Xiao-Xiao Chen, Xing-Yi Rao, Qi-Xin Guan, Peng Wang and Cai-Ping Tan*,
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引用次数: 0
摘要
内质网(ER)是金属免疫性细胞死亡(ICD)诱导剂的一个重要靶标细胞器。金属复合物可通过导致蛋白质错误折叠来诱导ER应激,这可通过微环境参数(包括粘度)的变化反映出来。我们在此介绍一种治疗性 Re(I)复合物(Re1),它的发射强度和寿命与粘度有关。Re1 可通过定位于 ER 并导致 ER 应激,引发 MDA-MB-231 细胞的免疫性细胞死亡(ICD)。我们证明 Re1 可同时诱导并定量监测 ER 粘度的逐渐增加。
Quantitative Determination of Endoplasmic Reticulum Viscosity during Immunogenic Cell Death by a Theranostic Rhenium Complex
The endoplasmic reticulum (ER) is an important targeting organelle for metal-based immunogenic cell death (ICD) inducers. Metal complexes can induce ER stress by causing protein misfolding, which can be reflected by alternations in microenvironmental parameters, including viscosity. We present here a theranostic Re(I) complex (Re1) that shows viscosity-dependent emission intensity and lifetime. Re1 can trigger immunogenic cell death (ICD) in MDA-MB-231 cells by localizing in the ER and causing ER stress. We demonstrate that Re1 can simultaneously induce and monitor the gradual increase in the ER viscosity quantitatively.
期刊介绍:
Chemical & Biomedical Imaging is a peer-reviewed open access journal devoted to the publication of cutting-edge research papers on all aspects of chemical and biomedical imaging. This interdisciplinary field sits at the intersection of chemistry physics biology materials engineering and medicine. The journal aims to bring together researchers from across these disciplines to address cutting-edge challenges of fundamental research and applications.Topics of particular interest include but are not limited to:Imaging of processes and reactionsImaging of nanoscale microscale and mesoscale materialsImaging of biological interactions and interfacesSingle-molecule and cellular imagingWhole-organ and whole-body imagingMolecular imaging probes and contrast agentsBioluminescence chemiluminescence and electrochemiluminescence imagingNanophotonics and imagingChemical tools for new imaging modalitiesChemical and imaging techniques in diagnosis and therapyImaging-guided drug deliveryAI and machine learning assisted imaging