Microfluidic Concatemer Sequencing Unveils Cooperative and Competitive Dynamics of Enhancer Clusters in Gene Regulation

Microfluidic Concatemer Sequencing Unveils Cooperative and Competitive Dynamics of Enhancer Clusters in Gene Regulation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Microfluidic Concatemer Sequencing Unveils Cooperative and Competitive Dynamics of Enhancer Clusters in Gene Regulation Zhihua Zhang, Qiwen Chen, Shiqi Zheng, Feifei Li, Zijian Li, Xiao Li, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7295477/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Chromatin looping plays a pivotal role in eukaryotic gene regulation; however, technical limitations have hindered the comprehensive characterization of higher-order interactions among genomic loci. As a result, key questions, such as whether enhancers function cooperatively or competitively at the single-molecule and ensemble levels, remain largely unresolved. Here, we present Drop-t, a novel microfluidics-based method that enables systematic profiling of higher-order chromatin architecture at both the single-molecule and population scales. We demonstrate that Drop-t is not only resistant to common ligation-induced DNA entanglements, which frequently cause nanopore clogging, but also provides greater relevance for elucidating regulatory mechanisms compared to existing ligation-free approaches. Using Drop-t, we uncovered extensive previously underappreciated non-specific enhancer–promoter interactions (EPIs) and showed that these may influence transcriptional variability by competing with specific EPIs. Notably, we identified two major gene classes, dictator genes and leader genes, based on whether their promoters are predominantly regulated by competitive or cooperative specific EPIs, respectively. Together, our findings established Drop-t as a powerful platform for decoding the higher-order organizational principles of chromatin architecture and their impact on transcriptional regulation and variability. Biological sciences/Molecular biology/Chromatin/Chromatin structure Biological sciences/Genetics/Gene regulation Biological sciences/Biological techniques/Gene expression analysis/Chromosome conformation capture-based methods Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementarytextandMethod.pdf Supplementary text and Method FigS1412.pdf Figure S1 FigS20710.pdf Figure S2 FigS30425.pdf Figure S3 FigS40625.pdf Figure S4 FigS50420.pdf Figure S5 FigS60629.pdf Figure S6 FigS70627.pdf Figure S7 TableS1.xlsx Table S1 TableS2.xlsx Table S2 TableS3.xlsx Table S3 TableS4.xlsx Table S4 Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7295477","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":497194055,"identity":"5d4b152e-de5d-4f56-bd4f-19f0404157f3","order_by":0,"name":"Zhihua Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYDACCTB5gMeAgYHxMYMBiVqYjUnSAlLMJk2Uu+RnNz97+OXPHRlzieRn1QUFtQz87QcYPxfg0cI455i5sQzPMx7LGWlmt2cYHGeQOJPALD0DjxZmiQQzaQmJwzwGNxLMbvMYHGNguMHAxsyDRwubRPo3aQkDkJb0b8UgLfKEtPBI5JhJfkgAackxY+YxqGEwIKRFQiKnTJrhAFDLmTfF0jMMDvAYnklslsanRX5G+jbJH38O2xscT9/4ueBPnZzc8cMHP+PTAgLIzjgMZDM2ENAAVPIDwa4jqHoUjIJRMApGHgAAn3JG2eZdQxsAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-7706-9247","institution":"China National Center for Bioinformation, Beijing, China","correspondingAuthor":true,"prefix":"","firstName":"Zhihua","middleName":"","lastName":"Zhang","suffix":""},{"id":497194056,"identity":"2ab9440b-4d00-4778-8214-78dba3b8416a","order_by":1,"name":"Qiwen Chen","email":"","orcid":"","institution":"China National Center for 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Technology","correspondingAuthor":false,"prefix":"","firstName":"Bingxiang","middleName":"","lastName":"Xu","suffix":""},{"id":497194063,"identity":"dab43233-1a74-46b8-9774-d7e6c94d13ea","order_by":8,"name":"Xiaoli Li","email":"","orcid":"","institution":"Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoli","middleName":"","lastName":"Li","suffix":""},{"id":497194064,"identity":"a5cb06ca-dc47-4ae6-ba69-41e13ffaa009","order_by":9,"name":"Qing Zhang","email":"","orcid":"","institution":"China National Center for Bioinformation","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Zhang","suffix":""},{"id":497194065,"identity":"157f044c-d9b5-4e0e-b40d-1036e9fdf50a","order_by":10,"name":"Yue Shi","email":"","orcid":"","institution":"China National Center for Bioinformation","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Shi","suffix":""},{"id":497194066,"identity":"c9e950de-3cec-4656-a482-23e4882790ae","order_by":11,"name":"Meizhen Zheng","email":"","orcid":"https://orcid.org/0000-0001-5569-1812","institution":"Southern University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Meizhen","middleName":"","lastName":"Zheng","suffix":""}],"badges":[],"createdAt":"2025-08-05 