TAD boundaries and gene activity are uncoupled.

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Learn more: PMC Disclaimer | PMC Copyright Notice eLife . 2026 Apr 15;15:RP110197. doi: 10.7554/eLife.110197 Search in PMC Search in PubMed View in NLM Catalog Add to search TAD boundaries and gene activity are uncoupled Faisal Almansour Faisal Almansour 1 National Cancer Institute, NIH, Bethesda, United States Find articles by Faisal Almansour 1 , Nadezda A Fursova Nadezda A Fursova 2 Systems Biology of Gene Expression, National Cancer Institute, NIH, Bethesda, United States Find articles by Nadezda A Fursova 2 , Adib Keikhosravi Adib Keikhosravi 3 High-throughput Imaging Facility, National Cancer Institute, NIH, Bethesda, United States Find articles by Adib Keikhosravi 3 , Kathleen S Metz Reed Kathleen S Metz Reed 1 National Cancer Institute, NIH, Bethesda, United States Find articles by Kathleen S Metz Reed 1 , Daniel R Larson Daniel R Larson 2 Systems Biology of Gene Expression, National Cancer Institute, NIH, Bethesda, United States Find articles by Daniel R Larson 2 , Gianluca Pegoraro Gianluca Pegoraro 3 High-throughput Imaging Facility, National Cancer Institute, NIH, Bethesda, United States Find articles by Gianluca Pegoraro 3 , Tom Misteli Tom Misteli 1 National Cancer Institute, NIH, Bethesda, United States Find articles by Tom Misteli 1, ✉ Editors: Timothy J Stasevich 4 , Adèle L Marston 5 Author information Article notes Copyright and License information 1 National Cancer Institute, NIH, Bethesda, United States 2 Systems Biology of Gene Expression, National Cancer Institute, NIH, Bethesda, United States 3 High-throughput Imaging Facility, National Cancer Institute, NIH, Bethesda, United States 4 Colorado State University, United States 5 University of Edinburgh, United Kingdom ✉ Corresponding author. Roles Timothy J Stasevich : Reviewing Editor Adèle L Marston : Senior Editor Collection date 2026. This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication . PMC Copyright notice PMCID: PMC13082786  PMID: 41983545 Previous version available: This article is based on a previously available preprint posted on bioRxiv on February 24, 2026: " TAD boundaries and gene activity are uncoupled ". Previous version available: This article is based on a previously available preprint with doi: https://doi.org/10.7554/eLife.110197.1 . Previous version available: This article is based on a previously available preprint with doi: https://doi.org/10.7554/eLife.110197.2 . Abstract Topologically associating domains (TADs) are prominent features of genome organization. A proposed function of TADs is to contribute to gene regulation by promoting chromatin interactions within a TAD and by suppressing interactions between TADs. Here, we directly probe the structure-function relationship of TADs by simultaneously assessing the behavior of TAD boundaries and gene activity at the single-cell and -allele level using high-throughput imaging. We find that while TAD boundaries pair more frequently than non-boundary regions, these interactions are infrequent and are uncorrelated with transcriptional activity of genes within the TAD. Similarly, acute global transcriptional inhibition or gene-specific activation does not alter TAD boundary proximity. Furthermore, while loss of the cohesin component RAD21 alters gene activity, disruption of TAD boundaries by depletion of the architectural chromatin protein CTCF is insufficient to alter expression of genes within the TAD. These results suggest that TAD boundary architecture and gene activity are largely uncoupled. Research organism: Human Introduction Beyond the linear DNA sequence, genomes are folded into higher-order structures ( Misteli, 2020 ). Some of the most prominent genome features are chromatin loops and domains. Chromatin conformation mapping techniques—most notably Hi-C and Micro-C—have particularly highlighted topologically associating domains (TADs) as ubiquitous architectural features of higher eukaryotic genomes ( Dixon et al., 2012 ; Lieberman-Aiden et al., 2009 ; Nora et al., 2012 ; Sexton et al., 2012 ). TADs are self-assembling, contiguous genomic regions that preferentially interact with each other rather than with neighboring regions, creating distinct chromatin domains ( Lieberman-Aiden et al., 2009 ). In human cells, TADs are typically 0.2–1 Mb in size and are defined by flanking boundary regions, which are marked by binding sites for the CTCF (CCCTC-binding factor) protein ( Lieberman-Aiden et al., 2009 ; Dekker and Mirny, 2016 ; Rowley M. and Corces V., 2018 ). Mammalian TADs form via a process referred to as loop extrusion, which is driven by the association of the cohesin complex with DNA, and via its ATP-driven motor activity, reels in DNA until it encounters bound CTCF molecules at the TAD boundaries, thus forming a domain ( Dekker and Mirny, 2016 ; Rowley M. and Corces V., 2018 ; Fudenberg et al., 2016 ). In other organisms, chromatin domains form by similar mechanisms, although, for example, in Drosophila , only a small fraction of boundaries are CTCF-dependent ( Kaushal et al., 2021 ). TADs are thought to have a gene-regulatory function by bringing control elements, such as enhancers, over large genomic distances into physical proximity with their target genes within the same TAD, while at the same time limiting their interactions with genes in other TADs ( Dixon et al., 2012 ; Nora et al., 2012 ). This model is in line with the known role of CTCF as an insulation factor ( Bell et al., 1999 ; Hark et al., 2000 ). A regulatory role for TADs is also supported by the observation that targeted deletions or inversions