Name: A Web-Based Workflow for Selecting Gene- and Tissue-Specific Enhancers (Video) | JoVE Journal
Uploaded: 2025-07-18T13:10:10.000+00:00
Duration: 8 min 12 s
Description: Learn a coding-free workflow for identifying tissue-specific gene enhancers using browser-based tools and public genomic data. This protocol enables biologists to discover regulatory elements without programming expertise.

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5 minutes

We thank the members of the Ramialison lab (Transcriptomics and Bioinformatics, reNEW Bioinformatics Hub) for helpful discussions. MR and HTN are supported by an NHMRC Ideas Grant (APP1180905). We thank Richard Saffery for the support. MR is funded by a Heart Foundation Future Leader Fellowship (107328). Additional infrastructure funding to the Murdoch Children&#39;s Research Institute was provided by the Australian Government National Health and Medical Research Council Independent Research Institute Infrastructure Support Scheme. The Australian Regenerative Medicine Institute is supported by grants from the State Government of Victoria and the Australian Government. The Novo Nordisk Foundation Center for Stem Cell Medicine is supported by Novo Nordisk Foundation grants (NNF21CC0073729).

NOTE: A step-by-step walkthrough is available at https://github.com/Ramialison-Lab/EnhancerWorkflow. Data used in the protocol is summarized in Table 1 and Table 2. Troubleshooting is available in Supplementary File 1.

1. Locating GoI (Figure 1)

<ol>
	<li>Open the EnsEMBL genome browser (https://www.EnsEMBL.org).</li>
	<li>Choose the appropriate genome assembly matching the species and version.</li>
	<li>Input GoI in the search field and click Go.</li>
	<li>Select the link to the appropriate EnsEMBL gene ID.</li>
	<li>Click the link to the Region in details tab viewer, located below the summary section, to navigate the region surrounding the GoI.</li>
</ol>

2. Defining enhancer detection region (Figure 2)

<ol>
	<li>&#160;Identify the two neighboring genes of the GoI to define the detection region. Use the Gene Legend track to search for these genes, which are annotated as visual elements related to merged EnsEMBL/Havana in the Basic Gene Annotations from GENCODE track. Determine the directionality of the GoI by the &#60; and &#62; signs on the appropriate gene name in the GENCODE track.</li>
	<li>Click and drag to select the intergenic region between these genes, and click Jump to region in the pop-up box to visualize the selected area. Redefine the region of interest at any time by repeating this step.</li>
	<li>(OPTIONAL) Customise the display by selecting Add/remove tracks at the top of the track viewer. Modulate the zoom level using the zoom/navigation controls above the track viewer. &#160;</li>
</ol>

3. Histone mark analysis (Figure 3)

<ol>
	<li>In the sidebar of the Region in detail tab viewer, select Configure this page.</li>
	<li>In the sidebar of the Configure Region Image tab display, under the Regulation dropdown, select Activity by Cell/ Tissue.</li>
	<li>Use the Cell/Tissue search bar to search and select tissues of interest. Alternatively, use the alphabetical navigation bar under the search bar to find tissues of interest.</li>
	<li>Select the Experiments tab, next to Cell/Tissue.</li>
	<li>Select H3K4me1 and H3K27ac as a marker of enhancers, and H3K4me3 as a marker of promoters.</li>
	<li>Select Configure track display.</li>
	<li>Select View tracks to visualise H3K4me1-marked regions in the enhancer detection region and H3K4me3-marked regions upstream of the GoI.</li>
	<li>For the enhancer candidates, retrieve the coordinates of genomic regions marked by H3K4me1 within the defined detection regions by clicking on the coloured visual/box elements in the newly added H3K4me1 tracks. This reveals the &#34;Hists &#38; Pols&#34; pop-up, which contains information on the element&#39;s genomic location in base pair (bp).
	<ol>
		<li>Retrieve active enhancer candidates by choosing H3K4me1 regions where the coloured visual/box elements of both H3K4me1 and H3K27ac overlap. The granularity of the zoom level may affect the number of enhancer candidates shown in this track.</li>
		<li>Alternatively, manually define the regions of interest for each genomic feature by clicking and dragging on the track to encapsulate graph peaks under the H3K4me1/H3K27ac track. Copy the genomic location coordinates into a text file and save as .bed format.</li>
	</ol>
	</li>
	<li>Likewise, for promoter regions, replicate step 3.8 using the H3K4me3 track, focusing on the region upstream of the GoI.</li>
</ol>

4. Chromatin conformation capture (Hi-C) analysis (Figure 4)

<ol>
	<li>Access the 4DN data portal (https://data.4dnucleome.org/). In the main stacked bar chart of the home page of 4DN (Figure 5), ensure that Experiment Sets is selected as the Y-axis, Experiment Type is selected as the X-axis, and the plot is grouped by Organism.</li>
	<li>Along the X Axis of the main bar chart, find the in situ Hi-C bar. Click on the portion of the bar that is grouped for Human Experiment Sets and then click on the Browse button in the pop-up. Filter relevant datasets using the left side panel.</li>
	<li>Click on the link in the Title column of the relevant Biosample for the tissue of interest.</li>
	<li>Click Explore Data in the Processed Files tab to explore the Hi-C dataset in more detail. &#160;</li>
	<li>Enter the coordinates of the identified promoter in the tissue of interest (from step 3.9) and mark the region horizontally by right-clicking the heatmap (Figure 4). The added lines ensure that the promoter region is visually tracked across the heatmap. Remove accidental lines by right-clicking the line and selecting horizontal/vertical rule and Close Series.</li>
	<li>Enter the coordinates of all experimentally validated control enhancers23 to calculate the interaction threshold based on their minimum. These control enhancers have been previously confirmed to interact with the promoter region, serving as benchmarks for defining the minimum significant interaction.</li>
	<li>Define the promoter-enhancer threshold, using the control enhancers, based on the lowest non-zero interaction score according to the colour key on the right side of the matrix.</li>
	<li>Enter the genomic coordinates of all H3K4me1-associated regions (from step 3.8) and mark vertically on the Hi-C heatmap. This allows for a cross-check pattern, where the intersection between H3K4me1-marked enhancers (vertical) and the promoter region (horizontal) can be examined for significant interactions.
	<ol>
		<li>Then, filter out weakly interacting regions by comparing the interaction scores of H3K4me3-marked regions with the interaction threshold (step 4.6). &#160;</li>
		<li>Select the genomic coordinates that exhibit interaction frequencies higher than the defined threshold in the 4DN portal heatmap. These regions appear as more concentrated (darker) signals in the heatmap, save in BED format.</li>
	</ol>
	</li>
</ol>

