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近红外调控正交生物成像及上转换纳米光敏剂的细胞内示踪

Near-infrared mediated orthogonal bioimaging and intracellular tracking of upconversion nanophotosensitizers.

机构信息

School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, China.

出版信息

Mikrochim Acta. 2022 Feb 24;189(3):120. doi: 10.1007/s00604-022-05218-4.

DOI:10.1007/s00604-022-05218-4
PMID:35201432
Abstract

Although upconversion photodynamic therapy (PDT) has gained extensive interests in disease treatment, the intracellular migration pathway of upconversion photosensitizers and underlying cell-particle interaction mechanism is still largely unexplored. In this work photoswitchable upconversion nanoparticles (UCNPs) are reported  that can release orthogonal emissions excited by two near-infrared lights, i.e., red color of 980-nm and green color of 808-nm light excitation. Taking advantage of the dual-emissive property, a methodology based on Pearson's correlation analysis is proposed to verify the accuracy of upconversion luminescence signals under different excitation lights, which has been previously neglected. Meanwhile, we have designed a near-infrared mediated bioimaging nanoplatform that can generate reactive oxygen species (ROS) using one light and simultaneously track the location of upconversion photosensitizers using another excitation light. Our study not only depicts the migration pathway of upconversion photosensitizers, but also demonstrates the organelle escape of these upconversion nanoparticles via PCI (photochemical internalization) process. It is believed that our results inspire more efficient synergistic therapy by combining PDT with other modalities in a programmable manner.

摘要

尽管上转换光动力疗法(PDT)在疾病治疗中引起了广泛的兴趣,但上转换光敏剂的细胞内迁移途径和潜在的细胞-颗粒相互作用机制仍在很大程度上尚未被探索。在这项工作中,我们报道了一种可光开关的上转换纳米粒子(UCNPs),它可以释放由两个近红外光激发的正交发射,即 980nm 的红色和 808nm 的绿色光激发。利用这种双发射特性,我们提出了一种基于 Pearson 相关分析的方法来验证在不同激发光下上转换发光信号的准确性,而这在以前是被忽视的。同时,我们设计了一种近红外介导的生物成像纳米平台,它可以使用一种光产生活性氧(ROS),同时使用另一种激发光跟踪上转换光敏剂的位置。我们的研究不仅描绘了上转换光敏剂的迁移途径,还通过光化学内化(PCI)过程证明了这些上转换纳米粒子的细胞器逃逸。我们相信,我们的研究结果通过以可编程的方式将 PDT 与其他模式相结合,为更有效的协同治疗提供了启示。

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