Understanding the heterogeneity and spatial brain environment of neurodegenerative diseases through conventional and future methods

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Background

There is a great shift in the different methodologies that are available to investigate the brain environment. A few examples include the increased use of single-cell approaches that are used to understand the individual functions on single-cell levels, spatial methods that combine microscopy information with omics technologies to determine the heterogeneity of the gene signatures that occur in tissue, integration of animal models and human data, and machine learning which is used to improving clustering, deconvolution and gene ontology annotations to investigate the biological relevance. These advances have shown to be of particular interest in the neuroscience field, since the tissue is mostly collected from antemortem conditions and postmortem delay, making the analysis of the microenvironment more challenging.

The brain can be divided into two types of brain cells, nerve cells, and glia. Both of which are known to lose their respective function when a person is afflicted with a neurodegenerative disease. This is of particular interest since the brain has its own immunological system where both the nerve and glia cells work to maintain this microenvironmental balance. The purpose of this research topic is to assemble various studies that implement the existing or novel approach while investigating neurodegenerative diseases. This can include computational methods that can be to explore or suggest mutations, open transcriptome regions, or potential biomarkers for the disease of focus.

This research topic encourages in-depth analyses of different omics technologies to determine the effect of neurodegenerative diseases on the microenvironment. This could include findings that target a whole region, making a distinction between the different cells that are present but it can also focus solely on one particular cell type. Although it would be of interest to perform one omics approach, it would be preferred to send in studies where multi-omics are integrated to explain the whole molecular mechanism during neurodegeneration or describe the differences between healthy and diseased subjects. Furthermore, it would be of interest to determine discrepancies that can be observed in animal models when compared to each other or human tissue.

Keywords: central nervous system, neurons, glia cells, computational methods, neurdegenerative diseases, omics analysis

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