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<li>Pennacchio, L. A., Bickmore, W., Dean, A., Nobrega, M. A., Bejerano, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Enhancers%3A+five+essential+questions%5BTitle%5D)+AND+%22Nat+Rev+Genet%22%5BJournal%5D)">Enhancers: five essential questions.</a> Nat Rev Genet. 14 (4), 288-295 (2013).</li>
<li>Kleinjan, D. A., van Heyningen, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Long-range+control+of+gene+expression%3A+emerging+mechanisms+and+disruption+in+disease%5BTitle%5D)+AND+%22Am+J+Hum+Genet%22%5BJournal%5D)">Long-range control of gene expression: emerging mechanisms and disruption in disease.</a> Am J Hum Genet. 76 (1), 8-32 (2005).</li>
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<li>Smith, E., Shilatifard, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Enhancer+biology+and+enhanceropathies%5BTitle%5D)+AND+%22Nat+Struct+Mol+Biol%22%5BJournal%5D)">Enhancer biology and enhanceropathies.</a> Nat Struct Mol Biol. 21 (3), 210-219 (2014).</li>
<li>Sur, I., Taipale, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+role+of+enhancers+in+cancer%5BTitle%5D)+AND+%22Nat+Rev+Cancer%22%5BJournal%5D)">The role of enhancers in cancer.</a> Nat Rev Cancer. 16 (8), 483-493 (2016).</li>
<li>Panigrahi, A., O'Malley, B. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Mechanisms+of+enhancer+action%3A+the+known+and+the+unknown%5BTitle%5D)+AND+%22Genome+Biol%22%5BJournal%5D)">Mechanisms of enhancer action: the known and the unknown.</a> Genome Biol. 22 (1), 108(2021).</li>
<li>Bulger, M., Groudine, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Functional+and+mechanistic+diversity+of+distal+transcription+enhancers%5BTitle%5D)+AND+%22Cell%22%5BJournal%5D)">Functional and mechanistic diversity of distal transcription enhancers.</a> Cell. 144 (3), 327-339 (2011).</li>
<li>Krivega, I., Dean, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Enhancer+and+promoter+interactions-long+distance+calls%5BTitle%5D)+AND+%22Curr+Opin+Genet+Dev%22%5BJournal%5D)">Enhancer and promoter interactions-long distance calls.</a> Curr Opin Genet Dev. 22 (2), 79-85 (2012).</li>
<li>Zhang, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Chromatin+connectivity+maps+reveal+dynamic+promoter-enhancer+long-range+associations%5BTitle%5D)+AND+%22Nature%22%5BJournal%5D)">Chromatin connectivity maps reveal dynamic promoter-enhancer long-range associations.</a> Nature. 504 (7479), 306-310 (2013).</li>
<li>Fang, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Mapping+of+long-range+chromatin+interactions+by+proximity+ligation-assisted+ChIP-seq%5BTitle%5D)+AND+%22Cell+Res%22%5BJournal%5D)">Mapping of long-range chromatin interactions by proximity ligation-assisted ChIP-seq.</a> Cell Res. 26 (12), 1345-1348 (2016).</li>
<li>Park, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((ChIP-seq%3A+advantages+and+challenges+of+a+maturing+technology%5BTitle%5D)+AND+%22Nat+Rev+Genet%22%5BJournal%5D)">ChIP-seq: advantages and challenges of a maturing technology.</a> Nat Rev Genet. 10 (10), 669-680 (2009).</li>
<li>Thurman, R. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+accessible+chromatin+landscape+of+the+human+genome%5BTitle%5D)+AND+%22Nature%22%5BJournal%5D)">The accessible chromatin landscape of the human genome.</a> Nature. 489 (7414), 75-82 (2012).</li>
<li>Heintzman, N. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Distinct+and+predictive+chromatin+signatures+of+transcriptional+promoters+and+enhancers+in+the+human+genome%5BTitle%5D)+AND+%22Nat+Genet%22%5BJournal%5D)">Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome.</a> Nat Genet. 39 (3), 311-318 (2007).</li>
<li>Visel, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((ChIP-seq+accurately+predicts+tissue-specific+activity+of+enhancers%5BTitle%5D)+AND+%22Nature%22%5BJournal%5D)">ChIP-seq accurately predicts tissue-specific activity of enhancers.</a> Nature. 457 (7231), 854-858 (2009).</li>
<li>Belton, J. -M., McCord, R. P., Gibcus, J. H., Naumova, N., Zhan, Y., Dekker, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Hi-C%3A+a+comprehensive+technique+to+capture+the+conformation+of+genomes%5BTitle%5D)+AND+%22Methods%22%5BJournal%5D)">Hi-C: a comprehensive technique to capture the conformation of genomes.</a> Methods. 58 (3), 268-276 (2012).</li>
<li>de Wit, E., de Laat, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+decade+of+3C+technologies%3A+insights+into+nuclear+organization%5BTitle%5D)+AND+%22Genes+Dev%22%5BJournal%5D)">A decade of 3C technologies: insights into nuclear organization.</a> Genes Dev. 26 (1), 11-24 (2012).</li>
<li>Gao, T., Qian, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((EnhancerAtlas+2.0%3A+an+updated+resource+with+enhancer+annotation+in+586+tissue%2Fcell+types+across+nine+species%5BTitle%5D)+AND+%22Nucleic+Acids+Res%22%5BJournal%5D)">EnhancerAtlas 2.0: an updated resource with enhancer annotation in 586 tissue/cell types across nine species.</a> Nucleic Acids Res. 47 (D1), D117-D123 (2019).</li>
<li>Erwin, G. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Integrating+diverse+datasets+improves+developmental+enhancer+prediction%5BTitle%5D)+AND+%22PLoS+Comput+Biol%22%5BJournal%5D)">Integrating diverse datasets improves developmental enhancer prediction.</a> PLoS Comput Biol. 10 (6), e1003677(2014).</li>
<li>Heintzman, N. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Histone+modifications+at+human+enhancers+reflect+global+cell-type-specific+gene+expression%5BTitle%5D)+AND+%22Nature%22%5BJournal%5D)">Histone modifications at human enhancers reflect global cell-type-specific gene expression.</a> Nature. 459 (7243), 108-112 (2009).</li>
<li>Spicuglia, S., Vanhille, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Chromatin+signatures+of+active+enhancers%5BTitle%5D)+AND+%22Nucleus%22%5BJournal%5D)">Chromatin signatures of active enhancers.</a> Nucleus. 3 (2), 126-131 (2012).</li>
<li>Borsari, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Enhancers+with+tissue-specific+activity+are+enriched+in+intronic+regions%5BTitle%5D)+AND+%22Genome+Res%22%5BJournal%5D)">Enhancers with tissue-specific activity are enriched in intronic regions.</a> Genome Res. 31 (8), 1325-1336 (2021).</li>
<li>Smemo, S., Campos, L. C., Moskowitz, I. P., Krieger, J. E., Pereira, A. C., Nobrega, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Regulatory+variation+in+a+TBX5+enhancer+leads+to+isolated+congenital+heart+disease%5BTitle%5D)+AND+%22Hum+Mol+Genet%22%5BJournal%5D)">Regulatory variation in a TBX5 enhancer leads to isolated congenital heart disease.</a> Hum Mol Genet. 21 (14), 3255-3263 (2012).</li>
<li>Boogerd, C. J., Evans, S. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((TBX5+and+NuRD+divide+the+heart%5BTitle%5D)+AND+%22Dev+Cell%22%5BJournal%5D)">TBX5 and NuRD divide the heart.</a> Dev Cell. 36 (3), 242-244 (2016).</li>
<li>Dickel, D. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Genome-wide+compendium+and+functional+assessment+of+in+vivo+heart+enhancers%5BTitle%5D)+AND+%22Nat+Commun%22%5BJournal%5D)">Genome-wide compendium and functional assessment of in vivo heart enhancers.</a> Nat Commun. 7 (1), 12923(2016).</li>
<li>Pennacchio, L. A., Visel, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Limits+of+sequence+and+functional+conservation%5BTitle%5D)+AND+%22Nat+Genet%22%5BJournal%5D)">Limits of sequence and functional conservation.</a> Nat Genet. 42 (7), 557-558 (2010).</li>
<li>Local, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Identification+of+H3K4me1-associated+proteins+at+mammalian+enhancers%5BTitle%5D)+AND+%22Nat+Genet%22%5BJournal%5D)">Identification of H3K4me1-associated proteins at mammalian enhancers.</a> Nat Genet. 50 (1), 73-82 (2018).</li>
<li>Creyghton, M. P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Histone+H3K27ac+separates+active+from+poised+enhancers+and+predicts+developmental+state%5BTitle%5D)+AND+%22Proc+Natl+Acad+Sci+U+S+A%22%5BJournal%5D)">Histone H3K27ac separates active from poised enhancers and predicts developmental state.</a> Proc Natl Acad Sci U S A. 107 (50), 21931-21936 (2010).</li>
<li>Visel, A., Minovitsky, S., Dubchak, I., Pennacchio, L. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((VISTA+Enhancer+Browser-a+database+of+tissue-specific+human+enhancers%5BTitle%5D)+AND+%22Nucleic+Acids+Res%22%5BJournal%5D)">VISTA Enhancer Browser-a database of tissue-specific human enhancers.</a> Nucleic Acids Res. 35 (Database), D88-D92 (2007).</li>
</ol>

