Table_3_Cell Type-Specific Predictive Models Perform Prioritization of Genes and Gene Sets Associated With Autism.XLSX

Bulk transcriptomic analyses of autism spectrum disorder (ASD) have revealed dysregulated pathways, while the brain cell type-specific molecular pathology of ASD still needs to be studied. Machine learning-based studies can be conducted for ASD, prioritizing high-confidence gene candidates and promoting the design of effective interventions. Using human brain nucleus gene expression of ASD and controls, we construct cell type-specific predictive models for ASD based on individual genes and gene sets, respectively, to screen cell type-specific ASD-associated genes and gene sets. These two kinds of predictive models can predict the diagnosis of a nucleus with known cell type. Then, we construct a multi-label predictive model for predicting the cell type and diagnosis of a nucleus at the same time. Our findings suggest that layer 2/3 and layer 4 excitatory neurons, layer 5/6 cortico-cortical projection neurons, parvalbumin interneurons, and protoplasmic astrocytes are preferentially affected in ASD. The functions of genes with predictive power for ASD are different and the top important genes are distinct across different cells, highlighting the cell-type heterogeneity of ASD. The constructed predictive models can promote the diagnosis of ASD, and the prioritized cell type-specific ASD-associated genes and gene sets may be used as potential biomarkers of ASD.

针对孤独症谱系障碍（Autism Spectrum Disorder, ASD）的批量转录组学分析已揭示了多条失调通路，然而孤独症谱系障碍的脑细胞类型特异性分子病理机制仍有待深入探究。针对孤独症谱系障碍开展基于机器学习的研究，可优先筛选高置信度的候选基因，推动有效干预策略的研发。本研究利用孤独症谱系障碍患者与健康对照的人类脑细胞核基因表达数据，分别以单个基因和基因集为基础构建孤独症谱系障碍的细胞类型特异性预测模型，进而筛选细胞类型特异性的孤独症谱系障碍关联基因及基因集。这两类预测模型可针对已知细胞类型的脑细胞核完成诊断预测。随后，本研究构建了多标签预测模型，可同时实现脑细胞核的细胞类型与诊断状态预测。本研究结果显示，2/3层与4层兴奋性神经元、5/6层皮层-皮层投射神经元、小白蛋白中间神经元以及原浆型星形胶质细胞在孤独症谱系障碍中更易受累。对孤独症谱系障碍具有预测效能的基因，其功能存在差异，且不同细胞类型中排名靠前的关键基因也各不相同，这凸显了孤独症谱系障碍的细胞类型异质性。本研究构建的预测模型可助力孤独症谱系障碍的临床诊断，而筛选得到的细胞类型特异性孤独症谱系障碍关联基因及基因集，有望作为孤独症谱系障碍的潜在生物标志物。