Advances of Brain Transcriptomics

Dear Colleagues,

Advancements in RNA-Seq technology in the last decade have underlined its power for elucidating the brain gene networks responsible for various stressful factors, as well as pathologies like Alzheimer and Parkinson, and other neurological diseases including schizophrenia and depressive disorders. Single cell RNA-seq (scRNA-seq) allows for ascertaining various neurons and glia cell identities by elucidating the specific marker genes within them. This technology underlinees multiple novel stable cell colonies in the course of scRNA-seq analysis. Associative networks between cell colonies have been provided over the course of cells’ maturation, rendering an exhaustive picture of cell population development dynamics.

As a separate relevant issue, RNA-Seq provides the basis for cell/tissue specific alternative splicing (AS) elucidation with a high resolution. As it is known, the brain maintains the most expanded AS-mediated proteome variability, as well as AS-mediated transcription regulation, via nonsense mediated decay. The research of Barres Lab reported a 10-fold expansion of transcript diversity analyzing mouse brain transcriptomes (Yan et al., 2015). Currently, it is reported that the AS-specific profile is more specific and robust in certain instances than gene expression for neuronal identity (Ha et al., 2021). Multiple databases like ASCOT and Genome (for elucidating the tissue/cell specific AS profiles for each gene) are arising, underlining the research of brain transcriptome structural and expression variability as a top priority.

Additionally, there is a genetic based vs. acquired trait paradigm, which would be represented by both genetic studies based on animal model strains/breeds (e.g., tame foxes, aggressive strains of rats, etc.), and those that acquired the trait within single generation upon administering certain stress-related protocols.

In this Issue, we hope to address the spectra of physiological studies, including, but not limited to, animal models of social stress response and various brain disease related data/models using the abovementioned approaches and methods.

Dr. Vladimir Babenko
Dr. Olga Redina
Guest Editors

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