The authors report no competing interests.

The web-based protocol described here functions as a workflow for enhancer retrieval, rather than enhancer prediction. By leveraging publicly available tissue-specific datasets, histone modifications (H3K4me1 and H3K27ac), and Hi-C interaction data, it narrows down potential enhancers associated with the GoI. Unlike computational prediction tools that depend on machine learning or sequence-based models, our approach focuses on retrieving enhancers only based on experimental data. The troubleshooting instructions for EnsEMBL genome browser is provided in Supplementary File 1.
This workflow integrates key elements such as histone marks and chromatin interaction data, similar to advanced methods like ChIA-PET and PLAC-seq, which map enhancer-promoter interactions with higher accuracy10,11. However, this method is beneficial when high-resolution experimental techniques are not feasible, as the protocol is less resource-intensive and saves significant time.
The primary limitation of the above-presented approach is its dependence on the availability and quality of existing datasets, which may affect the precision of the retrieved interactions. Investigating enhancer activity during cardiac development in the context of TBX5-associated genetic mutations requires tissue-specific resolution. For such analysis, embryonic cardiac tissue would be the most appropriate given its relevance to developmental regulation. However, no embryonic datasets included the histone modification data was publicly available at the time of analysis. To account for this, integrating alternative resources such as ENCODE, Enhancer Atlas 2.0, or EnhancerFinder can expand the pipeline&#39;s utility by providing additional datasets for enhancer identification and validation18,19.
In the TBX5 REPFIX, the H3K4me1-based analysis revealed 22 putative enhancers as a starting point for further investigation. The subsequent Hi-C analysis showed that 21 out of 22 enhancer candidates based on previous histone modification marks interacted with the TBX5 promoter in the cardiac muscle cells (Figure 5). This supports the reliability of the histone modification marks-based approach in predicting regions of interest.
We chose not to prioritise sequence conservation across species in this protocol, although it is a common criterion for identifying enhancers. As it previously proved to be less effective for tissue or species-specific enhancers, many of which are not strongly conserved during evolution26. Given this, we opted to focus on chromatin-based markers that are more directly indicative of functional enhancer activity. However, the sequence conservation may still hold value in specific contexts, such as studying enhancers with evolutionary significance. In this case, it could be added as an optional step for users interested in conserved regulatory elements.
The protocol proved effective in the retrieval of 21 heart-specific enhancers for the TBX5 gene from the EnsEMBL website, which had previously eluded identification by an existing platform, VISTA Cardiac Enhancers Browser. Although the protocol described here could not retrieve one of the enhancers, hs498, suggesting the possibility of limitation in detection, the approach uncovered some enhancers not detected by the VISTA Cardiac Enhancers Browser. However, further validation of the retrieved enhancers is necessary, as the protocol yields a larger number of putative regions compared to curated VISTA databases. This higher count increases the risk of false positives, and a larger number of predicted enhancers does not necessarily indicate improved specificity or functional relevance. Incorporating additional experimental datasets or functional assays such as gene expression analysis, CRISPR perturbation, or reporter assays will be crucial to confirm the biological validity of these candidates as performed in Smemoet al.&#39;s study23.
The cross-comparison with three experimentally validated enhancers showed partial overlap with the predicted regions (Figure 5). The predicted enhancer &#34;e1&#34; was more broadly positioned than validated Enhancer 2, while &#34;e18&#34; showed partial overlap with Enhancer 16 (Figure 5). These results suggest that this approach successfully identifies regions with known regulatory activity, although the broader span of the predicted enhancers may reflect the flexibility in enhancer boundaries. Enhancers often function as modular elements, and their activity can depend on chromatin context, cell type, and developmental timing2,27. Therefore, predicted regions may include the core active sites with adjacent sequences that contribute to regulatory function, despite the need for experimental validation to determine which portions of these broader predictions are functionally active in a tissue-specific or developmental context. While VISTA Cardiac Enhancer Browser identified four regions within the defined range, only one enhancer, mm370, partially overlapped with an experimentally validated enhancer, Enhancer 16, specifically in the region marked by H3K4me1 (Figure 5)23. The remaining portion of mm370, which does not overlap with Enhancer 16, may indicate a non-functional or inactive subregion of the enhancer28,29.

Enhancers are non-coding DNA regions that regulate gene transcription, development, and cellular differentiation1,2. Mutation of enhancers can lead to various diseases, including developmental disorders, cancers, and other genetic conditions3,4,5,6. Therefore, understanding enhancers is paramount to understanding gene expression, mutation, and diseases.
To understand how enhancers interact with their target genes, it is important to identify their locations within the genome. However, identifying the enhancer locations is not always straightforward as enhancers can be located both close to the transcriptional start site (TSS) and much further away, spanning tens to hundreds of kilobases2,7,8,9.
Despite their unpredictable genomic location, enhancers show distinct biochemical and structural signatures, allowing them to be systemically traced. Generally, enhancers tend to be enriched in intergenic and intronic regions, with a small number found within exons2,8. They are often marked by specific histone modifications and transcription factor binding, which define their regulatory roles and determine their spatio-temporal activity across different developmental stages and tissues10,11.
ChIP-seq is used to identify transcription factor binding sites (TFBSs) and histone modification marks, such as the hallmark of enhancers, H3K4me1, active enhancer marks, H3K27ac, and H3K4me3 marks, which are enriched at promoter regions1,12,13,14,15. Chromatin conformation capture (3C) techniques and their derivatives, such as 4C, 5C, Hi-C, and ChIA-PET, are used to map physical interactions between distant genomic regions. While 3C targets specific interactions in specific tissues, Hi-C offers a genome-wide architecture across cell types16,17.
In addition to the current methods, specialised approaches have been developed to characterise enhancers, including pooled enhancer databases, such as EnhancerAtlas or EnhancerFinder18,19. However, these tools often require researchers to integrate multiple datasets to investigate multiple enhancers across many tissues, which can be overwhelming for biologists without experience in bioinformatics and data mining.
Here, we describe a user-friendly protocol to select enhancers, entirely based on existing web tools. This allows researchers to query a gene of interest (GoI) and retrieve corresponding enhancers. The protocol here selects enhancers based on a specific set of criteria: histone modifications, chromatin interactions, and tissue specificity1,12,13,14,15,16,17,20,21. Enhancers found within introns are more likely to show tissue-specific activity compared to intergenic enhancers, which are positioned in the genomic regions between genes22. To ensure comprehensive coverage of potential active enhancers, we defined the search range between two neighboring GoIs to increase the likelihood of capturing regulatory elements located outside of gene bodies. We used an enhancer-specific epigenetic hallmark, H3K4me1, and an active enhancer mark, H3K27ac, to list enhancer candidates. These candidates were then refined based on Hi-C data, retaining enhancers with physical interactions with the corresponding promoter. This protocol is designed to guide biologists through the process of enhancer identification using only publicly available web-based tools. By integrating epigenetic and chromatin interaction data, the approach described here offers a practical framework to generate hypotheses about potential enhancers for further experimental validation.

