Philippe Andrey

Motivation: The bioimage informatics community is increasingly facing needs for the reproducible processing and analysis of large batches of multi-dimensional images through complex pipelines. We introduce BIP, a new open-source software for addressing these needs. The distinctive features of BIP is to be natively designed for batch and pipeline processing, relying on a unified, simple, and human-readable high-level syntax for processing single or multiple images and for specifying individual or combined operations. As a command-line tool, BIP also lends itself seamlessly to high-performance computing applications. BIP invests an original niche in the ecosystem of bioimage informatics software and should contribute to the development of reproducible research in bioimage processing and analysis. Availability: BIP is distributed as an open source software under the GNU General Public License, version 3. The source code is available at https://forge.inrae.fr/andreylab/bip and can also be found at the Software Heritage permalink https://archive.softwareheritage.org/swh:1:dir:dded82e71041943bdfdf7b4684c69dd742c67312. Executables for Linux and Windows are available from the BIP website at https://andreylab.versailles.inrae.fr/html/bip.html. The distribution also includes a detailed user manual (PDF and HTML formats). To enable users to identify the right operators for their tasks, the principles and effects of the operators are briefly exposed, and many illustrations are provided. The manual also includes a tutorial section, illustrating how to solve typical, commonly encountered problems in bioimage analysis. The material for the tutorials is publicly available at https://doi.org/10.57745/JIWVCE.

In multicellular organisms, cell division shapes tissue architecture, cell identity, and function. In walled organisms like plants, division plane orientation irreversibly defines tissue topology and is tightly regulated. While divisions often follow the shortest path enclosing cell volume, certain cells deviate, dividing perpendicular to the growth axis. Tissue-scale mechanical stress has been proposed to guide such orientation, but how these cues are integrated remains unclear. Here, we reexamine the role of the actin cytoskeleton in orienting cell division in Arabidopsis root epidermis. Combining cell biology, genetics, pharmacological treatments, 3D segmentation, and modeling, we show that actin is a central molecular actor required to establish cell division orientation against the geometrical rules, highlighting its role in integrating spatial information.

Meiosis is a cellular division, essential for sexual reproduction. During meiotic prophase, homologous chromosomes of maternal and paternal origin recognize each other and associate, a process that is facilitated by their rapid movements in the nucleoplasm. Meiotic prophase movements rely on the attachment of the chromosomes to the nuclear envelope and on cytoplasmic forces transmitted to the chromosomes through the nuclear envelope. In plants, the limited accessibility and fragility of male meiocytes inside the anthers make live imaging approaches and chromosome movement analysis technically challenging. Indeed, these movements are very rapid, and analyzing them requires finding the optimal acquisition conditions. Here, an efficient method to capture rapid chromosome movements during male meiotic prophase in Arabidopsis thaliana is described, in time-lapse movies, by following the movements of fluorescent-tagged centromeres. It is based on anther dissection and image acquisition using confocal laser scanning microscopy (CLSM), in 3D and over time. Acquiring meiotic z-stacks every 7 s allows sufficient resolution to reconstruct and analyze the trajectories of tagged centromeres. The extracted data is subsequently processed, enabling the quantitative analysis of these chromosomal movements. The development of models from this data is essential for understanding the mechanism of rapid chromosome movement, including the nature and intensity of the forces driving this process.

Meiosis is a cellular division, essential for sexual reproduction. During meiotic prophase, homologous chromosomes of maternal and paternal origin recognize each other and associate, a process that is facilitated by their rapid movements in the nucleoplasm. Meiotic prophase movements rely on the attachment of the chromosomes to the nuclear envelope and on cytoplasmic forces transmitted to the chromosomes through the nuclear envelope. In plants, the limited accessibility and fragility of male meiocytes inside the anthers make live imaging approaches and chromosome movement analysis technically challenging. Indeed, these movements are very rapid, and analyzing them requires finding the optimal acquisition conditions. Here, an efficient method to capture rapid chromosome movements during male meiotic prophase in Arabidopsis thaliana is described, in time-lapse movies, by following the movements of fluorescent-tagged centromeres. It is based on anther dissection and image acquisition using confocal laser scanning microscopy (CLSM), in 3D and over time. Acquiring meiotic z-stacks every 7 s allows sufficient resolution to reconstruct and analyze the trajectories of tagged centromeres. The extracted data is subsequently processed, enabling the quantitative analysis of these chromosomal movements. The development of models from this data is essential for understanding the mechanism of rapid chromosome movement, including the nature and intensity of the forces driving this process.