02:35:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7295477/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7295477/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91062081,"identity":"1e8b67a9-7fd9-4220-9bad-cb048c103fda","added_by":"auto","created_at":"2025-09-11 09:09:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3205491,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMuch DNA entanglements exists in the 3C heavy product. \u003c/strong\u003e(A) Distribution of read length of Pore-C and PacBio for 3C heavy products (≥ 10kb). Dash line indicates the average reads length. (B) The Cartoon of DNA entanglement formed during 3C ligation reduced mobility during electrophoresis. The DNA in the nucleus (left) formed entanglement during cut and ligation process of 3C (middle), which move as a whole in the gel and result in larger molecule size (right). The blue cube represents the molecular grids of gel. (C) DNA entanglements in the simulation. The simulation results of the 3C product in nucleus (left), and two examples of DNA entanglements. (D) The AMF image of DNA. Left panels: The AMF images for 3C heavy products (1 ng/μl, top) and linear plasmid DNA (1 ng/μl, bottom). Right panels: the computational recognized DNA intersection points. (E) The distribution of DNA intersection points between 3C heavy products and linear plasmid showed in (D). (F) The distribution of DNA traveling distance in 10 simulated electrophoresis. “Self” and “entangled” refer to DNA molecules traveling independently or being entangled, respectively. ****: representing P\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Fig1625.png","url":"https://assets-eu.researchsquare.com/files/rs-7295477/v1/6f394c61c6637db195cd481e.png"},{"id":91061147,"identity":"2a842d63-2a44-46cf-a3d8-45aa359629b6","added_by":"auto","created_at":"2025-09-11 09:01:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2161599,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDrop-t accurately recapitulates fine-scale pairwise chromosomal topologies of Hi-C. \u003c/strong\u003e(A) Basic experimental process of Drop-t. (B) The average correlation (SCC) between replications at different resolutions over all chromosomes for Drop-t and Hi-C. (C) Similarity of compartments in Drop-t, HiPore-C, and C-walks comparing to between in situ Hi-C, in K562. Chr8 was shown as an example at 1Mb resolution. The first eigenvector profiles indicating compartment score (CS) were also shown. (D) The average correlation (SCC) of pairwise contact matrix between in situ Hi-C and higher-order methods, at different resolutions. (E) The p(s) curves for Drop-t, HiPore-C, C-walks and in situ Hi-C in K562. (F) Comparison of TADs detected by Drop-t (lower diagonal) and Hi-C (upper diagonal) on 90-94Mb region of chromosome 1 at 10kb resolution. (G) Aggregated peak analysis (APA) using Drop-t pairwise contacts at Hi-C detected loop anchors. Peak to lower left (P2LL) is the ratio of the central pixel to the mean value of pixels in the lower left square.\u003c/p\u003e","description":"","filename":"Fig20425.png","url":"https://assets-eu.researchsquare.com/files/rs-7295477/v1/decfb6b891701e3bfed19f20.png"},{"id":91062612,"identity":"77d15d35-f761-45b1-835e-5894bd22b1e1","added_by":"auto","created_at":"2025-09-11 09:17:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1086253,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDrop-t accurately reveals the ensemble of higher-order chromatin complexes. \u003c/strong\u003e(A) Overview of the graph-based d-LHCC assembling algorithm. Unconnected components blue and purple in c-graph were connected if there is a directly links between them in f-graph. Unconnected components yellow and green in c-graph were connected if there is a node directly links both of them in f-graph. This process has been performed for all droplets, iteratively. (B) The size distribution of d-LHCC and hp-LHCC in K562. (C) Composition similarities between the technologies. The similarities were indexed by Jaccard coefficient (JC) score. In each panel, the upper triangular matrix represents the JC scores between d-LHCC and a technology, and the lower triangular matrix represents the JC score between the technology and permuted HCCs. The counts of JC scores in the matrix entries were coded in colors. (D) Distribution of the average physical distances among intra-LHCC fragments (30kb bins). The distance were calculated based on the super-resolution chromatin tracing data [18]. (E) Distributions of intrachromosomal genomic span of the HCCs. (F) Cumulative proportion of information entropy calculated from the compartment distribution. (G) Comparison of frequency distributions between d-LHCC and i-HCC. “Shared” indicates HCCs found in both datasets, while “d-LHCC specific”, “i-HCC specific” refers to HCCs found only in d-LHCC or i-HCC. (H) Comparison of the HCC ensembles as defined by d-LHCC and i-HCC. We coarse grained the higher-order contacts into the genes and enhancers resolution at the genome region from gene N6AMT1 to SE_01_03900037. The yellow, pink and blue headed lines represent the shared, d-LHCC specific and i-HCC specific composition forms, respectively. Each composition is aligned with pink and blue bars representing its frequency found in d-LHCC and i-HCC, respectively.