<table><tbody><tr><td>Computer workstation</td><td>N/A</td><td>N/A</td><td>Web-browser-capable computer, Windows/Mac/Linux operating system</td></tr><tr><td>4DN data portal</td><td>4DN data portal</td><td>https://data.4dnucleome.org/</td><td></td></tr><tr><td>Galaxy</td><td>Galaxy</td><td>https://usegalaxy.org/published/history?id=aff5db4e07064445</td><td></td></tr><tr><td>Github</td><td>Github</td><td>https://github.com/Ramialison-Lab/EnhancerWorkflow</td><td></td></tr><tr><td>VISTA&amp;nbsp;</td><td>VISTA Cardiac Enhancer Browser</td><td>https://portal.nersc.gov/dna/RD/heart/</td><td></td></tr></tbody></table>

a web-based workflow for selecting gene- and tissue-specific enhancers

A Web-Based Workflow for Selecting Gene- and Tissue-Specific Enhancers

Enhancers are DNA regions that regulate gene expression. Mutations within enhancers can result in abnormal gene regulation leading to disease. Therefore, identifying enhancers that regulate gene activity in specific tissues is crucial for understanding the genetic basis of disease. However, enhancers are difficult to identify as they do not encode proteins. While numerous enhancer repositories and identification tools are available, the complexity of these tools can present a challenge for biologists. To facilitate biologists in using these resources, we present a biologist-friendly protocol (https://github.com/Ramialison-Lab/EnhancerWorkflow) which leverages existing web-based genomics data such as H3K4me1 and H3K27ac histone marks and chromatin conformation analysis (Hi-C) data to discover enhancers associated with a gene of interest (GoI) in a target tissue where the enhancer is active. This protocol is entirely web-based and does not require programming skills from end-users. We demonstrated the utility of this approach by characterising candidate enhancers regulating&#160;TBX5, a gene critical for heart development. This protocol facilitates the identification of enhancers associated with this gene in the left ventricle.

