Synthetic genomic dissection of enhancer context sensitivity and synergy

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Ribeiro-dos-Santos , Colleen McLoughlin , Gwen Ellis , Hannah J. Ashe , View ORCID Profile Nerea Berastegui Zufiaurre , View ORCID Profile Ran Brosh , View ORCID Profile Milosz Majewski , View ORCID Profile Brendan Camellato , View ORCID Profile Jef D. Boeke , View ORCID Profile Matthew T. Maurano doi: https://doi.org/10.1101/2025.08.13.669251 Raquel Ordoñez 1 Institute for Systems Genetics, NYU School of Medicine , New York, NY 10016, USA 2 Department of Pathology, NYU School of Medicine , New York, NY 10016, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Raquel Ordoñez André M. Ribeiro-dos-Santos 1 Institute for Systems Genetics, NYU School of Medicine , New York, NY 10016, USA 7 Instituto de Ciências Biológicas, Universidade Federal do Pará , Belém, PA, 66075-110, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Colleen McLoughlin 1 Institute for Systems Genetics, NYU School of Medicine , New York, NY 10016, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gwen Ellis 1 Institute for Systems Genetics, NYU School of Medicine , New York, NY 10016, USA 8 Department of Biology, University of Vermont , Burlington, VT 05405, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hannah J. Ashe 1 Institute for Systems Genetics, NYU School of Medicine , New York, NY 10016, USA 9 School of Medicine, University of Maryland , Baltimore, MD 21201, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nerea Berastegui Zufiaurre 1 Institute for Systems Genetics, NYU School of Medicine , New York, NY 10016, USA 3 University of Navarra , Pamplona, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nerea Berastegui Zufiaurre Ran Brosh 1 Institute for Systems Genetics, NYU School of Medicine , New York, NY 10016, USA 2 Department of Pathology, NYU School of Medicine , New York, NY 10016, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ran Brosh Milosz Majewski 1 Institute for Systems Genetics, NYU School of Medicine , New York, NY 10016, USA 4 Maastricht University , Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Milosz Majewski Brendan Camellato 1 Institute for Systems Genetics, NYU School of Medicine , New York, NY 10016, USA 11 Developmental Biology Program, Memorial Sloan Kettering Cancer Center , New York, NY 10065, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Brendan Camellato Jef D. Boeke 1 Institute for Systems Genetics, NYU School of Medicine , New York, NY 10016, USA 5 Department of Biochemistry Molecular Pharmacology, NYU School of Medicine , New York, NY 10016, USA 6 Department of Biomedical Engineering, NYU Tandon School of Engineering , Brooklyn, NY 11201, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jef D. Boeke Matthew T. Maurano 1 Institute for Systems Genetics, NYU School of Medicine , New York, NY 10016, USA 2 Department of Pathology, NYU School of Medicine , New York, NY 10016, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Matthew T. Maurano For correspondence: maurano{at}nyu.edu Abstract Full Text Info/History Metrics Supplementary material Preview PDF ABSTRACT Noncoding disease and trait-associated genetic variation is frequently interpreted in the context of genomic regulatory elements such as DNase I hypersensitive sites (DHSs). But while most DHSs lie within a few kilobases of another DHS, regulatory elements are typically analyzed individually without accounting for their neighbors. Based on our Big-IN technology for scarless genome engineering, we have developed a high-throughput multiplexed delivery pipeline to analyze >100 engineered payloads in hundreds of mouse embryonic stem cell (mESC) clones. We characterize multiple heterotypic DHS combinations from different critical mESC regulator loci, all delivered in a constant chromosomal context replacing the Sox2 Locus Control Region (LCR). We identify widespread examples of context-dependent enhancers which have no activity on their own but can double the activity of a neighboring DHS. We find pervasive context dependencies that depend on the specific DHS pairings. We further show that synergy between neighboring DHSs decays as a characteristic function of distance, with its influence extending up to several kilobases. We fine map this context dependency to the contribution of individual transcription factor recognition sequences. Our approach implicates the specific sequence and architectural features underpinning pervasive genomic context effects, and outlines key directions for modeling the functional impact of noncoding regulatory variation on common human traits and diseases. INTRODUCTION Gene expression is tightly regulated by a complex network of enhancers which interact with gene promoters over long genomic distances. Enhancers were initially defined as short modular DNA sequences capable of autonomously driving promoter transcription regardless of their distance or orientation ( Banerji 1981 ; Schaffner 2015 ). In the genomic age, the term enhancer has come to include genomic sites identified by the presence of biochemical hallmarks including accessibility to DNase I or other enzymes, histone modifications, and eRNA transcription ( Catarino 2018 ). Yet there is an incomplete overlap between