
==== Front
Mol Biol Cell
Mol Biol Cell
molbiolcell
mboc
Molecular Biology of the Cell
1059-1524
1939-4586
The American Society for Cell Biology

38717453
E24-02-0082
10.1091/mbc.E24-02-0082
Special Issue on the Nucleus in Stem Cells and Development
new_hypothesis
new_methods
open_data
Cell type- and transcription-independent spatial proximity between enhancers and promoters
Mian Yasmine a
Wang Li a
Keikhosravi Adib a
Guo Konnie a
Misteli Tom a
Arda H. Efsun a *
Finn Elizabeth H. a b *
a National Cancer Institute, National Institutes of Health, Bethesda, MD 20892
b Cell Cycle and Cancer Biology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, 73104
Saitou Mitinori Monitoring Editor
Kyoto University
Conflicts of interests: The authors declare no financial conflict of interest.

Author contributions: Y.M. and L.W. performed the experiments; All authors drafted the article.; L.W., A.K., and K.G. analyzed the data; L.W. and E.F. prepared the digital images; T.M., H.A., and E.F. conceived and designed the experiments.

ORCID ID: Elizabeth H. Finn, 0000-0001-8320-2190

*Address correspondence to: Elizabeth H. Finn (elizabeth-finn@omrf.org); H. Efsun Arda (efsun.arda@nih.gov).
01 7 2024
01 7 2024
35 7 ar9623 2 2024
12 4 2024
29 4 2024
© 2024 Mian et al. “ASCB®,” “The American Society for Cell Biology®,” and “Molecular Biology of the Cell®” are registered trademarks of The American Society for Cell Biology.
2024
https://creativecommons.org/licenses/by-nc-sa/4.0/ This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 4.0 Unported Creative Commons License.

Cell type–specific enhancers are critically important for lineage specification. The mechanisms that determine cell-type specificity of enhancer activity, however, are not fully understood. Most current models for how enhancers function invoke physical proximity between enhancer elements and their target genes. Here, we use an imaging-based approach to examine the spatial relationship of cell type–specific enhancers and their target genes with single-cell resolution. Using high-throughput microscopy, we measure the spatial distance from target promoters to their cell type–specific active and inactive enhancers in individual pancreatic cells derived from distinct lineages. We find increased proximity of all promoter-enhancer pairs relative to non-enhancer pairs separated by similar genomic distances. Strikingly, spatial proximity between enhancers and target genes was unrelated to tissue-specific enhancer activity. Furthermore, promoter-enhancer proximity did not correlate with the expression status of target genes. Our results suggest that promoter-enhancer pairs exist in a distinctive chromatin environment but that genome folding is not a universal driver of cell-type specificity in enhancer function.

Traditional models of enhancer function invoke proximity between enhancers and their target genes, but recent observations have questioned this model. This raises the question of how cell-type specificity of enhancers is achieved.

The authors compared the expression pattern of target genes with their physical proximity with enhancers using high-throughput imaging techniques in three cell types. Expression did not correlate with proximity. Rather, promoter-enhancer pairs were in proximity more often than negative control regions regardless of expression.

These data suggest that that proximity may predispose enhancers for activity but regulation is more likely mediated by other cell type–specific factors.
==== Body
pmcINTRODUCTION

The human genome must be tightly regulated during development to produce many different cell types with distinct properties and functions. Regulatory elements known as enhancers are a major mechanism for cell type–specific gene expression. Enhancers are among the most evolutionarily conserved noncoding elements in the genome (Woolfe et al., 2005), and enhancer mutations are frequently associated with disease risk (Schaub et al., 2012). Enhancer elements can selectively drive gene expression in specific cell types, and are required for proper cell-type specification and lineage determination (Arnold and Stengel, 2023). However, enhancers are often located tens or hundreds of thousands of base pairs away from the genes they regulate (Carter et al., 2002), and how they exert regulatory effects on distant targets is poorly understood.

There are multiple models for how cell-type specificity of enhancers may be generated. The classic model suggests that physical juxtaposition of enhancer sequences with their target genes via spatial looping of the chromatin fiber determines enhancer activity. In support, high-throughput chromatin conformation capture (Hi-C) and Hi-C with chromatin immunoprecipitation (HiChIP) frequently demonstrate associations between enhancers and the promoters they regulate (Sanyal et al., 2012; Zhang et al., 2013). These associations are dynamically regulated during development and differ between cell types (Williamson et al., 2016; Bonev et al., 2017; Zhao et al., 2023), suggesting that the physical interaction between an enhancer and its promoter may be a source of cell-type specificity. Another model is that active enhancers nucleate the recruitment of high concentrations of transcription factors to form a transcription hub (Monahan et al., 2017; Sabari et al., 2018). In this model, the formation of a large nuclear body may abrogate the need for physical proximity between individual enhancers and promoters, as cell-type specificity would instead be established by the complement and local concentration of transcription factors recruited to the enhancer hub (Whyte et al., 2013; Monahan et al., 2017). It has been suggested that clustered enhancers such as superenhancers work through this mechanism. More generally, looping between enhancers and promoters may be a universal topological feature of enhancers and therefore largely invariant among cell types, with cell-type specificity determined by transcription factor occupancy or chromatin state rather than proximity. This hypothesis is supported by data demonstrating associations between promoters and enhancers even in cell types where target genes are inactive (Espinola et al., 2021), as well as cell-type specific chromatin marks and transcription factor occupancy at enhancer elements (Heintzman et al., 2009; Stergachis et al., 2013; Heinz et al., 2015). To distinguish between these models, it is critical to understand how the spatial proximity of an enhancer with its target genes differs in distinct cell types and how it relates to cell type–specific gene expression.

We therefore set out to probe the role of spatial proximity in enhancer function at the single-cell level using high-throughput imaging. To do so, we examined a set of cell type–specific enhancer elements in pancreatic cell lines derived from distinct lineages, where prior work had identified specific regions of differential chromatin accessibility that correspond to cell type–specific enhancers in α-, β-, duct-, and acinar cells and whose accessibility correlates with regulation of nearby genes (Arda et al., 2018). We used high-throughput FISH (hi-FISH) (Finn and Misteli, 2021) to directly visualize cell type–specific enhancers and their cognate promoters in two pancreatic lineages and, as a technical control, skin fibroblasts. This technique allows direct and quantitative measurement of three-dimensional (3D) spatial distances between enhancers and their target genes at the level of single chromosomes within single cells, allowing us to determine how frequently cell type–specific promoter-enhancer loops form in individual cells and whether proximity correlates with transcriptional status on a population level. While we find that enhancers are generally in closer proximity to their target genes than corresponding non-enhancer regions, we also note that the proximity of an enhancer to its target promoter is not indicative of the cell type–specific transcriptional activity of the target genes. Our results point to a proximity-independent mechanism for cell type–specific enhancer function.