To illustrate the use of the presented protocol, we investigated the TBX5 gene in the human heart, exploring TBX5-associated enhancers using the comprehensive workflow involving H3K4me1, H3K27ac, and Hi-C data. TBX5 is a gene that contributes to limb and heart development, including the formation of the four chambers and septum separation24. Mutation in this gene is a main cause of Holt-Oram syndrome (HOS), which causes limb abnormalities and congenital heart disease (CHD), including septum defects24. The mutation of TBX5-associated cardiac enhancers may critically influence CHD24. A previous study discovered three known TBX5 enhancers in human cardiac-specific tissue - namely "Enhancer 2", "Enhancer 9", and "Enhancer 16" (Supplementary File 2), which were demonstrated to have comparative phenotypes in transgenic mice23.
We investigated H3K4me1- and H3K27ac-enriched regions between RBM19 and TBX3, which are two flanking genes downstream and upstream of TBX5 in human, to retrieve putative enhancers at the TBX5 locus (Figure 1 and Figure 2). To identify heart-specific enhancers, the cardiac muscle cells were chosen. The putative TBX5 cardiac enhancer regions were retrieved as coordinates (chr12: start-end), and 22 both H3K4me1- and H3K27ac-associated regions were identified (Figure 3 and Supplementary File 3). Putative TBX5 cardiac enhancers were retrieved from the EnsEMBL genomic database to cross-reference Hi-C data held in the 4DNucleome database (Figure 4). This was done to gauge possible interactions between potential enhancers and the TBX5 cardiac promoter. Following the protocol described here, 21 out of 22 genomic regions were confirmed to interact with the TBX5 promoter (chr12: 114400143-114410103) in the cardiac muscle cells (Supplementary File 4). There was one region that had no physical interaction with the promoter (Figure 4, step 4.8). Finally, we compared this protocol with these biologically validated enhancers and the current gold-standard database of heart enhancers, VISTA Cardiac Enhancers Browser, and revealed additional enhancers not currently captured by the database25.
We performed a cross-comparison of the 21 TBX5 enhancers retrieved by the protocol presented here with existing databases. We retrieved 4 TBX5 enhancers from VISTA Cardiac Enhancer Browser (Supplementary File 5)25. Of the 4 VISTA-identified cardiac enhancers, 3 enhancers, hs2329, mm1282, and m370 overlapped with the regions identified by this web-based enhancer detection protocol (Figure 5). Each of the predicted enhancers also shared the genomic regions with previously experimentally validated enhancers from Smemo et al.23, Enhancer 2 (chr12:114025907-114026275, GRCh38), and Enhancer 16 (chr12:114415466-114420433, GRCh38), while they did not show overlap with Enhancer 9 (chr12:114263402-114266886, GRCh38). One of the VISTA-identified enhancers, hs498 did not overlap with any predicted enhancers by this protocol or Smemo et al.'s experimentally validated enhancers23 (Figure 5), even though the region showed partial overlap with H34Kme1-marks (Figure 5). Similarly, Enhancer 9 did not overlap with the predicted enhancers by this pipeline but was associated with H3K4me1 marks (Figure 5).
<img alt="Ensembl database search workflow for TBX5 gene; genomic data retrieval diagram." class="xfigimg" src="/files/ftp_upload/66840/66840fig1.jpg" data-altupdatedat="1762425657000" data-alt="Figure 1"> 
Figure 1: Step-by-step guide to locate the GoI in the EnsEMBL Genome Browser. The user first opens the EnsEMBL homepage (1.1), selects the species (Human), and enters the gene into the search bar (1.2-1.3). From the list of results, the appropriate gene ID is selected (1.4), which opens the gene summary page. The user then clicks the Region in Detail hyperlink (1.5) to visualize the genomic region surrounding the GoI, including neighboring elements and regulatory features. <a href="https://www.jove.com/files/ftp_upload/66840/66840fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Ensembl genome browser interface; gene alignment chart; chromosome region analysis." class="xfigimg" src="/files/ftp_upload/66840/66840fig2.jpg" data-altupdatedat="1762425657000" data-alt="Figure 2"> 
Figure 2: Defining the enhancer detection region around the GoI using the EnsEMBL Genome Browser. To define the enhancer detection region, identify the two neighboring genes flanking the GoI using the Basic Gene Annotations from GENCODE track, where genes are shown as dark yellow blocks labelled with merged EnsEMBL/Havana annotations. The transcriptional direction of each gene is indicated by arrowheads (&lt; or &gt;) next to the gene name (2.1). To select the intergenic region between the neighboring genes, click and drag across the region of interest, then choose Jump to region in the pop-up box to zoom in (2.2). To add regulatory or enhancer-related annotations, click Add/remove tracks (2.3). <a href="https://www.jove.com/files/ftp_upload/66840/66840fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Genome browser interface steps, highlighting settings, track options, and gene activity visualization." class="xfigimg" src="/files/ftp_upload/66840/66840fig3v2.jpg" data-altupdatedat="1762425657000" data-alt="Figure 3"> 
