Biology subjects

Tofanelli, R.

Publications and source records attributed to Tofanelli, R..

4 recordsLinked to original sources

bioRxiv2021

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. It enables rapid spatial annotation of cells even in highly curved biological shapes. With the help of 3DCoordX we obtained new insight by analyzing 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 thaliana. Proof-of-concept analyses suggested a preferential increase in cell length associated with neck elongation in the archegonium of Marchantia polymorpha and variations in cell volume linked to central morphogenetic features of a trap of the carnivorous plant 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.

plant biology↗

bioRxiv2021

MorphoGraphX 2.0: Providing context for biological image analysis with positional information

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. eLife 2015;4:e05864) 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.

developmental biology↗

bioRxiv2020

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 distinct processes that act at different scales, ranging from the molecular control of gene expression to cellular coordination in a tissue. Investigating morphogenesis of complex organs strongly benefits from three-dimensional representations of the organ under study. Here, we present a digital analysis of ovule development from Arabidopsis thaliana as a paradigm for a complex morphogenetic process. Using machine-learning-based image analysis we generated a three-dimensional atlas of ovule development with cellular resolution. It allows quantitative stage- and tissue-specific analysis of cellular patterns. Exploiting a fluorescent reporter enabled precise spatial determination of gene expression patterns, revealing subepidermal expression of WUSCHEL. Underlying the power of our approach, we found that primordium outgrowth progresses evenly, discovered a novel mode of forming a new cell layer, and detected a new function of INNER NO OUTER in restricting cell proliferation in the nucellus. Moreover, we identified two distinct subepidermal cell populations that make crucial contributions to ovule curvature. Our work demonstrates the expedience of a three-dimensional digital representation when studying the morphogenesis of an organ of complex cellular architecture and shape that eventually consists of 1,900 cells.

plant biology↗

bioRxiv2020

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, and acquisition settings. 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.

plant biology↗