bioRxivNeurosciencePreprint
Shared Alteration of Whole-Brain Connectivity and Olfactory Deficits in Multiple Autism Mouse Models
Tsan‐Ting HsuiD, Chih-Ping Chen, Ming-Hui LiniD, Tzyy‐Nan HuangiD, Chung-Yu Wang, Chia-Ming LeeiD, Bi‐Chang CheniD, Chien-Yao WangiD, Yi‐Ping HsuehiD
Abstract
Autism spectrum disorder (ASD) is a disconnection condition influenced by both heterogeneous genetic and environmental factors, yet it remains unclear whether common connectivity deficits exist. Here, we demonstrate that different ASD-linked mutations lead to distinct circuit abnormalities but share deficits in the piriform cortex and olfactory discrimination. Using advanced artificial intelligence, we developed a whole-brain mapping platform to analyze the distribution of the Thy1-YFP projection neurons in three ASD mouse models (Tbr1+/-, Nf1+/-, Vcp+/R95G). Our analysis revealed changes in axonal patterns and neuronal distribution, indicating deficits in projection neuron differentiation and maintenance. Notably, the piriform cortex consistently exhibited reduced YFP+ cells and signals and impaired functionality across all models. Visual and somatosensory cortices were also affected, but the patterns varied. These findings highlight that the sensory regions, especially the piriform cortex, are susceptible to ASD-related mutations, strengthening the notion that different sensory experiences are common in ASD. HighlightsO_LIAI-powered whole-mouse brain quantification accelerates neural connectivity study. C_LIO_LIAutism-linked mutations lead to various circuit abnormalities of Thy1-YFP neurons. C_LIO_LIMultiple sensory regions all exhibit circuit deficits among the three autism models. C_LIO_LIAbnormalities of olfaction and piriform cortex circuits are common in autism models. C_LI
- organism: mouse
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 1
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