Abstract CRISPR has enabled routine genome editing, but the lack of reproducibility across experiments, cell types, and laboratories remains a major barrier to translational applications. To address this, we developed an automated, industrial-scale gene-editing platform designed to minimize edit-to-edit variability and preserve genomic integrity across diverse cell types. The system performs thousands of concurrent genome-editing reactions under fully tracked and standardized conditions, minimizing batch effects, and ensuring consistent reagent handling and cell maintenance. This environment enables quantitative assessment of editing efficiency and quality across immortalized, primary, and induced pluripotent stem (iPS) cells, generating large-scale datasets that inform both product development and process improvement. Beyond the products and services created on this platform, EditCo Bio applies analytics generated from each project to learn from every edit. The resulting data guide the continuous evolution of our editing strategies and cell-maintenance processes. To demonstrate the analytical power of this approach, we applied the platform in a large-scale study of site-dependent homology-directed repair (HDR) efficiency across several hundred genomic safe-harbor sites in human iPSCs. By systematically varying donor design and chemical modifiers, we identified discrete genomic loci that reproducibly support high knock-in efficiency while maintaining cell viability. Expanded studies further revealed experimental conditions that promote recurrent structural genomic variations, including large deletions and rearrangements, which are undetectable by conventional short-read QC. The results of this work established methods, to (1) accurately measure these structural variations and (2), reduce their occurrence through optimized design and process control. Together, these findings describe an automated cell-engineering platform that transforms genome editing from an empirical procedure into a controlled engineering process. Citation Format: Travis J. Maures, Montse Morell. Altering is easy, engineering is hard: An automated platform for reproducible, high-fidelity genome engineering abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 3240.
Maures et al. (Fri,) studied this question.
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