automated measurement of stomatal response in arabidopsis thaliana in response to pseudomonas syringae pv. tomato dc3000 infection

Automatiserad mätning av klyvöppningar vid bakterieinvasion i Arabidopsis

Stomata are microscopic pores surrounded by a pair of guard cells on the surface of leaves and other aerial parts of plants. Under ever-changing environments, regulation of the stomatal aperture is central for plants to control the carbon dioxide uptake required for photosynthesis at the expense of water loss via transpiration. Thus, quantification of the stomatal aperture has been instrumental to understanding plant environmental adaptation. However, quantifying the stomatal aperture is inherently time-consuming and cumbersome as it requires human labor to spot and measure stomatal pores in a leaf image captured by a microscope. To circumvent these limitations, various methods have been developed to facilitate the quantification of stomatal aperture in Arabidopsis thaliana, a model plant extensively used to study stomatal biology1,2,3,4,5,6. For instance, a porometer can be used to measure transpiration rate as a metric of stomatal conductance. However, this method does not provide direct information on the stomatal number and aperture that determine stomatal conductance. Some studies have used confocal microscopy techniques highlighting stomatal pores using a fluorescent actin marker, a fluorescent dye, or cell wall autofluorescence1,2,3,4,5. While these approaches facilitate the detection of stomata, the cost of both operating a confocal microscopy facility and preparing microscopy samples can be an obstacle to routine application. In a ground-breaking work by Sai et al., a deep neural network model was developed to automatically measure stomatal aperture from bright-field microscopic images of A. thaliana epidermal peels6. Yet, this innovation does not exempt researchers from the task of preparing an epidermal peel for microscopic observation. Recently, this obstacle was overcome by developing a portable imaging device that can observe stomata by pinching a leaf of A. thaliana, together with a deep learning-based image analysis pipeline that automatically measures stomatal aperture from leaf images captured by the device7.
Stomata contribute to plant innate immunity against bacterial pathogens. The key to this immune response is stomatal closure that restricts bacterial entry through the microscopic pore into the leaf interior, where bacterial pathogens proliferate and cause diseases8. Stomatal closure is induced upon recognition of microbe-associated molecular patterns (MAMPs), immunogenic molecules that are often common to a class of microbes, by plasma membrane-localized pattern recognition receptors (PRRs)9. A 22 amino acid epitope of bacterial flagellin known as flg22 is a typical MAMP that induces stomatal closure through its recognition by the PRR FLS210. As a countermeasure, bacterial pathogens such as Pseudomonas syringae pv. tomato DC3000 (Pto) and Xanthomonas campestris pv. vesicatoria have evolved virulence mechanisms to reopen stomata9,11,12. These stomatal responses to bacterial pathogens have been conventionally analyzed in assays in which either leaf epidermal peels, leaf discs, or detached leaves are floated on bacterial suspension, and then stomata are observed under a microscope followed by manual measurement of stomatal aperture. However, these assays are cumbersome and may not reflect stomatal responses to natural bacterial invasion that occur in a leaf attached to the plant.
Here, a simple method is presented to investigate stomatal closure and reopening during Pto invasion under the condition that closely mimics the natural plant-bacteria interaction. This method leverages the portable imaging device for direct observation of A. thaliana stomata on a leaf attached to the plant inoculated with Pto, together with the image analysis pipeline for automated measurement of stomatal aperture.

Författare i fokus: Främja forskning om klyvöppningar med automatiserad bländarmätning

Direkt observation och automatiserad mätning av klyvöppningar på Pseudomonas syringae pv. tomat DC3000 i Arabidopsis thaliana

Plantal stomata play central roles in adaptation to stress conditions, so we need to measure stomata aperture to understand how plants respond to biotic and abiotic stresses. However, stomata aperture measurement is time-consuming and cumbersome. To measure stomata aperture, we peel the epidermis and observe stomata under a microscope.Then we manually measure stomata aperture, which takes a long time even for very experienced researchers. We challenged these barriers and developed a tool and a technique that can automatically measure stomata aperture in intact Arabidopsis leaves. So we strongly believe that stomata measuring device and deep-learning algorithm that we developed will facilitate functional analysis of stomata responses to various biotic and abiotic stresses because these technical advances significantly reduce time and human labor required for stomata aperture measurement.

Automatiserad mätning av klyvöppningar hos Arabidopsis thaliana som svar på Pseudomonas syringae pv. tomat DC3000-infektion

Börja med att ansluta en bärbar klyvöppning till en dator utrustad med programvara för bildinsamling. Använd ett papper för att försiktigt ta bort vattendroppar från de sprutinokulerade bladen från Arabidopsis thaliana-växten. Öppna nu den övre luckan på enheten, placera bladet på scenen och stäng locket.Justera fokus med justerskruven och klicka på Spara bild på datorn för att ta bilden. För att utföra automatisk mätning av klyvöppningen, öppna Google Colaboratory-anteckningsboken. Klicka på Arkiv följt av Spara en kopia på Drive för att göra en lokal kopia i Google Drive.När en ny flik öppnas stänger du fliken för den ursprungliga anteckningsboken. Tryck på knappen Kör under avsnittet Miljöinställningar i anteckningsboken för att importera nödvändiga bibliotek utan att fälla ut cellblocken. I avsnittet Kataloginställningar skapar du tre mappar i Google Drive för analys.Flytta de inhämtade bilderna till mappen example_result och kategorisera dem efter behandling eller prov för slutlig grafgenerering. Klicka på Ladda ned tränade modeller för att ladda ned ONNX-filerna för de tränade modellerna från Zenodo. Placera de nedladdade filerna i modellkatalogen.Kör inferensen och mätningen av klyvöppningen i anteckningsboken för att kvantifiera klyvöppningen från enskilda bilder. De resulterande bilderna med överlagrad inferens och example_result. CSV-filen exporteras till inference_results-katalogen.Kör avsnittet Grafgenerering för att skapa en graf om klyvöppningarnas bländarförhållande. Exportera det här diagrammet till inference_results katalogen. En minskning av klyvöppningen hos PTO-inokulerade plantor jämfört med låtsasinokulerade plantor observerades en timme efter inokuleringen.Tre timmar efter infektionen var klyvöppningen i både de Pto-inokulerade plantorna och de låtsasinokulerade plantorna praktiskt taget desamma. Automatiserad mätning av klyvöppningen tog cirka fem sekunder att bearbeta en bild, vilket resulterade i en minskning av mättiden med 95 %.

Research

JoVE Journal

Immunology and Infection

Automated Measurement of Stomatal Response in Arabidopsis thaliana in Response to Pseudomonas syringae pv. tomato DC3000 Infection

Direct Observation and Automated Measurement of Stomatal Responses to Pseudomonas syringae pv. tomato DC3000 in Arabidopsis thaliana

Stomata are microscopic pores found in the plant leaf epidermis. Regulation of stomatal aperture is pivotal not only for balancing carbon dioxide uptake ...

Induction of Pseudomonas syringae pv. tomato DC3000 Infection in Arabidopsis thaliana for Automated Stomatal Response Measurement

Induktion av Pseudomonas syringae pv. tomat DC3000-infektion i Arabidopsis thaliana för automatiserad mätning av klyvöppningar