Bukat Alicja, Bukowicki Marek, Bykowski Michał, Kuczkowska Karolina, Nowakowski Szymon, Śliwińska Anna, Kowalewska Łucja
Department of Plant Anatomy and Cytology, Faculty of Biology, University of Warsaw, Miecznikowa 1, 02-096 Warsaw, Poland.
Center for Machine Learning, Faculty of Physics, University of Warsaw, Pasteura 5, 02-093 Warsaw, Poland.
Plant Physiol. 2025 May 30;198(2). doi: 10.1093/plphys/kiaf212.
Grana are fundamental structural units of the intricate chloroplast membrane network. Investigating their nanomorphology is essential for understanding photosynthetic efficiency regulation. Here, we present GRANA (Graphical Recognition and Analysis of Nanostructural Assemblies), an artificial intelligence-enhanced, user-friendly software tool that recognizes grana on thylakoid network electron micrographs and generates a complex set of their structural parameters. GRANA employs 3 artificial neural networks of different architectures and binds them in a 1-click workflow. Its output is designed to facilitate hybrid intelligence analysis, securing fast and reliable results from large datasets. The GRANA tool is over 100 times faster compared with currently used manual approaches. As a proof of concept, we have successfully applied GRANA software to diverse grana structures across different land plant species grown under various conditions, demonstrating the wide range of potential applications for our software. GRANA tool supports large-scale analysis of grana nanomorphological features, facilitating advancements in photosynthesis-oriented studies.
基粒是复杂的叶绿体膜网络的基本结构单元。研究它们的纳米形态对于理解光合效率调控至关重要。在此,我们展示了GRANA(纳米结构组件的图形识别与分析),这是一种人工智能增强的、用户友好的软件工具,可识别类囊体网络电子显微镜照片上的基粒并生成一系列复杂的结构参数。GRANA采用了3种不同架构的人工神经网络,并将它们绑定在一个一键式工作流程中。其输出旨在促进混合智能分析,确保从大型数据集中快速获得可靠结果。与目前使用的手动方法相比,GRANA工具的速度快100多倍。作为概念验证,我们已成功将GRANA软件应用于在各种条件下生长的不同陆地植物物种的各种基粒结构,证明了我们软件的广泛潜在应用。GRANA工具支持对基粒纳米形态特征进行大规模分析,有助于推动以光合作用为导向的研究进展。