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

Medição Automatizada da Resposta Estomática à Invasão Bacteriana em 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.

Autor em destaque: Avançando na pesquisa estomática com medição automatizada de abertura

Observação Direta e Mensuração Automatizada das Respostas Estomáticas a Pseudomonas syringae pv. tomate DC3000 em 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.

Medida Automatizada da Resposta Estomática em Arabidopsis thaliana em Resposta a Pseudomonas syringae pv. tomate DC3000 infecção

Para começar, conecte um dispositivo portátil de imagem estomática a um computador equipado com software de aquisição de imagens. Use um pedaço de papel para remover cuidadosamente as gotículas de água das folhas inoculadas de Pseudomonas seringas da planta Arabidopsis thaliana. Agora abra a tampa superior do dispositivo, coloque a folha no palco e feche a tampa.Ajuste o foco usando o parafuso do ajustador e clique em Salvar imagem no computador para capturar a imagem. Para realizar a medição automatizada da abertura estomática, abra o notebook Google Colaborator. Clique em Arquivo seguido de Salvar uma cópia no drive para fazer uma cópia local no Google Drive.Quando uma nova guia for aberta, feche a guia do bloco de anotações original. Pressione o botão Executar na seção Configuração ambiental no bloco de anotações para importar as bibliotecas necessárias sem desdobrar os blocos de células. Na seção Configurações do diretório, crie três pastas no Google Drive para análise.Mova as imagens adquiridas para a pasta example_result, categorizando-as por tratamento ou amostra para geração final do gráfico. Clique em Baixar modelos treinados para baixar os arquivos ONNX dos modelos treinados da Zenodo. Coloque os arquivos baixados no diretório do modelo.Execute a inferência e a medição da parte da abertura estomática do notebook para quantificar a abertura estomática a partir de imagens individuais. As imagens resultantes com inferência sobreposta e o example_result. O arquivo CSV será exportado para o diretório inference_results.Execute a seção Geração de gráfico para criar um gráfico sobre a razão de abertura estomática. Exporte este gráfico para o diretório inference_results. Uma diminuição na abertura estomática em plantas inoculadas com Pto em comparação com plantas inoculadas simuladas foi observada uma hora após a inoculação.Três horas após a infecção, a abertura estomática nas plantas inoculadas com Pto e nas plantas inoculadas simuladas eram praticamente as mesmas. A medição automatizada da abertura estomática levou aproximadamente cinco segundos para processar uma imagem, resultando em uma redução de 95% no tempo de medição.

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 ...

Imunologia e Infecção

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

Indução de Pseudomonas syringae pv. tomate Infecção por DC3000 em Arabidopsis thaliana para Medição Automatizada da Resposta Estomática