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Quantitative Super-Resolution Microscopy to Assess Adhesion of Neuronal Cells on Single-Layer Graphene Substrates

Single Layer Graphene (SLG) has emerged as a critically important nanomaterial due to its unique optical and electrical properties and has become a potential candidate for biomedical applications, biosensors, and tissue engineering. Due to its intrinsic 2D nature, SLG is an ideal surface for the dev...

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Autores principales: Scalisi, Silvia, Pennacchietti, Francesca, Keshavan, Sandeep, Derr, Nathan D., Diaspro, Alberto, Pisignano, Dario, Pierzynska-Mach, Agnieszka, Dante, Silvia, Cella Zanacchi, Francesca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8621106/
https://www.ncbi.nlm.nih.gov/pubmed/34832107
http://dx.doi.org/10.3390/membranes11110878
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author Scalisi, Silvia
Pennacchietti, Francesca
Keshavan, Sandeep
Derr, Nathan D.
Diaspro, Alberto
Pisignano, Dario
Pierzynska-Mach, Agnieszka
Dante, Silvia
Cella Zanacchi, Francesca
author_facet Scalisi, Silvia
Pennacchietti, Francesca
Keshavan, Sandeep
Derr, Nathan D.
Diaspro, Alberto
Pisignano, Dario
Pierzynska-Mach, Agnieszka
Dante, Silvia
Cella Zanacchi, Francesca
author_sort Scalisi, Silvia
collection PubMed
description Single Layer Graphene (SLG) has emerged as a critically important nanomaterial due to its unique optical and electrical properties and has become a potential candidate for biomedical applications, biosensors, and tissue engineering. Due to its intrinsic 2D nature, SLG is an ideal surface for the development of large-area biosensors and, due to its biocompatibility, can be easily exploited as a substrate for cell growth. The cellular response to SLG has been addressed in different studies with high cellular affinity for graphene often detected. Still, little is known about the molecular mechanism that drives/regulates the cellular adhesion and migration on SLG and SLG-coated interfaces with respect to other substrates. Within this scenario, we used quantitative super-resolution microscopy based on single-molecule localization to study the molecular distribution of adhesion proteins at the nanoscale level in cells growing on SLG and glass. In order to reveal the molecular mechanisms underlying the higher affinity of biological samples on SLG, we exploited stochastic optical reconstruction microscopy (STORM) imaging and cluster analysis, quantifying the super-resolution localization of the adhesion protein vinculin in neurons and clearly highlighting substrate-related correlations. Additionally, a comparison with an epithelial cell line (Chinese Hamster Ovary) revealed a cell dependent mechanism of interaction with SLG.
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spelling pubmed-86211062021-11-27 Quantitative Super-Resolution Microscopy to Assess Adhesion of Neuronal Cells on Single-Layer Graphene Substrates Scalisi, Silvia Pennacchietti, Francesca Keshavan, Sandeep Derr, Nathan D. Diaspro, Alberto Pisignano, Dario Pierzynska-Mach, Agnieszka Dante, Silvia Cella Zanacchi, Francesca Membranes (Basel) Article Single Layer Graphene (SLG) has emerged as a critically important nanomaterial due to its unique optical and electrical properties and has become a potential candidate for biomedical applications, biosensors, and tissue engineering. Due to its intrinsic 2D nature, SLG is an ideal surface for the development of large-area biosensors and, due to its biocompatibility, can be easily exploited as a substrate for cell growth. The cellular response to SLG has been addressed in different studies with high cellular affinity for graphene often detected. Still, little is known about the molecular mechanism that drives/regulates the cellular adhesion and migration on SLG and SLG-coated interfaces with respect to other substrates. Within this scenario, we used quantitative super-resolution microscopy based on single-molecule localization to study the molecular distribution of adhesion proteins at the nanoscale level in cells growing on SLG and glass. In order to reveal the molecular mechanisms underlying the higher affinity of biological samples on SLG, we exploited stochastic optical reconstruction microscopy (STORM) imaging and cluster analysis, quantifying the super-resolution localization of the adhesion protein vinculin in neurons and clearly highlighting substrate-related correlations. Additionally, a comparison with an epithelial cell line (Chinese Hamster Ovary) revealed a cell dependent mechanism of interaction with SLG. MDPI 2021-11-15 /pmc/articles/PMC8621106/ /pubmed/34832107 http://dx.doi.org/10.3390/membranes11110878 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Scalisi, Silvia
Pennacchietti, Francesca
Keshavan, Sandeep
Derr, Nathan D.
Diaspro, Alberto
Pisignano, Dario
Pierzynska-Mach, Agnieszka
Dante, Silvia
Cella Zanacchi, Francesca
Quantitative Super-Resolution Microscopy to Assess Adhesion of Neuronal Cells on Single-Layer Graphene Substrates
title Quantitative Super-Resolution Microscopy to Assess Adhesion of Neuronal Cells on Single-Layer Graphene Substrates
title_full Quantitative Super-Resolution Microscopy to Assess Adhesion of Neuronal Cells on Single-Layer Graphene Substrates
title_fullStr Quantitative Super-Resolution Microscopy to Assess Adhesion of Neuronal Cells on Single-Layer Graphene Substrates
title_full_unstemmed Quantitative Super-Resolution Microscopy to Assess Adhesion of Neuronal Cells on Single-Layer Graphene Substrates
title_short Quantitative Super-Resolution Microscopy to Assess Adhesion of Neuronal Cells on Single-Layer Graphene Substrates
title_sort quantitative super-resolution microscopy to assess adhesion of neuronal cells on single-layer graphene substrates
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8621106/
https://www.ncbi.nlm.nih.gov/pubmed/34832107
http://dx.doi.org/10.3390/membranes11110878
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