these definitions, and only a fraction of the tested sequences show enhancer activity in reporter assays ( ENCODE Project Consortium 2020 ). A key open question is how their surrounding context modulates enhancer function. Functional testing of the enhancer activity has traditionally relied on sufficiency tests in ectopic reporter assays, or single deletion in an endogenous context. But while enhancers are usually considered as individual sequences, they are frequently found in close proximity within the genome, including at super-enhancers (Whyte 2013) or locus control regions (LCR) ( Li 2002 ). More recent studies addressing sufficiency in a native genomic context have identified genomic elements without autonomous activity that synergize with nearby enhancers (Thomas 2021; Brosh 2023 ; Blayney 2023 ) or sequences that contribute to enhancer-promoter communication through architectural interactions ( Batut 2022 ; Chen 2023 ; Bower 2025 ). Previous studies have shown that individual sequences within these clusters can interact in multiple ways: redundantly, additively or synergistically, when their combined activity exceed the sum of their individual effects ( Bothma 2015 ; Thomas 2021; Choi 2021 ; Martinez-Ara 2022 ; Lin 2022 ; Brosh 2023 ; Blayney 2023 ; Zhou 2025; Koeppel 2025 ). This suggests that what we call an “enhancer” may be in fact a composite regulatory unit whose function depends on context and cooperation. Understanding the regulatory logic of an enhancer cluster requires defining the predictive rules that explain how combinations of regulatory elements interact to fine-tune transcriptional outputs, and how this logic is encoded in the underlying DNA sequence. Synthetic regulatory genomics tools such as Big-IN technology ( Brosh 2021 ) address this challenge by enabling precise integration of engineered sequences into defined genomic loci. Big-IN uses a recombinase-mediated integration into a pre-engineered landing pad, which facilitates efficient and scarless insertion of synthetic payloads, from few base pairs to hundreds of kilobases, into the genome. This provides a powerful framework to systematically reconstruct and perturb entire regulatory landscapes, making it possible to test how combinations of regulatory elements encode transcriptional outcomes. In this study, we build on recent findings from the dissection of the Sox2 LCR, which showed that weak or inactive DHSs can boost the activity of nearby elements in a context-dependent manner (Brosh 2023). Using a synthetic regulatory genomics approach, we systematically investigate the sequence and organizational principles underlying enhancer context-dependence by replacing the endogenous Sox2 LCR with synthetic combinations of heterotypic DHSs derived from multiple key mouse embryonic stem cells (mESC) regulatory loci. We identify widespread context-dependent enhancers, characterize the impact of genomic distance on enhancer interaction, and fine-map the sequence determinants of synergy to specific transcription factor (TF) motif arrangements. Our work establishes a large-scale experimental approach for functional dissection of genomic organization, highlighting the importance of local sequence context for interpretion of regulatory function. Our findings suggest that evolution and function of regulatory DNA must be seen through the lens of context effects among nearby regulatory elements. RESULTS A multiplexed delivery platform for synthetic enhancer analysis To systematically dissect the principles of enhancer context dependence beyond natural genomic variation, we developed a scalable system capable of evaluating a higher number of synthetic enhancer constructs. We observed that Big-IN delivery of shorter (<20 kb) payloads regularly yields large numbers of colonies with high genotyping and Capture-seq success rates. We therefore designed a barcoding experiment to assess whether Big-IN can support multiplexed delivery of short payloads. We designed a 2.4-kb payload library including a barcode (BC) to uniquely identify independent insertions and a GFP expression cassette. We delivered this library to landing pads at Igf2/H19 and Hprt , counter-selected for successful delivery, and quantified BC diversity using an amplicon-based deep sequencing approach ( Fig. S1a ). Sequencing revealed hundreds of uniquely barcoded, on-target payload integrations both at the Igf2/H19 and Hprt loci ( Fig. S1b ). Fluorescent imaging of the colonies showed that >90% of the colonies showed GFP signal, confirming payload integration ( Fig. S1c ). These results demonstrate that the existing Big-IN protocol can be used for multiplexed delivery of hundreds of different assemblies. Encouraged by these results showing the feasibility of pooled delivery, we developed a scalable readout strategy using plate-based phenotyping ( Fig. 1 ). In this approach, a pooled library is delivered in a single transfection ( Table S1, Table S2 ), followed by Big-IN selection in pooled format. Clones are then picked and grown in a 96-well plate ( Table S3 ). Switching to an arrayed format only after selection significantly reduces the cell culture requirements. Download figure Open in new tab Fig. 1. Multiplexed Big-IN approach. a . Schematic of pooled-to-arrayed strategy for scalable Big-IN