Artificial Intelligence‐Enhanced Analysis of Genomic DNA Visualized with Nanoparticle‐Tagged Peptides under Electron Microscopy

Abstract: 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.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Artificial Intelligence‐Enhanced Analysis of Genomic DNA Visualized with Nanoparticle‐Tagged Peptides under Electron Microscopy ; day:09 ; month:10 ; year:2024 ; extent:17 ; Small ; (09.10.2024) (gesamt 17)

Contributors, Places and Time

• Creator: Sundharbaabu, Priyannth Ramasami ; Chang, Junhyuck ; Kim, Yunchul ; Shim, Youmin ; Lee, Byoungsang ; Noh, Chanyoung ; Heo, Sujung ; Lee, Seung Seo ; Shim, Sang‐Hee ; Lim, Kwang‐i. ; Jo, Kyubong ; Lee, Jung Heon

Further information

• DOI: 10.1002/smll.202405065
• URN: urn:nbn:de:101:1-2410091426119.614460184792
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:23 AM CEST