RESULTS

Selection and Hi-FISH quantification of cell type–specific pancreatic promoter-enhancer loops

To investigate the mechanistic role of chromatin looping in enhancer function, we sought to assess the spatial relationship of enhancer-promoter (E-P) pairs for genes with known cell type–specific expression in the pancreas. We performed HiChIP using an antibody against H3K27ac to map E-P interactions genome-wide in two pancreatic cell lines: EndoC-βH1 (Ravassard et al., 2011) (hereafter referred to as EndoC), an immortalized cell line derived from human islet β-cells, and PANC-1, human pancreatic ductal carcinoma cells (Lieber et al., 1975) (see Materials and Methods). We generated an average of 180 million reads per sample for a total of 1.6 billion reads. To call chromatin loops, we used two different computational pipelines to obtain high-confidence loop sets (see Materials and Methods for details). We defined E-P loops as those with one anchor overlapping with a promoter and the other with a distal region (Figure 1A). We used the edgeR package to identify differential cell type–specific E-P interactions between EndoC and PANC-1 cells (Robinson et al., 2010). Given our intent to use single-cell imaging approaches for our analysis, we focused on loops longer than 200 kb, well within the resolution limits of high-throughput imaging (see Materials and Methods). We then filtered the differential loops for those associated with known cell type–specific genes (Sturgill et al., 2024). For detailed imaging analysis, we selected a 522 kb loop with an upstream enhancer at the CCND1 gene, specific to PANC-1 cells, a 421 kb loop with an upstream enhancer at the ISL1 gene, specific to EndoC cells, and a 571 kb loop with a downstream enhancer at the PAX6 gene, specific to EndoC cells (Figure 1A, bolded arcs). Each of these loops occurred in tandem with other loops to the same promoter (unbolded arcs, Figure 1A), precluding the design of an ideal negative control probe located in the same genomic region. Consequently, we opted for a negative control located in a gene-rich region on chromosome 17, where we did not detect any loops in either cell type (Figure 1A). ChromHMM analysis confirmed cell type–specific variations in chromatin marks within our test regions, and also showed that despite having no loops, the chromatin state of the locus on chromosome 17 did not differ systematically, and included regions annotated by ChromHMM as active and inactive enhancers. In this locus, we selected one upstream probe and two downstream probes: one 420 kb downstream to serve as a negative control for ISL1, and one 550 kb downstream to serve as a negative control for both CCND1 and PAX6, as those loops were very similar in genomic distance (Figure 1A).

FIGURE 1: Probe locations and experimental design. (A) Locations of genes (boxes and arrows), BAC probes (bars), and HiChIP loops in both EndoC and PANC-1 cells (EndoC: teal, PANC-1: pink). Chromosome and position in Mbp as marked. FISH probe positions are marked and highlighted as follows. Orange: enhancer probe. Green: promoter probe. Yellow: control shared upstream probe. Blue: control downstream specific probe (dark blue: ISL1 genomic distance matched; light blue: CCND1/PAX6 genomic distance matched). (B) Distance measurements from FISH images. Two signals per genomic locus per nucleus were detected in each channel. Signals were segmented and distances measured in typically thousands of cells per sample to generate distance distributions (see Materials and Methods for details). Scale bar: 10 µm, Inset: 8.5 µm.

We performed hiFISH to measure the spatial distances between enhancer and promoter regions in single cells of each pancreatic cell type focusing on these five locus pairs. Our automated high-throughput imaging pipeline enabled us to analyze large numbers of cells, providing robust statistical significance to our findings. In addition, as a technical control, all interactions were measured in human immortalized foreskin fibroblasts (HFF), a non-pancreatic cell line that is stably diploid, for which the hiFISH technique has been optimized, and which also serves as a non-pancreatic lineage control (Finn et al., 2019). We selected bacterial artificial chromosome (BAC) clones surrounding each loop anchor or negative control locus, and performed two-color DNA FISH in a 384-well plate format against each pair (see Materials and Methods). For each image, we automatically segmented cells and the FISH signals within them (Figure 1B), and measured the distance between the centers of gravity of each green signal and the nearest red signal (Figure 1B) using a previously described pipeline (Finn and Misteli, 2021). For quality control filtering, we considered only cells with the correct number of detected signals and only promoter-enhancer or negative control pairs within 1.5 µm of each other, since larger pairing distances are expected infrequently for detection of chromatin interactions at the scale of several hundred kbs and are likely due to detection artifacts (Finn et al., 2019). We have previously observed few deviations in pairwise contact frequency over the course of the cell cycle (Finn et al., 2019), and none of the genes we studied are known to be strongly cell-cycle regulated, but this method also effectively filters for only G1-phase cells, as cells which have already undergone DNA replication will have twice the expected number of signals. While HFFs are diploid (Figure 2, right hand column), in PANC-1s and EndoCs our target loci were frequently aneuploid (Figure 2, left and central columns) as expected as both cell types are known to be rearranged and aneuploid (Ghadimi et al., 1999; Lawlor et al., 2019). Therefore, we filtered for cells with the same number of promoter and enhancer signals, and the most common number of signals in the population. The final dataset comprised 384,709 total signal pairs for all conditions and regions, with an average of 25,647 signal pairs per gene per cell type and we measured at least 3,780 signal pairs per gene per cell type (Supplemental Table S1). All analyses were done in three biological replicates performed on three different days, and each biological replicate included 12 technical replicate wells.

FIGURE 2: Representative FISH Images. Representative images of DNA FISH for all probe pairs and cell types tested. Blue: DAPI; Green: Promoter or upstream control; Red: Enhancer or downstream control. Scale bar: 10 µm; all images at the same scale. Small white box: magnified inset in larger white box.

E-P pairs colocalize more frequently than similarly spaced negative control pairs

To determine whether enhancers and promoters are more often in physical proximity than negative control pairs, we measured the distance between E-P pairs of CCND1, ISL1, and PAX6 and the negative control pairs (Figure 3). In all cases, two-dimensional (2D) spatial distances showed an enrichment for short distances and near-perfect colocalization, and the presence of rare long-distance separations (Williamson et al., 2015; Shi et al., 2018) (Figure 3A). Distance measurements were often within 500 nm and almost always within 1 µm (Figure 3A). Strikingly, the promoter-enhancer distances were always shorter than the controls in every cell type (Figure 3A, all P-values < 1.4*10−94, Wilcoxon–Mann–Whitney test). For example, the median promoter-enhancer distance for CCND1 was 199 nm in EndoC and 189 nm in PANC-1 cells, whereas the equidistant, non–E-P control pair was 295 nm in EndoC and 314 nm in PANC-1 (P < 2.2*10−22 in both cases). These extremely low P values, however, are reflective of a relatively more modest effect size (Figure 3A) and our extremely large dataset. As such, a comparison between experimental replicates is more relevant. We observed that the difference in distances between promoter-enhancer pairs and negative control pairs was highly reproducible between experimental replicates (Figure 3A). Median distances per technical replicate were significantly smaller for promoter-enhancer pairs than for negative control pairs (Figure 3B, P < 0.01 for all except ISL1 in PANC-1; P = 0.013 for ISL1 in PANC-1). Similarly, the proportion of pairs found within 120 nm was significantly larger for promoter-enhancer pairs than for negative control pairs (Figure 3C, P < 0.01 by two-sample t test for all comparisons). Mean distances for E-P pairs ranged from 150 to 300 nm (Figure 3B) and promoters and enhancers colocalized within 120 nm in between 20 and 25% of alleles (Figure 3C), consistent with prior studies of E-P pairs in other systems (Williamson et al., 2015; Chen et al., 2018), whereas non–E-P pairs were typically separated by 450–1000 nm and colocalized only in 3.7–15.2% of alleles (Finn et al., 2019).

