Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China.
School of Information Engineering, Minzu University of China, Beijing 100081, China.
ACS Nano. 2024 Aug 20;18(33):22378-22389. doi: 10.1021/acsnano.4c06953. Epub 2024 Aug 8.
It is crucial for understanding mechanisms of drug action to quantify the three-dimensional (3D) drug distribution within a single cell at nanoscale resolution. Yet it remains a great challenge due to limited lateral resolution, detection sensitivities, and reconstruction problems. The preferable method is using X-ray nano-computed tomography (Nano-CT) to observe and analyze drug distribution within cells, but it is time-consuming, requiring specialized expertise, and often subjective, particularly with ultrasmall metal nanoparticles (NPs). Furthermore, the accuracy of batch data analysis through conventional processing methods remains uncertain. In this study, we used radioenhancer ultrasmall HfO nanoparticles as a model to develop a modular and automated deep learning aided Nano-CT method for the localization quantitative analysis of ultrasmall metal NPs uptake in cancer cells. We have established an ultrasmall objects segmentation method for 3D Nano-CT images in single cells, which can highly sensitively analyze minute NPs and even ultrasmall NPs in single cells. We also constructed a localization quantitative analysis method, which may accurately segment the intracellularly bioavailable particles from those of the extracellular space and intracellular components and NPs. The high bioavailability of HfO NPs in tumor cells from deeper penetration in tumor tissue and higher tumor intracellular uptake provide mechanistic insight into HfO NPs as advanced radioenhancers in the combination of quantitative subcellular image analysis with the therapeutic effects of NPs on 3D tumor spheroids and breast cancer. Our findings unveil the substantial uptake rate and subcellular quantification of HfO NPs by the human breast cancer cell line (MCF-7). This revelation explicates the notable efficacy and safety profile of HfO NPs in tumor treatment. These findings demonstrate that this 3D imaging technique promoted by the deep learning algorithm has the potential to provide localization quantitative information about the 3D distributions of specific molecules at the nanoscale level. This study provides an approach for exploring the subcellular quantitative analysis of NPs in single cells, offering a valuable quantitative imaging tool for minute amounts or ultrasmall NPs.
为了深入理解药物作用机制，需要在纳米尺度上定量分析单个细胞内的三维（3D）药物分布。但由于横向分辨率、检测灵敏度和重构问题的限制，这仍然是一个巨大的挑战。首选方法是使用 X 射线纳米计算机断层扫描（Nano-CT）来观察和分析细胞内的药物分布，但这种方法耗时、需要专业知识，并且往往具有主观性，特别是对于超小金属纳米颗粒（NPs）更是如此。此外，通过传统处理方法对批量数据进行分析的准确性仍然不确定。在本研究中，我们使用放射性增强剂超小 HfO 纳米颗粒作为模型，开发了一种模块化和自动化的深度学习辅助 Nano-CT 方法，用于定位定量分析癌细胞中超小金属 NPs 的摄取。我们已经建立了一种用于单细胞 3D Nano-CT 图像的超小物体分割方法，该方法可以高度敏感地分析微小 NPs 甚至单细胞中超小 NPs。我们还构建了一种定位定量分析方法，可以准确地将细胞内可用的颗粒与细胞外空间和细胞内成分及 NPs 分离。HfO NPs 在肿瘤细胞中的高生物利用度源于其在肿瘤组织中的更深穿透性和更高的肿瘤细胞内摄取，为 HfO NPs 作为先进的放射性增强剂提供了机制见解，这与定量亚细胞图像分析与 NPs 对 3D 肿瘤球体和乳腺癌的治疗效果相结合。我们的研究结果揭示了 HfO NPs 被人乳腺癌细胞系（MCF-7）大量摄取并进行亚细胞定量。这一发现说明了 HfO NPs 在肿瘤治疗中的显著疗效和安全性。这些发现表明，这种由深度学习算法推动的 3D 成像技术有可能提供纳米级特定分子 3D 分布的定位定量信息。本研究为探索单细胞中 NPs 的亚细胞定量分析提供了一种方法，为微量或超小 NPs 提供了一种有价值的定量成像工具。
