Deep Learning to Decipher the Progression and Morphology of Axonal Degeneration

From pixels to connections: exploring in vitro neuron reconstruction software for network graph generation

Digital reconstruction has been instrumental in deciphering how in vitro neuron architecture shapes information flow. Emerging approaches reconstruct neural systems as networks with the aim of understanding their organization through graph theory. Computational tools dedicated to this objective build models of nodes and edges based on key cellular features such as somata, axons, and dendrites. Fully automatic implementations of these tools are readily available, but they may also be purpose-built from specialized algorithms in the form of multi-step pipelines. Here we review software tools informing the construction of network models, spanning from noise reduction and segmentation to full network reconstruction. The scope and core specifications of each tool are explicitly defined to assist bench scientists in selecting the most suitable option for their microscopy dataset. Existing tools provide a foundation for complete network reconstruction, however more progress is needed in establishing morphological bases for directed/weighted connectivity and in software validation.

Deep learning in mesoscale brain image analysis: A review

Mesoscale microscopy images of the brain contain a wealth of information which can help us understand the working mechanisms of the brain. However, it is a challenging task to process and analyze these data because of the large size of the images, their high noise levels, the complex morphology of the brain from the cellular to the regional and anatomical levels, the inhomogeneous distribution of fluorescent labels in the cells and tissues, and imaging artifacts. Due to their impressive ability to extract relevant information from images, deep learning algorithms are widely applied to microscopy images of the brain to address these challenges and they perform superiorly in a wide range of microscopy image processing and analysis tasks. This article reviews the applications of deep learning algorithms in brain mesoscale microscopy image processing and analysis, including image synthesis, image segmentation, object detection, and neuron reconstruction and analysis. We also discuss the difficulties of each task and possible directions for further research.

Downregulation of CDK5 signaling in the dorsal striatum alters striatal microcircuits implicating the association of pathologies with circadian behavior in mice

Dysfunction of striatal dopaminergic circuits has been implicated in motor impairment and Parkinson’s disease (PD)-related circadian perturbations that may represent an early prodromal marker of PD. Cyclin-dependent kinase 5 (CDK5) negatively regulates dopamine signaling in the striatum, suggesting a critical role of CDK5 in circadian and sleep disorders. Here, we used clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 gene editing to produce mice with a dorsal striatum (DS)-specific knockdown (KD) of the Cdk5 gene (referred to as DS-CDK5-KD mice) and investigate its role in vivo. DS-CDK5-KD mice exhibited deficits in locomotor activity and disturbances in activity/rest behavior. Additionally, Golgi staining of neurons in the DS revealed that CDK5 deletion reduced dendrite length and the number of functional synapses, which was confirmed by significant downregulation of MAP2, PSD-95, and synapsin I. Correlated with this, DS-CDK5-KD mice displayed reduced phosphorylation of Tau at Thr181. Furthermore, whole-cell patch-clamp recordings of green fluorescent protein-tagged neurons in the striatum of DS-CDK5-KD mice revealed a decreased frequency of spontaneous inhibitory postsynaptic currents and altered excitatory/inhibitory synaptic balance. Notably, anterograde labeling showed that CDK5 KD in the DS disrupted long-range projections to the secondary motor cortex, dorsal and ventral thalamic nuclei, and basolateral amygdala, which are involved in the regulation of motor and circadian rhythms in the brain. These findings support a critical role of CDK5 in the DS in maintaining the striatal neural circuitry underlying motor functions and activity/rest associated with circadian rhythms that are perturbed in neurodegenerative disorders.

Mesenchymal Stem Cell Application and Its Therapeutic Mechanisms in Intracerebral Hemorrhage

Intracerebral hemorrhage (ICH), a common lethal subtype of stroke accounting for nearly 10–15% of the total stroke disease and affecting two million people worldwide, has a high mortality and disability rate and, thus, a major socioeconomic burden. However, there is no effective treatment available currently. The role of mesenchymal stem cells (MSCs) in regenerative medicine is well known owing to the simplicity of acquisition from various sources, low immunogenicity, adaptation to the autogenic and allogeneic systems, immunomodulation, self-recovery by secreting extracellular vesicles (EVs), regenerative repair, and antioxidative stress. MSC therapy provides an increasingly attractive therapeutic approach for ICH. Recently, the functions of MSCs such as neuroprotection, anti-inflammation, and improvement in synaptic plasticity have been widely researched in human and rodent models of ICH. MSC transplantation has been proven to improve ICH-induced injury, including the damage of nerve cells and oligodendrocytes, the activation of microglia and astrocytes, and the destruction of blood vessels. The improvement and recovery of neurological functions in rodent ICH models were demonstrated via the mechanisms such as neurogenesis, angiogenesis, anti-inflammation, anti-apoptosis, and synaptic plasticity. Here, we discuss the pathological mechanisms following ICH and the therapeutic mechanisms of MSC-based therapy to unravel new cues for future therapeutic strategies. Furthermore, some potential strategies for enhancing the therapeutic function of MSC transplantation have also been suggested.