Plantorganelle Hunter is an effective deep-learning-based method for plant organelle phenotyping in electron microscopy

Xuping Feng, Zeyu Yu, Hui Fang, Hangjin Jiang, Guofeng Yang, Liting Chen, Xinran Zhou, Bing Hu, Chun Qin, Gang Hu, Guipei Xing, Boxi Zhao, Yongqiang Shi, Jiansheng Guo, Feng Liu, Bo Han, Bernd Zechmann, Yong He*, Feng Liu*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

Abstract

Accurate delineation of plant cell organelles from electron microscope images is essential for understanding subcellular behaviour and function. Here we develop a deep-learning pipeline, called the organelle segmentation network (OrgSegNet), for pixel-wise segmentation to identify chloroplasts, mitochondria, nuclei and vacuoles. OrgSegNet was evaluated on a large manually annotated dataset collected from 19 plant species and achieved state-of-the-art segmentation performance. We defined three digital traits (shape complexity, electron density and cross-sectional area) to track the quantitative features of individual organelles in 2D images and released an open-source web tool called Plantorganelle Hunter for quantitatively profiling subcellular morphology. In addition, the automatic segmentation method was successfully applied to a serial-sectioning scanning microscope technique to create a 3D cell model that offers unique views of the morphology and distribution of these organelles. The functionalities of Plantorganelle Hunter can be easily operated, which will increase efficiency and productivity for the plant science community, and enhance understanding of subcellular biology.

Original languageEnglish
Pages (from-to)1760-1775
Number of pages16
JournalNature Plants
Volume9
Issue number10
DOIs
Publication statusPublished - Oct 2023

Scopus Subject Areas

  • Plant Science

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