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Impact of Semiconducting Perylene Diimide Nanoparticle Size on Lymph Node Mapping and Cancer Imaging
Abstract
Semiconducting molecules of perylene diimide (PDI) with strong light absorption properties in the near-infrared region and good biocompatibility have received increasing attention in the field of theranostics, especially as photoacoustic (PA) imaging agents. Herein, we report a series of [ 64 Cu]-labeled PDI nanoparticles (NPs) of different sizes (30, 60, 100, and 200 nm) as dual positron emission tomography (PET) and PA imaging probes and photothermal therapy agents. The precise size control of the PDI NPs can be achieved by adjusting the initial concentration of PDI molecules in the self-assembly process, and the photophysical property of different sized PDI NPs was studied in detail. Furthermore, we systematically investigated the size-dependent accumulation of the PDI NPs in the lymphatic system after local administration and in tumors after intravenous injection by PA and PET imaging. The results revealed that 100 nm is the best size for differentiating popliteal and sciatic LNs since the interval is around 60 min for the NPs to migrate from popliteal LNs to sciatic LNs, which is an ideal time window to facilitate surgical sentinel LN biopsy and pathological examination. Furthermore, different migration times of the different-sized PDI NPs will provide more choices for surgeons to map the specific tumor relevant LNs. PDI NP theranostics can also be applied to imaging-guided cancer therapy. The NPs with a size of 60 nm appear to be the best for tumor imaging and photothermal cancer therapy due to the maximum tumor accumulation efficiency. Thus, our study not only presents organic PDI NP theranostics but also introduces different-sized NPs for multiple bioapplications.
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A small-molecule dye for NIR-II imaging
Antaris, A. L., Chen, H., Cheng, K., Sun, Y., Hong, G., Qu, C., Diao, S., Deng, Z., Hu, X., Zhang, B., et al. (2016) · Nature Materials · doi:10.1038/nmat4476 · Cited by 1,778
Chauhan, V. P., Stylianopoulos, T., Martin, J. D., Popović, Z., Chen, O., Kamoun, W. S., Bawendi, M. G., Fukumura, D., & Jain, R. K. (2012) · Nature Nanotechnology · doi:10.1038/nnano.2012.45 · Cited by 1,101
The role of micelle size in tumor accumulation, penetration, and treatment
Wang, J., Mao, W., Lock, L. L., Tang, J., Sui, M., Sun, W., Cui, H., Xu, D., & Shen, Y. (2015) · ACS Nano · doi:10.1021/acsnano.5b02017 · Cited by 635
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Li, J., Rao, J., & Pu, K. (2018) · Biomaterials · doi:10.1016/j.biomaterials.2017.11.025 · Cited by 469
Effect of physicochemical and surface properties on in vivo fate of drug nanocarriers
Zhao, Z., Ukidve, A., Krishnan, V., & Mitragotri, S. (2019) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2019.01.002 · Cited by 444
Jiang, Q., Liu, Y., Guo, R., Yao, X., Sung, S., Pang, Z., & Yang, W. (2019) · Biomaterials · doi:10.1016/j.biomaterials.2018.11.021 · Cited by 398
Nanomaterials for Nanotheranostics: Tuning Their Properties According to Disease Needs
Wong, X. Y., Sena-Torralba, A., Álvarez-Diduk, R., Muthoosamy, K., & Merkoçi, A. (2020) · ACS Nano · doi:10.1021/acsnano.9b08133 · Cited by 363
Li, J., Jiang, R., Wang, Q., Li, X., Hu, X., Yuan, Y., Lu, X., Wang, W., Huang, W., & Fan, Q. (2019) · Biomaterials · doi:10.1016/j.biomaterials.2019.119304 · Cited by 163
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Metadata
- Authors
- Yang, Z., Tian, R., Wu, J., Fan, Q., Yung, B. C., Niu, G., Jacobson, O., Wang, Z., Liu, G., Yu, G., Huang, W., Song, J., & Chen, X.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-04-03
- Volume
- 11
- Issue
- 4
- Pages
- 4247-4255
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W2602077418
- Cited by
- 193
- References
- 3
- FWCI
- 10.48
- Type
- journal article
- Language
- en
- Open access
- closed
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- Yang, Z., Tian, R., Wu, J., Fan, Q., Yung, B. C., Niu, G., Jacobson, O., Wang, Z., Liu, G., Yu, G., Huang, W., Song, J., & Chen, X. (2017). Impact of Semiconducting Perylene Diimide Nanoparticle Size on Lymph Node Mapping and Cancer Imaging. ACS Nano. https://doi.org/10.1021/acsnano.7b01261
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