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Trafficking of α‐Amino‐3–hydroxy‐5–methylisoxazole‐4–propionic acid (AMPA) receptors (AMPARs), mediated by AMPAR interacting proteins, enabled neurons to maintain tuning capabilities at rest or active state. α/β-Hydrolase domain-containing 6 (ABHD6), an endocannabinoid hydrolase, was an AMPAR auxiliary subunit found to negatively regulate the surface delivery of AMPARs. While ABHD6 was found to prevent AMPAR tetramerization in endoplasmic reticulum, ABHD6 was also reported to localize at postsynaptic site. Yet, the role of ABHD6 interacting with AMPAR at postsynaptic site, and the physiological significance of ABHD6 regulating AMPAR trafficking remains elusive. Here, we generated the ABHD6 knockout (ABHD6KO) mice and found that deletion of ABHD6 selectively enhanced AMPAR-mediated basal synaptic responses and the surface expression of postsynaptic AMPARs. Furthermore, we found that loss of ABHD6 impaired hippocampal long-term depression (LTD) and synaptic downscaling in hippocampal synapses. AMPAR internalization assays revealed that ABHD6 was essential for neuronal activity-dependent endocytosis of surface AMPARs, which is independent of ABHD6's hydrolase activity. The defects of AMPAR endocytosis and LTD are expressed as deficits in learning flexibility in ABHD6KO mice. Collectively, we demonstrated that ABHD6 is an endocytic accessory protein promoting AMPAR endocytosis, thereby contributes to the formation of LTD, synaptic downscaling and reversal learning. • Deletion of ABHD6 increases AMPAR-mediated basal synaptic responses in hippocampal CA1 neurons. • Deletion of ABHD6 impairs long-term depression in hippocampal Schaffer collateral pathway. • ABHD6 is required for homeostatic synaptic downscaling in cultured hippocampal neurons. • ABHD6 is essential for activity-dependent endocytosis of surface AMPARs. • ABHD6KO mice show deficits in reversal learning tests. [ABSTRACT FROM AUTHOR]

Parylene-C is a popular polymer material in biomedical applications, with excellent physicochemical properties and microfabrication capability. Like many aromatic polymers, parylene-C also has autofluorescence, which was usually taken as a negative background noise in biomedical detection studies. However, the fluorescence intensity of thin-film (＜1 μm) parylene-C was relatively weak, which may be a big limitation in visualization. In this work, we reported a simple annealing method to significantly enhance the fluorescence and achieve sufficient intensity as a visual marker. We studied the behaviors and mechanisms of the enhanced parylene-C fluorescence, then verified the feasibility and reliability of parylene-C for preparing fluorescent pipettes in targeted neuronal electrophysiology, where fluorescent guidance was strongly needed. The powerful parylene-C fabrication technique enables a precisely-controlled conformal coating along with a mass production capability, which further resulted in high-quality electrophysiological recordings of both cultured hippocampal neurons and acute hippocampal brain slices. Moreover, the enhanced parylene-C fluorescence can also be applied in more general biological operations, such as designable fluorescent micro-patterns for visualization in broader biomedical fields. [ABSTRACT FROM AUTHOR]

Synapses are the connections between neurons in the central nervous system (CNS) or between neurons and other excitable cells in the peripheral nervous system (PNS), where electrical or chemical signals rapidly travel through one cell to another with high spatial precision. Synaptic analysis, based on synapse numbers and fine morphology, is the basis for understanding neurological functions and diseases. Manual analysis of synaptic structures in electron microscopy (EM) images is often limited by low efficiency and subjective bias. We developed a multifunctional synaptic analysis system based on several advanced deep learning (DL) models. The system achieved synapse counting in low-magnification EM images and synaptic ultrastructure analysis in high-magnification EM images. The synapse counting system based on ResNet18 and a Faster R-CNN model had a mean average precision (mAP) of 92.55%. For synaptic ultrastructure analysis, the Faster R-CNN model based on ResNet50 achieved a mAP of 91.60%, the DeepLab v3 + model based on ResNet50 enabled high performance in presynaptic and postsynaptic membrane segmentation with a global accuracy of 0.9811, and the Faster R-CNN model based on ResNet18 achieved a mAP of 91.41% for synaptic vesicle detection. The proposed multifunctional synaptic analysis system may help to overcome the experimental bias inherent in manual analysis, thereby facilitating EM image–based synaptic function studies. • DL models achieve high performance for synaptic analysis using EM images. • Faster R-CNN model achieved accurate synapse counting in low-magnification EM images. • Integration of multiple DL models enables efficient synaptic ultrastructure analysis. • High-magnification EM provides accurate membrane segmentation and vesicle detection. [ABSTRACT FROM AUTHOR]

Lentiviral technology has proven to be a powerful tool to express exogenous genes in dividing and non-dividing cells. Currently, most protocols for generating high-titer lentivirus require ultracentrifugation, which can be an instrumental barrier for routine operations in a laboratory. In this study, the effect of relative centrifugal force (RCF) on the concentration efficiency of the lentivirus was systematically explored, and it was found that sucrose gradient centrifugation with a relatively low speed (≤10,000 g) robustly produces a high-titer virus (up to 2 × 108 TU/ml). The optimal sucrose concentration is 10%, and the recovery rate of the functional virus is greater than 80%. The infection efficiency of both concentrated and un-concentrated lentivirus decreases rapidly when the viruses are stored at 4 °C (τ ≈ 1.3 days) or subjected to multiple freeze-thaw cycles (τ = 1.1 rounds). In summary, we describe an efficient and easy-to-handle protocol for high-titer lentivirus purification. [ABSTRACT FROM AUTHOR]