Development of Computational Methods for the Analysis of CRISPR Experiments

Abstract

Recent technological developments in single-cell RNA-seq CRISPR screens enable high-throughput investigation of the genome. Through transduction of a gRNA library to a cell population followed by transcriptomic profiling by scRNA-seq, it is possible to characterize the effects of thousands of genomic perturbations on global gene expression. A major source of noise in scRNA-seq CRISPR screens are ambient gRNAs, which are contaminating gRNAs that likely originate from other cells. If not properly filtered, ambient gRNAs can result in an excess of false positive gRNA assignments. CRISPR Library Evaluation and Ambient Noise Suppression for Enhanced scRNA-seq (CLEANSER) is a mixture-model based statistical approach that has been developed to characterize ambient gRNA noise in single-cell CRISPR screen using information from scCRISPR experiments generated in-house. CLEANSER enables the assignment of gRNA to cells and filters out ambient gRNAs by using a mixture of Poisson distribution and negative binomial distribution for the ambient and transduced gRNA counts respectively, while using a normalization parameter that takes into account cell-specific biases.Next, in order to allow accurate gRNA assignment to cells for other gRNA capture methods, we utilize CRISPR barnyard assays to characterize ambient gRNA noise in both CROP-seq and direct capture single-cell CRISPR screens. We observed differences in gRNA UMI distributions between scCRISPR experiments generated using different gRNA capture methods. Using this information, we built two separate models, CROP-seq CLEANSER (csCLEANSER) and direct capture CLEANSER (dsCLEANSER), useful for analyzing gRNA assignment to cells in scCRISPR experimental data generated using each of the respective gRNA capture methods. We also find that ambient gRNA filtering methods impact differential gene expression analysis outcomes, and that CLEANSER outperforms alternate approaches by increasing gRNA-cell assignment accuracy. In the last section of this dissertation, we describe the Tn5-based Reference-Aligned CRISPR Editing Reporter (TRACER). Gene editing for therapeutic purposes often entails deletions of disease-causing genes or integration of therapeutically relevant genetic information to restore wild type protein function. However, these types of editing strategies could result in drastically different DNA fragment sizes, which cause PCR bias as well as varying downstream sequencing lengths and quality. Tn5 library preparation techniques are used in CRISPR experiments due to their unbiased nature in evaluating editing outcomes. However, computational methods to analyze CRISPR outcomes that are detected using Tn5 are still lacking. We built TRACER as a versatile tool that could be easily modified to detect editing outcomes for a large variety of experimental strategies.