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Semiconducting polymer nanoparticles as photoacoustic molecular imaging probes in living mice

Open access
Pu, Kanyi, Shuhendler, Adam J., Jokerst, Jesse V., Mei, Jianguo, Gambhir, Sanjiv S., Bao, Zhenan, Rao, Jianghong·2014·Nature Nanotechnology

Abstract

Photoacoustic (PA) imaging holds great promise for the visualization of physiology and pathology at the molecular level with deep tissue penetration and fine spatial resolution. To fully utilize this potential, PA molecular imaging probes have to be developed. Herein we introduce near infrared (NIR) light absorbing semiconducting polymer nanoparticles (SPNs) as a new class of contrast agents for PA molecular imaging. SPNs can produce stronger signal than commonly used single-wall carbon nanotubes and gold nanorods on a per mass basis, permitting whole-body lymph node PA mapping in living mice at a low systematic injection mass. Furthermore, SPNs possess high structural flexibility, narrow PA spectral profiles, and strong resistance to photodegradation and oxidation, which enables development of the first NIR ratiometric PA probe for in vivo real-time imaging of reactive oxygen species—vital chemical mediators of many diseases. These results demonstrate SPNs an ideal nanoplatform for developing PA molecular probes.

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Metadata

Authors
Pu, K., Shuhendler, A. J., Jokerst, J. V., Mei, J., Gambhir, S. S., Bao, Z., & Rao, J.
Venue
Nature Nanotechnology
Published
2014-01-26
Volume
9
Issue
3
Pages
233-239
ISSN
  • 1748-3387 (ISSN-L)
  • 1748-3395
OpenAlex
W2014358738
Cited by
1,185
References
5
FWCI
61.25
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Semiconducting polymer nanoparticles as photoacoustic molecular imaging probes in living mice. https://app.cochat.ai/papers/10.1038/nnano.2013.302
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Pu, K., Shuhendler, A. J., Jokerst, J. V., Mei, J., Gambhir, S. S., Bao, Z., & Rao, J. (2014). Semiconducting polymer nanoparticles as photoacoustic molecular imaging probes in living mice. Nature Nanotechnology. https://doi.org/10.1038/nnano.2013.302

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.