ABSTRACT KromaTiD’s current commercial therapeutic gene editing customers have expressed the critical need for a standard approach to screening engineered cells for quality and safety that yields a comprehensive genomic dataset with improved resolution, localization, and speed. Directional Genomic Hybridization (dGH™) has been developed to efficiently screen cell populations for the presence of simple, complex, and heterogenous structural variants. In this project, A Comprehensive Quality Control Testing Strategy for Engineered Cells, by combining five-color, whole genome dGH with the fit for purpose sequencing methods of a clinically important genome engineering system, we propose an approach to assess, for the first time, the complete outcomes of gene editing: successful edits, unsuccessful edits, off-target edits, sequence variants, structural variants, and gross genome integrity. Furthermore, we propose to develop a standardized data specification integrating the data from these methods into a regulatory ready data package. dGH is an in-situ hybridization technique utilizes high-density chromatid paints to directly interrogate the structure of a genome in a single cell without bioinformatic interpretation, providing a complete toolset for hypothesis-free, single-cell measurement of SVs at edit sites, per chromosome, and across the whole genome. For companies developing therapies based on gene editing and other cell engineering approaches, understanding editing systems and mis-repair of DSBs are critical for patient safety and regulatory approval. Currently, batches of edited cells are screened for edit-site errors by sequencing. Because DSBs do not all occur at the edit site, SVs in batches of edited cells exhibit a complex, heterogenous mixture of edit-site and random breakpoints. G-banding can be used to screen for gross genome defects but cannot detect small or complex structural variants. dGH assays detect structural variation from a reference genome without target information, resolve SVs of 5Kb and larger, and provide improved genomic structural assessment capable of displacing standard karyotyping. The potential of genome editing approaches such as CRISPR/Cas9, for the treatment of diseases is widely recognized, and realization of the promise of such therapeutic approaches will rely on accurate confirmation of the presence and absence of potentially risky structural variants. For these reasons, comprehensive detection and characterization of structural variations is a necessary step toward understanding gene editing and other cell engineering systems. dGH combined with best-fit sequencing can provide a complete analysis of the outcomes of gene editing from SNVs and indels though large, complex SVs.