ArticleCurrent protocols in chemical biology2016
Nanoparticle-Templated Molecular Recognition Platforms for Detection of Biological Analytes.
Article in Current protocols in chemical biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 32 citations in OpenAlex.
- Rational Design of Optical Single-Walled Carbon Nanotube-Based Nanosensors with Biological Recognition Elements.Advanced sensor research · 2026Article
- Near-infrared nanosensors enable optical imaging of oxytocin with selectivity over vasopressin in acute mouse brain slices.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Article
- Carbon nanocarriers deliver siRNA to intact plant cells for efficient gene knockdown.Science advances · 2020Article
- Fluorescent Single-Walled Carbon Nanotubes for Protein Detection.Sensors (Basel, Switzerland) · 2019Review
- High-throughput evolution of near-infrared serotonin nanosensors.Science advances · 2019Article
- Synthetic molecular recognition nanosensor paint for microalbuminuria.Nature communications · 2019Article
- High aspect ratio nanomaterials enable delivery of functional genetic material without DNA integration in mature plants.Nature nanotechnology · 2019Article
- Synthetic probe development for measuring single or few-cell activity and efflux.Methods in enzymology · 2019Article
- Ultralarge Modulation of Fluorescence by Neuromodulators in Carbon Nanotubes Functionalized with Self-Assembled Oligonucleotide Rings.Nano letters · 2018Article
- Advances in Nanomaterials for Brain Microscopy.Nano research · 2018Article
- Stochastic Simulation of Dopamine Neuromodulation for Implementation of Fluorescent Neurochemical Probes in the Striatal Extracellular Space.ACS chemical neuroscience · 2017Article
- Single-molecule detection of protein efflux from microorganisms using fluorescent single-walled carbon nanotube sensor arrays.Nature nanotechnology · 2017Article
- Surface Engineering of Nanoparticles to Create Synthetic Antibodies.Methods in molecular biology (Clifton, N.J.) · 2017Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Molecular recognition of biological analytes with optical nanosensors provides both spatial and temporal biochemical information. A recently developed sensing platform exploits near-infrared fluorescent single-wall carbon nanotubes combined with electrostatically pinned heteropolymers to yield a synthetic molecular recognition technique that is maximally transparent through biological matter. This molecular recognition technique is known as corona phase molecular recognition (CoPhMoRe). In CoPhMoRe, the specificity of a folded polymer toward an analyte does not arise from a pre-existing polymer-analyte chemical affinity. Rather, specificity is conferred through conformational changes undergone by a polymer that is pinned to the surface of a nanoparticle in the presence of an analyte and the subsequent modifications in fluorescence readout of the nanoparticles. The protocols in this article describe a novel single-molecule microscopy tool (near-infrared fluorescence and total internal reflection fluorescence [nIRF TIRF] hybrid microscope) to visualize the CoPhMoRe recognition process, enabling a better understanding of synthetic molecular recognition. We describe this requisite microscope for simultaneous single-molecule visualization of optical molecular recognition and signal transduction. We elaborate on the general procedures for synthesizing and identifying single-walled carbon nanotube-based sensors that employ CoPhMoRe via two biologically relevant examples of single-molecule recognition for the hormone estradiol and the neurotransmitter dopamine. © 2016 by John Wiley & Sons, Inc.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.