The positioning of new cellular walls during cell division plays a key role in shaping plant tissue organization. The influence of cell geometry on the positioning of division planes has been previously captured into various geometrical rules. Accordingly, linking cell shape to division orientation has relied on the comparison between observed division patterns and predictions under specific rules. The need to define a priori the tested rules is a fundamental limitation of this hypothesis-driven approach. As an alternative, we introduce a data-based approach to investigate the relation between cell geometry and division plane positioning, exploiting the ability of deep neural network to learn complex relationships across multidimensional spaces. Adopting an image-based cell representation, we show how division patterns can be learned and predicted from mother cell geometry using a UNet architecture modified to operate on cell masks. Using synthetic data and A. thaliana embryo cells, we evaluate the model performances on a wide range of diverse cell shapes and division patterns. We find that the trained model accounted for embryo division patterns that were previously irreconcilable under existing geometrical rules. Our work shows the potential of deep networks to understand cell division patterns and to generate new hypotheses on the control of cell division positioning.

Homologous pairing and recombination during meiosis are facilitated by rapid prophase movements (RPMs), which depend on chromosome attachment to the nuclear envelope (NE) and on cytoplasmic forces transmitted to the chromosomes through the NE, mediated by Linker of Nucleoskeleton and Cytoskeleton (LINC) complexes. In plants, only the NE-associated SUN-domain proteins SUN1 and SUN2 have been identified as components of the RPM process. Here we show that, during meiosis, SUN1 and SUN2 form a LINC complex with the KASH-domain protein SINE3, which recruits the meiosis-specific kinesin PSS1 to the NE. These proteins accumulate at telomere-binding sites in the NE, and their loss disrupts telomere attachment and bouquet formation and abolishes RPMs. These defects lead to defective synapsis and clustered crossovers, resulting in chromosome mis-segregation. Our results establish that the mechanism underlying RPMs is conserved in Arabidopsis thaliana, with RPMs primarily facilitating homologous recognition rather than preventing non-homologous interactions.

Summary: Posttranslational modification of histones during the cell cycle is a major process controlling many aspects of cell division. Among the variety of histone modifications, mitotic phosphorylation of histone H3 at serine 10 (H3S10ph) plays a crucial role, particularly in proper chromosome segregation. Here we aimed at precisely quantifying this phosphorylation dynamics during mitosis in plant cells in order to reveal molecular pathways involved in this process. We describe an analysis pipeline based on 3D image analysis that allows to semiautomatically quantify H3S10ph in mitotic Arabidopsis root cells. We also developed a new method for the compensation of signal attenuation in Z, based on measurement of objects of interest themselves. We show that this new attenuation correction method allows significant gains in accuracy and statistical power. Using this pipeline, we were able to reveal small H3S10ph differences between cells treated with hesperadin, an inhibitor of an H3S10ph kinase, or between Arabidopsis mutants affected in PP2A phosphatase activity. This tool opens new avenues to explore such regulatory pathways in plants, using the wealth of genetic materials available in Arabidopsis. It can also be applied to study other histone posttranslational modifications and more generally to any discrete 3D signals.

Summary: Posttranslational modification of histones during the cell cycle is a major process controlling many aspects of cell division. Among the variety of histone modifications, mitotic phosphorylation of histone H3 at serine 10 (H3S10ph) plays a crucial role, particularly in proper chromosome segregation. Here we aimed at precisely quantifying this phosphorylation dynamics during mitosis in plant cells, in order to reveal molecular pathways involved in this process. We describe an analysis pipeline based on 3D image analysis that allows to semi-automatically quantify H3S10 phosphorylation in mitotic Arabidopsis root cells. We also developed a new method for the compensation of signal attenuation in Z, based on measurement of objects of interest themselves. We show that this new attenuation correction method allows significant gains in accuracy and statistical power. Using this pipeline, we were able to reveal small H3S10ph differences between cells treated with hesperadin, an inhibitor of an H3S10ph kinase, or between Arabidopsis mutants affected in PP2A phosphatase activity. This tool opens new avenues to explore such regulation pathway in plants, using the wealth of genetic material available in Arabidopsis . It can also be applied to study other histone post-translational modifications, and more generally to any discrete 3D signals.