Immunomagnetic microscopy of tumor tissues using quantum sensors in diamond.
Sanyou ChenWanhe LiXiaohu ZhengPei YuPengfei WangZiting SunYao XuDefeng JiaoXiangyu YeMingcheng CaiMengze ShenMengqi WangQi ZhangFei KongYa WangJie HeHaiming WeiFazhan ShiJiangfeng DuPublished in: Proceedings of the National Academy of Sciences of the United States of America (2022)
Histological imaging is essential for the biomedical research and clinical diagnosis of human cancer. Although optical microscopy provides a standard method, it is a persistent goal to develop new imaging methods for more precise histological examination. Here, we use nitrogen-vacancy centers in diamond as quantum sensors and demonstrate micrometer-resolution immunomagnetic microscopy (IMM) for human tumor tissues. We immunomagnetically labeled cancer biomarkers in tumor tissues with magnetic nanoparticles and imaged them in a 400-nm resolution diamond-based magnetic microscope. There is barely magnetic background in tissues, and the IMM can resist the impact of a light background. The distribution of biomarkers in the high-contrast magnetic images was reconstructed as that of the magnetic moment of magnetic nanoparticles by employing deep-learning algorithms. In the reconstructed magnetic images, the expression intensity of the biomarkers was quantified with the absolute magnetic signal. The IMM has excellent signal stability, and the magnetic signal in our samples had not changed after more than 1.5 y under ambient conditions. Furthermore, we realized multimodal imaging of tumor tissues by combining IMM with hematoxylin-eosin staining, immunohistochemistry, or immunofluorescence microscopy in the same tissue section. Overall, our study provides a different histological method for both molecular mechanism research and accurate diagnosis of human cancer.
Keyphrases
- high resolution
- molecularly imprinted
- deep learning
- single molecule
- magnetic nanoparticles
- endothelial cells
- papillary thyroid
- gene expression
- high speed
- optical coherence tomography
- high throughput
- convolutional neural network
- induced pluripotent stem cells
- squamous cell
- poor prognosis
- squamous cell carcinoma
- magnetic resonance
- air pollution
- childhood cancer
- binding protein
- artificial intelligence
- fluorescence imaging
- quantum dots
- pain management
- energy transfer
- long non coding rna
- tandem mass spectrometry