Dataset Information

Nanosensors for the Chemical Imaging of Acetylcholine Using Magnetic Resonance Imaging.

ABSTRACT: A suite of imaging tools for detecting specific chemicals in the central nervous system could accelerate the understanding of neural signaling events critical to brain function and disease. Here, we introduce a class of nanoparticle sensors for the highly specific detection of acetylcholine in the living brain using magnetic resonance imaging. The nanosensor is composed of acetylcholine-catalyzing enzymes and pH-sensitive gadolinium contrast agents co-localized onto the surface of polymer nanoparticles, which leads to changes in T1 relaxation rate (1/ T1). The mechanism of the sensor involves the enzymatic hydrolysis of acetylcholine leading to a localized decrease in pH which is detected by the pH-sensitive gadolinium chelate. The concomitant change in 1/ T1 in vitro measured a 20% increase from 0 to 10 ?M acetylcholine concentration. The applicability of the nanosensors in vivo was demonstrated in the rat medial prefrontal cortex showing distinct changes in 1/ T1 induced by pharmacological stimuli. The highly specific acetylcholine nanosensor we present here offers a promising strategy for detection of cholinergic neurotransmission and will facilitate our understanding of brain function through chemical imaging.

SUBMITTER: Luo Y

PROVIDER: S-EPMC6281809 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Nanosensors for the Chemical Imaging of Acetylcholine Using Magnetic Resonance Imaging.

Luo Yi Y Kim Eric H EH Flask Chris A CA Clark Heather A HA

ACS nano 20180606 6

A suite of imaging tools for detecting specific chemicals in the central nervous system could accelerate the understanding of neural signaling events critical to brain function and disease. Here, we introduce a class of nanoparticle sensors for the highly specific detection of acetylcholine in the living brain using magnetic resonance imaging. The nanosensor is composed of acetylcholine-catalyzing enzymes and pH-sensitive gadolinium contrast agents co-localized onto the surface of polymer nanopa ...[more]

PMID: 29851460

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Project description:PurposeTo explore, at a high field strength of 7T, the performance of various fat spectral models on the quantification of triglyceride composition and proton density fat fraction (PDFF) using chemical-shift encoded MRI (CSE-MRI).MethodsMR data was acquired from CSE-MRI experiments for various fatty materials, including oil and butter samples and in vivo brown and white adipose mouse tissues. Triglyceride composition and PDFF were estimated using various a priori 6- or 9-peak fat spectral models. To serve as references, NMR spectroscopy experiments were conducted to obtain material specific fat spectral models and triglyceride composition estimates for the same fatty materials. Results obtained using the spectroscopy derived material specific models were compared to results obtained using various published fat spectral models.ResultsUsing a 6-peak fat spectral model to quantify triglyceride composition may lead to large biases at high field strengths. When using a 9-peak model, triglyceride composition estimations vary greatly depending on the relative amplitudes of the chosen a priori spectral model, while PDFF estimations show small variations across spectral models. Material specific spectroscopy derived spectral models produce estimations that better correlate with NMR spectroscopy estimations in comparison to those obtained using non-material specific models.ConclusionAt a high field strength of 7T, a material specific 9-peak fat spectral model, opposed to a widely accepted or generic human liver model, is necessary to accurately quantify triglyceride composition when using CSE-MRI estimation methods that assume the spectral model to be known as a priori information. CSE-MRI allows for the quantification of the spatial distribution of triglyceride composition for certain in vivo applications. Additionally, PDFF quantification is shown to be independent of the chosen a priori spectral model, which agrees with previously reported results obtained at lower field strengths (e.g. 3T).

Dataset Information

Nanosensors for the Chemical Imaging of Acetylcholine Using Magnetic Resonance Imaging.

Publications

Nanosensors for the Chemical Imaging of Acetylcholine Using Magnetic Resonance Imaging.

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