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Bacterial DNA Recognition by SERS Active Plasma-Coupled Nanogold

[Image: see text] It is shown that surface-enhanced Raman spectroscopy (SERS) can identify bacteria based on their genomic DNA composition, acting as a “sample-distinguishing marker”. Successful spectral differentiation of bacterial species was accomplished with nanogold aggregates synthesized throu...

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Detalles Bibliográficos
Autores principales: Shvalya, Vasyl, Vasudevan, Aswathy, Modic, Martina, Abutoama, Mohammad, Skubic, Cene, Nadižar, Nejc, Zavašnik, Janez, Vengust, Damjan, Zidanšek, Aleksander, Abdulhalim, Ibrahim, Rozman, Damjana, Cvelbar, Uroš
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9756328/
https://www.ncbi.nlm.nih.gov/pubmed/36301628
http://dx.doi.org/10.1021/acs.nanolett.2c02835
Descripción
Sumario:[Image: see text] It is shown that surface-enhanced Raman spectroscopy (SERS) can identify bacteria based on their genomic DNA composition, acting as a “sample-distinguishing marker”. Successful spectral differentiation of bacterial species was accomplished with nanogold aggregates synthesized through single-step plasma reduction of the ionic gold-containing vapored precursor. A high enhancement factor (EF = 10(7)) in truncated coupled plasmonic particulates allowed SERS-probing at nanogram sample quantities. Simulations confirmed the occurrence of the strongest electric field confinement within nanometric gaps between gold dimers/chains from where the molecular fingerprints of bacterial DNA fragments gained photon scattering enhancement. The most prominent Raman modes linked to fundamental base-pair molecular vibrations were deconvoluted and used to proceed with nitrogenous base content estimation. The genomic composition (percentage of guanine-cytosine and adenine-thymine) was successfully validated by third-generation sequencing using nanopore technology, further proving that the SERS technique can be employed to swiftly specify bioentities by the discriminative principal-component statistical approach.