Simple and sensitive assays for DNA detection still represent a highly pursued research area with important implications in biomedical-related sciences. Graphene oxide (GO) is a highly efficient quenching platform for fluorophore-tagged DNA, which is why its use for fluorescent sensing has been widespread over the past decade. GO-based biosensing systems frequently rely upon the isolation of biomolecule-material complexes prior to detection via hybridization-induced desorption of the fluorescent dye. Simple mix-and-read detection formats that do not require purification/isolation/wash steps are envisioned as promising schemes for decentralized analysis, with potential for commercial scalability. For GO-based mix-and-read assays, the aging process of the quenching material in aqueous media can be a crucial parameter affecting the analytical performance, which has so far not been addressed in the literature. To get this goal, top-down characterization microstructures to atomic levels is needed. Herein, we revisit GO as a well-known quenching system, aiming at a centrifugation-free, mix-and-read, no-wash format, toward the detection of an apolipoprotein-E-encoding DNA sequence as a model analyte. We look into the progression of GO aging in water medium through a top-down characterization and investigate the analytical performance of fresh versus aged dispersions in terms of hybridization-based detection. We found that aged GO, while still retaining a high quenching efficiency, undergoes morphological changes over time with concomitant detrimental effects on its analytical performance toward DNA detection.
Bibliographical noteFunding Information:
This work was supported by the project Advanced Functional Nanorobots (reg. no. CZ.02.1.01/0.0/0.0/15_003/0000444 financed by the EFRR). C.L.M.P. acknowledges the financial support of the European Union’s Horizon 2020 Research and innovation programme under the Marie Skłodowska-Curie Actions IF grant agreement no. 795347. Z.S. was supported by the Czech Science Foundation (GACR No. 16-05167S) and by the specific university research (MSMT no. 20-SVV/2018). This work was created with the financial support of the Neuron Foundation for science support.
© 2019 American Chemical Society.
All Science Journal Classification (ASJC) codes
- Chemical Engineering(all)