payload delivery and phenotyping. Payloads are assembled either by arrayed Golden Gate Assembly (GGA) followed by pooling, or direct pooled cloning. Pooled payload DNA is then extracted and delivered to mESC using Big-IN. Following selection on pools, individual clones are expanded in arrayed 96-well format. PCR genotyping and Capture-seq are used to assign payload identity, and allele-specific qRT-PCR is performed to quantify expression in each clone. To validate correct delivery and clone identity, we employed 96-well plate-based genomic DNA extraction and library construction, followed by targeted capture sequencing ( Brosh 2021 ). To deconvolute payload identity in individual clones, we developed a strategy that combines three orthogonal signals: junction mapping using a modified version of bamintersect ( Brosh 2021 ), read coverage analysis, and unique sequence detection (kmer genotyping). This approach enables flexible deconvolution across different payload library designs. To enable efficient cloning of multiple Big-IN payloads, we used Golden Gate assembly (GGA), to streamline construction of a diverse library of payloads ( Fig. 1 ). We developed a new pINE-APLE vector (plasmid for INnovativE Assembly of PayLoads in E. coli) specifically to enable scalable assembly and delivery of Big-IN payloads. Compared to the original pLM1110 backbone ( Brosh 2021 ), it is significantly smaller (5.5 kb vs. 14 kb), supports high-copy growth in standard bacteria strains, includes a green/white GFP-based screening system, and includes a simplified mammalian selection cassette and modular design features to allow swapping between both systems. Each payload was flanked by unique nucleotide sequences (UNS) to facilitate genotyping after delivery. To generate equimolar pools of payload DNA for delivery, we optimized a pooling strategy to ensure uniform representation of each construct (see Methods for details), which was further confirmed by sequencing the pooled DNA. This approach enabled consistent and scalable preparation of multiplexed Big-IN payloads suitable for pooled delivery. Combinatorial enhancer libraries show widespread enhancer cluster synergy Having established a scalable platform for pooled delivery and phenotyping of synthetic sequences, we next applied this system to test whether context-dependent activity is a general feature of developmental enhancers. We expanded our analysis beyond elements of the Sox2 locus to include additional DHSs from other loci. We selected candidate DHSs from the Nanog , Sall1 , and Prdm14 loci previously characterized through deletion analysis ( Hnisz 2015 ; Blinka 2016 ; Moorthy 2017 ; Singh 2021; Vos 2021) ( Fig. 2a-d ). Download figure Open in new tab Fig. 2. Identification of novel context-sensitive DHSs. a-d . Sox2 ( a ), Nanog ( b ), Prdm14 ( c ), and Sall1 ( d ) loci showing. Del, deletion: red indicates deletion reported to affect expression of the target gene, gray indicates no effect ( Hnisz 2015 ; Blinka 2016 ; Moorthy 2017 ; Singh 2021; Vos 2021). Boundaries of full-length enhancer clusters are marked with black boxes. The Big-IN landing pad replacing the BL6 allele of the Sox2 LCR is denoted by orange shading. e . Schematic of strategy to identify enhancer context sensitivity delivering payloads to replace the Sox2 LCR. f-g . Candidate DHSs were tested alone (gray), or in combination with Sox2 DHSs (orange or blue). f . Sox2 expression analysis. Each bar represents the mean ± SD. Expression from the BL6 allele was normalized to the CAST allele and scaled between 0 (Δ Sox2 ) to 1 (WT LCR). Vertical dashed lines indicate median expression of baseline payloads. g. Contribution of each DHS, computed as the difference in Sox2 expression between payload pairs differing solely by the presence of the candidate DHS. Points indicate mean expression change. Error bars indicate SD across all pairwise combinations of clones. We cloned candidate DHSs individually or in pairs with Sox2 23 (context-dependent) and Sox2 24 (autonomous), then delivered them in place of the Sox2 LCR, and profiled Sox2 expression ( Fig. 2e , Fig. S2 ). None of the candidates had strong activity on their own, and all were weaker than Sox2 24 ( Fig. 2f ). But some of the pairs had significantly higher activity than the constituent DHSs alone. We explored how each individual context DHS ( Sox2 DHSs 23 and 24) modulated enhancer activity by comparing each of the candidate DHSs to the activity of the payload without the candidate ( Fig. 2g , Fig. S3 ). This analysis resolved multiple categories of enhancer context dependence: (i) DHSs with no activity at all, whether on their own or with a partner DHS; (ii) DHSs that are inactive on their own but have negative synergy with Sox2 24; (iii) DHSs that synergize specifically with Sox2 23; (iv) with Sox2 24; or (v) with both. The differences observed between DHS23 and DHS24 when paired with the same candidate DHSs can reflect both variability of TF compatibility as well as context sensitivity, meaning that the regulatory outcome of a given DHS depends on its pairing partner. These data provide evidence that DHS synergy is prevalent across the genome and is highly specific to the exact pair of DHSs. To identify predictors of these interactions, we examined different