Visualization of Vaccine Dynamics with Quantum Dots for Immunotherapy

Nanomaterials in Immunology: Bridging Innovative Approaches in Immune Modulation, Diagnostics, and Therapy

The intersection of immunology and nanotechnology has provided significant advancements in biomedical research and clinical applications over the years. Immunology aims to understand the immune system's defense mechanisms against pathogens. Nanotechnology has demonstrated its potential to manipulate immune responses, as nanomaterials' properties can be modified for the desired application. Research has shown that nanomaterials can be applied in diagnostics, therapy, and vaccine development. In diagnostics, nanomaterials can be used for biosensor development, accurately detecting biomarkers even at very low concentrations. Therapeutically, nanomaterials can act as efficient carriers for delivering drugs, antigens, or genetic material directly to targeted cells or tissues. This targeted delivery improves therapeutic efficacy and reduces the adverse effects on healthy cells and tissues. In vaccine development, nanoparticles can improve vaccine durability and extend immune responses by effectively delivering adjuvants and antigens to immune cells. Despite these advancements, challenges regarding the safety, biocompatibility, and scalability of nanomaterials for clinical applications are still present. This review will cover the fundamental interactions between nanomaterials and the immune system, their potential applications in immunology, and their safety and biocompatibility concerns.

Accurate Cancer Screening and Prediction of PD-L1-Guided Immunotherapy Efficacy Using Quantum Dot Nanosphere Self-Assembly and Machine Learning

Accurate quantification of exosomal PD-L1 protein in tumors is closely linked to the response to immunotherapy, but robust methods to achieve high-precision quantitative detection of PD-L1 expression on the surface of circulating exosomes are still lacking. In this work, we developed a signal amplification approach based on aptamer recognition and DNA scaffold hybridization-triggered assembly of quantum dot nanospheres, which enables bicolor phenotyping of exosomes to accurately screen for cancers and predict PD-L1-guided immunotherapeutic effects through machine learning. Through DNA-mediated assembly, we utilized two aptamers for simultaneous ultrasensitive detection of exosomal antigens, which have synergistic roles in tumor diagnosis and treatment prediction, and thus, we achieved better sample classification and prediction through machine-learning algorithms. With a drop of blood, we can distinguish between different cancer patients and healthy individuals and predict the outcome of immunotherapy. This approach provides valuable insights into the development of personalized diagnostics and precision medicine.