Umeå University's logo

Defects in cell wall integrity (CWI) profoundly affect plant growth, although, underlying mechanisms are not well understood. We show that in Arabidopsis mur1 mutant, CWI defects from compromising dimerization of RG-II pectin, a key component of cell wall, attenuate the expression of auxin response factors ARF7-ARF19. As a result, polar auxin transport components are misexpressed, disrupting auxin response asymmetry, leading to defective apical hook development. Accordingly, mur1 hook defects are suppressed by enhancing ARF7 expression. In addition, expression of brassinosteroid biosynthesis genes is down-regulated in mur1 mutant, and supplementing brassinosteroid or enhancing brassinosteroid signaling suppresses mur1 hook defects. Intriguingly, brassinosteroid enhances RG-II dimerization, showing hormonal feedback to the cell wall. Our results thus reveal a previously unrecognized link between cell wall defects from reduced RG-II dimerization and growth regulation mediated via modulation of auxin-brassinosteroid pathways in early seedling development.

Formation of the apical hook in etiolated dicot seedlings results from differential growth in the hypocotyl apex and is tightly controlled by environmental cues and hormones, among which auxin and gibberellins (GAs) play an important role. Cell expansion is tightly regulated by the cell wall, but whether and how feedback from this structure contributes to hook development are still unclear. Here, we show that etiolated seedlings of the Arabidopsis (Arabidopsis thaliana) quasimodo2-1 (qua2) mutant, defective in pectin biosynthesis, display severe defects in apical hook formation and maintenance, accompanied by loss of asymmetric auxin maxima and differential cell expansion. Moreover, qua2 seedlings show reduced expression of HOOKLESS1 (HLS1) and PHYTOCHROME INTERACTING FACTOR4 (PIF4), which are positive regulators of hook formation. Treatment of wild-type seedlings with the cellulose inhibitor isoxaben (isx) also prevents hook development and represses HLS1 and PIF4 expression. Exogenous GAs, loss of DELLA proteins, or HLS1 overexpression partially restore hook development in qua2 and isx-treated seedlings. Interestingly, increased agar concentration in the medium restores, both in qua2 and isx-treated seedlings, hook formation, asymmetric auxin maxima, and PIF4 and HLS1 expression. Analyses of plants expressing a F & ouml;rster resonance energy transfer-based GA sensor indicate that isx reduces accumulation of GAs in the apical hook region in a turgor-dependent manner. Lack of the cell wall integrity sensor THESEUS 1, which modulates turgor loss point, restores hook formation in qua2 and isx-treated seedlings. We propose that turgor-dependent signals link changes in cell wall integrity to the PIF4-HLS1 signaling module to control differential cell elongation during hook formation. Loss of cell wall integrity suppresses gibberellic acid accumulation and HOOKLESS1 and PHYTOCHROME INTERACTING FACTOR4 expression, ultimately repressing apical hook formation in Arabidopsis.

Purpose: Wood comprises different cell types, such as fibers, tracheids and vessels, defining its properties. Studying cells’ shape, size, and arrangement in microscopy images is crucial for understanding wood characteristics. Typically, this involves macerating (soaking) samples in a solution to separate cells, then spreading them on slides for imaging with a microscope that covers a wide area, capturing thousands of cells. However, these cells often cluster and overlap in images, making the segmentation difficult and time-consuming using standard image-processing methods.

Results: In this work, we developed an automatic deep learning segmentation approach that utilizes the one-stage YOLOv8 model for fast and accurate segmentation and characterization of macerated fiber and vessel form aspen trees in microscopy images. The model can analyze 32,640 x 25,920 pixels images and demonstrate effective cell detection and segmentation, achieving a mAP0.5-0.95 of 78 %. To assess the model’s robustness, we examined fibers from a genetically modified tree line known for longer fibers. The outcomes were comparable to previous manual measurements. Additionally, we created a user-friendly web application for image analysis and provided the code for use on Google Colab.

Conclusion: By leveraging YOLOv8’s advances, this work provides a deep learning solution to enable efficient quantification and analysis of wood cells suitable for practical applications.

BACKGROUND: Plants can perceive and respond to mechanical signals. For instance, cortical microtubule (CMT) arrays usually reorganize following the predicted maximal tensile stress orientation at the cell and tissue level. While research in the last few years has started to uncover some of the mechanisms mediating these responses, much remains to be discovered, including in most cases the actual nature of the mechanosensors. Such discovery is hampered by the absence of adequate quantification tools that allow the accurate and sensitive detection of phenotypes, along with high throughput and automated handling of large datasets that can be generated with recent imaging devices.

RESULTS: Here we describe an image processing workflow specifically designed to quantify CMT arrays response to tensile stress in time-lapse datasets following an ablation in the epidermis - a simple and robust method to change mechanical stress pattern. Our Fiji-based workflow puts together several plugins and algorithms under the form of user-friendly macros that automate the analysis process and remove user bias in the quantification. One of the key aspects is also the implementation of a simple geometry-based proxy to estimate stress patterns around the ablation site and compare it with the actual CMT arrays orientation. Testing our workflow on well-established reporter lines and mutants revealed subtle differences in the response over time, as well as the possibility to uncouple the anisotropic and orientational response.

CONCLUSION: This new workflow opens the way to dissect with unprecedented detail the mechanisms controlling microtubule arrays re-organization, and potentially uncover the still largely elusive plant mechanosensors.