FIGURE 3: Regions with loops interact more frequently than control regions. (A) Probability density functions for 2D spatial distance between two probes. Each line represents one of three biological replicates; negative control pairs in pink, promoter enhancer pairs in teal. (B) Median 2D spatial distances for negative control (pink) and loop (teal); each dot represents the median in a single well, pooled population medians in cross-hatches. (C) Percentage of spot pairs found within 120 nm for negative control (pink) and loop (teal); each dot represents data from a single well; pooled population proportions in cross-hatches. (D) Heatmap showing colocalization frequency (top row) compared with HiChIP PET count (bottom row; log10).

Among the three E-P interactions, we compared colocalization frequency with PET count (Figure 3D). We observed very high PET counts and very frequent colocalizations between ISL1 and its enhancer and substantially lower PET counts and less frequent colocalizations between PAX6 and its enhancer. However, while CCND1 colocalized with its enhancer more frequently than did PAX6, the PET counts were very similar between these two loops (Figure 3D). It is possible that observed discrepancies reflect the increased complexity around CCND1, which might act as a “tether” and increase spatial colocalizations at this region compared with PAX6, or that HiChIP data only imperfectly mirror spatial colocalization frequencies.

We examined available Hi-C data to determine whether these differences were due to the presence of insulator elements or TAD boundaries between tested locus pairs. This is unlikely to be the case, as a boundary is visible between promoter and enhancer at PAX6 in HFF, and almost no structure is visible bridging promoter and enhancer at ISL1 in PANC-1, but no boundary appears between the negative control probes in EndoC (Supplemental Figure S2). Thus, the presence or position of TAD boundaries is insufficient to explain the increased colocalization frequencies between promoters and enhancers as compared with negative controls and the genome architecture of the loci does not explain cell type–specific loops identified via HiChIP in PANC-1 or EndoC cells. We conclude that these enhancers are generally in closer proximity to their target promoters than equidistant non-enhancer regions and this positioning occurs regardless of cell type–specific local structure identified by Hi-C or active loops identified by HiChIP.

Colocalization between promoters and enhancers is independent of cell type and expression status of the target gene

According to the classic model of promoter-enhancer interactions, colocalization between enhancers and promoters is required for gene activation and therefore enhancers are predicted to be positioned closer to their cognate genes in the cell types where those genes are actively transcribed. In fact, we observed cell type–specific differences in promoter-enhancer distances at two of our three genes. However, these cell type–specific differences did not consistently align with cell type–specific observed associations in HiChIP data. The ISL1 enhancer is closer to its promoter in EndoC than in either PANC-1 (median distance: 170 nm vs. 203 nm, P = 0.011; pairs within 120 nm: 30% vs. 23%, P = 0.0019) or HFF (170 nm vs. 239 nm, P = 2.0*10−5; 30% vs. 17%, P = 2.6*10−6). This is consistent with HiChIP data showing EndoC-specific loops. However, the PAX6 enhancer is significantly farther away from its promoter in HFF than in either PANC-1 (275 nm vs. 205 nm, P = 1.1*10−5; 12% vs. 21%, P = 1.9*10−6) or EndoC (275 nm vs. 222 nm, P = 2.5*10−9; 12% vs. 18%, P = 6.3*10−12). All other cell-type comparisons were nonsignificant (P > 0.05). Reassuringly, the negative control regions never showed cell-type specificity, suggesting that the differences we observe are not due to differences between cell types in nuclear geometry, nuclear size, or global chromatin packaging (Figure 4A). Similarly, comparing colocalization frequencies determined by FISH with PET counts determined by HiChIP in all pairs and cell types reveals frequent instances where low PET counts are nonetheless reflected by high colocalization frequencies (Supplemental Figure S3). We conclude that E-P distances are unrelated to the tissue-specific formation of E-P interactions in the tested regions.

FIGURE 4: E-P proximity is not cell-type or gene expression specific. (A) Probability density functions showing distribution of 2D spatial distances between two probes. Each line represents one of three biological replicates. (B) dCT values as a boxplot (box: interquartile range; line: 95% of distribution) for the genes of interest, INS (insulin; control for EndoC specificity), VIM (vimentin, control for PANC-1 specificity), and ACTB (actin beta, normalization control). (C) Bubble plot showing pooled percentage of colocalizing spot pairs (% within 120 nm, size of bubble) and dCT (color) for each probe pair in each cell type. (D) Heatmap showing Expression (dCT value; E) and Colocalization (% within 120 nm; C) for all three genes in all three cell types.

Finally, we tested the prediction that physical E-P proximity correlates with cell type–specific gene expression. QRT-PCR results demonstrated, as expected, high expression of ISL1 specific to EndoC, CCND1 in HFF and PANC-1, and PAX6 in EndoC (Figure 4B). Positive controls for cell-type specificity, INS and VIM, showed the expected cell type–specific expression in EndoC and PANC-1, respectively (Figure 4B). The gene expression data were also consistent with HiChIP data: EndoC expressed and exhibited loops at ISL1 and PAX6, but did not express or exhibit loops at CCND1. In contrast, PANC-1 expressed CCND1, where we observed associations in HiChIP, but not ISL1 or PAX6, for which we did not detect loops in HiChIP data. However, expression status was only poorly correlated with spatial proximity (Figure 4, C and D); in some cases both the proportion of FISH signals within 120 nm and the expression level was high (as for ISL1 in EndoC), moderate (as for CCND1 in PANC-1), or low (as for ISL1 in HFF). But in other cases highly expressed genes were only rarely in proximity as for example for PAX6 in EndoC and some promoters were in relatively frequent proximity with enhancers without detectable transcription such as for CCND1 in EndoC. In total, we observe two different patterns in our three genes: at ISL1 and PAX6, promoters and enhancers are in relatively frequent proximity in both pancreatic cell types despite being expressed in only one of the two; at CCND1, the promoter and enhancer colocalize at similar rates in all cell types despite silenced expression in EndoC.

There was also no clear-cut pattern whereby promoters and their enhancers were found in more frequent proximity at active rather than inactive genes within a cell type. While HFF exhibited frequent colocalizations and high expression at CCND1, and rare colocalizations and low expression at ISL1 and PAX6, this correlation did not hold up in the other two cell types (Figure 4, C and D). PANC-1 showed fairly constant colocalization frequencies across all three genes, despite very different expression levels, and EndoC showed frequent proximity at ISL1 and CCND1, yet ISL1 and PAX6 but not CCND1 were expressed (Figure 4, C and D). We conclude that 3D spatial proximity is not correlated with cell type–specific enhancer-mediated gene regulation in the genes and cell types tested.

DISCUSSION

We have spatially mapped at the single-cell level the physical proximity of a set of cell type–specific enhancers in different pancreatic cell lines and have related E-P proximity to gene expression status. Our data demonstrate that cell-type specificity in spatial proximity between enhancers and promoters is not absolutely required for cell type–specific enhancer function because we find no consistent correlation between E-P proximity and cell type–specific chromatin contact frequency as detected by HiChIP nor to cell type–specific gene activity. Remarkably, however, we show that the E-P pairs, tested in three cell types, were in closer spatial proximity than non–E-P regions at similar genomic distances. This was not due to preferential location of E-P pairs within TADs compared with our control pairs, because apparent boundaries exist between both the E-P pairs and control loci pairs. For instance, the CCND1 enhancer is separated from its target gene by a TAD boundary in Endo-C cells, and does not loop in HiChIP data, but nonetheless the two colocalize more frequently than negative control regions. Our findings suggest that regions containing enhancers may interact more frequently with their putative target genes than equidistant non-enhancer regions, regardless of cell type or gene activity. It thus appears that mechanisms other than spatial proximity, such as chromatin changes or differential transcription factor binding, must be considered as drivers of cell-type specificity in enhancer function, alongside architectural features.

