Significance: This study analyzed the dynamic protein corona on the surface of nanoparticles as they traversed from blood to cell lysosomes and escaped from lysosomes to cytoplasm in the target cells. We found with proteomic analysis an abundance of chaperone and glycolysis coronal proteins (i.e., heat shock cognate protein 70, heat shock protein 90, and pyruvate kinase M2 [PKM2]) after escape of the nanoparticles from lysosomes to the cytosol. Alterations of the coronal proteins (e.g., PKM2 and chaperone binding) induced proteostasis collapse, which subsequently led to elevated chaperone-mediated autophagy (CMA) activity in cells. As PKM2 is a key molecule in cell metabolism, we also revealed that PKM2 depletion was causative to CMA-induced cell metabolism disruption from glycolysis to lipid metabolism.

Messenger RNA (mRNA) vaccine has fueled a great hope for cancer immunotherapy. However, low immunogenicity, caused by inefficient mRNA expression and weak immune stimulation, hampers the efficacy of mRNA vaccines. Here, we present an mRNA compartmentalization–based cancer vaccine, comprising a multimodule DNA nanostructure (MMDNS)–assembled compartment for efficient mRNA translation via in situ localizing mRNA concentration and relevant reaction molecules. The MMDNS is constructed via programmable DNA hybridization chain reaction (HCR)–based strategy, with integrating antigen-coded mRNA, CpG oligodeoxynucleotides (ODNs), acidic-responsive DNA sequence, and dendritic cells targeting aptamer. MMDNS undergoes in situ assembly in acidic lysosomes to form a micro-sized aggregate, inducing an enhanced CpG ODN adjuvant efficacy. Subsequently, the aggregates escape into cytoplasm, providing a moderate compartment which supports the efficient translation of spatially proximal mRNA transcripts via localizing relevant reaction molecules. The mRNA compartmentalization–based vaccine boosts a strong immune response and effectively inhibits tumor growth and metastasis, offering a robust strategy for cancer immunotherapy.

In this study, a deep reinforcement learning approach is proposed to handle tactical driving in complex highway traffic environments for unmanned ground vehicles. Tactical driving is a challenging topic for unmanned ground vehicles because of its interplay with routing decisions as well as real-time traffic dynamics. The core of our deep reinforcement learning approach is a deep Q-network that takes dynamic traffic information as input and outputs typical tactical driving decisions as action. The reward is designed with the consideration of successful highway exit, average traveling speed, and driving safety and comfort. In order to endow an unmanned ground vehicle with situational traffic information that is critical for tactical driving, the vehicle’s sensor information such as vehicle position and velocity are further augmented through the assessment of the ego-vehicle’s collision risk, potential field, and kinematics and used as input for the deep Q-network model. A convolutional neural network is built and fine-tuned to extract traffic features which facilitate the decision-making process of Q-learning. For model training and testing, a highway simulation platform is constructed with realistic parameter settings obtained from a real-world highway traffic dataset. The performance of the deep Q-network model is validated with extensive simulation experiments under different parameter settings such as traffic density and risk level. The results exhibit the important potentials of our deep Q-network model in learning challenging tactical driving decisions given multiple objectives and complex traffic environment.