Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA.
Department of Psychiatry, Albert Einstein College of Medicine, Bronx, NY, USA.
J Neurodev Disord. 2022 May 2;14(1):29. doi: 10.1186/s11689-022-09441-1.
Autism spectrum disorder is a neurodevelopmental disorder, affecting 1-2% of children. Studies have revealed genetic and cellular abnormalities in the brains of affected individuals, leading to both regional and distal cell communication deficits.
Recent application of single-cell technologies, especially single-cell transcriptomics, has significantly expanded our understanding of brain cell heterogeneity and further demonstrated that multiple cell types and brain layers or regions are perturbed in autism. The underlying high-dimensional single-cell data provides opportunities for multilevel computational analysis that collectively can better deconvolute the molecular and cellular events altered in autism. Here, we apply advanced computation and pattern recognition approaches on single-cell RNA-seq data to infer and compare inter-cell-type signaling communications in autism brains and controls.
Our results indicate that at a global level, there are cell-cell communication differences in autism in comparison with controls, largely involving neurons as both signaling senders and receivers, but glia also contribute to the communication disruption. Although the magnitude of changes is moderate, we find that excitatory and inhibitor neurons are involved in multiple intercellular signaling that exhibits increased strengths in autism, such as NRXN and CNTN signaling. Not all genes in the intercellular signaling pathways show differential expression, but genes in the affected pathways are enriched for axon guidance, synapse organization, neuron migration, and other critical cellular functions. Furthermore, those genes are highly connected to and enriched for genes previously associated with autism risks.
Overall, our proof-of-principle computational study using single-cell data uncovers key intercellular signaling pathways that are potentially disrupted in the autism brains, suggesting that more studies examining cross-cell type effects can be valuable for understanding autism pathogenesis.
自闭症谱系障碍是一种神经发育障碍，影响 1-2%的儿童。研究揭示了受影响个体大脑中的遗传和细胞异常，导致区域和远端细胞通讯缺陷。
单细胞技术，尤其是单细胞转录组学的最新应用，极大地扩展了我们对脑细胞异质性的理解，并进一步表明自闭症中存在多种细胞类型和脑层或区域受到干扰。潜在的高维单细胞数据为多层次计算分析提供了机会，这些分析可以共同更好地推断自闭症中改变的分子和细胞事件。在这里，我们应用先进的计算和模式识别方法对单细胞 RNA-seq 数据进行推断和比较自闭症大脑和对照中的细胞间信号通讯。
我们的结果表明，在全局水平上，自闭症大脑中的细胞间通讯存在差异，与对照相比，神经元作为信号发送者和接收者在很大程度上涉及其中，但胶质细胞也有助于通讯中断。尽管变化幅度适中，但我们发现兴奋性和抑制性神经元参与了多种细胞间信号，这些信号在自闭症中表现出增强的强度，如 NRXN 和 CNTN 信号。细胞间信号通路中的并非所有基因都表现出差异表达，但受影响通路中的基因富集了轴突导向、突触组织、神经元迁移和其他关键细胞功能。此外，这些基因与先前与自闭症风险相关的基因高度连接和富集。
总体而言，我们使用单细胞数据进行的原理验证计算研究揭示了自闭症大脑中可能受到破坏的关键细胞间信号通路，这表明更多研究检查跨细胞类型的影响对于理解自闭症发病机制可能是有价值的。
