Rapid non-destructive method to phenotype stomatal traits

Abstract

Background Stomata are tiny pores on the leaf surface that are central to gas exchange. Stomatal number, size and aperture are key determinants of plant transpiration and photosynthesis, and variation in these traits can affect plant growth and productivity. Current methods to screen for stomatal phenotypes are tedious and not high throughput. This impedes research on stomatal biology and hinders efforts to develop resilient crops with optimised stomatal patterning. We have developed a rapid non-destructive method to phenotype stomatal traits in three crop species: wheat, rice and tomato. Results The method consists of two steps. The first is the non-destructive capture of images of the leaf surface from plants in their growing environment using a handheld microscope; a process that only takes a few seconds compared to minutes for other methods. The second is to analyse stomatal features using a machine learning model that automatically detects, counts and measures stomatal number, size and aperture. The accuracy of the machine learning model in detecting stomata ranged from 88 to 99%, depending on the species, with a high correlation between measures of number, size and aperture using the machine learning models and by measuring them manually. The rapid method was applied to quickly identify contrasting stomatal phenotypes. Conclusions We developed a method that combines rapid non-destructive imaging of leaf surfaces with automated image analysis. The method provides accurate data on stomatal features while significantly reducing time for data acquisition and analysis. It can be readily used to phenotype stomata in large populations in the field and in controlled environments.

Characterization of carbon fluxes, stock and nutrients in the sacred forest groves and invasive vegetation stands within the human dominated landscapes of a tropical semi-arid region

“…Clear nail polish was applied on the adaxial and abaxial sides of the mature leaf from the selected eight tree species; later dry nail polish layer was peeled off using clear tape and pasted on a clear sterile microscopic slide. Mounted specimens are observed under a light microscope at 40 × magnification using the software Image View; microscopic images of the stomata were captured using APTINA trinocular camera 143 .…”

REGULATOR OF FLOWERING AND STRESS manipulates stomatal density and size in Brachypodium

“…The 10‐day water deprivation treatment was conducted as aforementioned. To image the stomatal features on the abaxial surface, leaves were processed with the nail polish method (Pathoumthong et al, 2023). Stomata on leaf imprints were observed using a light microscope (OLYMPUS IXplore compound microscope) with 10× or 40× objectives.…”

Natural variation in the pattern‐triggered immunity response in plants: Investigations, implications and applications

“…When measuring stomatal aperture size, it is important to quickly fix the stomata at the time point desired, which can be achieved by several methods (Wu & Zhao, 2017 ). Stomatal closure has also been measured in live plants using handheld microscopes and indirectly via its effect on leaf temperature using thermal imaging, both of which allow higher‐throughput phenotyping than traditional microscopy (Ceulemans et al., 1995 ; Merlot et al., 2002 ; Pathoumthong et al., 2023 ). Using this approach to measure PTI would require applying a PAMP directly to the live plant, which may not always be feasible.…”