Neurogenomics Overview

Principles

Definition

Neurogenomics is an interdisciplinary field of research that examines the complex interactions between genetic factors and the structure, function, and development of the nervous system. It utilizes advanced genomic technologies, such as gene expression profiling, RNA sequencing, and single-cell genomics, often in conjunction with neuroimaging and bioinformatic analyses, to investigate the genetic architecture underlying neural processes, cellular heterogeneity within brain tissue, and the etiology of neurodevelopmental and psychiatric disorders. This field is significant for elucidating the molecular basis of neural circuit formation, plasticity, and dysfunction, providing insights into both healthy brain function and neurological or psychiatric disease.

Ontological type

Disease Applications

Core Methods

Cellular Heterogeneity

Key developments

Key Figures

Genome-Informed Neurodevelopment era

Eric Lander helped catalyze genome-scale gene discovery and network analyses that shaped approaches to neural development through genome-wide data. The Drosophila genome project, led by Gerald Rubin and Susan Celniker, established a comprehensive framework for identifying conserved neural-development regulators and regulatory networks via comparative genomics. Barbara Wold and colleagues advanced vertebrate neural development by applying expression microarrays to map transcriptional programs that drive neuronal differentiation. Mark Gerstein and collaborators exemplified genome-scale data integration and network-centered analyses that linked trophic signaling pathways and transcriptional regulators to neurodevelopment and disease susceptibility.

Atlas and Multi-Omic Integration era

Stephen M. Smith [1] is associated with Boston University [3] and University College London [4] during the Atlas and Multi-Omic Integration era. Stephen M. Smith [1] contributed to the anatomically comprehensive atlas of the adult human brain transcriptome [7], providing a spatially resolved map of regional gene expression that anchored subsequent multi-omic integration and cross-regional comparisons. Steve Horvath [2], with affiliations to University of California, San Francisco [5] and The University of Texas Southwestern Medical Center [6], contributed to this era's atlas-driven framework for linking transcriptional landscapes to development and circuit-level function. Horvath [2]'s involvement with the same anatomically comprehensive brain transcriptome atlas [7] helped advance data integration and cross-species analyses that underpinned the era's goal of connecting genetic variation to brain development and function.

Spatial Single-Cell Genomics era

In the Spatial Single-Cell Genomics era (2017–2023) of neurogenomics, Sten Linnarsson and collaborators advanced high-resolution brain cell-type taxonomies by integrating single-cell RNA sequencing with spatial localization to map transcriptional identities onto precise anatomical locations. Avil Regev and her teams contributed scalable brain cell atlas frameworks and regulatory-program analyses that connect gene regulation to cell-type-specific function and cross-species comparisons. Fabian Theis and the spatial-transcriptomics community developed methods such as SpatialDE and standardized pipelines that quantify spatially variable gene expression and enable interoperable analyses across brain regions. Rahul Satija and the Seurat ecosystem, together with cortex-focused work from the Hodge group, pushed forward dataset integration and cross-region mapping that link disease-associated variants to defined cell types and regulatory circuits.