of boundary elements alter enhancer-promoter communication and, in some cases, gene expression ( Chakraborty et al., 2025 ; Franke et al., 2016 ; Lupiáñez et al., 2015 ). Furthermore, comparative mapping of the Ubx and AbdA TADs in Drosophila embryos showed that an ~2-fold change in enhancer-gene contact frequency leads to an ~7-fold difference in gene expression ( Mateo et al., 2019 ). Disruption of TAD boundaries has also been linked to human disease, as structural variations that alter domain architecture can cause pathogenic rewiring of enhancer-gene contacts, e.g., in cancers, and in neurological and congenital disorders ( Spielmann et al., 2018 ). Moreover, mutations in components of the cohesin complex cause developmental disorders, known as cohesinopathies, such as Cornelia de Lange syndrome, in which impaired chromatin architecture and altered transcriptional regulation are thought to drive the phenotype ( Dorsett and Krantz, 2009 ). On the other hand, several lines of evidence suggest that TADs are not strictly required for transcription regulation. Global removal of cohesin or CTCF produces surprisingly modest effects on genome-wide transcription, suggesting that most genes can be expressed relatively accurately without intact TAD structures ( Nora et al., 2017 ; Rao et al., 2017 ). Although many CTCF and cohesin binding sites overlap with enhancers and promoters, a substantial fraction does not, indicating that domain boundaries are not universally tied to regulatory elements ( Kagey et al., 2010 ; Merkenschlager and Odom, 2013 ; Phillips-Cremins et al., 2013 ). Furthermore, in Drosophila , large-scale rearrangements of chromatin domains lead to only modest transcriptional changes ( Ghavi-Helm et al., 2019 ) and cis-regulatory transcription hubs form before domain establishment and prior to transcriptional activation, indicating that gene regulatory contacts may emerge independently of domain architecture ( Espinola et al., 2021 ). Similarly, during dorsoventral patterning in Drosophila , tissue-specific gene expression patterns emerge despite largely invariant chromatin domains across tissues ( Ing-Simmons et al., 2021 ). Finally, in mammals, enhancer-promoter contacts persist even after the global loss of CTCF or cohesin, underscoring that regulatory interactions can be maintained without stable TAD anchoring ( Chakraborty et al., 2023 ; Hsieh et al., 2022 ; Platania et al., 2024 ; Taylor et al., 2022 ). These observations point to a limited functional role of TAD architecture in gene regulation. A confounding factor in assessing the functional role of TADs on gene regulation is the recent realization that TAD structure is highly dynamic, resulting in variable TAD conformations in individual cells and alleles ( Bintu et al., 2018 ; Cattoni et al., 2017 ; Finn et al., 2019 ; Gabriele et al., 2022 ). High-throughput DNA FISH studies indicate that TAD boundary pairing only occurs in typically 5–20% of alleles at any given time in a population ( Finn et al., 2019 ). In agreement, live-cell imaging demonstrates that TAD boundaries undergo continuous motion, and that the persistence time of pairing is on the order of ~10–30 min before they separate again, consistent with polymer simulations of cohesin-mediated loops ( Gabriele et al., 2022 ; Sabaté et al., 2023 ). In addition to the variable nature of TAD architecture, gene expression itself is similarly dynamic, with most genes undergoing rapid cycles of activity and inactivity, referred to as gene bursting ( Rodriguez et al., 2019 ; Sood et al., 2025 ). Furthermore, recent observations point to a bidirectional relationship in which transcriptional activity itself also affects chromatin structure ( Platania et al., 2024 ; Chahar et al., 2023 ; Luppino et al., 2022 ; Shaban et al., 2024 ). Neither the variability in TAD organization nor the dynamics of gene activity is captured by commonly used population-based profiling methods, confounding the assessment of the effect of TAD structure on gene expression at the level of individual alleles. Here, we directly probe the structure-function relationship of TADs at the single-cell and single-allele level by use of high-throughput imaging to simultaneously visualize TAD boundaries by DNA-FISH and nascent RNA production by RNA-FISH. Using the TADs containing the EGFR and MYC genes, respectively, as model systems, we quantitatively compare boundary distances at individual active and inactive alleles or upon transcriptional perturbation or stimulation. We also probe the effect of loss of the architectural TAD protein CTCF on gene expression. We find that TAD boundary proximity is unrelated to gene activity. Results Simultaneous assessment of TAD boundaries and gene activity by high-throughput imaging To quantitatively analyze the relationship between TAD boundaries and gene activity at the single-cell and single-allele level, we developed a high-throughput FISH imaging (HiFISH) and image analysis pipeline ( Almansour et al., 2024 ; Keikhosravi et al., 2024 ) comprised of three components: (1) detection of TAD boundaries and nascent RNA using combined DNA- and RNA-FISH in a 384-well high-throughput format (DNA/RNA HiFISH) ( Almansour et al., 2024 ), (2) measurement of center-to-center TAD boundary distances using HiTIPS, a customized image analysis software to probe features of nuclear architecture ( Keikhosravi et al., 2024 ), and (3) quantitative comparison of boundary distances and gene expression status at each visualized allele ( Figure 1A ; see Materials and Methods). DNA/RNA HiFISH was performed simultaneously in a single hybridization step, as previously described ( Almansour et al., 2024 ). Boundary distances were measured in 2D maximum-intensity projections generated from 3D imaging stacks using center-to-center distance measurements and a pixel resolution of 152 nm, as previously described ( Finn et al., 2017 ; see Materials and Methods). Similar results were obtained using 2D maximum-intensity projections and 3D imaging ( Finn et al., 2017 ; see below). Figure 1. High-throughput DNA/RNA FISH. Open in a new tab ( A ) Schematic overview of the DNA/RNA high-throughput FISH imaging (HiFISH) pipeline used to simultaneously measure topologically associating domain (TAD) boundary distance and gene activity at the single-cell and single-allele levels. Step 1: Design of DNA FISH probes based on Micro-C profiling and detection of DNA and nascent RNA by HiFISH. Step 2: Measurement of center-to-center TAD boundary distances and RNA signal at individual alleles by image analysis using HiTIPS ( Keikhosravi et al., 2024 ). Step 3: Quantitative comparison of TAD boundary distances with gene activity at each allele. ( B ) DNA/RNA HiFISH detection of 5’ (green) and 3’ (red) MYC TAD boundaries and nascent RNA (blue) in human bronchial epithelial cells (HBECs). Scale bar: 10 μm. ( C ) Quantification of MYC nascent RNA signals using DNA/RNA HiFISH in fixed HBECs or an MS2-tagged MYC reporter in living HBECs. Bars represent means ± SEM from two experiments. Dots indicate means from individual experiments. 166,953 cells were analyzed for MS2, and 30,137 cells for DNA/RNA HiFISH. Statistical significance was calculated using two-way ANOVA with Bonferroni correction: ns, not significant (p≥0.05). © 2026, Guin Panel A was created with BioRender and is published under a Creative Commons Attribution License . Further reproductions must adhere to the terms of this license. The combined DNA/RNA HiFISH imaging approach resulted in robust simultaneous detection of TAD boundaries and nascent RNA in multiple cell types ( Almansour et al., 2024 ; Figure 1B ; see below). The correct number of DNA FISH signals was routinely detected in >95% of cells in non-transformed hTERT-HFFc6 fibroblasts (HFFs) or in human bronchial epithelial cells (HBECs), as previously reported ( Almansour et al., 2024 ). Similarly, the detected RNA-FISH signals accurately reflected the number of active alleles as demonstrated by comparison with the number of active MYC alleles visualized by live-cell imaging using an MS2-tagged MYC reporter system in HBECs ( Figure 1C ). The high detection efficiency underscores the sensitivity and specificity of our approach for probing TAD boundary distances and nascent transcription at the single-cell and single-allele level ( Almansour et al., 2024 ). The high-throughput nature of this approach enabled routine probing of thousands of individual alleles per experimental condition, providing high statistical power in comparative analyses. Selection and validation of model TADs Two TADs containing the EGFR and MYC genes, respectively, were selected as models for our analysis based on their high biological relevance in signaling and transcription, respectively, and their presence in structurally well-defined TADs, as mapped by high-resolution (1 kb) publicly available Micro-C datasets of HFF cells ( Krietenstein et al., 2020 ) and human embryonic stem cells (ESCs) ( Akgol Oksuz et al., 2021 ). Both TADs are conserved in both cell types and display well-defined corner peaks and side streaks, both hallmarks of stable and structurally distinct TADs ( Krietenstein et al., 2020 ; Akgol Oksuz et al., 2021 ). The EGFR TAD spans ~500 kb and has two sub-TADs of ~250 kb each, while the MYC TAD extends over ~3 Mb and comprises two large sub-TADs (~1 Mb and ~2 Mb, respectively) ( Figure 2A ). The MYC TAD is more insulated from flanking chromatin than the EGFR TAD, which itself may reside in a sub-TAD within a broader domain not visible at lower Hi-C resolution ( Krietenstein et al., 2020 ; Akgol Oksuz et al., 2021 ). The two TADs also differ in their gene content and chromatin landscape based on ChromHMM analysis ( Ernst and Kellis, 2012 ; Figure 2—figure supplement 1 ), with the EGFR TAD enriched in transcriptionally active regions, while the MYC TAD is predominantly quiescent, with isolated active chromatin features clustered near the long noncoding RNAs PVT1 and PCAT1 . The MYC TAD contains only two protein-coding genes, MYC and POU5F1B, and multiple noncoding elements ( Figure 2—figure supplement 1 ). The MYC gene lies close to the 5′ boundary, while POU5F1B is within the upstream sub-TAD. The EGFR TAD harbors three protein-coding genes— EGFR , LANCL2 , and VOPP1 —with the EGFR gene positioned relatively distally upstream ( Figure 2—figure supplement 1 ). The fraction of the TAD covered by each gene also varies: EGFR occupies ~40% of its TAD, whereas LANCL2 and VOPP1 cover ~17% and~20%, respectively. In contrast, MYC and POU5F1B together span less than 1% of the MYC TAD ( Figure 2—figure supplement 1 ). The MYC and EGFR TAD boundaries lie in largely quiescent chromatin, with the exception of the 3′ EGFR TAD boundary, which contains some active marks due to the proximity to the EGFR gene ( Figure 2—figure supplement 1 ). The differences in size and variation in structural and functional features between the two TADs make them attractive and robust models for investigating the relationship between TAD boundaries and gene activity at single-allele resolution. Figure 2. Topologically associating domain (TAD) boundaries interact more frequently than non-TAD regions. ( A ) Micro-C contact maps for EGFR and MYC TADs and adjacent regions in hTERT-HFFc6 fibroblast (HFF) cells, highlighting TAD boundaries and genomically equidistant non-TAD regions. Squares denote the probe positions used for 3’ (green), 5’ boundary (red), and equidistant non-TAD controls (purple). Interactions between the 5’ TAD boundaries and the 3’ TAD (yellow) or non-TAD boundaries (gray) are highlighted, and total Micro-C contacts between regions are quantified, emphasizing high TAD boundary contact frequency in both EGFR and MYC TADs, as well as weaker signals in the non-TAD regions. ( B–C ) Representative DNA high-throughput FISH imaging (HiFISH) images of EGFR and MYC TAD boundary and non-TAD regions in HFF cells ( B ) and human bronchial epithelial cells (HBECs) ( C ). Scale bar: 10 μm. ( D–E ) Measurement of boundary distances. Distance distributions of EGFR and MYC TAD boundaries vs. matched non-TAD regions in HFF cells ( D ) and HBECs ( E ). Dashed line indicates 250 nm threshold used to define physical interaction. Between 2,000 and 18,000 alleles were analyzed per sample. Values represent an individual dataset from a single experiment of multiple experiments. Mann-Whitney U test p-values are: ***p<1 × 10 –100 ; ** 1×10 –100 ≤p<1×10 –20 ; * 1×10 –20 ≤p<0.01. © 2026, Guin Panel A was created with BioRender and is published under a Creative Commons Attribution License . Further reproductions must adhere to the terms of this license. Figure 2—figure supplement 1. Micro-C chromosome interaction maps and ChromHMM analysis of EGFR , MYC , ERRFI1 , FKBP5 , and VARS2 topologically associating domains (TADs) in HFFc6. Diamonds denote probe interaction sites in Micro-C for both the non-TAD probes (gray) and the TAD boundary probes (yellow). The cytogenetic chromosome band track (black) indicates the chromosome location of the indicated loci. The gene reference track shows all coding and noncoding genes. The binding sites track shows CTCF (navy blue) and RAD21 (purple). ChromHMM chromatin states for two different foreskin fibroblast cell lines. Figure 2—figure supplement 2. Sequence and location of DNA and RNA probes binding sites for DNA/RNA high-throughput FISH imaging (HiFISH). Schematic representation of DNA and RNA probes target regions. RNA probes targeting sequences are indicated in blue. Open in a new tab To detect the EGFR and MYC TAD boundaries, we selected specific BAC DNA FISH probes of typically ~165 kb that directly target the 5’ and 3’ boundaries, respectively ( Figure 2A ; Figure 2—figure supplements 1 and 2 ; Supplementary file 1 ). The large probe size ensures high detection sensitivity without loss of accuracy as previously described ( Finn and Misteli, 2021 ). To control for regional variability and to distinguish boundary-specific behavior from broader chromatin effects, control DNA FISH probes targeting non-TAD control regions were also used. These probes were positioned on the same chromosome arm, equidistant upstream of the respective 5′ TAD boundaries and located within the same or adjacent cytogenetic bands ( Figure 2A ; Figure 2—figure supplements 1 and 2 ). The EGFR -associated non-TAD control region contains one protein-coding gene and fewer noncoding elements relative to its corresponding TAD boundary ( Figure 2—figure supplement 1 ). In contrast, the MYC non-TAD control region contains multiple genes and exhibits a more complex local topology, bordering multiple looped domains and small TADs ( Figure 2—figure supplement 1 ). Both non-TAD regions share a relatively quiescent chromatin state, with sparse transcriptional and regulatory element enrichment. Consistent with their function as boundaries, Micro-C contact frequencies were at least 10-fold higher for the MYC and EGFR TAD boundary regions compared to the corresponding control regions ( Figure 2A ). TAD boundaries pair at low frequency We first used DNA/RNA HiFISH to determine the distance distribution profiles and interaction frequencies of TAD boundaries at the single-allele level ( Almansour et al., 2024 ; Figure 2B–E ; see Materials and Methods). As expected, the median distance for MYC TAD boundaries was smaller compared to non-TAD regions in both HFF (0.41±0.30 µm vs. 0.81±0.63 µm; median ±interquartile range) and HBEC (0.46±0.33 µm vs. 0.71±0.50 µm; Mann-Whitney U test, p<1 × 10⁻¹⁰ for both) ( Figure 2D and E ). Similarly, in HFF, EGFR TAD boundaries were in closer proximity than non-TAD regions (0.41±0.30 µm vs. 0.56±0.48 µm; U test, p<1 × 10⁻²⁰) ( Figure 2D ). Interestingly, the EGFR TAD boundaries exhibited a more uniform distribution in HBEC, with TAD boundaries showing a similar distribution to non-TAD regions (0.39±0.28 µm vs. 0.42±0.29 µm; U test, p<0.01) ( Figure 2E ). The lack of a strong difference in proximity at EGFR boundaries in HBECs likely reflects the smaller size of the EGFR TAD (~0.5 Mb) ( Figure 2A ; Figure 2—figure supplement 1 ) and the smaller nuclear size in HBECs ( Figure 2C ). To quantify the pairing frequency of MYC and EGFR TAD boundaries, we calculated the percentage of alleles with TAD boundary distances below a 250 nm threshold, a value previously used to define chromatin interactions ( Finn et al., 2019 ; Gabriele et al., 2022 ). EGFR TAD boundaries were within 250 nm in 31 ± 14% (mean ± SD) of alleles in HBEC and 33 ± 17% in HFF, and MYC TAD boundaries were within this range in 23 ± 9% of HBEC and 27 ± 9% of HFF alleles ( Figure 2D and E ). Non-TAD control regions showed lower interaction frequencies with 27 ± 13% and 20 ± 8% for EGFR in HBEC and HFF, respectively, and 8 ± 1% and 6 ± 0.3% for MYC in HBEC and HFF ( Figure 2D and E ; p<0.05 for all comparisons). As previously noted, while adjusting the distance threshold changes the absolute percentage