\u003c/p\u003e","description":"","filename":"Fig30425.png","url":"https://assets-eu.researchsquare.com/files/rs-7295477/v1/af498217bdafe2b82bca1ad1.png"},{"id":91061157,"identity":"ee4d6783-63df-4ebe-8b33-19de61d2038f","added_by":"auto","created_at":"2025-09-11 09:01:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1780723,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKey distinguishes between d-LHCC and s-HCC. \u003c/strong\u003e(A) Distribution of HCC (solid lines) and reads counts (dash lines) over different HCC size. (B) Distribution of chromosomes numbers that an HCC (s-HCCs and d-LHCCs) spanned across various sizes. (C) The distribution of log2 ratio of fragment entropies, log2(d-LHCC/s-HCC), over various chromosome number and HCC size. (D) Examples of the different intra-/inter-chromosomal connection trends in mini, middle and large HCC, which showed increasing number of spanned chromosomes with fragment size in d-LHCC, while a fraction of large s-HCCs spanning small or medium number of chromosomes. Four randomly selected s-HCCs and d-LHCCs of each size (3, 8, 24 representing mini, middle and large HCC, respectively) were shown as examples. Different chromosomes were distinguished by colors. The inter- and intra-chromosomal interactions were represented as arcs inside and outside the circle, respectively. (E) Gap length distribution of intrachromosomal d-LHCCs (left) and s-HCCs (right) for different size on a chromosome. The gap distances are log scaled and the color indicate the frequency. Dash lines indicate the threshold for proximal, middle and distal gaps. (F) and (G) shows the distribution of the cluster number and cluster size over various HCC size, respectively. (H) Cartoon for the different model of hieratical organization in d-LHCCs and s-HCCs. Clusters, sub-clusters and components are present as ellipses, circles and bars, respectively. The horizontal line represents a chromosome with multiple HCCs. Clusters forming the same HCCs were connected by polygonal lines in the same color. (I) The distribution of pairwise contacts between genes, typical-enhancers, and super-enhancers, marked as “G”, “T”, “S”, respectively. (J) Example showing the difference between s-HCC and d-LHCC at MYC locus. The enhancer annotation was taken from Enhancer Atlas. Each line in the tracks of d-LHCCs and s-HCCs represents a HCC, and a dot represents a fragment. The MYC and enhancers interactions were detected by 4C [39].\u003c/p\u003e","description":"","filename":"Fig40627.png","url":"https://assets-eu.researchsquare.com/files/rs-7295477/v1/306b3a2d6605b83c1e0122a5.png"},{"id":91062084,"identity":"3a2e0d4a-cd37-47f1-8060-e9704eb28c59","added_by":"auto","created_at":"2025-09-11 09:09:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1258972,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCompetition between EPIs associate with the strength and stability of gene expression. \u003c/strong\u003e(A) Histogram showing the distribution of EPI number of all genes.\u003c/p\u003e\n\u003cp\u003e(B) Correlation between gene expression level and the number of EPI that the gene involved. The number of genes for each group is annotated. (C-F) Relationships between POT/POC and gene expression/noise. In C and E, circular bars represent genes firstly divided into seven groups according to number of EPIs, and each group was then subdivided into five POT quantiles (classes) represented by colored blocks. Expression\u003c/p\u003e\n\u003cp\u003e(C) or noise (E) level was compared between classes of each group. Arcs outside (red) and inside (blue) the circle showed significantly positive (i.e., higher expression/noise level in higher POT/POC quantiles) or negative (i.e., lower expression/noise level in higher POT/POC quantiles) trends, respectively. The height of arcs represents the - log10(P) of Mann-Whitney U test. Strongly negative (blue arcs) and positive (red arcs) trends between POT and gene expression (C), and noise (E), were illustrated, respectively. In D and F, genes were firstly partitioned into four super-groups according to POT ranges. Each super-group was further divided into four groups according to the number of EPIs, and each group was subdivided into five classes according to the proportion of POC. Similarly, strongly negative (blue arcs) and positive (red arcs) trends between POC and gene expression (D), and noise (F), were illustrated, respectively.