biochemical hallmarks of regulatory activity in the candidate DHSs, such as TF occupancy patterns, histone modifications, PRO-seq signal, STARR-seq activity (as a proxy for reporter assay output), and sequence conservation ( Fig. S4 ). None of these features alone reliably predicted the observed interaction categories. Notably, some DHSs with high STARR-seq activity failed to behave as autonomous enhancers in our genomic context, such as Sall1 +39 & +40 and Sall1 +27 & +28, highlighting that reporter-based activity does not necessarily translate to autonomous function in a different context. DHS pair spacing modulates enhancer synergy While the candidate enhancer pairs originated from disparate genome sites, they showed strong synergy when delivered together. This raises the question of whether genomic distance might limit their interaction. However, dissecting the role of DHS spacing is cofounded by the difficulty of deconvolving distance per se from function of the intervening sequence. To this end, we employed synHPRT1R, a synthetic payload representing a “backwards” version (i.e., reversed but not complemented) of the 101-kb human HPRT1 locus ( Camellato 2024 ). This DNA preserves certain features of the native HPRT1 locus, such as base composition and repetitive sequence, while disrupting higher-order features such as coding sequence and TF recognition sites (except palindromes). synHPRT1R has previously been demonstrated to have no ATAC-seq peaks upon delivery into mESC ( Camellato 2024 ). To assess the role of DHS spacing on activity, we employed a pooled library cloning strategy using a ladder of BsiWI-digested synHPRT1R fragments ranging from 56 bp to 10.8 kb. Sourcing the library from a restriction-enzyme digest ensures that fragments have independent and non-overlapping sequences, even those with closely related sizes. These fragments were cloned between Sox2 DHSs 23 and 24 ( Fig. 3a-b ). Upon delivery replacing the Sox2 LCR, Capture-seq data was used to deconvolute the spacer identity for each mESC clone. The results showed that the synergy between DHS23 and DHS24 decays with increasing distance. Sox2 expression decreased monotonically to the activity of DHS24 alone as the spacer distance increased to 4 kb ( Fig. 3c ), consistent with a functional dissociation of both DHSs. Download figure Open in new tab Fig. 3. Synergistic enhancer function depends on distance between DHSs. a . Schematic of spacing library cloning strategy: spacer fragments (6 bp–10 kb) derived from restriction-digested donor BAC DNA were cloned between a pair of the Sox2 LCR DHSs and integrated at the endogenous locus using Big-IN. Individual clones were isolated for analysis. b . Schematic of the Sox2 LCR showing DHSs 19, 23, 24, and 26 included in the following spacing experiments. Inter-DHS spacing distances within the native core LCR DHSs are indicated for reference. c-d . Sox2 expression analysis using qRT-PCR in individual mESC clones including DHS pairs Sox2 DHS23 and 24, spacer donor synHPRT1R ( c ) or DHS 24 and 26, spacer donor syn-HPRT1R noCpG ( d ) separated by spacers of varying lengths. Each point represents Sox2 expression from the engineered allele in an independent mESC clone. Expression from the BL6 allele was normalized to the CAST allele and scaled between 0 (Δ Sox2 ) to 1 (WT LCR). Orange line indicates second-order polynomial fit with 95% confidence interval shaded in gray. Horizontal dashed lines indicate median expression for baseline payloads. Red and blue points represent spacer orientation; gray indicates no spacer. To investigate whether this distance-dependent synergy was specific to a context-dependent DHS (i.e. Sox2 23), we next investigated Sox2 24 and 26, both of which show autonomous activation of Sox2 ( Brosh 2023 ). To further generalize our spacer strategy, we tested our strategy with Syn-HPRT1R noCpG , a derivative of SynHPRT1R wherein CpGs were mutated away and which lacks any detectable H3K27me3 signal in mESC ( Camellato 2024 ). AvrII-digested synHRPT1R noCpG fragments were cloned between Sox2 24 and 26. We observed a similar distance dependence between DHS24 and DHS26 ( Fig. 3d ), demonstrating that distance-dependent synergy is a broad feature of neighboring enhancers and not limited to highly context-dependent DHSs. Together, these results demonstrate that enhancer synergy is highly sensitive to spatial arrangement, and that genomic distance constrains the functional interaction of individual DHSs. Sox2 23 & 24 synergy relies on a highly structured TF binding pattern To assess the interaction between specific TF site contributions and synergy between enhancers, we considered a series of TF recognition motifs within Sox2 DHS24 previously shown to affect the activity of Sox2 DHS24 alone ( Brosh 2023 ). We rederived each deletion in the context of the Sox2 23 & 24 DHS pair, and delivered them in place of the Sox2 LCR ( Fig. 4a ). Deletion of different TF sites showed a wide range of impact on Sox2 activation ( Fig. 4b ). Comparing their effects on Sox2 expression across both contexts revealed a strong correlation ( Fig. 4c-d ): deletions that most reduced expression in the Sox2 24-alone context, had a proportionally larger effect in the Sox2 23 & 24 background. The positive correlation indicates