of close contacts, it does not affect the relative differences between TAD boundaries and non-TAD regions ( Finn et al., 2019 ). These results are consistent with single-cell FISH and Hi-C studies showing that TAD boundaries pair two- to threefold more frequently than non-TAD regions, and with live-cell imaging studies showing transient boundary pairing ( Cattoni et al., 2017 ; Finn et al., 2019 ; Gabriele et al., 2022 ; Carstens et al., 2016 ; Flyamer et al., 2017 ; Giorgetti et al., 2014 ; Nagano et al., 2013 ; Stevens et al., 2017 ; Szabo et al., 2018 ). Our findings confirm that TAD boundaries exhibit higher interaction frequencies and shorter distances than non-TAD regions, but that boundary pairing is a relatively infrequent and transient event, as previously observed by FISH ( Finn et al., 2019 ) and live-cell imaging ( Gabriele et al., 2022 ). TAD boundary distance is not related to gene activity status To assess whether TAD boundary proximity correlates with gene activity at the single-allele level, boundary distances were compared between transcriptionally active and inactive alleles using high-throughput DNA/RNA HiFISH ( Figure 3A ). Active alleles were identified by the presence of nascent RNA FISH signals in the proximity (<1 µm) of a TAD boundary signal (see Materials and Methods), while inactive alleles lacked detectable RNA signals near TAD boundary signals ( Figures 1A and 3A ). RNA detection was efficient, as indicated by the comparable number of active transcription sites detected by RNA-FISH as in living cells using the MS2-RNA detection system ( Figure 1C ). Figure 3. Topologically associating domain (TAD) boundary proximity is not related to gene activity status. ( A ) Representative DNA/RNA high-throughput FISH imaging (HiFISH) image of EGFR nascent RNA (blue) and its associated 5’ (red) and 3’ (green) TAD boundaries in HBECs, illustrating detection of active (RNA-positive) and inactive (RNA-negative) alleles. Scale bar: 10 μm. ( B – E ) Comparison of TAD boundary distances for EGFR and MYC alleles based on transcriptional activity status. Histograms of allele-specific distance distributions from a representative dataset from a single experiment; Mann-Whitney U test p-values are indicated as follows: ns, not significant (p≥0.05). Dot plots of the mean of median distances from multiple experiments (500–20,000 alleles per condition); error bars represent SEM, and statistical significance was calculated using two-way ANOVA with Bonferroni correction: ns, not significant (p≥0.05). © 2026, Guin Panel A was created with BioRender and is published under a Creative Commons Attribution License . Further reproductions must adhere to the terms of this license. Figure 3—figure supplement 1. Topologically associating domain (TAD) boundary proximity is uncoupled from allelic gene activity in single nuclei in both 2D and 3D imaging. ( A ) Comparison of TAD boundary distances for EGFR and MYC alleles in HCT116 based on transcriptional activity status. Histograms of allele-specific distance distributions from a representative dataset from a single experiment. Mann-Whitney U test p-values are indicated as follows: ****p<0.0001; ns, not significant (p≥0.05). ( B ) Comparative analysis of TAD boundary distances between active and inactive alleles within the same nucleus for EGFR and MYC loci in human bronchial epithelial cells (HBECs) and hTERT-HFFc6 fibroblast (HFF) cells. ( C ) Reciprocal analysis of MYC alleles in HBEC stratified by TAD boundary distance, shown for both 2D and 3D measurements. Alleles were grouped into TAD boundary-distance bins using a histogram bin width of 0.108 µm, corresponding to the xy pixel size of the imaging system, and transcriptional activity was then assessed across these distance categories. Histograms show data from a representative dataset of a single experiment. Mann-Whitney U test p-values are indicated as follows: ns, not significant (p≥0.05). Open in a new tab Comparative analyses of multiple independent DNA/RNA HiFISH experiments—each comprising up to 20,000 alleles—revealed no consistent difference in TAD boundary distances between active and inactive alleles of EGFR and MYC loci in HBEC, HFF, and HCT116 cells ( Figure 3B–E ; Figure 3—figure supplement 1 ). In HBEC, EGFR boundary distances were identical between active (0.29±0.07 μm, mean of medians ± SD) and inactive (0.29±0.07 μm) alleles (Mann-Whitney U test, p=0.57) ( Figure 3B ). Similarly, in HFF, both active and inactive EGFR alleles exhibited identical boundary distances (0.32±0.08 μm; p=0.75) ( Figure 3C ). A similar pattern was observed for MYC . In HBEC, median boundary distances were 0.39±0.05 μm for active MYC alleles and 0.37±0.06 μm for inactive alleles (p=0.31) ( Figure 3D ). In HFF, active and inactive MYC alleles also had similar distances (0.39±0.04 μm vs. 0.41±0.04 μm; p=0.46) ( Figure 3E ). A similar pattern was observed for both genes in HCT116 ( Figure 3—figure supplement 1 ). Furthermore, the TAD boundary distances of the two alleles in the same nucleus were uncorrelated ( Figure 3—figure supplement 1 ) and not significantly different between active and inactive alleles, either as measured by 2D or 3D imaging ( Figure 3—figure supplement 1 ). Similar results were observed when the data were stratified by distance rather than activity status ( Figure 3—figure supplement 1 ). Taken together, these results indicate that the proximity of MYC and EGFR TAD boundaries is not related to gene activity at individual alleles. Inhibition of gene activity does not alter TAD boundary pairing To further test the relationship between TAD boundaries and gene activity, we