Single-cell approaches to deconvolution of disease-associated signals

University Of California, San Diego, La Jolla CA

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Abstract

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.