Genetic engineering experiments and therapies are constrained by the size of DNA integrations into human cell’s genomes. Existing AAV, lentiviral, and non-viral methods rapidly decrease in integration efficiency beyond ∼5kb of sequence. Through systematic evaluation of non-viral DNA template formats, we identified circular ssDNA and dsDNA as capable of mediating >5kb integrations. Large circular DNA delivery efficiency and its impacts on cell viability and payload expression could be significantly improved with small DNA “helper” plasmids, mRNA-encoded nucleases, and sequence design optimizations. Collectively, these modifications enabled ultra-large—up to 10 kb DNA—integrations at >20% efficiency in primary human T cells at the TRAC locus and at >60% efficiency in human iPSCs at the AAVS1 locus. Finally, we demonstrate that GMP clinical-manufactured T cells with ultra-large integrations are functional in vitro and in vivo. Overall, we identified optimal template architectures, delivery modes, and sequence design rules for ultra-large DNA integrations in both research and clinical settings to accelerate basic genetic research and next-generation cellular therapies.

Protein-coding genes in the human genome evolved via modular rearrangement of domains from ancestral genes¹. Here, we develop a scalable, evolutionarily guided method to assemble novel protein-coding genes from constituent domains within a protein family, termed DESynR (Domain Engineered via Synthesis and Recombination) genes. Using primary human chimeric antigen receptor T cells as a model system, we find that the expression of DESynR Activator Protein-1 (AP-1) transcription factors (TFs) significantly outperforms the overexpression of natural AP-1 TFs in multiple functional assays in vitro and in vivo. Top DESynR AP-1 TFs exhibit non-intuitive architectures of constituent domains, including from TFs that are not canonically expressed in T cells. DESynR AP-1 TFs induce broad transcriptional and epigenetic reprogramming of T cells and, in some cases, lead to the development of non-natural T cell states, engaging gene expression modules from disparate human cell types. Taken together, we demonstrate that novel configurations of existing protein domains may uncover non-evolved genes that program cell states with therapeutically relevant functions.

Genome-wide Perturb-seq (GWPS) has emerged as a powerful approach for unbiased mapping of gene regulatory networks. A key assumption underlying many Perturb-seq analyses is that each guide RNA exclusively perturbs a single target locus. Without methods to identify and filter off-target events, erroneous gene-pathway associations driven by off-target activity can propagate into downstream analyses. Here, we present a workflow for the systematic identification of candidate off-target events in CRISPRi Perturb-seq experiments. Our approach exploits the observation that cells harboring a guide which represses an off-target gene display transcriptional similarity to cells in which that gene is directly targeted by an on-target guide. We apply our workflow to multiple GWPS datasets and nominate off-target events in which a guide nominally targeting one gene also represses a distinct gene producing a phenotype likely attributable to the off-target perturbation. We use both off-target gene repression and guide seed sequence alignments at the off-target promoter locus as evidence for off-target effects and find independent evidence of putative off-target events in separate GWPS datasets. Together, these results establish a principled framework for the identification and filtering of off-target guide effects in Perturb-seq experiments.