assessed whether acute global transcriptional inhibition alters TAD boundary proximity. We treated HBEC or HFF cells with 5,6-dichlorobenzimidazole 1-β-D-ribofuranoside (DRB), an inhibitor of CDK9 and CDK7 that acutely blocks RNA polymerase II (RNAPII) transcription elongation and initiation ( Baumli et al., 2010 ; Rahl et al., 2010 ). As expected, after 2 hr of DRB treatment, nascent RNA signals for EGFR and MYC decreased by over 90%, confirming effective transcriptional inhibition ( Figure 4A ). Figure 4. Global transcription inhibition does not alter topologically associating domain (TAD) boundary pairing. ( A ) Representative DNA/RNA high-throughput FISH imaging (HiFISH) images of EGFR and MYC TAD boundary and nascent RNA in HBECs and HFF cells with and without 2 hr 5,6-dichlorobenzimidazole 1-β-D-ribofuranoside (DRB) treatment. Scale bars: 10 μm. ( B ) Quantification of TAD boundary distances for EGFR and MYC in the presence or absence of DRB. Dot plots of the mean of median distances from multiple experiments (500–20,000 alleles per condition). Error bars represent SEM. Statistical significance was calculated using two-way ANOVA with Bonferroni correction: ns, not significant (p≥0.05). Figure 4—figure supplement 1. Transcriptional inhibition does not affect the spatial organization of non-topologically associating domain (TAD) control regions. ( A ) Comparison of TAD boundary and non-TAD distances for EGFR and MYC alleles in human bronchial epithelial cell (HBEC) and hTERT-HFFc6 fibroblast (HFF) based on transcriptional inhibition status. Histograms show allele-specific distance distributions from a representative dataset of a single experiment. Open in a new tab TAD boundary distances remained unchanged for both EGFR and MYC upon transcriptional inhibition ( Figure 4B ). In HBEC and HFF cells, EGFR TAD boundary median distances were similar in untreated controls (0.31±0.10 μm and 0.31±0.10 μm; mean of medians ± SD) and DRB-treated cells (0.30±0.11 μm and 0.31±0.11 μm; U-test p-values = 0.51 and 1.00, respectively). Likewise, MYC TAD boundary distances showed minimal changes in HBEC (0.38±0.07 μm control vs. 0.37±0.08 μm treated; p=0.83) nor in HFF (0.37±0.07 μm control vs. 0.35±0.08 μm treated; p=0.83) ( Figure 4B ). Transcriptional inhibition also did not affect the distance distribution of non-TAD control regions ( Figure 4—figure supplement 1 ). These results indicate that acute inhibition of global RNAPII-dependent transcription does not significantly impact TAD boundary distances, suggesting that the behavior of TAD boundaries is uncoupled from short-term gene expression dynamics. Stimulation of gene activity does not change TAD boundary distances To conversely assess whether transcriptional activation influences TAD boundaries, HBECs were treated for 2 hr with dexamethasone (Dex), a glucocorticoid receptor (GR) agonist known to selectively induce GR-target genes ( Bothe et al., 2021 ). We selected for this analysis ERRFI1 , FKBP5 , and VARS2 , which are all robustly induced (2- to 7- fold) as measured by RNA-seq ( Figure 5—figure supplement 1 ) and reside in relatively large TADs of 400, 1000, and 700 kb, respectively ( Figure 5A ). Interestingly, unlike in TADs for the Dex-insensitive MYC and EGFR genes, for the inducible ERRFI1 , FKBP5 , and VARS2 genes TAD boundary distances were largely similar to the corresponding non-TAD boundry controls ( Figure 5B ). Distances for TAD boundaries were 0.31±0.01 µm (mean of medians ± SD) 0.40±0.02 µm, and 0.35±0.01 µm for ERRFI1 , FKBP5 , and VARS2 , respectively, and were comparable to those for non-TAD regions (0.28±0.00 µm, 0.40±0.01 µm, and 0.39±0.00 µm). Statistical comparison by Bonferroni’s multiple-comparison test of TAD vs. non-TAD distances showed no significant difference for ERRFI1 (p=0.1572, ns) or FKBP5 (p=1.0000, ns), but a modest yet significant difference for VARS2 (p=0.0057) ( Figure 5B ). Figure 5. Transcription stimulation does not alter topologically associating domain (TAD) boundary interactions. ( A ) Micro-C maps for the ERRFI1 , FKBP5 , and VARS2 TADs and neighboring regions, showing TAD boundaries (green, red) and equidistant non-TAD control regions (red, purple) in hTERT-HFFc6 fibroblast (HFF); corresponding probe positions are indicated. Interactions between the 5’ TAD boundaries and the 3’ TAD (yellow) or equidistant non-TAD control regions (gray) are highlighted, and total Micro-C contacts between regions are quantified, showing prominent contact frequency between ERRFI1 , VARS2 , and FKBP5 TAD boundaries, as well as the non-TAD region of ERRFI1 . ( B ) Comparison of TAD boundary and non-TAD region distances for EGFR , MYC , ERRFI1 , FKBP5 , and VARS2 in human bronchial epithelial cells (HBECs) as measured by DNA HiFISH. Dot plots of the mean of median distances from two experiments (11,000–49,000 alleles per condition). Error bars represent SEM. Statistical significance was calculated using two-way ANOVA with Bonferroni correction: ****p<0.0001; **p<0.01; ns, not significant (p≥0.05). ( C ) Measurement of boundary distances. Distance distributions of ERRFI1 , FKBP5 , and VARS2 TADs in untreated and 2 hr dexamethasone (Dex)-treated HBEC. Between 2,500 and 6,000 alleles were analyzed per condition. Values represent an individual dataset from a single experiment representative of multiple experiments. Mann-Whitney U test p-values are indicated as follows: ns, not significant (p≥0.05). Figure 5—figure supplement 1. RNA levels following dexamethasone (Dex) treatment. ( A ) RNA-seq analysis of ERRFI1, VARS2, and FKBP5 RNA levels following Dex treatment for the indicated durations in human bronchial