In cancer plasmonic photothermal therapy (PPTT)， plasmonic nanoparticles are used to convert light into localized heat， leading to cancer cell death. Among plasmonic nanoparticles， gold nanorods (AuNRs) with specific dimensions enabling them to absorb near-infrared laser light have been widely used. The detailed mechanism of PPTT therapy， however， still remains poorly understood. Typically， surface-enhanced Raman spectroscopy (SERS) has been used to detect time-dependent changes in the intensity of the vibration frequencies of molecules that appear or disappear during different cellular processes. A complete proven assignment of the molecular identity of these vibrations and their biological importa... More
In cancer plasmonic photothermal therapy (PPTT)， plasmonic nanoparticles are used to convert light into localized heat， leading to cancer cell death. Among plasmonic nanoparticles， gold nanorods (AuNRs) with specific dimensions enabling them to absorb near-infrared laser light have been widely used. The detailed mechanism of PPTT therapy， however， still remains poorly understood. Typically， surface-enhanced Raman spectroscopy (SERS) has been used to detect time-dependent changes in the intensity of the vibration frequencies of molecules that appear or disappear during different cellular processes. A complete proven assignment of the molecular identity of these vibrations and their biological importance has not yet been accomplished. Mass spectrometry (MS) is a powerful technique that is able to accurately identify molecules in chemical mixtures by observing their m/z values and fragmentation patterns. Here， we complemented the study of changes in SERS spectra with MS-based metabolomics and proteomics to identify the chemical species responsible for the observed changes in SERS band intensities during PPTT. We observed an increase in intensity of the bands at around 1000， 1207， and 1580 cm， which were assigned in the literature to phenylalanine， albeit with dispute. Our metabolomics results showed increased levels of phenylalanine， its derivatives， and phenylalanine-containing peptides， providing evidence for more confidence in the SERS peak assignments. To better understand the mechanism of phenylalanine increase upon PPTT， we combined metabolomics and proteomics results through network analysis， which proved that phenylalanine metabolism was perturbed. Furthermore， several apoptosis pathways were activated via key proteins (e.g.， HADHA and ACAT1)， consistent with the proposed role of altered phenylalanine metabolism in inducing apoptosis. Our study shows that the integration of the SERS with MS-based metabolomics and proteomics can assist the assignment of signals in SERS spectra and further characterize the related molecular mechanisms of the cellular processes involved in PPTT.
