Parallel, Continuous Monitoring and Quantification of Programmed Cell Death in Plant Tissue

Abstract

Abstract Accurate quantification of hypersensitive response (HR) programmed cell death is imperative for understanding plant defense mechanisms and developing disease‐resistant crop varieties. Here, a phenotyping platform for rapid, continuous‐time, and quantitative assessment of HR is demonstrated: Parallel Automated Spectroscopy Tool for Electrolyte Leakage (PASTEL). Compared to traditional HR assays, PASTEL significantly improves temporal resolution and has high sensitivity, facilitating detection of microscopic levels of cell death. Validation is performed by transiently expressing the effector protein AVRblb2 in transgenic Nicotiana benthamiana (expressing the corresponding resistance protein Rpi‐blb2) to reliably induce HR. Detection of cell death is achieved at microscopic intensities, where leaf tissue appears healthy to the naked eye one week after infiltration. PASTEL produces large amounts of frequency domain impedance data captured continuously. This data is used to develop supervised machine‐learning (ML) models for classification of HR. Input data (inclusive of the entire tested concentration range) is classified as HR‐positive or negative with 84.1% mean accuracy (F1 score = 0.75) at 1 h and with 87.8% mean accuracy (F1 score = 0.81) at 22 h. With PASTEL and the ML models produced in this work, it is possible to phenotype disease resistance in plants in hours instead of days to weeks.

Plant phenotyping relevance

植物組織のプログラム細胞死・過敏感反応を電気インピーダンスで連続定量するフェノタイピング基盤を開発し、機械学習分類も検証しているため、方法が研究の中心である。

Code and data availability

The paper's impedance/phenotyping data and ML models are not publicly deposited; the Data Availability Statement says they are available from the corresponding author upon reasonable request. The only URLs present are the CC-BY license and the AD5933 commercial datasheet, neither of which is a paper-specific asset.

No evidence-backed public reproduction asset is currently recorded.