that while the relative contribution of each TF motif is preserved, their impact is amplified in the higher activity Sox2 23 & 24 context. Download figure Open in new tab Fig. 4. Individual TF contributions are preserved across different enhancer contexts. a . Detail of Sox2 DHS 24, showing DNase-seq and ChIP-nexus data for TF occupancy in mESC. Below, schematic representation of Sox2 24 payload boundaries and annotated TF motif positions. b. Analysis of Sox2 DHS24 TF site deletions within the context of Sox2 DHSs 23 and 24. The presence (green dots) or absence (gray dots) of each TF site is indicated. Each point represents Sox2 expression from the engineered allele in an independent mESC clone. Bars indicate median. Expression from the BL6 allele was normalized to the CAST allele and scaled between 0 (Δ Sox2 ) to 1 (WT LCR). Vertical dashed lines indicate median expression of baseline payloads. c . Comparison of the effect of matched TF motif deletions under Sox2 24-only ( Brosh 2023 ) and Sox2 23 & 24 assemblies, suggesting that individual TF contributions are preserved between both contexts. d . Bar plot comparing the effect on Sox2 expression of each TF motif deletion under Sox2 24-only ( Brosh 2023 ) and Sox2 23 & 24 assemblies. Horizontal dashed lines indicate median expression of the corresponding baseline payloads. We next asked whether the overall synergy between the core Sox2 LCR DHSs 23 and 24 depends on a specific arrangement of TF recognition sequences across both elements. Based on ChIP-nexus data and TF motif analysis, we identified key TF recognition sites in Sox2 23 and Sox2 24, numbered 23.1–23.8 and 24.1–24.4, respectively ( Brosh 2023 ). These sites were predicted to be occupied by key mESC regulators, including ZIC3, the ESRR family, nuclear receptors, SOX, and forkhead proteins ( Fig. 5a ). Delivery of synthetic payloads containing targeted deletions of these sites confirmed their activity, showing that Sox2 23 is a composite element with multiple regions contributing to the interaction with Sox2 24 ( Fig. 5b ). Notably, even after deletion of all identified motifs in Sox2 23 ( Sox2 23 & 24 (Δ23.1-23.8)), expression of the DHS pair remained higher than with Sox2 24 alone, suggesting that Sox2 23 retains residual regulatory activity beyond the TF recognition sites we have mapped. Download figure Open in new tab Fig. 5. Sox2 DHSs 23 & 24 4 synergy relies on a highly structured TF binding pattern. a . Detail of Sox2 DHSs 23 and 24, showing DNase-seq and ChIP-nexus data for TF occupancy in mESC. Below, schematic representation of Sox2 23 & 24 payload boundaries, annotated TF motif positions, and the relative positions A-G used for TF motif 24.2 relocation. b. Dissection of Sox2 23 TF recognition sites. The presence (green dots) or absence (gray dots) of each TF site is indicated for each payload; truncations are indicated by the absence of dots. Each point represents Sox2 expression from the engineered allele in an independent mESC clone. Bars indicate median. Expression from the BL6 allele was normalized to the CAST allele and scaled between 0 (Δ Sox2 ) to 1 (WT LCR). Vertical dashed lines indicate median expression of baseline payloads. Sox2 23 & 24, Sox2 23 & 24 (Δ23.1-23.7) and Sox2 23 & 24 (Δ23.1-23.8) were previously reported ( Brosh 2023 ). c-d . Contribution of TF site 24.2 by position within different contexts. Relocation (red) or addition (orange, blue, or green) of TF site 24.2 to Sox2 23, Sox2 24, or Sox2 23 & 24 payloads at positions A-G (triangles in a ). c . Sox2 expression analysis. The DHS schematic (left) illustrates the presence or absence of the original 24.2 motif (green ticks) and the position of the newly inserted copy (white ticks). d . Δ Sox2 expression was computed as the mean Sox2 expression difference between payload pairs differing solely by the presence of TF site 24.2. Vertical gray line indicates original TF site 24.2 location. We then asked to what extent this arrangement of TF sites required specific positioning. Previous deletion analysis had identified the motif 24.2, which overlaps nuclear receptor and SOX motif matches, as the single most important TF site in DHS24 ( Brosh 2023 ). We therefore relocated or inserted a copy of the motif 24.2 at 6 different positions ( Fig. 5a and c ). While both relocation of 24.2 and insertion of a second copy within Sox2 23 or 24 increased activity above baseline, the original position consistently supported the highest expression ( Fig. 5d ). The effect was strongest within 1 kb of the native 24.2 site, and declined steeply beyond that. These results suggest that enhancer context is shaped not only by the identity of the TF recognition sequences but also by their precise spatial configuration, highlighting a highly structured regulatory grammar underlying the synergy between DHSs. DISCUSSION Enhancer clusters are key units of transcriptional regulation, but the principles governing the interactions between their constituent elements remain poorly understood. We have applied a scalable approach to test enhancer function in a defined genomic context. This platform has enabled profiling the activity of complex regulatory interactions in a controlled sequence environment. Cooperative enhancer function has previously been described at