Our finding does not contradict previous studies which observe a general and systematic correlation between Hi-C data and average spatial distances. Indeed, several studies have shown that while Hi-C and DNA FISH are frequently highly correlated, nevertheless exceptions to this rule occur when considering individual pairs or individual cells (Bintu et al., 2018; Finn et al., 2019; Takei et al., 2021). The apparent discordance between Hi-C data and DNA FISH data at some genome loci is a longstanding observation (Williamson et al., 2015), and recent data examining promoter-enhancer loops in multiple systems have come to similar conclusions as this study (Benabdallah et al., 2019). In addition, it is worth noting that the Hi-C data were perhaps more closely aligned with the spatial distances we measured than was HiChIP, for instance a shifting boundary apparent in Hi-C maps between PAX6 and its enhancer agreeing with increased spatial distances specifically in HFF, or the presence of a very strong looped structure between in Hi-C maps agreeing with strongly conserved colocalization frequencies at CCND1. HiChIP is a more sensitive method to identify active promoter-enhancer loops, but it is possible that Hi-C more accurately recapitulates average spatial position. Thus, our data fit with the rich and growing body of literature suggesting that the relation between biochemical methods such as Hi-C and HiChIP and imaging-based methods such as DNA FISH is highly concordant at the genome-wide level but may be more variable at individual locus pairs.

In addition to the classic E-P model which envisions direct physical interaction between the enhancer and promoter sequences, alternative models for how enhancers may mediate cell-type specificity have been proposed (Arnold and Stengel, 2023). Two models have particular explanatory power for our data. The first of these is that enhancers may function via the formation of relatively large phase-separated biocondensates containing transcription factors such as Mediator and YY1, and the RNA Polymerase machinery (Cho et al., 2018; Wang et al., 2022). The second is that individual enhancers rarely regulate their target promoters in a one-to-one manner, but instead many enhancers work together in concert to regulate genes (Ing-Simmons et al., 2015).

Biochemical and sequencing-based approaches to study genome organization are not well suited to distinguish between close spatial association between chromatin elements and the formation of larger biomolecular condensates (Gómez Acuña et al., 2023). This is due to the fact that the cross-linkers used in these approaches are promiscuous, and link DNA to protein as well as protein to protein. Cross-linking artifacts have long been suggested as one way to explain divergent observations of spatial proximity observed by DNA FISH and interaction data generated by Hi-C (Williamson et al., 2015). Furthermore, because HiChIP signal depends on enrichment for specific chromatin marks (in our case H3K27ac) as well as chemical cross-linking and ligation, differences in the chromatin environment or nuclear environment could result in cell-type specificity in HiChIP signal without any change in spatial distance. The global enrichment for promoter-enhancer proximity we observed in all cell types, regardless of gene expression status, is in line with a model in which the transcriptional status of cognate promoters is largely determined by the presence of relevant transcription factors without the need to increase spatial proximity between promoter and enhancer.

Clusters of enhancer elements regulating individual genes are frequently found in Hi-C data (Markenscoff-Papadimitriou et al., 2014; Ing-Simmons et al., 2015; Oudelaar et al., 2018; Chen et al., 2019), and individual sequence variants at enhancers contribute synergistically to disease risk (Chatterjee et al., 2016), suggesting cooperativity between enhancers in gene regulation. These observations have led to a model in which enhancer clustering, or cooperativity, leads to cell type–specific activation, while absolute rates of association of individual promoter-enhancer pairs remain unchanged (Liang and Perez-Rathke, 2021). The regions studied had multiple enhancers identified by HiChIP contacting each promoter, and further work with methodologies designed to test multivalent associations is necessary to conclusively test the hypothesis that these enhancers act cooperatively.

Recent live cell imaging studies support the hypothesis that multiple mechanisms may regulate promoter-enhancer specificity. In transgenic systems in Drosophila, there is a very strong correlation between active transcription and looping (Chen et al., 2018), in which it appears that transcriptional activation requires and stabilizes looping. Further studies in similar systems at multiple genomic distances highlighted a three-state model, in which spatially separated promoters and enhancers created a “stably off” state, whereas a looped conformation was either “poised” or actively transcribing (Brückner et al., 2023). Translating these models to mammalian systems has been significantly more difficult, and some endogenous genes in human cells show no correlation between spatial distance and transcription (Alexander et al., 2019). It is possible that this could be due to the fact that many if not most mammalian genes are regulated by clustering of enhancers rather than any individual promoter enhancer loop (Li et al., 2020; Cheng et al., 2023), or that enhancer activity may be linked to movement more than position (Gu et al., 2018; Platania et al., 2023).

Our study is limited to very long-range interactions and it is possible that distinct enhancer mechanisms are used for short- and long-range E-P interactions and that the role of TADs differs for short-range interactions. This would be reminiscent of regulation of neuronal signaling, where Hi-C data show that associations between immediate-early genes and their proximal enhancers were independent of cohesin, but associations between secondary response genes and more distal enhancers required cohesin (Calderon et al., 2022). To resolve these two issues, and determine how TADs and promoter-enhancer loops contribute to the specific action of an enhancer and whether shorter loops are regulated via different mechanisms than longer ones, better imaging techniques with increased spatial and genomic resolution must be developed and employed. Furthermore, our study is limited in its interpretations due to the size of our DNA FISH probes (∼200 kb in length) which may preclude detection of changes in short-range chromatin interactions. Future work with higher genomic resolution will be needed to examine in more detail the relationship of E-P interactions and gene activity. Our data suggest however that large-scale changes in genome organization upon activation are not a universal feature.

We only examined three genes in three cell types, and this significantly limits the generalizability of our data. However, we observed different behaviors at each gene. This suggests that further study, of more promoter-enhancer pairs and more cell types, is necessary in order to determine what the “usual” behavior of promoters and enhancers is, and to identify ideal model systems for how promoters and enhancers behave. Furthermore, none of our chosen E-P interactions are implicated in human disease; expanding this work to those loops particularly relevant to human health may highlight that only some promoter-enhancer loops are functionally relevant. Regardless, it appears from our findings that the simple model of physical interaction between an enhancer with its target gene promoter is insufficient to explain cell type–specific functions of enhancers.

MATERIALS AND METHODS

Request a protocol through Bio-protocol.

Cell lines

PANC-1 is a human pancreatic cancer cell line isolated from the pancreatic duct of a 56-year-old white male with epithelioid carcinoma (Lieber et al., 1975). PANC-1 cells were obtained from ATCC (CRL-1469). Cells were maintained in DMEM with 10% FBS.

HFF is an hTert-immortalized human foreskin fibroblast cell line (Benanti and Galloway, 2004). HFF cells were grown in DMEM with 10% FBS, 2 mM glutamine, and penicillin/streptomycin and split 1:4 twice weekly.

EndoC-βH1 is an immortalized human beta cell line (Ravassard et al., 2011). Cells were grown in DMEM low glucose (1 g/l), 2% albumin from bovine serum fraction V, 50 µM 2-mercaptoethanol, 10 mM nicotinamide, 5.5 μg/ml transferrin, 6.7 ng/ml sodium selenite, and penicillin (100 units/ml)/streptomycin (100 mg/ml). Cells were seeded onto plates coated with 1% extra-cellular matrix (Sigma, E1270) in DMEM (1 g/l glucose).

