UCSF

Cellular biology has traditionally relied upon simple, low-dimensional single-cell measurements (e.g., microscopy and flow cytometry) which fail to adequately address cellular complexity, or high-dimensional aggregative measurements (e.g., bulk RNA-sequencing) which obscure cellular heterogeneity. Single-cell genomics technologies strike the ideal balance between measurement complexity and resolution, but have historically been hampered by two technological limitations: cell-cell doublets which confound data interpretation, and scalability limitations due to high reagent costs and complex parallel sample preparation workflows.

In this dissertation, I present solutions to both of these issues. First, I describe DoubletFinder, a machine learning approach for finding cell-cell doublets in scRNA-seq data by identifying real cells with heightened similarity to in silico-generated artificial doublets. Second, I describe MULTI-seq, a method enabling pooled single-cell genomics sample processing by labeling plasma membranes with sample-specific DNA barcodes prior to cellular isolation. After describing these technologies, I demonstrate three MULTI-seq applications. First, I explore the effects of sample pooling on single-cell RNA-sequencing (scRNA-seq) data-quality. Second, I extend MULTI-seq to single-cell epigenomics assays. And third, I leverage MULTI-seq to perform the largest-ever single-cell screen-by-sequencing experiment on PBMCs. Collectively, this dissertation documents molecular and computational tools for improving single-cell genomics scalability and data interpretability, and illustrates how these improvements expand the boundaries of feasibility for single-cell genomics experiments.