A fundamental challenge facing neuroscientists is to understand how the functional specializations and orchestrated activities of the myriad cell types that comprise the human brain emerge from a common set of molecular instructions. These instructions are manifest in the brain's transcriptome, which is broadly defined as the set of all RNA molecules that are present in a given population of cells at a given point in time. Recent technological advances such as microarrays and RNA-seq have enabled researchers to sample transcriptomes with greater depth and accuracy than ever before, leading to an explosion of data. Our laboratory is interested in understanding how the human brain transcriptome is organized in space and time, and how this organization reflects the functional diversity of the disparate cell types that comprise the human brain.
Using network methods that treat transcriptomes as holistic systems, instead of collections of discrete elements, we have recently shown that microarray data generated from whole human brain tissue exhibit highly reproducible and functionally significant patterns of organization. We have identified modules of co-expressed genes that correspond to oligodendrocytes, astrocytes, and neurons (including specific neuronal subtypes), demonstrating that cell type-specific information can be recovered from whole brain tissue without isolating homogeneous populations of cells and providing an initial description of transcriptional patterns that distinguish the major cell classes of the human brain.
We are currently extending this approach to identify patterns of transcriptome organization in additional regions from the developing and adult human brain. We hypothesize that the identification and experimental characterization of these patterns will enable us to refine the cellular taxonomy of the nervous system on the basis of gene expression, while simultaneously providing a new approach for annotating gene function in the human brain through the principle of "guilt-by-association". In addition, we are attempting to leverage the consistency of these patterns to provide a new framework for studying the molecular and cellular evolution of the human brain, as well as perturbations in gene expression that are associated with specific neurological and neuropsychiatric diseases.