A spatiotemporal systems biology approach to understanding autism spectrum disorder

Autism spectrum disorder (ASD) is a complex developmental syndrome of unknown etiology. Recent studies employing exome- and genome-wide sequencing have identified nine high-confidence ASD (hcASD) genes. Working from the hypothesis that ASD-associated mutations in these biologically pleiotropic genes will disrupt intersecting developmental processes to contribute to a common phenotype, the work presented here attempts to identify time periods, brain regions, and cell types in which these genes converge. Given that current systems biology methods are not optimized for this type of question, a novel spatiotemporal approach was developed based on the concept that ASD-associated genes need to be analyzed in a human neurodevelopment context in order to best understand their contribution to the underlying biology. Leveraging a rich expression dataset, encompassing multiple human brain regions across human development and into adulthood, coexpression networks were constructed around the hcASD 'seed' genes. Subdivision of the gene expression data into smaller developmental epochs and discrete brain regions increased the spatiotemporal resolution, and enrichment of an independent set of probable ASD (pASD) genes, derived from the same sequencing studies, pinpointed a key point of convergence during midfetal development of the prefrontal cortex. Incorporation of additional layer- and cell-specific datasets refined this result to midfetal layer 5 and 6 cortical projection neurons. These results were confirmed with multiple different validation strategies. Importantly, this approach generates testable hypotheses regarding when, where, and in which cell types mutations in these specific genes may be productively studied to clarify ASD pathology, thereby providing a critical step forward in translating genetic findings to treatment. The novel paradigm presented here provides a rigorous framework for investigating other highly heritable, complex neuropsychiatric disorders. Further refinement of this method and translation into model systems will provide a more complete understanding of the underlying biology.