Photosensitizing deep-seated cancer cells with photoprotein-conjugated upconversion nanoparticles

To resolve the problem of target specificity and light transmission to deep-seated tissues in photodynamic therapy (PDT), we report a cancer cell-targeted photosensitizer using photoprotein-conjugated upconversion nanoparticles (UCNPs) with high target specificity and efficient light transmission to deep tissues. Core-shell UCNPs with low internal energy back transfer were conjugated with recombinant proteins that consists of a photosensitizer (KillerRed; KR) and a cancer cell-targeted lead peptide (LP). Under near infrared (NIR)-irradiating condition, the UCNP-KR-LP generated superoxide anion radicals as reactive oxygen species via NIR-to-green light conversion and exhibited excellent specificity to target cancer cells through receptor-mediated cell adhesion. Consequently, this photosensitizing process facilitated rapid cell death in cancer cell lines (MCF-7, MDA-MB-231, and U-87MG) overexpressing integrin beta 1 (ITGB1) receptors but not in a cell line (SK-BR-3) with reduced ITGB1 expression and a non-invasive normal breast cell line (MCF-10A). In contrast to green light irradiation, NIR light irradiation exhibited significant PDT efficacy in cancer cells located beneath porcine skin tissues up to a depth of 10 mm, as well as in vivo tumor xenograft mouse models. This finding suggests that the designed nanocomposite is useful for sensing and targeting various deep-seated tumors.

Automated summary

To address the issues of target specificity and light transmission in photodynamic therapy (PDT), a cancer cell-targeted photosensitizer using photoprotein-conjugated upconversion nanoparticles (UCNPs) was developed. The UCNP-KR-LP showed high target specificity and efficient light transmission to deep tissues, generating reactive oxygen species via NIR-to-green light conversion. This nanocomposite demonstrated significant PDT efficacy in deep-seated tumors located beneath porcine skin tissues and in tumor xenograft mouse models.