Linking autism risk genes to morphological and pharmaceutical screening by high-content imaging: Future directions and opinion.

This review looks at a new way to study autism genes using special imaging technology called high-content analysis. Scientists can grow cells in the lab and watch how they change when autism risk genes are modified. They can measure things like how cells grow, develop, and connect with each other. This technology might help find new medicines that could help with the effects of autism genes.

This 2025 review examines high-content analysis (HCA) as a method for studying autism risk genes through morphological profiling in cultured cells. The authors discuss how HCA can systematically analyze cellular changes following genetic modifications in ASD cell models, measuring features like cell proliferation, differentiation, synapse formation, and morphological changes across neurons, astrocytes, and microglia. The review covers recent genetic and pharmacological screening campaigns using HCA systems, highlighting advances in machine learning for unbiased feature identification. The authors propose that comprehensive screening approaches could identify compounds that might ameliorate effects of ASD risk gene mutations through morphological profiling, potentially informing future therapeutic development.

High-content analysis represents a promising research methodology for understanding autism pathomechanisms and potentially identifying therapeutic targets. However, translation from cellular models to clinical applications remains uncertain and requires further validation.

As a review paper, findings represent synthesis of existing literature rather than new empirical data. The clinical translation of morphological profiling findings to therapeutic outcomes remains unclear and speculative.

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.