The growth and impact of gene editing technologies in the past few years have demonstrated a high potential to correct for genetic aberrations causative of disease, with some trials even actively reversing disease state. Regrettably, this is not true for all trials using gene editing or gene edited products, with some reporting unintended consequences, like insertional mutagenesis. Due in part to these unintended consequences, regulatory agencies are starting to draft guidance around development of cell and gene therapy products. In this analysis of previously published data, we focused on identifying the on- and off-target gene editing events in individual cells that occurred following CRIPSR/Cas9 gene editing.
To characterize on-target and quantify off-target gene editing with CRISPR/Cas9, we leveraged primary template-directed amplification (PTA), commercially available in ResolveDNA®, to perform single-cell whole genome sequencing on two cell types that had undergone gene editing with two different guide RNAs. Analysis was performed using BaseJumperTM bioinformatics platform. Through this application, we demonstrate a workflow for detecting single nucleotide variants, changes in copy number, and structural variants that highlight heterogeneity across these gene-edited cells. Multiple controls were employed for data interpretation, including bulk cell cultures, absence of guide RNAs, and untreated cells. We show noticeable intercellular editing variability, even within well-performing guided edits. PTA's very low allele dropout rate enhances our understanding of gene editing variability by providing zygosity within the cell. In an effort to understand unintended consequences of gene editing, we characterized a wide array of heterogeneous off-target events, many of which were not captured by prediction algorithms. Most notable of these are copy number and structural variant off-target effects that would not be seen through standard bulk screening. Taken together, these observations suggest single-cell whole genome sequencing is an appropriate method to characterize genomic consequences of CRISPR/Cas9 gene editing.
Dr. Victor Weigman, PhD
Sr. Director of Computational Biology, BioSkryb Genomics
Dr. Victor Weigman currently leads BioSkryb Genomics’ Computational Biology organization, focused on driving and automating biological insights from the multiomic lens of a single cell. He brings more than 18 years of biomarker discovery and translational research within genomics. He has published papers on biomarker identification and assay development and has contributed to the development and launch of several genomic lab developed tests and companion diagnostics. He currently serves on the NIST Genome Editing Consortium and as an advisor to the Carolina Health Informatics Program at UNC-Chapel Hill. In addition, he has facilitated research within the MAQC Society and served with the Friends of Cancer Research’s TMB Working Group to better define complex biomarker use in clinical testing and treatment-based decisions.