Multi-dimensional bioimage analyses with machine learning to reveal the dynamics of membrane vesicles and microtubules in stomatal guard cells
| Project/Area Number |
20H03289
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Review Section |
Basic Section 44040:Morphology and anatomical structure-related
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| Research Institution | Kumamoto University |
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Principal Investigator |
Higaki Takumi 熊本大学, 大学院先端科学研究部(理), 教授 (90578486)
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥16,120,000 (Direct Cost: ¥12,400,000、Indirect Cost: ¥3,720,000)
Fiscal Year 2022: ¥5,330,000 (Direct Cost: ¥4,100,000、Indirect Cost: ¥1,230,000)
Fiscal Year 2021: ¥5,200,000 (Direct Cost: ¥4,000,000、Indirect Cost: ¥1,200,000)
Fiscal Year 2020: ¥5,590,000 (Direct Cost: ¥4,300,000、Indirect Cost: ¥1,290,000)
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| Keywords | 気孔開閉運動 / 微小管 / 膜小胞 / イメージング / 画像解析 |
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| Outline of Research at the Start |
本研究では、多次元ライブセルイメージングと機械学習（いわゆる人工知能（AI））を活用した画像解析技術に基づいて、植物の気孔開閉運動を司るH+-ATPaseの細胞膜への局在化機構の全貌解明を目指す。気孔開口を駆動するH+-ATPase AHA1の細胞膜輸送に必須の因子であるPATROL1およびその相互作用因子群に注目し、気孔開閉運動におけるPATROL1関連因子群の三次元分布および共局在性を描出する。取得画像群から膜小胞と微小管を高精度に検出・評価する機械学習技術を活用した次世代型の定量的細胞生物学研究を展開し、気孔応答を制御するPATROL1依存的なAHA1の細胞膜輸送機構を明らかにする。
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| Outline of Final Research Achievements |
In this study, we aimed to elucidate the mechanism of H+-ATPase plasma membrane delivery through next-generation quantitative cell biology research by leveraging AI technology to dramatically reduce the human cost associated with image analysis of multidimensional live cell imaging, a bottleneck until now. Specifically, we (1) explored the application of image transformations using deep learning for segmentation, an indispensable task for quantitatively evaluating the cytoskeletal structure; (2) investigated the improvement of phototoxicity and time resolution through the enhancement of microscopic image quality by deep learning; (3) automated the classification and analysis of cytoskeletal patterns through machine learning; and (4) using these technologies, we conducted dynamic analysis related to drug resistance of membrane vesicles and superficial microtubules that control stomatal movement.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究では、AI技術を利用して植物細胞生物学研究を推進する方法を実践的に検討した。具体的には、細胞骨格構造の定量評価を改善するセグメンテーション手法、顕微鏡画像の画質改善を目指した深層学習モデルの開発、細胞骨格パターンの自動分類と分析など、研究者の分析作業を省力化するだけでなく、従来よりもより健全な状況の細胞をより詳しく調べることのできる技術の開発に取り組んだ。さらに、これらの手法を用いて植物の孔辺細胞に焦点を当て、気孔の環境応答性を担うH+-ATPase細胞膜輸送機構の解明を目指して実践的な研究を行った。このAIを活用した手法は、生物学の幅広い分野での利用が期待される。
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Report
(4 results)
Research Products
(37 results)
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[Journal Article] Ab-GALFA, A bioassay for insect gall formation using the model plant Arabidopsis thaliana2023
Author(s)
Tomoko Hirano , Ayaka Okamoto , Yoshihisa Oda , Tomoaki Sakamoto , Seiji Takeda , Takakazu Matsuura , Yoko Ikeda , Takumi Higaki , Seisuke Kimura , Masa H Sato
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Journal Title
Scientific Reports
Volume: 13
Issue: 1
Pages: 2554-2554
DOI
Related Report
Peer Reviewed / Open Access
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[Journal Article] Tracking metabolites at single-cell resolution reveals metabolic dynamics during plant mitosis.2022
Author(s)
Okubo-Kurihara E, Ali A, Hiramoto M, Kurihara Y, Abouleila Y, Abdelazem EM, Kawai T, Makita Y, Kawashima M, Esaki T, Shimada H, Mori T, Hirai MY, Higaki T, Hasezawa S, Shimizu Y, Masujima T, Matsui M
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Journal Title
Plant Physiol
Volume: 189
Issue: 2
Pages: 459-464
DOI
Related Report
Peer Reviewed / Open Access
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[Journal Article] Discovery, characterization and functional improvement of kumamonamide as a novel plant growth inhibitor that disturbs plant microtubules.2021
Author(s)
Ishida T, Yoshimura H, Takekawa M, Higaki T, Ideue T, Hatano M, Igarashi M, Tani T, Sawa S, Ishikawa H.
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Journal Title
Sci Rep
Volume: 23
Issue: 1
Pages: 6077-6077
DOI
Related Report
Peer Reviewed / Open Access
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