HiChIP assays

HiChIP libraries were prepared following procedures described previously (Mumbach et al., 2016). Briefly, cells were fixed in 1% formaldehyde (made fresh) at 1 × 106 cell/ml for 10 min at room temperature with rotation, quenched with glycine at a final concentration of 125 mM for 5 min, then pelleted at 500 RCF for 5 min and washed once with PBS/Pluronic before proceeding to the next steps.

To isolate nuclei, each cell line sample was resuspended in 250 μl of ice-cold Hi-C Lysis Buffer (10 mM Tris-HCl pH 7.5, 10 mM NaCl, 0.2% NP-40, 1 × Roche protease inhibitors, 11697498001) and rotated at 4°C for 30 min. The nuclei were pelleted at 2500 RCF for 5 min and washed with 250 μl ice-cold Hi-C Lysis Buffer. The washed nuclei pellet was resuspended in 50 μl 0.5% SDS and incubated at 62°C for 10 min. SDS was quenched by adding 146 μl H2O and 25 μl of 10% Triton X-100, and incubated at 37°C for 15 min, rotating end-to-end. To digest the chromatin in situ, 100U MboI and 25 μl 10xNEB buffer 2 (room temperature) were added to the reaction and incubated at 37°C for 2 h with rotation. Enzymes were heat-inactivated at 62°C for 20 min. To label ends of the digested chromatin fragments, 14 μl of dNTP (40 μM each of dATP-14-biotin, dTTP, dCTP, and dGTP) and 15U of DNA Polymerase I, Large (Klenow) Fragment (NEB, M0210) were added and incubated at 37°C for 45 min. Ends were ligated by adding 484 μl of ligation mixture containing 75 μl 10x T4 ligase buffer, 62.5 µL 10% Triton X-100, 3.75 μl 20 mg/ml BSA, 2000U of T4 ligase (NEB, M0202S), and 337.75 μl H2O and incubated at room temperature for 2 h with rotation. The ligation mix was pelleted at 2500 RCF for 5 min at 4°C.

To fragment the ligated chromatin, the in situ Hi-C pellet was lysed in 130 μl of Nuclei Lysis Buffer then transferred to an AFA microtube (Covaris Inc. 520135) and sonicated on a Covaris E220 under the following setting: PIP 105W, duty factor 2%, CPB 200, time 4 min, temperature 6°C. The sheared lysate was cleared by centrifugation at 16,100 RCF, 4°C for 15 min. Cleared lysate was diluted 2x in ChIP Dilution Buffer and precleared with 15 μl Dynabeads Protein A at 4°C for 1 h with rotation. 1 μl of anti-H3K27ac (Abcam, ab4729, lot GR3211959-1) was added to the precleared lysate to immunoprecipitate (IP) H3K27ac-associated, proximity ligated chromatin fragments overnight at 4°C with rotation. 15 μl Dynabeads Protein A were added and incubated at 4°C for 2 h to pull down the immunoprecipitated complex. Beads were washed nine times in the following order: 3x Low Salt, 3x High Salt, and 3x LiCl buffers. Washing was performed at room temperature on a magnet stand by adding to a sample tube 300 μl of a wash buffer, turning the tube 180° relative to the magnet several times, allowing the beads to set for 2 min then removing the supernatant. ChIP DNA was eluted by incubation in 50 μl ChIP Elution Buffer on a thermomixer at 37°C with 300 rpm mixing. Two elution cycles were performed per sample and the eluates were pooled. To reverse cross-link, 5 μl Proteinase K (20 mg/ml, Invitrogen 25530049) was added and the mixture was incubated at 55°C for 45 min followed by 67°C for 2.5 h. The DNA was purified using a Zymo Research DNA Clean and Concentrator kit (D4014) following the manufacturer's instructions, eluted to 12 μl H2O, and 2 μl was used for quantitation on Agilent TapeStation 4000.

For biotin pull-down and sequencing library preparation, 5 μl Streptavidin C-1 beads (Invitrogen, 65001) were washed with 300 μl Tween Wash Buffer, resuspended in 10 μl 2x Binding Buffer and mixed with each sample. The mixtures were incubated at room temperature for 15 min with rotation. Beads were washed in 300 μl Tween Wash Buffer twice at 55°C with shaking (400 rpm), followed by one wash in 100 μl 2x TD Buffer. For on-bead tagmentation, beads were resuspended in 50 μl mixture containing 25 μl 2xTD buffer, 0.05 μl Tn5 per 1 ng post-ChIP DNA and H2O and incubated at 55°C with interval shaking for 10 min. The tagmented beads were incubated in 300 μl 50 mM ethylenediaminetetraacetic acid (EDTA) at 50°C for 30 min, followed by washes in 2 × 50 mM EDTA (300 μl) at 50°C for 3 min, 3x Tween Wash Buffer (300 μl) at 55°C for 2 min then once in 200 μl 10 mM Tris. To prepare the sequencing library, beads were resuspended in 50 μl PCR mix (25 μl 2xNEB HF master mix, 1 μl 12.5 μM Nextera ad-noMix, 1 μl barcoded 12.5 μM Nextera ad2.x, and 23 μl H2O) and amplified by PCR program: 72°C for 5 min, 98°C for 1 min, followed by N cycles of [98°C for 15 s, 63°C for 30 s, 72°C for 1 min] then hold at 4°C. N was determined by the amount of post-ChIP DNA as described in Mumbach protocol. The libraries were cleaned up using the Zymo kit and eluted in 15–18 μl H2O, 2 μl of which was used to determine the quantity and size distribution on an Agilent TapeStation 4000. High-quality libraries were sequenced with 2 × 75 bp runs on an Illumina NextSeq instrument.

HiChIP data analysis

Data processing and loop calling.

Nine HiChIP libraries (five from EndoC and four from PANC-1) were sequenced using paired-end sequencing to achieve over 180 million reads per sample. To negate the variability between HiChIP replicates and identify high-confidence chromatin interactions, we built “reference” chromatin interaction datasets for each cell type by merging the raw reads (.fastq files) from replicate samples. These merged reads were processed using the Hi-C Pro pipeline (Servant et al., 2015). Briefly, reads were aligned to the human reference genome (GRCh38/hg38) with Bowtie2, using default parameters. Valid interaction pairs were identified and filtered to remove duplicates and self-ligated reads. We then called loops using both hichipper (Lareau and Aryee, 2018) and FitHiChIP (Bhattacharyya et al., 2019) from Hi-C Pro outputs. Hichipper parameters for significant interactions were set to a PET count of at least 2 and an FDR of 0.01 or less, using the EACH, SELF loop calling setting which aggregates self-ligation reads from each sample. FitHiChIP parameters for significant interactions were set to the “loose” method for calling loops with a 5000 bp bin size, with an FDR cutoff less than 0.05. The loop lists were intersected using bedtools’ pairToPair function to identify common chromatin interactions among these two computational methods (Quinlan and Hall, 2010). These intersecting loop sets were considered high-confidence loops, designated as the reference loop set for each cell line.

Differential loop analysis.

