Nf-Root: A Best-Practice Pipeline for Deep-Learning-Based Analysis of Apoplastic pH in Microscopy Images of Developmental Zones in Plant Root Tissue.

Abstract

Hormonal mechanisms associated with cell elongation play a vital role in the development and growth of plants. Here, we report Nextflow-root (nf-root), a novel best-practice pipeline for deep-learning-based analysis of fluorescence microscopy images of plant root tissue from A. thaliana. This bioinformatics pipeline performs automatic identification of developmental zones in root tissue images. This also includes apoplastic pH measurements, which is useful for modeling hormone signaling and cell physiological responses. We show that this nf-core standard-based pipeline successfully automates tissue zone segmentation and is both high-throughput and highly reproducible. In short, a deep-learning module deploys deterministically trained convolutional neural network models and augments the segmentation predictions with measures of prediction uncertainty and model interpretability, while aiming to facilitate result interpretation and verification by experienced plant biologists. We observed a high statistical similarity between the manually generated results and the output of the nf-root.

Plant phenotyping relevance

植物根組織の発達ゾーンを画像から自動抽出し、アポプラストpHを測定する再現可能な深層学習パイプラインを開発・検証しており、植物表現型取得が中心である。

Code and data availability

The paper publicly releases the PHDFM fluorescence microscopy image dataset, a test dataset, the trained U-Net^2 segmentation model, the nf-root Nextflow pipeline, the segmentation training module, and the prediction package implementing uncertainty/interpretability, all with explicit availability statements and Zenodo