Figure 3: Configuration of histone modification tracks in the enhancer detection region using the EnsEMBL Genome Browser. In the left tool bar, click Configure this page (3.1) to access the track configuration panel and navigate to "Activity by Cell/Tissue" under the Regulation section (3.2). In the opened tab, select the "Experiments" section (3.3), and use the Cell/Tissue search bar to locate and select your tissue of interest (cardiac muscle cell) (3.4). In the histone mark panel (3.5), enable H3K4me1 and H3K27ac as active enhancer marks and H3K4me3 as a promoter mark then click "Configure track display" (3.6). After confirming the track selections, click "View tracks" (3.7) to return to the genome viewer. Histone mark peaks are now shown in the detection region (3.8) as coloured blocks under the corresponding tissue label (yellow: H3K4me1, blue: H3K27ac, and orange: H3K4me3). "Hists &amp; Pols" pop-up containing the genomic coordinates of the region in base pairs (chr:start-end), which can be copied and saved for downstream analysis. A "Hists &amp; Pols" pop-up appears after clicking on the coloured elements in the track. The pop-up contains the genomic coordinates of the region in base pairs (e.g., chr12:11443450-114451611 for the promoter region), which can be copied and saved for downstream analysis (3.8). Likewise, to extract candidate enhancers, prioritize regions where H3K4me1 and H3K27ac peaks overlap, as shown by vertical alignment of peaks and boxes across tracks (3.9). Overlapping regions can be selected directly by clicking their boxes or by manually click-dragging across the aligned peaks to define a region (e.g., chr12:114400143-114410103 for an active candidate region). Coordinates shown in the pop-up should be saved in BED format for downstream validation or visualization. <a href="https://www.jove.com/files/ftp_upload/66840/66840fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="4D nucleome data portal interface with interactive charts, data visualization, and analysis features." class="xfigimg" src="/files/ftp_upload/66840/66840fig4v2.jpg" data-altupdatedat="1762425657000" data-alt="Figure 4"> 
Figure 4: Visualisation of promoter-enhancer chromatin interactions using Hi-C heatmaps from the 4D Nucleome Data Portal. The 4D Nucleome Data Portal homepage displays a stacked bar chart summarizing available experiment types by organism. The "in situ Hi-C" dataset for human samples is selected by clicking the corresponding section of the bar (4.1). A filtered list of relevant datasets is displayed; a Hi-C dataset derived from H9 cells differentiated into cardiac myoblasts is selected (4.2). The selected dataset (4.3) is opened in the HiGlass browser via the Explore Data button (4.4). The genomic region of interest is entered in the coordinate box (4.5), and the contact matrix is rendered as a colour-scaled heatmap. Darker colours (deep red to black) indicate stronger chromatin contact frequency, while lighter colours (white to orange) represent weaker interactions. A horizontal rule is placed at the promoter coordinate, and vertical rules are drawn at the positions of three experimentally validated control enhancers (4.6). These intersections are used to define a strict interaction threshold, set by the strongest visible signal (darkest colour) among the promoter-enhancer contacts (4.7). Additional vertical rules are drawn at the locations of candidate H3K27ac and H3K4me1-marked enhancers (from step 3.8). Candidates whose promoter-enhancer intersections are equal to or darker than the threshold are retained, while those with weaker signals (lighter color squares) are excluded (4.8). Retained coordinates are extracted manually and saved in BED format for downstream analyses. (a. Enhancer 2, b. Enhancer 9, and c. Enhancer 16) <a href="https://www.jove.com/files/ftp_upload/66840/66840fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Chromosome 12 genomic sequence analysis diagram with detailed region annotations and gene locations." class="xfigimg" src="/files/ftp_upload/66840/66840fig5.jpg" data-altupdatedat="1762425657000" data-alt="Figure 5"> 
Figure 5: Genomic browser view of predicted TBX5 enhancers compared with VISTA-validated cardiac enhancers and control enhancers. Genome browser snapshots display the enhancer searching range (STEP 2) comparing predicted enhancers retrieved by the web-based protocol (bottom) with VISTA-validated enhancers (top) and experimentally validated control enhancers (center). The main panel shows the full genomic locus with annotated regulatory elements, including cardiac muscle cell-specific H3K4me1 (yellow), H3K27ac (blue), and H3K4me3 peaks (orange). Three zoomed-in figures capture the alignment between the retrieved by the protocol, VISTA, and control enhancers. The overlap with control enhancers is outlined with red boxes. Coordinates for each sub-region are displayed in the lower browser panels. <a href="https://www.jove.com/files/ftp_upload/66840/66840fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Table 1: Data used in the study. <a href="https://www.jove.com/files/ftp_upload/66840/Table%201.xlsx" target="_blank">Please click here to download of this Table.</a>
Table 2: Web-based tools used in the study. <a href="https://www.jove.com/files/ftp_upload/66840/Table%202.xlsx" target="_blank">Please click here to download of this Table.</a>
Supplementary File 1: Troubleshooting instructions for EnsEMBL Genome Browser. <a href="https://www.jove.com/files/ftp_upload/66840/Supplementary%20File%201.docx" target="_blank">Please click here to download of this File.</a>
Supplementary File 2: A BED file in GRCh38 format, the experimentally validated TBX5 cardiac control enhancers23. <a href="https://www.jove.com/files/ftp_upload/66840/Supplementary%20File%202.bed" target="_blank">Please click here to download of this File.</a>
Supplementary File 3: A BED file in GRCh38 format, TBX5 cardiac enhancers retrieved by STEP3 from EnsEMBL. <a href="https://www.jove.com/files/ftp_upload/66840/Supplementary%20File%203.bed" target="_blank">Please click here to download of this File.</a>
Supplementary File 4: A BED file in GRCh38 format, TBX5 cardiac enhancers retrieved by STEP4 from EnsEMBL. <a href="https://www.jove.com/files/ftp_upload/66840/Supplementary%20File%204.bed" target="_blank">Please click here to download of this File.</a>
Supplementary File 5: A BED file in GRCh38 format, TBX5 cardiac enhancers retrieved from the VISTA cardiac enhancer browser25. <a href="https://www.jove.com/files/ftp_upload/66840/Supplementary%20File%205.bed" target="_blank">Please click here to download of this File.</a>

