Bioeffects of Inhaled Nanoplastics on Neurons and Alteration of Animal Behaviors through Deposition in the Brain.

The potential toxicity of nanoplastics on plants has previously been illustrated, but whether nanoplastics could cause neurotoxicity, especially to higher animals, remains unclear. We now demonstrate that nanoplastics can be deposited in the brain via nasal inhalation, triggering neuron toxicity and altering the animal behavior. Polystyrene nanoparticles (PS-NPs) of PS-COOH and PS-NH2 are used as models for nanoplastics. We designed a microfluidic chip to evaluate the PS-NPs with different concentrations, surface ligands, and sizes to interact with neurons. Smaller PS-NPs can induce more cellular uptake than larger PS-NPs. PS-NPs with a size of 80 nm can reach and deposit in the brain of mice via aerosol inhalation. Mice inhaling PS-NPs exhibit fewer activities in comparison to those inhaling water droplets. An obvious neurotoxicity of the nanoplastics could be observed from the results of the inhibition of AChE activities. Our results show the potential significance of the physiochemical properties of organic nanoplastics on depositing in mammalian brains by nasal inhalation.

Tumor Microenvironment Based on Extracellular Matrix Hydrogels for On-Chip Drug Screening.

Recent advances in three-dimensional (3D) culturing and nanotechnology offer promising pathways to overcome the limitations of drug screening, particularly for tumors like neuroblastoma. In this study, we develop a high-throughput microfluidic chip that integrates a concentration gradient generator (CGG) with a 3D co-culture system, constructing the vascularized microenvironment in tumors by co-culturing neuroblastoma (SY5Y cell line) and human brain microvascular endothelial cells (HBMVECs) within a decellularized extracellular matrix (dECM) hydrogels. The automated platform enhances the simulation of the tumor microenvironment and allows for the precise control of the concentrations of nanomedicines, which is crucial for evaluating therapeutic efficacy. The findings demonstrate that the high-throughput platform can significantly accelerate drug discovery. It efficiently screens and analyzes drug interactions in a biologically relevant setting, potentially revolutionizing the drug screening process.

Advances in Single-Cell Techniques for Linking Phenotypes to Genotypes.

Single-cell analysis has become an essential tool in modern biological research, providing unprecedented insights into cellular behavior and heterogeneity. By examining individual cells, this approach surpasses conventional population-based methods, revealing critical variations in cellular states, responses to environmental cues, and molecular signatures. In the context of cancer, with its diverse cell populations, single-cell analysis is critical for investigating tumor evolution, metastasis, and therapy resistance. Understanding the phenotype-genotype relationship at the single-cell level is crucial for deciphering the molecular mechanisms driving tumor development and progression. This review highlights innovative strategies for selective cell isolation based on desired phenotypes, including robotic aspiration, laser detachment, microraft arrays, optical traps, and droplet-based microfluidic systems. These advanced tools facilitate high-throughput single-cell phenotypic analysis and sorting, enabling the identification and characterization of specific cell subsets, thereby advancing therapeutic innovations in cancer and other diseases.

Coupling Nanoscale Precision with Multiscale Imaging: A Multifunctional Near-Infrared Dye for the Brain.

Live imaging of primary neural cells is crucial for monitoring neuronal activity, especially multiscale and multifunctional imaging that offers excellent biocompatibility. Multiscale imaging can provide insights into cellular structure and function from the nanoscale to the millimeter scale. Multifunctional imaging can monitor different activities in the brain. However, this remains a challenge because of the lack of dyes with a high signal-to-background ratio, water solubility, and multiscale and multifunctional imaging capabilities. In this study, we present a neural dye with near-infrared (NIR) emissions (>700 nm) that enables ultrafast staining (in less than 1 min) for the imaging of primary neurons. This dye not only enables multiscale neural live-cell imaging from vesicles in neurites, neural membranes, and single neurons to the whole brain but also facilitates multifunctional imaging, such as the monitoring and quantifying of synaptic vesicles and the changes in membrane potential. We also explore the potential of this NIR neural dye for staining brain slices and live brains. The NIR neural dye exhibits superior binding with neural membranes compared to commercial dyes, thereby achieving multiscale and multifunctional brain neuroimaging. In conclusion, our findings introduce a significant breakthrough in neuroimaging dyes by developing a category of small molecular dyes.

Deep learning unlocks label-free viability assessment of cancer spheroids in microfluidics.

Despite recent advances in cancer treatment, refining therapeutic agents remains a critical task for oncologists. Precise evaluation of drug effectiveness necessitates the use of 3D cell culture instead of traditional 2D monolayers. Microfluidic platforms have enabled high-throughput drug screening with 3D models, but current viability assays for 3D cancer spheroids have limitations in reliability and cytotoxicity. This study introduces a deep learning model for non-destructive, label-free viability estimation based on phase-contrast images, providing a cost-effective, high-throughput solution for continuous spheroid monitoring in microfluidics. Microfluidic technology facilitated the creation of a high-throughput cancer spheroid platform with approximately 12 000 spheroids per chip for drug screening. Validation involved tests with eight conventional chemotherapeutic drugs, revealing a strong correlation between viability assessed via LIVE/DEAD staining and phase-contrast morphology. Extending the model's application to novel compounds and cell lines not in the training dataset yielded promising results, implying the potential for a universal viability estimation model. Experiments with an alternative microscopy setup supported the model's transferability across different laboratories. Using this method, we also tracked the dynamic changes in spheroid viability during the course of drug administration. In summary, this research integrates a robust platform with high-throughput microfluidic cancer spheroid assays and deep learning-based viability estimation, with broad applicability to various cell lines, compounds, and research settings.