Spatiotemporal Brain Transcriptomics Reveal Risk Gene Hot-Spots in Major Neuropsychiatric Disorders

TL;DR

This study maps how risk genes for brain disorders are expressed over time and space, revealing key areas linked to various neuropsychiatric conditions.

Contribution

The study introduces a systematic analysis of spatiotemporal gene expression patterns across multiple neuropsychiatric disorders.

Findings

Risk genes show distinct spatiotemporal enrichment patterns across neuropsychiatric traits.

Integration of MRI data confirmed structural changes in identified gene hot-spots.

Cell-type specificity and functional pathways were identified for risk gene enrichment.

Abstract

The temporal onset of polygenic brain disorders has been closely linked to the developmental dynamics of genome-wide risk gene expression. In this study, we systematically characterized the spatiotemporal expression patterns of these risk genes and their relevance in differentiating major neuropsychiatric disorders. We analyzed genome-wide risk gene sets for Intelligence Quotient (IQ), Autism Spectrum Disorders (ASD), Attention Deficit Hyperactive Disorder (ADHD), Tourette’s Syndrome (TS), Obsessive Compulsive Disorder (OCD), Anorexia Nervosa (ANO), Neuroticism, Panic disorder, Major Depressive Disorder (MDD), Bipolar Disorder (BIP), Schizophrenia (SZ), Epilepsy, Alzheimer’s Disease (AD), and Parkinson’s Disease (PD). Our results reveal distinct patterns of spatiotemporal enrichment across these traits, allowing their classification into three clusters. To validate the biological significance of these enrichment patterns, we integrated clinical MRI datasets and confirmed structural alterations within the identified spatiotemporal “hot-spots”. Furthermore, by combining gene co-expression network analysis and single-cell transcriptomic data, we delineated the cell-type specificity and functional pathways underlying risk gene enrichment. In situ hybridization data from the marmoset brain further provided a comprehensive map of risk gene related module expression. This work reinforces the link between dynamic gene expression and disease mechanisms, while highlighting potential biomarkers and therapeutic targets arising from these identified “hot-spots” and pathways.

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