DeepD&Cchl: an AI tool for automated 3D single-cell chloroplast detection, counting, and cell type clustering
Chloroplast density in cells varies among different types of cells and plants. In current single-cell spatiotemporal analysis, the automatic detection and quantification of chloroplasts at the single-cell level is crucial. We developed DeepD&Cchl (Deep-learning-based Detecting-and-Counting-c...
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| Format: | Article |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1513953/full |
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| author | Qun Su Le Liu Zhengsheng Hu Tao Wang Huaying Wang Huaying Wang Qiuqi Guo Xinyi Liao Yan Sha Feng Li Zhao Dong Zhao Dong Shaokai Yang Shaokai Yang Ningjing Liu Qiong Zhao Qiong Zhao Qiong Zhao |
| author_facet | Qun Su Le Liu Zhengsheng Hu Tao Wang Huaying Wang Huaying Wang Qiuqi Guo Xinyi Liao Yan Sha Feng Li Zhao Dong Zhao Dong Shaokai Yang Shaokai Yang Ningjing Liu Qiong Zhao Qiong Zhao Qiong Zhao |
| author_sort | Qun Su |
| collection | DOAJ |
| description | Chloroplast density in cells varies among different types of cells and plants. In current single-cell spatiotemporal analysis, the automatic detection and quantification of chloroplasts at the single-cell level is crucial. We developed DeepD&Cchl (Deep-learning-based Detecting-and-Counting-chloroplasts), an AI tool for single-cell chloroplast detection and cell-type clustering. It utilizes You-Only-Look-Once (YOLO), a real-time detection algorithm, for accurate and efficient performance. DeepD&Cchl has been proved to identify chloroplasts in plant cells across various imaging types, including light microscopy, electron microscopy, and fluorescence microscopy. Integrated with an Intersection Over Union (IOU) module, DeepD&Cchl precisely counts chloroplasts in single- or multi-layered images, while eliminating double-counting errors. Furthermore, when combined with Cellpose, a single-cell segmentation tool, DeepD&Cchl enhances its effectiveness at the single-cell level. By counting chloroplasts within individual cells, it supports cell-type-specific clustering based on chloroplast number versus cell size, offering valuable morphological insights for single-cell studies. In summary, DeepD&Cchl is a significant advancement in plant cell analysis. It offers accuracy and efficiency in chloroplast identification, counting and cell-type classification, providing a useful tool for plant research. |
| format | Article |
| id | doaj-art-d93333ae09b94b5f9e58c0e2046d4896 |
| institution | OA Journals |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-d93333ae09b94b5f9e58c0e2046d48962025-08-20T02:26:19ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-05-011610.3389/fpls.2025.15139531513953DeepD&Cchl: an AI tool for automated 3D single-cell chloroplast detection, counting, and cell type clusteringQun Su0Le Liu1Zhengsheng Hu2Tao Wang3Huaying Wang4Huaying Wang5Qiuqi Guo6Xinyi Liao7Yan Sha8Feng Li9Zhao Dong10Zhao Dong11Shaokai Yang12Shaokai Yang13Ningjing Liu14Qiong Zhao15Qiong Zhao16Qiong Zhao17School of Mathematics and Physics, Hebei University of Engineering, Handan, Hebei, ChinaSchool of Life Sciences, East China Normal University, Shanghai, ChinaSchool of Mathematics and Physics, Hebei University of Engineering, Handan, Hebei, ChinaNational Satellite Meteorological Centre, Beijing, ChinaSchool of Mathematics and Physics, Hebei University of Engineering, Handan, Hebei, ChinaNational Satellite Meteorological Centre, Beijing, ChinaSchool of Life Sciences, East China Normal University, Shanghai, ChinaSchool of Life Sciences, East China Normal University, Shanghai, ChinaUniversity of Alberta, Edmonton, AB, CanadaThe High School Affiliated to Renmin University of China, Beijing, ChinaSchool of Mathematics and Physics, Hebei University of Engineering, Handan, Hebei, ChinaHebei Computational Optical Imaging and Photoelectric Detection Technology Innovation Center, Hebei University of Engineering, Handan, Hebei, ChinaUniversity of Alberta, Edmonton, AB, CanadaDepartment of Physics, University of Alberta, Edmonton, AB, CanadaSchool of Life Sciences, East China Normal University, Shanghai, ChinaSchool of Life Sciences, East China Normal University, Shanghai, ChinaInstitute of Eco-Chongming, Shanghai, ChinaZhejiang Zhoushan Island Ecosystem Observation and Research Station, Zhoushan, Zhejiang, ChinaChloroplast density in cells varies among different types of cells and plants. In current single-cell spatiotemporal analysis, the automatic detection and quantification of chloroplasts at the single-cell level is crucial. We developed DeepD&Cchl (Deep-learning-based Detecting-and-Counting-chloroplasts), an AI tool for single-cell chloroplast detection and cell-type clustering. It utilizes You-Only-Look-Once (YOLO), a real-time detection algorithm, for accurate and efficient performance. DeepD&Cchl has been proved to identify chloroplasts in plant cells across various imaging types, including light microscopy, electron microscopy, and fluorescence microscopy. Integrated with an Intersection Over Union (IOU) module, DeepD&Cchl precisely counts chloroplasts in single- or multi-layered images, while eliminating double-counting errors. Furthermore, when combined with Cellpose, a single-cell segmentation tool, DeepD&Cchl enhances its effectiveness at the single-cell level. By counting chloroplasts within individual cells, it supports cell-type-specific clustering based on chloroplast number versus cell size, offering valuable morphological insights for single-cell studies. In summary, DeepD&Cchl is a significant advancement in plant cell analysis. It offers accuracy and efficiency in chloroplast identification, counting and cell-type classification, providing a useful tool for plant research.https://www.frontiersin.org/articles/10.3389/fpls.2025.1513953/fullchloroplastsDeepD&Cchldeep learningautomatic detection and countingsingle cellcell type clustering |
| spellingShingle | Qun Su Le Liu Zhengsheng Hu Tao Wang Huaying Wang Huaying Wang Qiuqi Guo Xinyi Liao Yan Sha Feng Li Zhao Dong Zhao Dong Shaokai Yang Shaokai Yang Ningjing Liu Qiong Zhao Qiong Zhao Qiong Zhao DeepD&Cchl: an AI tool for automated 3D single-cell chloroplast detection, counting, and cell type clustering Frontiers in Plant Science chloroplasts DeepD&Cchl deep learning automatic detection and counting single cell cell type clustering |
| title | DeepD&Cchl: an AI tool for automated 3D single-cell chloroplast detection, counting, and cell type clustering |
| title_full | DeepD&Cchl: an AI tool for automated 3D single-cell chloroplast detection, counting, and cell type clustering |
| title_fullStr | DeepD&Cchl: an AI tool for automated 3D single-cell chloroplast detection, counting, and cell type clustering |
| title_full_unstemmed | DeepD&Cchl: an AI tool for automated 3D single-cell chloroplast detection, counting, and cell type clustering |
| title_short | DeepD&Cchl: an AI tool for automated 3D single-cell chloroplast detection, counting, and cell type clustering |
| title_sort | deepd cchl an ai tool for automated 3d single cell chloroplast detection counting and cell type clustering |
| topic | chloroplasts DeepD&Cchl deep learning automatic detection and counting single cell cell type clustering |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1513953/full |
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