Background In recent years, significant progress has been made in the treatment of nasopharyngeal carcinoma (NPC). The application of immunotherapy, especially the use of Programmed Cell Death Protein 1 inhibitors, has demonstrated excellent therapeutic effects. However, inconsistent immunotherapy outcomes due to individual differences in patients remain a major challenge, making the search for specific biomarkers to screen for the population who may benefit from immunotherapy a priority. Surface-enhanced Raman spectroscopy (SERS) has shown great potential as a highly sensitive and specific optical analytical tool for identifying and monitoring tumor-related markers in NPC. Results In this study, plasma samples from Nasopharyngeal cancer patients before and after immunotherapy, along with those from healthy volunteers, were tested using label-free SERS combined with plasmapheresis. Especially, the components with varying molecular weight sizes were analyzed via the separation process, thereby preventing the potential loss of diagnostic information that could result from competitive adsorption. Subsequently, a robust machine learning algorithm based on principal component analysis and linear discriminant analysis (PCA-LDA) was used to extract features from plasma SERS data and establish an effective discriminant model. The results showed that in the upper plasma layer, the most optimal discrimination was found between normal patients and those post-treatment, with sensitivity and specificity of 88.5% and 92.3%, respectively. In the lower plasma layer, the most optimal discrimination was found between the pre-treatment and post-treatment patients, with sensitivity and specificity of 84.6% and 80.8%, respectively. Significance and novelty Plasmapheresis can reveal latent diagnostic information in the plasma and holds promise for treating NPC, the screening of the population who may benefit from immunotherapy, and the postoperative evaluation of immunotherapy. Further exploration of the feasibility of using SERS to detect tumor markers in the population who may benefit from immunotherapy for NPC will help improve the therapeutic efficacy, optimize clinical practice, and promote the development of individualized treatment strategies.
The heterogeneity of extracellular vesicles (EVs) surface information represents different functions, which is neglected in previous studies. In this study, a label-free SERS analysis approach is demonstrated to study fundamental EV biological and physical information heterogeneity by matching specific sizes of nano-enhanced particles. This strategy reveals informative, comprehensive, and high-quality SERS spectra of the overall exosome surface, and effectively circumvents the key information loss caused by the spatial resistance of NPs binding to the 293 exosomes' concave structure. The classification of normal and cancerous cell-derived exosomes by PCA method, the accuracy is improved from 91.2% to 95.1% by optimizing sizes of nano-enhanced particles. In addition, stem cell-derived EVs of diverse sizes and morphologies similarly show acuity of spectrum variation to NPs size, which is conductive to qualitative studies. This new strategy will offer a widened in-depth understanding of the surface information, size, and morphology of EVs, which can be applied to the study of biological functions.
A prototype of label-free SERS sensor, based on the formation of T-Hg 2+ -T pairs and the corresponding orientation variation of DNA on Au shell surface, was developed for detection of Hg 2+ in aqueous solution quantitatively and selectively. The DNA aptamer immobilized on the surface of SiO 2 @Au core/shell nanoparticles (NPs) consisted of two segments, namely, the segment which has the consecutive thymines (T) severing as the Hg 2+ recognition elements, and the segment which contains the guanine (G) and adenine (A) bases working as the signal reporter. With interaction of Hg 2+ ions and the thymines between the adjacent single-stranded DNA (ssDNA), the DNA molecule adopted vertical orientation, resulting in increase of Raman intensity ratio I(660 cm −1 )/I(736 cm −1 ) with increase of Hg 2+ concentration, which thus allowed to measure trace amounts of Hg 2+ in aqueous solution selectively and quantitatively. Our results revealed that this label-free SERS sensor could sensitively respond to Hg 2+ ions within a wide concentration range (from 1 × 10 −8 to 1 × 10 −3 M). This work therefore demonstrates that proper design of aptamer-modified SiO 2 @Au core/shell NPs can be utilized for label-free SERS detection of heavy metal ions in the environment.
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