RESUMO
Arthropod herbivory poses a serious threat to crop yield, prompting plants to employ intricate defense mechanisms against pest feeding. The generalist pest two-spotted spider mite (Tetranychus urticae) inflicts rapid damage and remains challenging due to its broad target range. In this study, we explored the Arabidopsis (Arabidopsis thaliana) response to T. urticae infestation, revealing the induction of abscisic acid (ABA), a hormone typically associated with abiotic stress adaptation, and stomatal closure during water stress. Leveraging a FRET-based ABA biosensor (nlsABACUS2-400n), we observed elevated ABA levels in various leaf cell types post-mite feeding. While ABA's role in pest resistance or susceptibility has been debated, an ABA-deficient mutant exhibited increased mite infestation alongside intact canonical biotic stress signaling, indicating an independent function of ABA in mite defense. We established that ABA-triggered stomatal closure effectively hinders mite feeding and minimizes leaf cell damage through genetic and pharmacological interventions targeting ABA levels, ABA signaling, stomatal aperture, and density. This study underscores the critical interplay between biotic and abiotic stresses in plants, highlighting how the vulnerability to mite infestation arising from open stomata, crucial for transpiration and photosynthesis, reinforces the intricate relationship between these stress types.
RESUMO
Current live-cell imaging techniques make possible the observation of live events and the acquisition of large datasets to characterize the different parameters of the visualized events. They provide new insights into the dynamics of biological processes with unprecedented spatial and temporal resolutions. Here we describe the implementation and application of a new tool called TrackAnalyzer, accessible from Fiji and ImageJ. Our tool allows running semi-automated single-particle tracking (SPT) and subsequent motion classification, as well as quantitative analysis of diffusion and intensity for selected tracks relying on the graphical user interface (GUI) for large sets of temporal images (X-Y-T or X-Y-C-T dimensions). TrackAnalyzer also allows 3D visualization of the results as overlays of either spots, cells or end-tracks over time, along with corresponding feature extraction and further classification according to user criteria. Our analysis workflow automates the following steps: (1) spot or cell detection and filtering, (2) construction of tracks, (3) track classification and analysis (diffusion and chemotaxis), and (4) detailed analysis and visualization of all the outputs along the pipeline. All these analyses are automated and can be run in batch mode for a set of similar acquisitions.
RESUMO
Fluorescence microscopy techniques have experienced a substantial increase in the visualization and analysis of many biological processes in life science. We describe a semiautomated and versatile tool called Cell-TypeAnalyzer to avoid the time-consuming and biased manual classification of cells according to cell types. It consists of an open-source plugin for Fiji or ImageJ to detect and classify cells in 2D images. Our workflow consists of (a) image preprocessing actions, data spatial calibration, and region of interest for analysis; (b) segmentation to isolate cells from background (optionally including user-defined preprocessing steps helping the identification of cells); (c) extraction of features from each cell; (d) filters to select relevant cells; (e) definition of specific criteria to be included in the different cell types; (f) cell classification; and (g) flexible analysis of the results. Our software provides a modular and flexible strategy to perform cell classification through a wizard-like graphical user interface in which the user is intuitively guided through each step of the analysis. This procedure may be applied in batch mode to multiple microscopy files. Once the analysis is set up, it can be automatically and efficiently performed on many images. The plugin does not require any programming skill and can analyze cells in many different acquisition setups.
