Biology |
| Volume | 21 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 2025 |
Bibliographical note
Publisher Copyright:© 2025 Gros et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding
This work was supported by: the European Molecular Biology Laboratory to SK, AK, JMC, by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - project number 452616889 to SK, and by the Eindhoven, Wageningen Utrecht Alliance through the Centre for Living Technologies to JP, WN and LCK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank Ivana \u010Cavka for help with gonad linearization, Johannes Hugger for adapting the pytorch ResNet for 3D data, and Niccol\u00F2 Banterle for discussions. We thank the high-performance computing cluster at the European Molecular Biology Laboratory (EMBL) for its support. The EMBL Advanced Light Microscopy Facility (ALMF), Zeiss, and Evident/Olympus are acknowledged for support in image acquisition. Some C. elegans strains were provided by the Caenorhabditis Genetics Center, which is funded by NIH Office of Research Infrastructure Programs (P40 OD010440).
| Funders | Funder number |
|---|---|
| European Molecular Biology Laboratory | |
| Centre for Living Technologies | |
| Deutsche Forschungsgemeinschaft | 452616889 |
| National Institutes of Health | P40 OD010440 |