Abstract
Next-generation sequencing has identified risk genes with large effect sizes for autism spectrum disorders (ASD). Although functional analysis of individual risk genes has progressed, the overall picture of ASD pathogenesis is unclear. Therefore, there is a need for morphological profiling of variants in these genes to fully comprehend their pathomechanism in cultured cells. High-content analysis (HCA) is a powerful approach to thoroughly analyze cellular alterations following genetic modifications in many disorders, including ASD. We begin this review with the latest phenotypic descriptions of ASD risk variants and different ASD cell models, which provide a basis to select features for extraction in image-based analysis to best capture ASD mechanisms. We then describe recent genetic and pharmacological screening campaigns for ASD using HCA systems. Generally, HCA enables imaging of ASD-derived cell models using measurements such as cell proliferation, differentiation, process growth, synapse numbers, and other morphological changes to neurons, astrocytes, and microglia. Advances in machine learning are reducing bias in feature identification and extraction. These data can be transformed for downstream analyses and visualization, such as clustering using heatmaps for morphological profiling. This provides image-based profiling data that can be used to determine the mechanisms of action of genetic modifications. Additionally, comprehensive methods, such as mixture-based and common structure ranking approaches, which can systematically examine the effects of millions of compounds, could identify compounds that might ameliorate the effects of ASD risk gene mutations using morphological profiling.