We used the edgeR pipeline (Robinson et al., 2010) to identify differential loops between cell types, which requires replicate count data. For this purpose, we generated individual loop sets from each replicate sample using the same analysis pipeline detailed above, except for calling loops only with hichipper. These sets of loops were cross-checked with the high-confidence reference loop sets appropriate for each cell type by using bedtools’ pairToPair function, and then discarding nonoverlapping loops. Using the GenomicRanges package (Lawrence et al., 2013), we constructed a PET counts matrix that included a union of all filtered, high-confidence loops across replicates for EndoC and PANC-1 cells. The counts were normalized using the calcNormFactors() function in edgeR. We estimated both common and tagwise dispersions, and then fitted the data to a negative binomial model using edgeR glmFIT(). After defining contrasts between cell types (EndoC and PANC-1), the significance of these loops was determined by glmLRT(), which conducts a likelihood ratio test comparing loop counts among the cell types. From the significance testing, we then considered loops with an FDR of 0.01 or less as differential loops. Lastly, we identified cell type–specific clusters of these loops using the kmeans() function in R, with the number of clusters set to two for the two cell types and the distance metric based on centered correlations.

DNA FISH

High-throughput fluorescence in situ hybridization was performed as described previously (Finn and Misteli, 2021). Probes were generated from BACs containing target regions. Bacteria from a single colony were grown into a large-scale culture and target DNA was purified via alkaline lysis using the Nucleobond BAC 100 Maxiprep kit (from Takara). DNA was then quantified and stored at −20°C for future use. Probes were generated from BAC DNA by a nick translation reaction incubated at 16°C for 1 h 20 min with the following mix: 40 ng/µl DNA, 0.05 M Tris-HCl pH 8.0, 5 mM MgCl2, 0.05 mg/ml BSA, 0.05 mM dNTPs with all dTTP replaced with fluorescently-labeled dUTP, 1 mM β-mercaptoethanol, 0.5 U/μl Escherichia coli DNA Polymerase I and 0.5 μg/μl DNAse I. The reaction was stopped with the addition of 1 μl EDTA per 50 μl reaction volume and heat shocked to 72°C for 10 min, then stored at −20°C overnight. QC gels were run in 2% agarose to verify successful nick translation with a smear of less than 1 kb. Combinations of two probes (1 μg per probe) were mixed, ethanol precipitated, resuspended in 15 μl of hybridization buffer (50% formamide pH 7.0, 10% dextran sulfate, and 1% Tween-20 in 2X SSC) per well and warmed to 72°C before plating.

Cells were plated at an appropriate density and grown overnight at 37°C before fixation for 10 min in 4% paraformaldehyde in PBS, two PBS rinses, and storage in 70% ethanol at −20°C. To perform DNA FISH, cells were warmed to room temperature and rinsed three times in PBS to remove ethanol. Cells were then permeabilized in 0.5% wt/vol saponin/0.5% vol/vol Triton X-100 in PBS at room temperature for 20 min, rinsed twice with PBS, deproteinated for 15 min at room temperature in 0.1 N HCl, and neutralized for 5 min at room temperature in 2X SSC before equilibration in 50% formamide/2X SSC for at least 20 min at room temperature. 13.5 μl of resuspended probe mix was added per well, pipetting to mix before adding, and plates were spun to remove air bubbles. Cells were denatured for 7.5 min at 85°C and immediately moved to a 37°C water for 72 h hybridization. After hybridization, plates were rinsed once at room temperature with 2X SSC, and then thrice each with 1X SSC and 0.1X SSC both warmed to 45°C. Cells were stained with DAPI at high concentration (0.5 μg/ml) for 15 min to counterstain nuclei and simultaneously test for Mycoplasma in every experiment, rinsed, mounted in PBS, and imaged. All experiments were performed in 12 technical replicate wells per experiment.

Imaging

Automated imaging was performed in three channels (405, 488, and 561 nm excitation lasers) on a CV8000 dual spinning disk confocal microscope with a 60X water immersion lens (NA = 1.2) and no pixel binning for a final pixel size of 108 nm. We imaged 16 fields per well with a z-stack of 10 μm at 1 μm intervals. In the first exposure, cells were excited with the 405 nm laser and the light path included a short pass emission dichroic mirror and a sCMOS camera in front of a 445/45 nm bandpass filter. In the second exposure, cells were excited with both 488 and 561 nm lasers and emission detected through the same light path by two sCMOS cameras in front of 525/50 nm and 600/37 nm bandpass emission filters, respectively. Laser power and exposure time was optimized per experiment to ensure good signal-to-noise ratios.

Image analysis

Analysis of imaging data was carried out using HiTIPS, a high-throughput image analysis software to analyze DNA FISH data (Keikhosravi et al., 2023). For each experimental plate, specific analysis parameters were selected and tailored to align with the average nucleus size, as well as the size and brightness of the DNA FISH spots observed. Within HiTIPS, the GPU-based CellPose algorithm for nuclei segmentation was used in conjunction with the Laplacian of Gaussian method for spot detection (Stringer et al., 2021). Spot positions were determined as a center of gravity of the segmented spot. Parameter selection was guided by real-time visual feedback, enabling iterative refinement of measurement parameters. Image processing was done on the NIH HPC Biowulf cluster (NIH Biowulf HPC Cluster).

Statistical analysis

Distances between green and red spots were calculated in 2D in R (R Core Team, 2015), using the SpatialTools package (French, 2015). Further analyses for statistics and plotting used the plyr (Wickham, 2011), dplyr (Wickham and Francois, 2015), ggplot2 (Wickham, 2009), data.table (Dowle et al., 2015), knitr (Xie, 2014), and stringr (Wickham, 2015) packages. Total counts for each biological replicate in Supplemental Table S1. Distributions were compared between biological replicates, generally comparable in shape, and subsequently pooled for analysis. For comparisons of overall central tendency, a Wilcoxon—Mann–Whitney test was used as distributions were universally highly skewed. For comparisons of well-level median spatial distance or proportion interacting, a t test was used as the distributions of these summary statistics were more normal in shape, as expected.

Data availability

The HiChIP data presented in this publication have been deposited in NCBI's Gene Expression Omnibus (GEO) (Edgar et al., 2002) and are accessible through GEO Series accession number GSE245471 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE245471).

The Hi-C data shown in Supplemental Figure S1 are published and publicly available processed datasets visualized using Juicebox (https://aidenlab.org/juicebox/). HFF (Krietenstein et al., 2020) is accessible through 4D Nucleome accession number 4DNES2R6PUEK (https://data.4dnucleome.org/experiment-set-replicates/4DNES2R6PUEK/); Panc-1 (Ren et al., 2021) through GEO Series accession number GSE149103 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE149103); and EndoC (Lawlor et al., 2019) through GEO Series accession number GSE118588 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE118588).

Spot positions derived from imaging data have been deposited and are available at the Data Dryad general-purpose repository (https://datadryad.org/stash) DOI: 10.5061/dryad.6t1g1jx5v

Supplementary Material

The authors would like to thank Gianluca Pegoraro for support in high-throughput imaging and analysis. Computational resources of the NIH High Performance Cluster (Biowulf) supported the analysis in this work ( https://hpc.nih.gov). This work was supported by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research (grant no. ZIA BC011798 to H.E.A. and BC010309-24 to T.M.).

