Cell Type Enrichment Analysis to Identify Cellular Targets for Autism Spectrum Disorder

Background: The brain is the most complex organ of the entire body containing hundreds of distinct cell types, each with unique morphologies, projections, functional roles, and gene expression profiles.  Yet, there are clear examples of neurological disruptions caused by deficiencies in just one cell type or circuit. However, the cellular disruptions that lead to the behavioral abnormalities in autism are not clear.  If there were a method to identify the cell types that serve as the intermediaries between a set of genetic lesions and a particular behavioral disruption, then one could identify cellular targets for treatment.  Importantly, there is a remarkable diversity of gene expression across the nervous system. We hypothesize that across a large number of candidate disease genes implicated in a disorder, we may be able to identify the vulnerable cell type(s) by a relative overabundance in their expression of candidate disease genes.

Objectives: To leverage human genetic and cell type specific gene expression information to identify cells and circuits that are likely to be disrupted in autism spectrum disorders.

Methods: High-throughput methods have identified dozens of candidate genes that may contribute to disorders, utilizing common variant genome-wide association studies, rare variant analyses (such as exome sequencing and copy number variation), and postmortem gene expression analysis of patient brains.  In previous work, we generated dozens of ‘bacTRAP’ transgenic mouse lines specifically with the goal of systematically examining the gene expression profiles in targeted cell types.  Here, we combine these two sources of information and test an approach for allowing the selective expression of genes to guide us towards the neurobiology of disorder. We test two statistical methods, a non-parametric approach and permutation based approach, for identifying cell types by the intersection of gene expression with disease gene information.  As a likely positive control for the method, we include retinal cell type gene expression and candidate gene lists derived from dominant and recessive forms of human retinopathies.  As a negative control, we include candidate gene lists for genes that influence height, which should be unrelated to cell specific gene expression in the brain.

Results: Our approach successfully identifies a robust enrichment of Rod and Cone expressed genes in human retinopathy disease genes, and no enrichment in any cell types for genes related to height.   Analysis of patient post-mortem gene expression data and candidate gene lists from human genetic studies both indicate a common feature of autism may be the disruption of cortical interneurons.  Some modestly significant signal was also seen in corticothalamic and striatal cell types after multiple testing correction. There was no enrichment signal for other autism candidate regions or cells, for example, in any cell type of the cerebellum.

Conclusions: We have developed a novel approach to identify potential cellular mechanisms mediating psychiatric disorder, and indentified candidate cell types that may mediate features of autism spectrum disorders. We hope that our identification of candidate cell types from the genetic information may suggest new targets for treatments.