Science· 2026Q1
Transcriptome-based classification in mice with ASD-risk mutations
- 1citations
- Q1SCImago
- 2026year
Short summary
A prefrontal RNA sequencing atlas of 1008 profiles from 17 mouse lines with ASD-risk mutations identified two opposing transcriptomic states that differ in sex bias, regional specificity, developmental stability, cell type remodeling, and responses to fluoxetine and lithium.
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Key points
- Developed a sex-balanced atlas of 1008 prefrontal RNA sequencing profiles from 17 mouse lines with ASD-risk mutations.
- Identified two opposing transcriptomic states within these mouse models.
- These states differ significantly in sex bias, regional specificity, developmental stability, cell type remodeling, and drug responses (fluoxetine, lithium).
- Single-nucleus RNA-seq revealed broader cell type remodeling in one group, with reciprocal associations mirroring bulk transcriptomic signatures.
- The framework enables classification of independent mouse lines and identification of subgroups with conserved synaptic directionality.
AI-generated from the title and abstract; the full text is not read.
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental condition with a strong genetic component. Large-scale human genetic studies have identified >1200 ASD-risk genes. We report a sex-balanced atlas of 1008 prefrontal RNA sequencing (RNA-seq) profiles from 17 mouse lines carrying ASD-risk mutations. Our analysis identified two opposing transcriptomic states. The two groups differed in sex bias, regional specificity, developmental stability, cell type remodeling, and responses to fluoxetine and lithium. Single-nucleus RNA-seq revealed broader cell type remodeling in group 1 than in group 2, and cell type–specific modules showed reciprocal associations that mirrored bulk transcriptomic signatures. The framework classifies independent mouse lines and identifies subgroups with conserved synaptic directionality, supporting molecular stratification.
The authors' abstract, as published at the source. Science, 2026 · DOI ↗
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Field: Cognitive Neuroscience
Cognitive NeuroscienceNeuroscience