Dendritic thickness: a morphometric parameter to classify mouse retinal ganglion cells

The crucial role that hippocampus Cyclooxygenase-2 plays in memory

It is generally accepted that Cyclooxygenase-2 (COX-2) is activated to cause inflammation. However, COX-2 is also constitutively expressed at the postsynaptic dendrites and excitatory terminals of the cortical and spinal cord neurons. Although some evidence suggests that COX-2 release during neuronal signalling may be pivotal for regulating the function of memory, the significance of constitutively expressed COX-2 in neuron is still unclear. This research aims to discover the role of COX-2 in memory beyond neuroinflammation and to determine whether the inhibition of COX-2 can cause cognitive dysfunction by influencing dendritic plasticity and its underlying mechanism. We found COX-2 gene knockout (KO) could significantly impact the learning and memory ability, cause neuronal structure disorder and influence gamma oscillations. These might be mediated by the inhibition of prostaglandin (PG) E2/cAMP pathway and phosphorylated protein kinase A (p-PKA)-phosphorylated cAMP response element binding protein (p-CREB)-brain derived neurotrophic factor (BDNF) axis. It suggested COX-2 might play a critical role in learning, regulating neuronal structure and gamma oscillations in the hippocampus CA1 by regulating COX-2/BDNF signalling pathway.

Neuronal Cell Types in the Spinal Trigeminal Nucleus of the Camel Brain

Neurons in the spinal trigeminal nucleus of a camel were morphologically studied by the Golgi impregnation method. The neurons were classified based on the size and shape of their cell bodies, the density of their dendritic trees, and the morphology and distribution of their appendages. At least 12 morphological types of neurons were found in the camel spinal trigeminal nucleus, including the following: stalked, islets, octopus-like, lobulated, boat-like, pyramidal, multipolar, round, oval, and elongated neurons. These neurons exhibited large numbers of various forms of appendages that arise not only from their dendrites but also from their cell bodies. Moreover, neurons with unique large dilatations especially at their dendritic branching points were also reported. The neurons reported in this study displayed an array of different sizes and shapes and featured various forms of appendages arising from cell bodies and dendrites. Such morphologically distinctive neuronal cell types might indicate an evolutionary adaptation to pain and temperature processing pathways at the level of the spinal trigeminal nucleus in camels, which traditionally live in a very harsh climatic environment and are frequently exposed to painful stimuli.

Future of PD-1/PD-L1 axis modulation for the treatment of triple-negative breast cancer

Triple-negative breast cancer (TNBC) has the worst prognosis among the subtypes of breast cancer, with no targeted therapy available. Immunotherapy targeting programmed cell death protein-1 (PD-1) and its ligand (PD-L1) has resulted in some promising outcomes in cancer patients. The common treatments are monoclonal antibodies (mAbs). Despite novel methodologies in developing mAbs, there are several drawbacks with these medications. Immunological reactions, expensive and time-consuming production and requiring refrigeration are some of the challenging characteristics of mAbs that are addressed with using aptamers. Aptamers are nucleotide-based structures with high selectivity and specificity for target. Their small size helps aptamers penetrate the tissue better. In this review, we have discussed the nature of PD-1/PD-L1 interaction and summarised the available mAbs and aptamers specific for these two targets. This review highlights the role of aptamers as a future pathway for PD-1/PD-L1 modulation.

Abnormal dendritic morphology in the cerebellum of cyclooxygenase-2- knockin mice

Prostaglandin E2 (PGE2) is a bioactive signalling molecule metabolized from the phospholipid membranes by the enzymatic activity of cycloxygenase-2 (COX-2). In the developing brain, COX-2 constitutively regulates the production of PGE2, which is important in neuronal development. However, abnormal COX-2/PGE2 signalling has been linked to neurodevelopmental disorders including autism spectrum disorders (ASDs). We have previously demonstrated that COX-2^- -KI mice show autism-related behaviours including social deficits, repetitive behaviours and anxious behaviours. COX-2-deficient mice also have deficits in pathways involved in synaptic transmission and dendritic spine formation. In this study, we use a Golgi-COX staining method to examine sex-dependent differences in dendritic and dendritic spine morphology in neurons of COX-2^- -KI mice cerebellum compared with wild-type (WT) matched controls at postnatal day 25 (P25). We show that COX-2^- -KI mice have increased dendritic arborization closer to the cell soma and increased dendritic looping. We also observed a sex-dependent effect of the COX-2^- -KI on dendritic thickness, dendritic spine density, dendritic spine morphology, and the expression of β-actin and the actin-binding protein spinophilin. Our findings show that changes in COX-2/PGE2 signalling lead to impaired morphology of dendrites and dendritic spines in a sex-dependant manner and may contribute the pathology of the cerebellum seen in individuals with ASD. This study provides further evidence that the COX-2^- -KI mouse model can be used to study a subset of ASD pathologies.

Optimization of Traced Neuron Skeleton Using Lasso-Based Model

Reconstruction of neuronal morphology from images involves mainly the extraction of neuronal skeleton points. It is an indispensable step in the quantitative analysis of neurons. Due to the complex morphology of neurons, many widely used tracing methods have difficulties in accurately acquiring skeleton points near branch points or in structures with tortuosity. Here, we propose two models to solve these problems. One is based on an L1-norm minimization model, which can better identify tortuous structure, namely, a local structure with large curvature skeleton points; the other detects an optimized branch point by considering the combination patterns of all neurites that link to this point. We combined these two models to achieve optimized skeleton detection for a neuron. We validate our models in various datasets including MOST and BigNeuron. In addition, we demonstrate that our method can optimize the traced skeletons from large-scale images. These characteristics of our approach indicate that it can reduce manual editing of traced skeletons and help to accelerate the accurate reconstruction of neuronal morphology.

Identifying Weak Signals in Inhomogeneous Neuronal Images for Large-Scale Tracing of Sparsely Distributed Neurites

Tracing neurites constitutes the core of neuronal morphology reconstruction, a key step toward neuronal circuit mapping. Modern optical-imaging techniques allow observation of nearly complete mouse neuron morphologies across brain regions or even the whole brain. However, high-level automation reconstruction of neurons, i.e., the reconstruction with a few of manual edits requires discrimination of weak foreground points from the inhomogeneous background. We constructed an identification model, where empirical observations made from neuronal images were summarized into rules for designing feature vectors that to classify foreground and background, and a support vector machine (SVM) was used to learn these feature vectors. We embedded this constructed SVM classifier into a previously developed tool, SparseTracer, to obtain SparseTracer-Learned Feature Vector (ST-LFV). ST-LFV can trace sparsely distributed neurites with weak signals (contrast-to-noise ratio < 1.5) against an inhomogeneous background in datasets imaged by widely used light-microscopy techniques like confocal microscopy and two-photon microscopy. Moreover, 12 sub-blocks were extracted from different brain regions. The average recall and precision rates were 99% and 97%, respectively. These results indicated that ST-LFV is well suited for weak signal identification with varying image characteristics. We also applied ST-LFV to trace long-range neurites from images where neurites are sparsely distributed but their image intensities are weak in some cases. When tracing this long-range neurites, manual edit was required once to obtain results equivalent to the ground truth, compared with 20 times of manual edits required by SparseTracer. This improvement in the level of automatic reconstruction indicates that ST-LFV has the potential to rapidly reconstruct sparsely distributed neurons at the large scale.