There is a pressing need for new and more effective treatments for central nervous system (CNS) disorders. A large body of evidence now suggests that alterations in synaptic transmission and neuronal excitability represent underlying factors for many neurological and psychiatric diseases. However, it has been challenging to target these complex functional domains for therapeutic discovery using traditional neuronal assay methods. Here we review advances in neuronal screening technologies and cellular model systems that enable phenotypic screening of neuronal function as a basis for novel CNS drug discovery approaches.
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http://dx.doi.org/10.1016/j.ddtec.2017.03.005 | DOI Listing |
J Comp Neurol
January 2025
Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India.
Direction selectivity is a fundamental feature in the visual system. In the retina, direction selectivity is independently computed by ON and OFF circuits. However, the advantages of extracting directional information from these two independent circuits are unclear.
View Article and Find Full Text PDFMol Biol Rep
January 2025
Department of Pharmacology, School of Health Sciences, Central University of Punjab, Bathinda, 151401, India.
Background: Multiple sclerosis (MS) is a chronic autoimmune condition that damages the myelin sheath of neurons in the central nervous system, resulting in compromised nerve transmission and motor impairment. The astrocytopathy is considered one of the prominent etiological factor in the pathophysiology of demyelination in MS. The expression level of ceramide synthase-2 (CS-2) is yet to be established in the pathophysiology of astrocytopathy although the derailed ceramide biosynthetic pathways is well demonstrated in the pathophysiology of demyelination.
View Article and Find Full Text PDFNat Methods
January 2025
Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
The phenotypic and functional states of cells are modulated by a complex interactive molecular hierarchy of multiple omics layers, involving the genome, epigenome, transcriptome, proteome and metabolome. Spatial omics approaches have enabled the study of these layers in tissue context but are often limited to one or two modalities, offering an incomplete view of cellular identity. Here we present spatial-Mux-seq, a multimodal spatial technology that allows simultaneous profiling of five different modalities: two histone modifications, chromatin accessibility, whole transcriptome and a panel of proteins at tissue scale and cellular level in a spatially resolved manner.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Neurosurgery of the Second Affiliated Hospital, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China.
Horizontal connections in anterior inferior temporal cortex (ITC) are thought to play an important role in object recognition by integrating information across spatially separated functional columns, but their functional organization remains unclear. Using a combination of optical imaging, electrophysiological recording, and anatomical tracing, we investigated the relationship between stimulus-response maps and patterns of horizontal axon terminals in the macaque ITC. In contrast to the "like-to-like" connectivity observed in the early visual cortex, we found that horizontal axons in ITC do not preferentially connect sites with similar object selectivity.
View Article and Find Full Text PDFSci Rep
January 2025
Instituto de Ingeniería Energética, Universitat Politècnica de València, Valencia, Spain.
Reliable prediction of photovoltaic power generation is key to the efficient management of energy systems in response to the inherent uncertainty of renewable energy sources. Despite advances in weather forecasting, photovoltaic power prediction accuracy remains a challenge. This study presents a novel approach that combines genetic algorithms and dynamic neural network structure refinement to optimize photovoltaic prediction.
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