Harnessing Deep Learning to Analyze Cryptic Morphological Variability of Marchantia polymorpha

Author:

Tomizawa Yoko1,Minamino Naoki2,Shimokawa Eita3,Kawamura Shogo3ORCID,Komatsu Aino3ORCID,Hiwatashi Takuma2,Nishihama Ryuichi34ORCID,Ueda Takashi25ORCID,Kohchi Takayuki3ORCID,Kondo Yohei167ORCID

Affiliation:

1. Quantitative Biology Research Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences , 5-1 Higashiyama, Myodaiji-cho, Okazak, Aichii, 444-8787 Japan

2. Division of Cellular Dynamics, National Institute for Basic Biology , Nishigonaka 38, Myodaiji, Okazaki, Aichi, 444-8585 Japan

3. Graduate School of Biostudies, Kyoto University , Kitashirakawa-Oiwakecho, Sakyo, Kyoto, 606-8502 Japan

4. Department of Applied Biological Science, Faculty of Science and Technology, Tokyo University of Science , 2641 Yamazaki, Noda, Chiba, 278-8510 Japan

5. Department of Basic Biology, SOKENDAI (The Graduate University for Advanced Studies) , Nishigonaka 38, Myodaiji, Okazaki, Aichi, 444-8585 Japan

6. Division of Quantitative Biology, National Institute for Basic Biology, National Institutes of Natural Sciences , 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi, 444-8787 Japan

7. Department of Basic Biology, School of Life Science, SOKENDAI (The Graduate University for Advanced Studies) , 5-1 Higashiyama, Myodaiji-cho, Okazaki, Aichi, 444-8787 Japan

Abstract

Abstract Characterizing phenotypes is a fundamental aspect of biological sciences, although it can be challenging due to various factors. For instance, the liverwort Marchantia polymorpha is a model system for plant biology and exhibits morphological variability, making it difficult to identify and quantify distinct phenotypic features using objective measures. To address this issue, we utilized a deep-learning-based image classifier that can handle plant images directly without manual extraction of phenotypic features and analyzed pictures of M. polymorpha. This dioicous plant species exhibits morphological differences between male and female wild accessions at an early stage of gemmaling growth, although it remains elusive whether the differences are attributable to sex chromosomes. To isolate the effects of sex chromosomes from autosomal polymorphisms, we established a male and female set of recombinant inbred lines (RILs) from a set of male and female wild accessions. We then trained deep learning models to classify the sexes of the RILs and the wild accessions. Our results showed that the trained classifiers accurately classified male and female gemmalings of wild accessions in the first week of growth, confirming the intuition of researchers in a reproducible and objective manner. In contrast, the RILs were less distinguishable, indicating that the differences between the parental wild accessions arose from autosomal variations. Furthermore, we validated our trained models by an ‘eXplainable AI’ technique that highlights image regions relevant to the classification. Our findings demonstrate that the classifier-based approach provides a powerful tool for analyzing plant species that lack standardized phenotyping metrics.

Funder

Japan Society for the Promotion of Science

Publisher

Oxford University Press (OUP)

Subject

Cell Biology,Plant Science,Physiology,General Medicine

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