We present a coding-free workflow for biologists to identify tissue-specific gene enhancers using only browser-based tools. Our protocol leverages public H3K4me1/H3K27ac histone marks and Hi-C data, enabling researchers without programming expertise to access, analyse, and identify potential regulatory elements associated with their genes of interest.

Enhancers are DNA regions that regulate gene expression. Mutations within enhancers can result in abnormal gene regulation leading to disease. Therefore, ...

We are interested in decoding the non-coding genome to understand how genes are regulated, to be expressed at the right time and the right place. We develop user-friendly protocols using web-based genomics tools to make enhanced discovery accessible to all biologists. Wide availability of multimodal data that can help narrow down the location of enhancers and the easy access of this data through open source web interfaces.This enables comprehensive enhancer identification without requiring programming expertise from researchers. This protocol provides a foundation for anyone without a background in enhancer biology to start navigating publicly available data sets to retrieve enhancers for the genes of interest. We use TBX5, a well-known player in heart biology, as a case study.Our workflow identified 21 enhancers, which may shed light on heart development mechanisms and congenital heart disease. We'll expand our protocol with new data sources, including spatial genomics, while developing additional user-friendly tools for biologists. To begin, open the Ensembl Genome Browser.Select the genome assembly that matches the species and version of interest. Enter the gene of interest in the search field and click Go.From the results, click on the appropriate Ensembl Gene ID.Then scroll to the Summary"section and click the Region in Detail"link to visualize the surrounding region of the gene of interest. Now, search for the two genes flanking the gene of interest using the Gene Legend track.These genes appear as visual elements representing merged Ensembl and Havana annotations within the basic gene annotations from the GENCODE track. Determine the directionality of the gene of interest by observing the greater than or less than signs beside the gene names. Click and drag the cursor across the intergenic region between these genes, then click Jump to Region"in the popup box to view the selected region.Customize the display by clicking Add"or Remove Tracks"at the top of the track viewer. Use the zoom and navigation controls to adjust the view for enhanced visualization of the region. In the Region in Detail"tab viewer, click Configure This Page"in the sidebar.In the Configure Region Image"sidebar, under Regulation, select Activity by Cell or Tissue. Use the cell or tissue search bar to find and select the desired tissues, or use the alphabetic index below the bar. Click on the Experiments"tab adjacent to the Cell or Tissue"option.Choose H3K4me1 and H3K27ac as a marker for enhancers and H3K4me3 as a marker for promoters and click Configure Track Display. Then, select View Tracks"to visualize regions marked by H3K4me1 within the enhancer detection region and regions marked by H3K4me3 upstream of the gene of interest. Click on the colored visual or box elements in the H3K4me1 track to retrieve the genomic coordinates of regions marked within the defined detection region.This opens the Hists and Pols popup, which displays the element's genomic location in base pairs. Alternatively, manually define the regions of interest for each genomic feature by clicking and dragging on the track to enclose graph peaks under the H3K4me1 or H3K27ac tracks. Then copy the genomic location coordinates into a text file and save the file in BED format.Access the 4DN Data Portal. On the homepage, ensure that Experiment Sets"is selected as the Y-axis of the main stacked bar, Experiment Type"is selected as the X-axis, and the plot is grouped by organism. Locate the In Situ Hi-C bar along the X-axis and click on the portion representing human experiment sets.In the popup, click the Browse"button and select cardiac muscle myoblasts"and our gene of interest, TBX5, associated with heart organogenesis. Click on the link in the Title"column of the relevant biosample corresponding to the tissue of interest. Then, in the Processed Files"tab, click Explore Data"to examine the Hi-C dataset in more detail.Enter the coordinates of the identified promoter in the tissue of interest and right click the heat map to mark the region horizontally. These lines allow the promoter region to be visually tracked across the heat map. If required, adjust the view by dragging it vertically until the upper and lower bounds in the Y coordinates of the search bar align with the boundaries of the region of interest.To remove any unwanted lines, right click the line and select Horizontal"or Vertical Rule, then click Close Series. Enter the coordinates of all experimentally validated control enhancers to calculate the interaction threshold based on their minimum interaction values. Display the three control enhancers from the literature alongside the promoter region from Ensembl using the pre-prepared view.Then define the promoter enhancer threshold using these control enhancers by selecting the lowest non-zero interaction score as indicated by the color key on the right side of the heat map matrix. Enter the genomic coordinates of all H3K4me1-associated enhancer regions and mark vertically on the Hi-C heat map. Filter out weakly interacting regions by comparing the interaction scores of H3K4me3-marked regions against the interaction threshold.Select the genomic coordinates showing interaction frequencies above this threshold, which appear as more concentrated or darker signals on the heat map, and save them in BED format. Three of the four cardiac enhancers identified in the VISTA Cardiac Enhancer Browser, HS2329, mm1282, and mm370, overlapped with regions predicted by the web-based enhancer detection protocol. Enhancer Two overlapped with both a region predicted by the enhancer detection protocol and an experimentally validated enhancer.Enhancer 16 showed overlap with both predicted enhancer regions and prior experimental data. Enhancer Nine did not overlap with any predicted enhancers from the pipeline but showed partial H3K4me1 signal enrichment.

