Zhou Huige, Guo Yuecong, Fu Tianyu, Peng Yufeng, Chen Ziwei, Cui Yanyan, Guo Mengyu, Zhang Kai, Chen Chunying, Wang Yaling
New Cornerstone Science Laboratory, CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety and CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Chinese Academy of Sciences, Beijing 100190, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
ACS Nano. 2024 Jul 13. doi: 10.1021/acsnano.4c06095.
Understanding the intracellular behavior of nanoparticles (NPs) plays a key role in optimizing the self-assembly performance of nanomedicine. However, conducting the 3D, label-free, quantitative observation of self-assembled NPs within intact single cells remains a substantial challenge in complicated intracellular environments. Here, we propose a deep learning combined synchrotron radiation hard X-ray nanotomography approach to visualize the self-assembled ultrasmall iron oxide (USIO) NPs in a single cell. The method allows us to explore comprehensive information on NPs, such as their distribution, morphology, location, and interaction with cell organelles, and provides quantitative analysis of the heterogeneous size and morphologies of USIO NPs under diverse conditions. This label-free, in situ method provides a tool for precise characterization of intracellular self-assembled NPs to improve the evaluation and design of a bioresponsive nanomedicine.
了解纳米颗粒（NPs）的细胞内行为对于优化纳米药物的自组装性能起着关键作用。然而，在复杂的细胞内环境中，对完整单细胞内自组装纳米颗粒进行三维、无标记、定量观察仍然是一项重大挑战。在此，我们提出一种深度学习结合同步辐射硬X射线纳米断层扫描方法，以可视化单个细胞内自组装的超小氧化铁（USIO）纳米颗粒。该方法使我们能够探索纳米颗粒的全面信息，如它们的分布、形态、位置以及与细胞器的相互作用，并对不同条件下USIO纳米颗粒的异质尺寸和形态进行定量分析。这种无标记的原位方法为精确表征细胞内自组装纳米颗粒提供了一种工具，以改进生物响应性纳米药物的评估和设计。