\u003c/p\u003e","description":"","filename":"Fig50627.png","url":"https://assets-eu.researchsquare.com/files/rs-7295477/v1/c94635f1c43c4c8717111886.png"},{"id":91061161,"identity":"cc6f44c7-f332-4318-8ae8-2d3dff70f541","added_by":"auto","created_at":"2025-09-11 09:01:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2011810,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCompetition and cooperation between enhancers. \u003c/strong\u003eThe definitions in (A)-(F) are similar to Figure 5C-F. (A). The strongly negative biased trends (enriched blue arcs) between EPIE and gene expression for dictator genes. (B). Mixed trends (comparable number of red and blue arcs) between POH and expression for dictator genes. (C). Mixed trends between EPIE and expression for leader genes. (D). The strongly positive biased trends (enriched red arcs) between POH and gene expression for leader genes. (E). Strongly positive trends between EPIE and noise for dictator genes. (F). Strongly negative trends between POH and noise for leader genes. (G) Distribution of EPD for gene sets annotated below each box. “High EPIE” refers to dictators with top 20% EPIE in each group (i.e. all classes annotated with red in A-F), and “high POH” refers to leaders with top 20% POH in each group. (H) Distribution of inter-TAD EPI proportion for same gene sets in (G). (I) Top 15 motifs enriched for dictators and leaders. The x-axis represents log10 p-value of enrichment with all promoters as background. (J) Distribution of number of motifs within ± 1kb of promoters for the same gene sets in (G). (K-L) GO enrichment of leaders with top 20% POH(K) and dictators with top 20% EPIE (L). ****: representing P\u0026lt;0.0001; ***: representing P\u0026lt;0.001; **: representing P\u0026lt;0.01; *: representing P\u0026lt;0.05 for mann-whitney U test.\u003c/p\u003e","description":"","filename":"Fig60629.png","url":"https://assets-eu.researchsquare.com/files/rs-7295477/v1/ea2d08d0d020c1ed2323a69a.png"},{"id":91061164,"identity":"26dfe123-5659-4b31-ae91-0ff8392dd963","added_by":"auto","created_at":"2025-09-11 09:01:39","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":285466,"visible":true,"origin":"","legend":"\u003cp\u003eThe proposed competition and cooperation model of enhancer–promoter interaction (EPI) regulation within higher-order chromatin structures. Upper panel shows a model for leader gene’s promoter while bottom panel shows a model for dictator gene’s promoter.\u003c/p\u003e","description":"","filename":"fig70629.png","url":"https://assets-eu.researchsquare.com/files/rs-7295477/v1/503af2e463b3a05a2081b98c.png"},{"id":91063530,"identity":"6354c281-c0ae-446a-8eae-237979aebba7","added_by":"auto","created_at":"2025-09-11 09:25:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4428531,"visible":true,"origin":"","legend":"Article File","description":"","filename":"MainText2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7295477/v1_covered_e7c00576-67b3-42e9-a584-f81a1ebff22a.pdf"},{"id":91061148,"identity":"257934d6-ec55-4d20-9652-35142002712d","added_by":"auto","created_at":"2025-09-11 09:01:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":400626,"visible":true,"origin":"","legend":"Supplementary text and Method","description":"","filename":"SupplementarytextandMethod.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7295477/v1/62586afd06b318c1a82d3d12.pdf"},{"id":91061151,"identity":"b30c34a5-4f4e-45ea-89d5-1089fc86bbf1","added_by":"auto","created_at":"2025-09-11 09:01:39","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1649104,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1\u003c/p\u003e","description":"","filename":"FigS1412.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7295477/v1/9a2a3b81c0b4a75577cb099f.pdf"},{"id":91062083,"identity":"59e68c88-b08d-4eca-a6d3-f7eeba9f7aab","added_by":"auto","created_at":"2025-09-11 09:09:39","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2114116,"visible":true,"origin":"","legend":"Figure 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As a result, key questions, such as whether enhancers function cooperatively or competitively at the single-molecule and ensemble levels, remain largely unresolved. Here, we present Drop-t, a novel microfluidics-based method that enables systematic profiling of higher-order chromatin architecture at both the single-molecule and population scales. We demonstrate that Drop-t is not only resistant to common ligation-induced DNA entanglements, which frequently cause nanopore clogging, but also provides greater relevance for elucidating regulatory mechanisms compared to existing ligation-free approaches. Using Drop-t, we uncovered extensive previously underappreciated non-specific enhancer–promoter interactions (EPIs) and showed that these may influence transcriptional variability by competing with specific EPIs. Notably, we identified two major gene classes, dictator genes and leader genes, based on whether their promoters are predominantly regulated by competitive or cooperative specific EPIs, respectively. Together, our findings established Drop-t as a powerful platform for decoding the higher-order organizational principles of chromatin architecture and their impact on transcriptional regulation and variability. 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