Issue 13, 2021

An intelligent T1T2 switchable MRI contrast agent for the non-invasive identification of vulnerable atherosclerotic plaques

Abstract

Unlike stable atherosclerotic plaques, vulnerable plaques are very likely to cause serious cardio-cerebrovascular diseases. Meanwhile, how to non-invasively identify vulnerable plaques at early stages has been an urgent but challenging problem in clinical practices. Here, we propose a macrophage-targeted and in situ stimuli-triggered T1T2 switchable magnetic resonance imaging (MRI) nanoprobe for the non-invasive diagnosis of vulnerable plaques. Precisely, single-dispersed iron oxide nanoparticles (IONPs) modified with hyaluronic acid (HA), denoted as IONP-HP, show macrophage targetability and T1 MRI enhancement (r2/r1 = 3.415). Triggered by the low pH environment of macrophage lysosomes, the single-dispersed IONP-HP transforms into a cluster analogue, which exhibits T2 MRI enhancement (r2/r1 = 13.326). Furthermore, an in vivo switch of T1T2 enhancement modes shows that the vulnerable plaques exhibit strong T1 enhancement after intravenous administration of the nanoprobe, followed by a switch to T2 enhancement after 9 h. In contrast, stable plaques show only slight T1 enhancement but without T2 enhancement. It is therefore imperative that the intelligent and novel nanoplatform proposed in this study achieves a substantial non-invasive diagnosis of vulnerable plaques by means of a facile but effective T1T2 switchable process, which will significantly contribute to the application of materials science in solving clinical problems.

Graphical abstract: An intelligent T1–T2 switchable MRI contrast agent for the non-invasive identification of vulnerable atherosclerotic plaques

Supplementary files

Article information

Article type
Paper
Submitted
10 Nov 2020
Accepted
17 Feb 2021
First published
26 Feb 2021

Nanoscale, 2021,13, 6461-6474

An intelligent T1T2 switchable MRI contrast agent for the non-invasive identification of vulnerable atherosclerotic plaques

C. Pan, J. Lin, J. Zheng, C. Liu, B. Yuan, O. U. Akakuru, M. Zubair Iqbal, Q. Fang, J. Hu, J. Chen, J. Lin, Q. Dai, X. Guo, Z. Li, T. Zhang, C. Xu, X. Ma, T. Chen, A. Wu and Y. Jin, Nanoscale, 2021, 13, 6461 DOI: 10.1039/D0NR08039J

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