Quantitative Three-Dimensional\nImaging Analysis of\nHfO<sub>2</sub> Nanoparticles in Single Cells via Deep Learning Aided\nX‑ray Nano-Computed Tomography

It is crucial for understanding mechanisms of drug action\nto quantify\nthe three-dimensional (3D) drug distribution within a single cell\nat nanoscale resolution. Yet it remains a great challenge due to limited\nlateral resolution, detection sensitivities, and reconstruction problems.\nThe preferable method is using X-ray nano-computed tomography (Nano-CT)\nto observe and analyze drug distribution within cells, but it is time-consuming,\nrequiring specialized expertise, and often subjective, particularly\nwith ultrasmall metal nanoparticles (NPs). Furthermore, the accuracy\nof batch data analysis through conventional processing methods remains\nuncertain. In this study, we used radioenhancer ultrasmall HfO<sub>2</sub> nanoparticles as a model to develop a modular and automated\ndeep learning aided Nano-CT method for the localization quantitative\nanalysis of ultrasmall metal NPs uptake in cancer cells. We have established\nan ultrasmall objects segmentation method for 3D Nano-CT images in\nsingle cells, which can highly sensitively analyze minute NPs and\neven ultrasmall NPs in single cells. We also constructed a localization\nquantitative analysis method, which may accurately segment the intracellularly\nbioavailable particles from those of the extracellular space and intracellular\ncomponents and NPs. The high bioavailability of HfO<sub>2</sub> NPs\nin tumor cells from deeper penetration in tumor tissue and higher\ntumor intracellular uptake provide mechanistic insight into HfO<sub>2</sub> NPs as advanced radioenhancers in the combination of quantitative\nsubcellular image analysis with the therapeutic effects of NPs on\n3D tumor spheroids and breast cancer. Our findings unveil the substantial\nuptake rate and subcellular quantification of HfO<sub>2</sub> NPs\nby the human breast cancer cell line (MCF-7). This revelation explicates\nthe notable efficacy and safety profile of HfO<sub>2</sub> NPs in\ntumor treatment. These findings demonstrate that this 3D imaging technique\npromoted by the deep learning algorithm has the potential to provide\nlocalization quantitative information about the 3D distributions of\nspecific molecules at the nanoscale level. This study provides an\napproach for exploring the subcellular quantitative analysis of NPs\nin single cells, offering a valuable quantitative imaging tool for\nminute amounts or ultrasmall NPs.