Single-cell approaches to deconvolution of disease-associated signals

项目摘要 对大量细胞混合物的全基因组转录组图谱分析不是识别RNA或蛋白质组的理想方法 患者来源的自闭症谱系障碍中相对于正常基因类型功能障碍的特征 细胞。项目3的目标是开发计算和实验工作流程来识别疾病- 皮层神经元和神经胶质细胞的异质混合物在单细胞水平上的相关差异。 我们将执行多参数优化并测试我们的实验工作流程的透明度和 健壮性。我们将把我们优化的工作流程应用于项目1中针对自闭症的HiPSC模型，以确定 疾病特定、细胞类型特定的RNA签名。

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.