Sundharbaabu Priyannth Ramasami, Chang Junhyuck, Kim Yunchul, Shim Youmin, Lee Byoungsang, Noh Chanyoung, Heo Sujung, Lee Seung Seo, Shim Sang-Hee, Lim Kwang-Il, Jo Kyubong, Lee Jung Heon
School of Advanced Materials Science and Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, South Korea.
Department of Chemistry & Interdisciplinary Program of Integrated Biotechnology, Sogang University, Seoul, 04107, South Korea.
Small. 2025 Mar;21(12):e2405065. doi: 10.1002/smll.202405065. Epub 2024 Oct 9.
DNA visualization has advanced across multiple microscopy platforms, albeit with limited progress in the identification of novel staining agents for electron microscopy (EM), notwithstanding its ability to furnish a broad magnification range and high-resolution details for observing DNA molecules. Herein, a non-toxic, universal, and simple method is proposed that uses gold nanoparticle-tagged peptides to stain all types of naturally occurring DNA molecules, enabling their visualization under EM. This method enhances the current DNA visualization capabilities, allowing for sequence-specific, genomic-scale, and multi-conformational visualization. Importantly, an artificial intelligence (AI)-enabled pipeline for identifying DNA molecules imaged under EM is presented, followed by classification based on their size, shape, or conformation, and finally, extraction of their significant dimensional features, which to the best of authors' knowledge, has not been reported yet. This pipeline strongly improved the accuracy of obtaining crucial information such as the number and mean length of DNA molecules in a given EM image for linear DNA (salmon sperm DNA) and the circumferential length and diameter for circular DNA (M13 phage DNA), owing to its image segmentation capability. Furthermore, it remained robust to several variations in the raw EM images arising from handling during the DNA staining stage.
尽管电子显微镜(EM)在提供广泛放大范围和高分辨率细节以观察DNA分子方面具有优势,但在鉴定用于EM的新型染色剂方面进展有限,DNA可视化已在多个显微镜平台上取得了进展。本文提出了一种无毒、通用且简单的方法,该方法使用金纳米颗粒标记的肽对所有类型的天然存在的DNA分子进行染色,从而能够在EM下对其进行可视化。这种方法增强了当前的DNA可视化能力,实现了序列特异性、基因组规模和多构象的可视化。重要的是,本文还介绍了一种基于人工智能(AI)的流程,用于识别在EM下成像的DNA分子,然后根据其大小、形状或构象进行分类,最后提取其重要的尺寸特征,据作者所知,这尚未见报道。由于其图像分割能力,该流程极大地提高了在给定EM图像中获取关键信息的准确性,如线性DNA(鲑鱼精DNA)的DNA分子数量和平均长度,以及环状DNA(M13噬菌体DNA)的周长和直径。此外,对于DNA染色阶段处理过程中产生的原始EM图像的多种变化,该流程仍具有鲁棒性。