Single-cell approaches to deconvolution of disease-associated signals

项目摘要 细胞混合物的全基因组转录组分析对于鉴定RNA或蛋白质组学而言并不理想。 在自闭症谱系障碍中相对于患者来源的正常基因型功能障碍的特征 细胞项目3的目标是开发计算和实验工作流程，以识别疾病- 相关的差异，在单细胞水平的异质混合物的皮质神经元和胶质细胞。 我们将进行多参数优化，并测试我们的实验工作流程的透明度和 鲁棒性我们将把我们优化的工作流程应用于项目1中的自闭症特异性hIPSC模型， 疾病特异性、细胞类型特异性RNA特征。

Project Summary Genome-wide transcriptome profiling of the bulk mixture of cells is not ideal for identifying RNA or proteomic signatures that are dysfunctional in Autism spectrum disorder relative to normal genotypes in patient-derived cells. The objective of Project 3 is to develop computational and experimental workflows to identify disease- associated differences at the single-cell level from heterogeneous mixtures of cortical neurons and glial cells. We will perform multi parameter optimization and test our experimental workflow for transparency and robustness. We will apply our optimized workflows to autism-specific hIPSC models from Project 1 to identify disease-specific, cell-type specific RNA signatures.