epithelial cells (HBECs). Values were calculated for RPKM fold-change (Dex/No_Dex) ratio. Data represent the mean of three independent experiments. ( B ) Histograms of the distribution of nascent RNA transcription sites per nucleus in HBECs upon Dex treatment. Data represent values from at least two independent experiments (diamonds and circles); diamonds (EtOH control) and circles (Dex 2 hr) represent the mean of means, and error bars indicate SD. p-Values from two-way ANOVA with Bonferroni correction are shown as: ****p<0.0001; ***p<0.001; **p<0.01; *p<0.05; ns, not significant (p≥0.05). Open in a new tab As expected, based on the increased steady-state RNA levels detected by RNA-seq data upon Dex stimulation, the number of cells with one or two nascent RNA signals for ERRFI1 , FKBP5 , and VARS2 increased after Dex treatment for either 2 or 4 hr compared to untreated controls ( Figure 5—figure supplement 1 ). However, despite robust transcriptional induction, TAD boundary distances remained unchanged (p>0.3 for all comparisons) ( Figure 5C ). The proportion of alleles within 250 nm also did not differ between Dex-treated and control conditions (Mann-Whitney U test, p>0.2) ( Figure 5C ). Together, these results demonstrate that TAD boundary proximity is unaffected by acute transcriptional activation, reinforcing the notion that TAD boundary structure and gene activity are uncoupled. TAD boundary architecture and gene expression Our results indicate that gene expression status does not affect TAD boundaries. We next asked whether, conversely, alterations in TAD boundary structure affect transcription. To do so, we depleted the cohesin complex component RAD21 or the boundary protein CTCF in HCT116 cells using previously characterized auxin-inducible degron (AID) systems ( Rao et al., 2017 ; Natsume et al., 2016 ; Yesbolatova et al., 2020 ) and assessed the effect of depletion of either factor on boundary structure and transcription by DNA/RNA HiFISH ( Figure 6 ; Figure 6—figure supplement 1 ). Figure 6. Effects of RAD21 and CTCF depletion on topologically associating domain (TAD) boundary distances and gene expression. ( A ) Representative DNA/RNA high-throughput FISH imaging (HiFISH) images of EGFR and MYC nascent RNA FISH (blue) and its 3’ (green) and 5’ (red) TAD boundaries DNA FISH in HCT116-RAD21-AID1 and HCT116-CTCF-AID2 cells, respectively, in control and auxin-treated conditions. Scale bar: 10 μm. ( B ) TAD boundary distances and non-TAD controls after RAD21 or CTCF depletion for 3 hr. Values represent an individual dataset from a single experiment representative of multiple experiments. Between 13,000 and 127,500 alleles were analyzed per condition. Mann-Whitney U test p-values are indicated as follows: ***p<1 × 10 –100 ; ns, not significant (p≥0.05). ( C ) Fraction of silent (0), monoallelic (1), biallelic (2), and triallelic or more (≥3) expression of the indicated genes in individual cells after 3 hr or no auxin treatment in HCT116-RAD21-AID1 or HCT116-CTCF-AID2 cells. At least 20,000 cells were measured per experiment. Data represent values from at least two independent experiments (diamonds and circles); diamonds (DMSO control) and circles (RAD21 or CTCF-depleted) represent the mean of means, and error bars indicate SD. p-Values from two-way ANOVA with Bonferroni correction are shown as: ****p<0.0001; **p<0.01; *p<0.05; ns, not significant (p≥0.05). Figure 6—figure supplement 1. Depletion of RAD21 and CTCF. ( A ) Loss of RAD21 or CTCF in HCT116-RAD21-AID1 or HCT116-CTCF-AID2 cells, respectively, following DMSO (control) or auxin treatment. RAD21 and CTCF degradation were assessed using mClover fluorescence (green). Scale bar: 20  μm. ( B–C ) Fraction of silent (0), monoallelic (1), biallelic (2), and triallelic or more (≥3) expression of the indicated genes in individual cells after 3 hr or no auxin treatment in HCT116-RAD21-AID1 ( B ) or HCT116-CTCF-AID2 ( C ) cells. Data represent values from at least two independent experiments (diamonds and circles); diamonds (DMSO control) and circles (RAD21 or CTCF-depleted) represent the mean of means, and error bars indicate SD. p-Values from two-way ANOVA with Bonferroni correction are shown as: *p<0.05; ns, not significant (p≥0.05). Open in a new tab Consistent with prior studies, treatment of HCT116-RAD21-AID cells with auxin for 3 or 6 hr resulted in near-complete degradation of RAD21 ( Rao et al., 2017 ; Natsume et al., 2016 ; Figure 6—figure supplement 1 ). RAD21 depletion significantly increased the 5’–3’ TAD boundary distances for both the EGFR and MYC TADs ( Figure 6A and B ). For EGFR , the median boundary distance increased from 0.25±0.20 μm (control; median ±IQR) to 0.39±0.36 μm (RAD21-depleted, Mann-Whitney U test, p<1 × 10⁻¹⁰). For MYC , the median increased from 0.34±0.26 μm to 0.49±0.49 μm (U test, p<1 × 10⁻¹⁰). The fraction of alleles with TAD boundaries within 250 nm decreased from 49% to 26% for EGFR and from 29% to 18% for MYC upon depletion of RAD21 ( Figure 6B ). RAD21 depletion also reduced the expression of EGFR and MYC , with the median number of transcription sites per cell decreasing by 1.6-fold and 2.1-fold, respectively ( Figure 6C ). In line with gene repression, the percentage of cells with no detectable nascent RNA signal increased for both EGFR and MYC (Bonferroni-adjusted p-values = 2.70e-3 and <0.0001, respectively) ( Figure 6C ), and monoallelic expression frequencies also modestly decreased ( Figure 6C ). Similar results were observed for the ERRFI1 gene upon loss of RAD21 ( Figure 6—figure supplement 1 ). The reduction in gene expression upon loss of RAD21 may either be due to changes