individual loci (Thomas 2021; Brosh 2023 ; Blayney 2023 ), but the extent of synergy between enhancer elements remains unclear. By testing multiple heterotypic DHS pairs, we found that roughly half of them displayed some form of interaction (positive or negative synergy) with DHSs from the Sox2 LCR. This synergy was highly dependent on the identity, arrangement, and distance between the DHSs. Although we inspected several hallmarks of regulatory activity for each candidate DHSs (including TF occupancy, histone modifications, or nascent transcripts), none of these alone predicted enhancer activity or enhancer cooperativity in our system. Some robust DHSs with robust reporter activity (such as Sall1 +39&+40 or Sall1 +27&28) failed to drive Sox2 expression when placed alone at Sox2 . We note the rate of inactive candidate DHSs was higher from the Prdm14 , Nanog , and Sall1 loci than from the Sox2 LCR, all of whose DHSs contribute some activity ( Brosh 2023 ). Thus it is possible that the Sox2 locus regulatory architecture is tuned for certain enhancer sequences. We expect that patterns will emerge with screening of additional candidates, loci, and cell types, investigation of additional parameters such as enhancer spacing, and profiling of more detailed molecular readouts such as long-read single-molecule footprinting (Stergachis 2020; Swanson 2024). Regulatory activity fundamentally relies on proximity, making genomic distance a key parameter in cis-regulatory function. The influence of distance spans multiple distinct scales: from promoter-enhancer communication and locus architecture across tens to hundreds of kilobases, to the organization of enhancer clusters at the kilobase scale, and down to the precise arrangement of TF recognition sequences at base pair resolution within individual regulatory elements. While the impact of enhancer-promoter distance is well-recognized (Zuin 2022; Thomas 2025; Jensen 2025 ), there has been less attention on the distance between enhancers on the order of hundreds to thousands of base pairs (Small 1993). It remains an open question what mechanism might underlie synergy on the order of 4 kb, whether mediated by nucleosomes ( Mirny 2010 ) or diffusion ( Karr 2022 ). Single-molecule methyltransferase footprinting has identified distance-dependent correlation in accessibility among DHSs on a similar kilobase scale (Stergachis 2020), as has analysis of allelic imbalance in bulk DNase-seq data ( Maurano 2015 ; Halow 2021 ). Our work supports the notion that enhancer cluster spatial organization reflects a functional arrangement that contributes to the precision, robustness and cell-type specificity of gene regulation. Beyond a conceptual framework which scaffolds genomic elements on distance, both the position and orientation of Sox2 DHSs influence their activity ( Brosh 2023 ), and even larger enhancer clusters also exhibit polarity ( Brosh 2023 ; Ordoñez 2024; Kassouf 2025 ). Moreover, while distance and orientation imply a focus on two points, multiple functional genomic elements are in play at any given genomic locus. Thus, attempts to disentangle this confusion must also consider “position”, which implies a coordinate within a multi-dimensional landscape of genomic elements. Our premise is that a reductive and systematic genetic approach to interrogate these functional interactions along key axes can resolve confounding observations and derive mechanistic understanding of genomic regulatory architecture. Future studies will be necessary to systematically dissect how position, orientation, and polarity contribute to enhancer cluster function. Previous studies show that TFs binding the same regulatory element may cooperate to recruit shared or complementary co-activators, with greater TF diversity linked to stronger activation (Singh 2021). These interactions suggest a regulatory grammar that cannot be fully explained by individual motif presence alone, raising the question of whether enhancer activity is driven by the qualitative arrangement of key TFs or the quantitative buildup of multiple inputs. Mechanistically, enhancer context sensitivity has been modelled through two TF interaction frameworks: the bill-board model, which allows flexible motif positioning and emphasizes TF composition, and the enhanceosome model, which requires strict motif spacing and orientation for precise cooperative binding ( Arnosti 2005 ). While most enhancers likely lie along a continuum between these extremes, our results at the Sox2 locus are more consistent with the enhanceosome model, in which precise motif arrangement is necessary for synergy. Still, within the billboard framework, it remains possible that introducing only a single motif (24.2) into Sox2 DHS23 was insufficient to recruit the border TF machinery needed for autonomous activity. Understanding how TFs cooperate within their native cellular and genomic environments, particularly in the context of noncoding genomic variation, remains a key challenge to understand cis-regulatory architecture. METHODS Payload assembly Different strategies were used to assemble payloads into these vectors, using different oligos and synthetic DNA fragments: Golden Gate Assembly (GGA) DNA fragments were assembled into three different entry vectors: pLM1110 ( Brosh 2023 ), pRB051 (a derivative of pLM1110) ( Brosh 2023 ) or pINEAPLE. pINEAPLE was cloned by performing BsaI GGA of the synthetic fragment qRB_005 into pAV10 (Addgene #63213). Payloads were assembled with GGA as previously described ( Brosh 2023 ), briefly 100 ng of entry vector was mixed with 20 ng of each DNA fragment, 0.4 μL of the appropriate restriction enzymes, either Esp3I (NEB R0734S) or BsaI-HFv2 (NEB R3733S), 1.5 μL 1 mg/mL BSA, 1 μL T4 Quick Ligase (NEB M2200S) and 1.5 μL T4 Ligase Buffer (NEB) in a total volume of 15 μL. Reactions were cycled 40 times between 37 °C (5 min) and 16°C (4 min), heat inactivated at 60 °C (5 min) and 80 °C (5 min) before transforming into TransforMax EPI300 cells for pLM1110 or pRB051-derived payloads and JM109 for pINE-APLE-derived payloads. Restriction enzyme cloning Spacer fragments were cloned into source payloads using restriction enzyme-based cloning. Briefly, source payloads were digested with the appropriate restriction enzymes, either BsiWI-HF (NEB R3553S) or AvrII (NEB R0174S), in a reaction containing 1× NEB CutSmart buffer at 37 °C for 1 hour. Quick CIP (NEB M0525S) was included to prevent vector recircularization. synHPRT1R or synHPRT1RnoCpG BAC DNA was digested with the appropriate restriction enzyme, followed by an additional digestion step with a secondary restriction enzyme (NotI-HF (NEB R3189S) or I-SceI (NEB R0694L)) to prevent cloning of unwanted backbone fragments into the final payload. Digested fragments were purified using 0.8x 18% Sera-Mag Magnetic Beads (Cytiva 65152105050250) in polyethylene glycol. Purified inserts were ligated into the digested vector using T4 DNA ligase (NEB M0202S) at 16 °C overnight. Ligated products were transformed into E. coli JM109 by heat shock at 42 °C for 45 seconds, followed by recovery in SOC medium at 37 °C for 1 hour. Bacterial cultures were expanded in 100 mL LB medium overnight at 37 °C, and plasmid DNA was extracted using the ZymoPURE II Plasmid Midiprep Kit (D4201). Cloned constructs were verified by short read sequencing to confirm pool representation. Payload DNA screening and preparation For initial verification, each payload DNA was prepped differently according to the backbone replication conditions. pLM1110 and pRB051-derived payloads, including an inducible copy number backbone, were picked into 5 mL LB-Kan supplemented with 1X CopyControl Induction Solution (Lucigen CCIS125) and cultured overnight at 30 °C with shaking at 220 RPM. Payload DNAs were isolated using the ZR BAC DNA Miniprep Kit (Zymo Research D4048). pINEAPLE-derived payloads, including a high copy number backbone, were picked into 5 mL LB-Amp and cultured overnight at 37 °C with shaking at 220 RPM. Payload DNAs were isolated using the Zyppy Plasmid Miniprep Kit (Zymo Research D4020). Payload assembly verification was then performed by Sanger Sequencing, short read or long sequencing methods. Sequence-verified clones were then prepped at larger scale for delivery into mESC cells: Individual payloads were grown according to their backbone replication conditions. pLM1110 and pRB051-derived payloads were picked into 2.5 mL LB+Kan and grown overnight at 30 °C, then subcultured 1:100 in 250 mL of LB+Kan supplemented with 1X CopyControl Induction Solution (Lucigen CCIS125) and grown for an additional 8 hours at 30 °C. pINEAPLE-derived payloads were picked into 50 mL LB+Amp and grown overnight at 37 °C. For pooled deliveries of payloads cloned individually, single colonies for each payload clone were picked into 5 mL of LB+Amp and cultured overnight at 30 °C with shaking at 220 RPM. Cultures were then pooled based on OD600 to 5 mL total volume and expanded in 45 mL of fresh LB+Amp (1:10 dilution) for 4 h at 37 °C. For transfection, DNA was purified using the ZymoPURE II Midiprep kit (Zymo Research D4201) for DNAs 20 kb. DNA preps were stored at 4 °C. mESC culture and engineering C57BL6/6J x CAST/EiJ (BL6xCAST) clone 4 male mESCs49 were kindly provided by David Spector (Cold Spring Harbor Laboratory, Cold Spring Harbor, NY) and cultured as previously described ( Brosh 2021 ). Cells were maintained on 0.1% gelatin-coated plates (EMD Millipore ES006-B) in 80/20 medium comprising 80% 2i medium and 20% mESC medium. 2i medium contained a 1:1 mixture of Advanced DMEM/F12 (ThermoFisher 12634010) and Neurobasal-A (ThermoFisher 10888022) supplemented with 1% N2 Supplement (ThermoFisher 17502048), 2% B27 Supplement (ThermoFisher 17504044), 1% GlutaMAX (ThermoFisher 35050061), 1% PenStrep (ThermoFisher 15140122), 0.1 mM 2-mercaptoethanol (Sigma M3148), 1,250 U/mL LIF (Sigma ESG1107), 3 mM CHIR99021 (R&D Systems 4423), and 1 mM PD0325901 (Sigma PZ0162). mESC medium contained KnockOut DMEM (ThermoFisher 10829018) supplemented with 15% FBS (BenchMark 100-106), 0.1 mM 2-mercaptoethanol (Sigma M3148), 1% GlutaMAX (ThermoFisher 35050061), 1% MEM nonessential amino acids (ThermoFisher 11140050), 1% nucleosides (EMD Millipore ES008-D), 1% Pen-Strep (ThermoFisher 15140122), and 1,250 U/mL LIF (Sigma ESG1107). Cells were grown at 37 °C in a humidified atmosphere of 5% CO2 and passaged on average twice per week. Cells were routinely tested for mycoplasma contamination. Payload deliveries were performed as previously described ( Brosh 2021 ) with minor adjustments based on