a-web-based-workflow-for-selecting-gene-and-tissue-specific-enhancers

Research

JoVE Journal

Genetics

In this work we provide an experimental workflow of how active enhancers can be identified and experimentally validated.

Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation

Unlike DNA sequence data, epigenomic data are not readily subjected to text-based searches. Presented here are the procedures to use an upgraded version of GeNemo, a web-based bioinformatics tool, to conduct pattern-based searches for similarities in epigenomic data comparing available online databases including Encyclopedia of DNA Elements with user's data.

Pattern-based Search of Epigenomic Data Using GeNemo

This protocol describes the steps needed to design and perform multiplexed targeting of enhancers with the deactivating fusion protein SID4X-dCas9-KRAB, also known as enhancer interference (Enhancer-i). This protocol enables the identification of enhancers that regulate gene expression and facilitates the dissection of relationships between enhancers regulating a common target gene.

Dissection of Enhancer Function Using Multiplex CRISPR-based Enhancer Interference in Cell Lines

This protocol outlines the steps needed to generate a model system in which the transcription of an endogenous gene of interest can be conditionally controlled in live animals or cells using enhanced lac repressor and/or tet activator systems.

In vivo Application of the REMOTE-control System for the Manipulation of Endogenous Gene Expression

We report the application of quantitative chromosome conformation capture followed by high-throughput sequencing in embryoid bodies generated from embryonic stem cells. This technique allows to identify and quantitate the contacts between putative enhancers and promoter regions of a given gene during embryonic stem cell differentiation.

Identification of Enhancer-Promoter Contacts in Embryoid Bodies by Quantitative Chromosome Conformation Capture (4C)

This protocol describes a novel rAAV-based transient enhancer-reporter assay. This assay can be used to induce enhancer-driven expression in vivo in the mouse brain.

AAV Deployment of Enhancer-Based Expression Constructs In Vivo in Mouse Brain

Here, we present a protocol for the in vitro selection of engineered transcriptional repressors (ETRs) with high, long-term, stable, on-target silencing efficiency and low genome-wide, off-target activity. This workflow allows for reducing an initial, complex repertoire of candidate ETRs to a short list, suitable for further evaluation in therapeutically relevant settings.

In Vitro Selection of Engineered Transcriptional Repressors for Targeted Epigenetic Silencing

Gene expression is regulated by interactions of gene promoters with distal regulatory elements. Here, we descirbe how low input Capture Hi-C (liCHi-C) allows the identification of these interactions in rare cell types, which were previously unmeasurable.

An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

This protocol provides a streamlined computational pipeline for quantifying nascent enhancer transcripts. By integrating chromatin accessibility, chromatin feature, and transcriptional data, it enables accurate detection and strand-specific analysis of enhancer activity in complex intragenic regions, while remaining accessible to researchers without extensive bioinformatics training.

A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

We describe a method to sort single mammalian cells and to quantify the expression of up to 96 target genes of interest in each cell. This method includes the use of internal qPCR standards to enable the estimation of absolute transcript counts.

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Learn a coding-free workflow for identifying tissue-specific gene enhancers using browser-based tools and public genomic data. This protocol enables biologists to discover regulatory elements without programming expertise.

A Web-Based Workflow for Selecting Gene- and Tissue-Specific Enhancers

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