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## Section: New Results

### Signaling and transport for tissue patterning

Participants : Romain Azaïs, Guillaume Cerutti, Christophe Godin, Bruno Leggio, Jonathan Legrand, Teva Vernoux [External Collaborator] .

• Related Research Axes: RA1 (Representations of forms in silico) & RA2 (Data-driven models)

• Related Key Modeling Challenges: KMC3 (Realistic integrated digital models)

One central mechanism in the shaping of biological forms is the definition of regions with different genetic identities or physiological properties through bio-chemical processes operating at cellular level. Such patterning of the tissue is often controlled by the action of molecular signals for which active or passive transport mechanisms determine the spatial precision of the targeting. The shoot apical meristem (SAM) of flowering plants is a remarkable example of such finely controlled system where the dynamic interplay between the hormone auxin and the polarization of efflux carriers PIN1 govern the rhythmic patterning of organs, and the consequent emergence of phyllotaxis.

Using Arabidopsis thaliana as a model system, we develop an integrated view of the meristem as a self-organizing dynamical form by reconstructing the dynamics of physiological processes from living tissues, and by proposing computational models integrating transport and signaling to study tissue patterning in silico.

Automatic quantification of auxin transport polarities. Time-lapse imaging of living SAM tissues marked with various fluorescent proteins allows monitoring the dynamics of cell-level molecular processes. Using a co-visualization of functional fluorescent auxin transporter (PIN1-GFP) with a dye staining of cell walls with propidium iodide (PI), we developed an original method to quantify in 3D the polarization of auxin transport for every anticlinal wall of the first layer of cells in confocal images. The developed method [13] was thoroughly evaluated against super-resolution acquisitions of the same tissue obtained using radial fluctuations (SRRF), and show to provide highly consistent results (less than 10% incorrect polarities, 80% of cells with a polarity vector error lesser than 30${}^{\circ }$). The digitally reconstructed networks evidenced an overall stable convergence of PIN1 polarities towards the center of the meristem, with a local convergence and divergence pattern that could explain the dynamics of auxin distributions in the meristem [19].

Landmark-based registration for the averaging of meristem patterning. To perform statistics of meristem patterning at the scale of a population, we developed a series of tools to compute a rigid 3D transformation that registers any individual meristem into a common cylindrical reference frame in which point-wise comparison is meaningful. The orignal method relies on the identification of biological landmarks (apex and main symmetry axis of the meristematic dome, position of the lastly emerged organ primordium and direction of the phyllotactic spiral) to compute this transform. These landmarks can be extracted from image acquisitions of meristems carrying the right fluorescent bio-markers (CLV3 central zone marker for the apex, DIIV auxin bio-sensor for the organ primordia) using an original method that relies on the computation of 2D continuous maps of epidermal signal from discrete point clouds. The use of this registration method allowed to evidence key features of the transcriptional response of mersitematic cells to auxin [19].

In a second time, we aim to generalize the method to images without specific bio-markers, using only the geometry of the tissue to identify the relevant landmarks. To do so, machine learning approaches making use of the data processed for [19] are being developed and evaluated. This new landmark-based registration method would drastically improve the ability of comparing different individual meristems, open the way to spatial statistics over of multiple genetic and molecular signals, and contribute to an integrated tissue-level view of meristem patterning.

Computational models of integrated transport and signaling. Guided by new discoveries on auxin patterning dynamics in the shoot apical meristem (SAM) of A. thaliana, we developed a theoretical model of active and passive auxin transport. This model, built on existing view of auxin active transport [30], [31], naturally integrates the role of deeper cellular layers in the SAM and the mutual feedbacks between different components of the auxin-transport machinery. Through numerical simulation, the consequences of competing theories on PIN polarisation mechanism on auxin dynamics were explored. These results will serve, in quantitative comparisons with in vivo observation, to validate hypotheses on molecular mechanisms of auxin transport and to provide information on the role of memory effects and information fluxes during patterning.

These works were part of the BioSensors HFSP project and are carried out in the Phyllo ENS-Lyon project. These works gave rise to a journal article which is currently under review and have been partly presented at the International Worskhop on Image Analysis Methods for the Plant Sciences in Bron in July 2019.