payload size. Briefly, 1.5-5×10 6 mESCs were transfected with 1.5-5 μg of payload DNA and 1.5–5 μg of pCAG-iCre plasmid (Addgene #89573), and plated at densities ranging from 1.5×10 5 to 5×10 6 cells per 10 cm plate. Following transfection, mESCs were selected with 10 mg/mL blasticidin for 2 days starting day 1 post-transfection and with 2 nM proaerolysin for 2 days starting day 7 post-transfection. Individual mESC clones were manually picked approximately 10 days post-transfection into gelatinized 96-well plates prefilled with 30 μL TrypLE-Select, which was neutralized with 200 μL mESC medium. Immediately after, 25 μL were transferred into a new plate with 75 μL of 2i media, obtaining two replica plates with 90% and 10% relative densities. Three days later, crude gDNA was extracted from the 90% plate using the Wizard SV 96 Genomic DNA Purification System (Promega A2370) for qPCR genotyping and DNA library preparation. Candidate clones were then expanded from the 10% density plate for further phenotypic characterization. mESC genotyping Genotyping of mESC clones was performed by real-time quantitative PCR (qPCR) as previously described (Brosh 2023). Briefly, qPCR reactions were carried out in 384-well format using KAPA SYBR FAST (Kapa Biosystems KK4610) on a LightCycler 480 Real-Time PCR System (Roche). Each well was prefilled with 5 μL of KAPA SYBR FAST and 4 μL of water. Primers (50 nL of 100 μM stock) and genomic DNA (500 nL per reaction) were dispensed by the Echo 550 liquid handler system to ensure consistency. Thermal cycling conditions were 95 °C for 3 min, then 40 cycles of 95 °C for 3 sec, 63 °C for 20 sec, and 72 °C for 20 sec. Assays targeted the newly formed left and right homology arm junctions, loss of the landing pad, and absence of the payload vector backbone. For payloads flanked by unique nucleotide sequences (UNS), UNS-specific primers were included. RNA Isolation, cDNA synthesis and mRNA expression analysis by qRT-PCR RNA extraction, cDNA synthesis and qRT-PCR were performed as previously described (Brosh 2023). Briefly, RNA was isolated using the RNeasy-mini Kit (QIAGEN 74106) and treated with Turbo DNA-free kit (ThermoFisher AM1907) following the “rigorous DNase treatment”. cDNA synthesis was performed from 1-2 µg total RNA using the High-Capacity cDNA Reverse Transcription Kit (ThermoFisher 4368814), including -RT controls for a subset of samples. Real-time quantitative reverse transcription PCR (qRT-PCR) was performed using KAPA SYBR FAST (Kapa Bio-systems KK4610) on a LightCycler 480 (Roche). An Echo 550 liquid handler was used for loading 348-well plates as described above. Allele-specific primers were used with thermal cycling conditions as previously described (Brosh 2021) (95 °C for 3 min, then 40 cycles of 95 °C for 3 sec, 57 °C for 20 sec, and 72 °C for 20 sec). Assays were performed in triplicate wells, and replicate wells on the same plate were averaged after masking technical outliers. Ct values were normalized to Gapdh, and allele-specific expression was quantified using the 2^ΔCt[CAST–BL6] method. Fold change was scaled from 0 (Δ Sox2 ) to 1 (WT) by subtracting the mean fold change for ΔSox2 from all data points then dividing by the mean fold change of the WT LCR samples ( Table S4 ) SUPPLEMENTARY FIGURES Download figure Open in new tab Fig. S1. Multiplexed Big-IN delivery. a . Experiment schematic including payload encoding GFP with a 16-bp barcode (BC) in its 5’UTR. The payload was delivered to two previously established mESC lines harboring Big-IN landing pads at Igf2 / H19 and Hprt (Ordoñez 2024; Camellato 2024 ). Two-stage nested PCR with an 8-bp unique molecular identifier (UMI) for single-molecule counting was used to survey BC diversity. b . Number of unique BCs identified per delivery. c . Percentage of surviving clones that were GFP+. Download figure Open in new tab Fig. S2. qRT-PCR results. Sox2 expression from all mESC clones described in this paper. Displayed is the expression of the BL6 allele of Sox2 relative to the CAST allele, calculated as 2 ΔCt[CAST-BL6] . Points represent individual clones and bars indicate median. Vertical dashed lines indicate median expression of baseline payloads. Download figure Open in new tab Fig. S3. Contribution of candidates for each context DHS. Contribution of each DHS, computed as the difference in Sox2 expression between payload pairs differing solely by the presence of the context DHS. Points indicate mean expression change. Error bars indicate SD across all pairwise combinations of clones. Expression is scaled between 0 (Δ Sox2 ) and 2 (WT LCR). Download figure Open in new tab Fig. S4. Candidate DHSs. Genome browser view of candidate DHSs analyzed in this study, showing GC content, DNase-seq, CTCF ChIP-seq, ChIP-nexus data for TF occupancy, EP300 ChIP-seq, histone marks in mESC, and sequence conservation. Each DHS is shaded in a different color according to its mode of interaction with neighboring DHSs. SUPPLEMENTARY TABLES Table S1. Payloads and assembly details. Table S2. Delivery pools. Summary of different pools delivered in independent Big-IN experiments, including the specific payloads including in each pool. Table S3. Summary Table for mESC clones described in this paper. 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