Abbreviations used:

BAC bacterial artificial chromosome

ChromHMM chromatin hidden markov model

DAPI 4’,6-diamidino-2-phenylindole

dCT delta cycle threshold

DNA deoxyribonucleic acid

E-P enhancer-promoter

FISH fluorescence in situ hybridization

Hi-C high-throughput chromatin conformation capture

HiChIP Hi-C with chromatin immunoprecipitation

hiFISH high-throughput FISH

kb kilobase pairs

Mbp megabase pairs

PET paired-end tag

QRT-PCR quantitative reverse transcription polymerase chain reaction

RNA ribonucleic acid

This article was published online ahead of print in MBoC in Press (http://www.molbiolcell.org/cgi/doi/10.1091/mbc.E24-02-0082) on May 8, 2024.
==== Refs
REFERENCES

Alexander JMGuan JLi BMaliskova LSong MShen YHuang BLomvardas SWeiner OD (2019). Live-cell imaging reveals enhancer-dependent Sox2 transcription in the absence of enhancer proximity. eLife 8 , e41769.31124784
Arda HETsai JRosli YRGiresi PBottino RGreenleaf WJChang HYKim SK (2018). A chromatin basis for cell lineage and disease risk in the human pancreas. Cell Syst 7 , 310–322.e4.30145115
Arnold MStengel KR (2023). Emerging insights into enhancer biology and function. Transcription 14 , 68–87.37312570
Benabdallah NSWilliamson IIllingworth RSKane LBoyle SSengupta DGrimes GRTherizols PBickmore WA (2019). Decreased enhancer-promoter proximity accompanying enhancer activation. Mol Cell 76 , 473–484.e7.31494034
Benanti JAGalloway DA (2004). Normal human fibroblasts are resistant to RAS-induced senescence. Mol Cell Biol 24 , 2842–2852.15024073
Bhattacharyya SChandra VVijayanand PAy F (2019). Identification of significant chromatin contacts from HiChIP data by FitHiChIP. Nat Commun 10 , 4221.31530818
Bintu BMateo LJSu J-HSinnott-Armstrong NAParker MKinrot SYamaya KBoettiger ANZhuang X (2018). Super-resolution chromatin tracing reveals domains and cooperative interactions in single cells. Science 362 , eaau1783.30361340
Bonev BMendelson Cohen NSzabo QFritsch LPapadopoulos GLLubling YXu XLv XHugnot J-PTanay ACavalliG (2017). Multiscale 3D genome rewiring during mouse neural development. Cell 171 , 557–572.e24.29053968
Brückner DBChen HBarinov LZoller BGregor T (2023). Stochastic motion and transcriptional dynamics of pairs of distal DNA loci on a compacted chromosome. Science 380 , 1357–1362.37384691
Calderon LWeiss FDBeagan JAOliveira MSGeorgieva RWang Y-FCarroll TSDharmalingam GGong WTossell K, et al. (2022). Cohesin-dependence of neuronal gene expression relates to chromatin loop length. eLife 11 , e76539.35471149
Carter DChakalova LOsborne CSDai Y-FFraser P (2002). Long-range chromatin regulatory interactions in vivo. Nat Genet 32 , 623–626.12426570
Chatterjee SKapoor AAkiyama JAAuer DRLee DGabriel SBerrios CPennacchio LAChakravarti A (2016). Enhancer variants synergistically drive dysfunction of a gene regulatory network in hirschsprung disease. Cell 167 , 355–368.e10.27693352
Cheng LDe CLi JPertsinidis A (2023). Mechanisms of transcription control by distal enhancers from high-resolution single-gene imaging. bioRxiv, 10.1101/2023.03.19.533190.
Chen HLevo MBarinov LFujioka MJaynes JBGregor T (2018). Dynamic interplay between enhancer-promoter topology and gene activity. Nat Genet 50 , 1296–1303.30038397
Chen HXiao JShao TWang LBai JLin XDing NQu YTian YChen X, et al. (2019). Landscape of enhancer-enhancer cooperative regulation during human cardiac commitment. Mol Ther Nucleic Acids 17 , 840–851.31465963
Cho W-KSpille J-HHecht MLee CLi CGrube VCisse II (2018). Mediator and RNA polymerase II clusters associate in transcription-dependent condensates. Science 361 , 412–415.29930094
Dowle MSrinivasan AShort TLiangolou SSaporta RAntonyan E (2015). data.table: Extension of Data.frame. R package version 1.9.6. Available from: https://CRAN.R-project.org/package=data.table (accessed June 2023).
Edgar RDomrachev MLash AE (2002). Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30 , 207–210.11752295
Espinola SMGötz MBellec MMessina OFiche J-BHoubron CDejean MReim ICardozo Gizzi AMLagha M, et al. (2021). Cis-regulatory chromatin loops arise before TADs and gene activation, and are independent of cell fate during early Drosophila development. Nat Genet 53 , 477–486.33795867
Finn EHMisteli T (2021). A high-throughput DNA FISH protocol to visualize genome regions in human cells. STAR Protoc 2 , 100741.34458868
Finn EHPegoraro GBrandão HBValton A-LOomen MEDekker JMirny LMisteli T (2019). Extensive heterogeneity and intrinsic variation in spatial genome organization. Cell 176 , 1502–1515.e10.30799036
French J (2015). SpatialTools: tools for spatial data analysis. R package version 1.0.2. Available at: https://CRAN.R-project.org/package=SpatialTools (accessed June 2023).
Ghadimi BMSchröck EWalker RLWangsa DJauho AMeltzer PSRied T (1999). Specific chromosomal aberrations and amplification of the AIB1 nuclear receptor coactivator gene in pancreatic carcinomas. Am J Pathol 154 , 525–536.10027410
Gómez Acuña LIFlyamer IBoyle SFriman EBickmore WA (2023). Transcription decouples estrogen-dependent changes in enhancer-promoter contact frequencies and spatial proximity. bioRxiv, 10.1101/2023.03.29.534720.
Gu BSwigut TSpencley ABauer MRChung MMeyer TWysocka J (2018). Transcription-coupled changes in nuclear mobility of mammalian cis-regulatory elements. Science 359 , 1050–1055.29371426
Heintzman NDHon GCHawkins RDKheradpour PStark AHarp LFYe ZLee LKStuart RKChing CW, et al. (2009). Histone modifications at human enhancers reflect global cell-type-specific gene expression. Nature 459 , 108–112.19295514
Heinz SRomanoski CEBenner CGlass CK (2015). The selection and function of cell type-specific enhancers. Nat Rev Mol Cell Biol 16 , 144–154.25650801
Ing-Simmons ESeitan VCFaure AJFlicek PCarroll TDekker JFisher AGLenhard BMerkenschlager M (2015). Spatial enhancer clustering and regulation of enhancer-proximal genes by cohesin. Genome Res 25 , 504–513.25677180
Keikhosravi AAlmansour FBohrer CHFursova NAGuin KSood VMisteli TLarson DRPegoraro G (2023). HiTIPS: high-throughput image processing software for the study of nuclear architecture and gene expression. bioRxiv, 10.1101/2023.11.02.565366.
Krietenstein NAbraham SVenev SVAbdennur NGibcus JHsieh T-HSParsi KMYang LMaehr RMirny LA, et al. (2020). Ultrastructural details of mammalian chromosome architecture. Mol Cell 78 , 554–565.e7.32213324
Lareau CAAryee MJ (2018). hichipper: a preprocessing pipeline for calling DNA loops from HiChIP data. Nat Methods 15 , 155–156.29489746
