Using positional information to provide context for biological image analysis with MorphoGraphX 2.0.

Positional information is a central concept in developmental biology. In developing organs, positional information can be idealized as a local coordinate system that arises from morphogen gradients controlled by organizers at key locations. This offers a plausible mechanism for the integration of the molecular networks operating in individual cells into the spatially coordinated multicellular responses necessary for the organization of emergent forms. Understanding how positional cues guide morphogenesis requires the quantification of gene expression and growth dynamics in the context of their underlying coordinate systems. Here, we present recent advances in the MorphoGraphX software (Barbier de Reuille et al., 2015⁠) that implement a generalized framework to annotate developing organs with local coordinate systems. These coordinate systems introduce an organ-centric spatial context to microscopy data, allowing gene expression and growth to be quantified and compared in the context of the positional information thought to control them.

The annotation and analysis of complex 3D plant organs using 3DCoordX.

A fundamental question in biology concerns how molecular and cellular processes become integrated during morphogenesis. In plants, characterization of 3D digital representations of organs at single-cell resolution represents a promising approach to addressing this problem. A major challenge is to provide organ-centric spatial context to cells of an organ. We developed several general rules for the annotation of cell position and embodied them in 3DCoordX, a user-interactive computer toolbox implemented in the open-source software MorphoGraphX. 3DCoordX enables rapid spatial annotation of cells even in highly curved biological shapes. Using 3DCoordX, we analyzed cellular growth patterns in organs of several species. For example, the data indicated the presence of a basal cell proliferation zone in the ovule primordium of Arabidopsis (Arabidopsis thaliana). Proof-of-concept analyses suggested a preferential increase in cell length associated with neck elongation in the archegonium of Marchantia (Marchantia polymorpha) and variations in cell volume linked to central morphogenetic features of a trap of the carnivorous plant Utricularia (Utricularia gibba). Our work demonstrates the broad applicability of the developed strategies as they provide organ-centric spatial context to cellular features in plant organs of diverse shape complexity.

A digital 3D reference atlas reveals cellular growth patterns shaping the Arabidopsis ovule.

A fundamental question in biology is how morphogenesis integrates the multitude of processes that act at different scales, ranging from the molecular control of gene expression to cellular coordination in a tissue. Using machine-learning-based digital image analysis, we generated a three-dimensional atlas of ovule development in Arabidopsis thaliana, enabling the quantitative spatio-temporal analysis of cellular and gene expression patterns with cell and tissue resolution. We discovered novel morphological manifestations of ovule polarity, a new mode of cell layer formation, and previously unrecognized subepidermal cell populations that initiate ovule curvature. The data suggest an irregular cellular build-up of WUSCHEL expression in the primordium and new functions for INNER NO OUTER in restricting nucellar cell proliferation and the organization of the interior chalaza. Our work demonstrates the analytical power of a three-dimensional digital representation when studying the morphogenesis of an organ of complex architecture that eventually consists of 1900 cells.

Accurate and versatile 3D segmentation of plant tissues at cellular resolution.

Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on fixed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, acquisition settings even on non plant samples. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface.

Shaping the genome of plants.

Fertilization of an egg cell by more than one sperm cell can produce viable progeny in a flowering plant.

Protocol for rapid clearing and staining of fixed Arabidopsis ovules for improved imaging by confocal laser scanning microscopy.

BACKGROUND: A salient topic in developmental biology relates to the molecular and genetic mechanisms that underlie tissue morphogenesis. Modern quantitative approaches to this central question frequently involve digital cellular models of the organ or tissue under study. The ovules of the model species Arabidopsis thaliana have long been established as a model system for the study of organogenesis in plants. While ovule development in Arabidopsis can be followed by a variety of different imaging techniques, no experimental strategy presently exists that enables an easy and straightforward investigation of the morphology of internal tissues of the ovule with cellular resolution. RESULTS: We developed a protocol for rapid and robust confocal microscopy of fixed Arabidopsis ovules of all stages. The method combines clearing of fixed ovules in ClearSee solution with marking the cell outline using the cell wall stain SCRI Renaissance 2200 and the nuclei with the stain TO-PRO-3 iodide. We further improved the microscopy by employing a homogenous immersion system aimed at minimizing refractive index differences. The method allows complete inspection of the cellular architecture even deep within the ovule. Using the new protocol we were able to generate digital three-dimensional models of ovules of various stages. CONCLUSIONS: The protocol enables the quick and reproducible imaging of fixed Arabidopsis ovules of all developmental stages. From the imaging data three-dimensional digital ovule models with cellular resolution can be rapidly generated using image analysis software, for example MorphographX. Such digital models will provide the foundation for a future quantitative analysis of ovule morphogenesis in a model species.