Lawlor NMárquez EJOrchard PNarisu NShamim MSThibodeau AVarshney AKursawe RErdos MRKanke M, et al. (2019). Multiomic profiling identifies cis-regulatory networks underlying human pancreatic β cell identity and function. Cell Rep 26 , 788–801.e6.30650367
Lawrence MHuber WPagès HAboyoun PCarlson MGentleman RMorgan MTCarey VJ (2013). Software for computing and annotating genomic ranges. PLoS Comput Biol 9 , e1003118.23950696
Liang JPerez-Rathke A (2021). Minimalistic 3D chromatin models: sparse interactions in single cells drive the chromatin fold and form many-body units. Curr Opin Struct Biol 71 , 200–214.34399301
Lieber MMazzetta JNelson-Rees WKaplan MTodaro G (1975). Establishment of a continuous tumor-cell line (panc-1) from a human carcinoma of the exocrine pancreas. Int J Cancer 15 , 741–747.1140870
Li JHsu AHua YWang GCheng LOchiai HYamamoto TPertsinidis A (2020). Single-gene imaging links genome topology, promoter-enhancer communication and transcription control. Nat Struct Mol Biol 27 , 1032–1040.32958948
Markenscoff-Papadimitriou EAllen WEColquitt BMGoh TMurphy KKMonahan KMosley CPAhituv NLomvardas S (2014). Enhancer interaction networks as a means for singular olfactory receptor expression. Cell 159 , 543–557.25417106
Monahan KSchieren ICheung JMumbey-Wafula AMonuki ESLomvardas S (2017). Cooperative interactions enable singular olfactory receptor expression in mouse olfactory neurons. eLife 6 , e28620.28933695
Mumbach MRRubin AJFlynn RADai CKhavari PAGreenleaf WJChang HY (2016). HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat Methods 13 , 919–922.27643841
NIH Biowulf HPC Cluster. Available from: https://hpc.nih.gov/. accessed 2 January 2024.
Oudelaar AMDavies JOJHanssen LLPTelenius JMSchwessinger RLiu YBrown JMDownes DJChiariello AMBianco S, et al. (2018). Single-allele chromatin interactions identify regulatory hubs in dynamic compartmentalized domains. Nat Genet 50 , 1744–1751.30374068
Platania AErb CBarbieri MMolcrette BGrandgirard Ede Kort MAMeaburn KTaylor TShchuka VMKocanova S, et al. (2023). Competition between transcription and loop extrusion modulates promoter and enhancer dynamics. bioRxiv, 10.1101/2023.04.25.538222
Quinlan ARHall IM (2010). BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26 , 841–842.20110278
Ravassard PHazhouz YPechberty SBricout-Neveu EArmanet MCzernichow PScharfmann R (2011). A genetically engineered human pancreatic β cell line exhibiting glucose-inducible insulin secretion. J Clin Invest 121 , 3589–3597.21865645
R Core Team (2015). R: a language and environment for statistical computing. Available from: http://R-project.org (accessed June 2023).
Ren BYang JWang CYang GWang HChen YXu RFan XYou LZhang T, et al. (2021). High-resolution Hi-C maps highlight multiscale 3D epigenome reprogramming during pancreatic cancer metastasis. J Hematol Oncol 14 , 120.34348759
Robinson MDMcCarthy DJSmyth GK (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26 , 139–140.19910308
Sabari BRDall'Agnese ABoija AKlein IACoffey ELShrinivas KAbraham BJHannett NMZamudio AVManteiga JC, et al. (2018). Coactivator condensation at super-enhancers links phase separation and gene control. Science 361 , eaar3958.29930091
Sanyal ALajoie BRJain GDekker J (2012). The long-range interaction landscape of gene promoters. Nature 489 , 109–113.22955621
Schaub MABoyle APKundaje ABatzoglou SSnyder M (2012). Linking disease associations with regulatory information in the human genome. Genome Res 22 , 1748–1759.22955986
Servant NVaroquaux NLajoie BRViara EChen C-JVert J-PHeard EDekker JBarillot E (2015). HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol 16 , 259.26619908
Shi GLiu LHyeon CThirumalai D (2018). Interphase human chromosome exhibits out of equilibrium glassy dynamics. Nat Commun 9 , 3161.30089831
Stergachis ABNeph SReynolds AHumbert RMiller BPaige SLVernot BCheng JBThurman RESandstrom R, et al. (2013). Developmental fate and cellular maturity encoded in human regulatory DNA landscapes. Cell 154 , 888–903.23953118
Stringer CWang TMichaelos MPachitariu M (2021). Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18 , 100–106.33318659
Sturgill DWang LArda HE (2024). PancrESS - a meta-analysis resource for understanding cell-type specific expression in the human pancreas. BMC Genomics 25 , 76.38238687
Takei YYun JZheng SOllikainen NPierson NWhite JShah SThomassie JSuo SEng C-HL, et al. (2021). Integrated spatial genomics reveals global architecture of single nuclei. Nature 590 , 344–350.33505024
Wang WQiao SLi GCheng JYang CZhong CStovall DBShi JTeng CLi D, et al. (2022). A histidine cluster determines YY1-compartmentalized coactivators and chromatin elements in phase-separated enhancer clusters. Nucleic Acids Res 50 , 4917–4937.35390165
Whyte WAOrlando DAHnisz DAbraham BJLin CYKagey MHRahl PBLee TIYoung RA (2013). Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 153 , 307–319.23582322
Wickham H (2009). ggplot2: Elegant Graphics for Data Analysis, New York: Springer-Verlag.
Wickham H (2011). The split-apply-combine strategy for data analysis. J Stat Softw 40 , 1–29.
Wickham H (2015). stringr: simple, consistent wrappers for common string operations. R package version 1.0.0. Available from: http://CRAN.R-project.org/package=stringr (accessed June 2023).
Wickham HFrancois R (2015). dplyr: a grammar of data manipulation. R package version 0.4.3. Available from: https://CRAN.R-project.org/package=dplyr (accessed June 2023).
Williamson IBerlivet SEskeland RBoyle SIllingworth RSPaquette DBickmore WA (2015). Spatial genome organization: contrasting views from chromosome conformation capture and fluorescence in situ hybridization. Genes Dev 28 , 2778–2791.
Williamson ILettice LAHill REBickmore WA (2016). Shh and ZRS enhancer colocalisation is specific to the zone of polarising activity. Development 143 , 2994–3001.27402708
Woolfe AGoodson MGoode DKSnell PMcEwen GKVavouri TSmith SFNorth PCallaway HKelly K, et al. (2005). Highly conserved non-coding sequences are associated with vertebrate development. PLoS Biol 3 , e7.15630479
Xie Y (2014). knitr: A Comprehensive Tool for Reproducible Research in R. In: Implementing Reproducible Computational Research, eds. V StoddenF LeischR. D Peng, New York, NY: Chapman and Hall/CRC.
Zhang YWong C-HBirnbaum RYLi GFavaro RNgan CYLim JTai EPoh HMWong E, et al. (2013). Chromatin connectivity maps reveal dynamic promoter-enhancer long-range associations. Nature 504 , 306–310.24213634
Zhao YDing YHe LZhou QChen XLi YAlfonsi MVWu ZSun HWang H (2023). Multiscale 3D genome reorganization during skeletal muscle stem cell lineage progression and aging. Sci Adv 9 , eabo1360.36800432