in TAD architecture or, more likely, due to local effects of RAD21, such as in enhancer-promoter interactions ( Kagey et al., 2010 ; Merkenschlager and Odom, 2013 ; Phillips-Cremins et al., 2013 ). To more directly assess a possible role of TAD structure on gene expression, we depleted the boundary factor CTCF via degron as previously described ( Yesbolatova et al., 2020 ; Figure 6—figure supplement 1 ). Depletion of CTCF increased MYC TAD boundary distances (median increased from 0.33±0.25 µm to 0.44±0.30 µm; Mann-Whitney U test, p<1 × 10⁻¹⁰⁰). No change in EGFR TAD boundary distances was detected upon CTCF depletion, likely due to the smaller size of the EGFR TAD (U test, p≥0.01; Figure 6C ). Regardless, no significant differences were observed in the expression of MYC or EGFR following CTCF depletion (Bonferroni-adjusted p-value>0.5; Figure 6C ). A similar lack of an effect on gene expression was observed for the ERRFI1 gene upon depletion of CTCF ( Figure 6—figure supplement 1 ). Altogether, these results demonstrate that while loss of the cohesin component RAD21 alters boundary distances and reduces gene activity, disruption of TAD boundary architecture by depletion of CTCF does not alter gene expression. Discussion We have used high-throughput DNA/RNA FISH to directly and quantitatively probe the relationship between TAD boundaries and gene activity at the single-cell and -allele level. We find in various experimental settings that TAD boundary proximity is largely unrelated to gene activity. Uncoupling of TAD boundary structure and gene activity is supported by several observations. TAD boundary distances were indistinguishable between transcriptionally active and inactive alleles across several loci and cell types, even when measured in the same cell nucleus. Furthermore, neither global transcription inhibition nor gene-specific induction of transcription altered TAD boundary proximity, suggesting that short-term transcription dynamics do not affect TAD boundary interactions. These results align with genome-wide findings from population-based studies, indicating that global transcription inhibition does not disrupt TAD structure ( Rao et al., 2017 ). A lack of correlation between chromatin domain structure and gene expression has also been noted in early Drosophila development, where domain architecture was found to be unrelated to cell-type-specific gene expression patterns ( Espinola et al., 2021 ; Ing-Simmons et al., 2021 ), and local chromatin loops formed before the emergence of chromatin domains ( Espinola et al., 2021 ; Ing-Simmons et al., 2021 ). In addition, disruption of intra- and inter-TAD interactions in Drosophila does not alter the expression of a majority of genes ( Ghavi-Helm et al., 2019 ). We also find that while loss of RAD21 altered gene expression, depletion of the architectural boundary protein CTCF did not alter the expression of MYC and EGFR, nor did it change boundary distances. Combined, these findings suggest that the behavior of TAD boundaries is largely decoupled from gene activity. These observations point to a model in which the precise demarcation of TAD boundaries plays a relatively minor role in determining the activity of the genes within the TAD. Our results are consistent with the view that transcriptional regulation occurs primarily at finer scales of genome organization—such as enhancer-promoter loops or sub-TAD structures—rather than being governed by the overall configuration of a TAD. In line with this interpretation, we find that depletion of RAD21 reduces MYC and EGFR expression, likely due to its local effects within the TAD, whereas loss of the bona fide boundary factor CTCF does not alter gene expression. A more local role of chromatin structure on gene expression is also suggested by our finding that the overall TAD structure is not sensitive to the transcriptional status of its genes. Similar observations have been made by others, demonstrating local, intra-TAD effects of transcription on chromatin structure ( Hsieh et al., 2022 ; Luppino et al., 2022 ; Shaban et al., 2024 ). The complex interplay of gene expression, local chromatin organization, and TAD structure is further highlighted by the observation of distinct, and only partially overlapping, effects on gene expression upon loss of either of the two cohesin regulators, WAPL and NIPBL ( Schuijers et al., 2018 ). Our findings argue against a role of TADs as stringent regulators of gene expression. One emerging view is that, rather than constituting discrete, stable structures, TADs are probabilistic genome features that represent the integrated sum of all chromatin-chromatin interactions within a genome region ( Natsume et al., 2016 ; Lee et al., 2025 ). This interpretation is in line with the observed high degree of single-cell heterogeneity of chromatin interactions, including boundaries ( Finn et al., 2019 ), and the documented highly dynamic nature of TAD boundaries which show that the fully formed CTCF loop which defines a specific TAD is a rare event ( Gabriele et al., 2022 ). Further support for this view is provided by recent ultra-resolution live-cell imaging, which revealed that at short genomic distances (<200 kb), chromatin loci encounter one another frequently due to spontaneous dynamic motion ( Lee et al., 2025 ). Beyond this range, encounter probability declines sharply, and cohesin becomes essential to dynamically bridge distal regulatory elements through active loop extrusion ( Lee et al., 2025 ). This extrusion-driven process enables long-range interactions, including between promoters and enhancers and between TAD boundaries ( Lee et al., 2025 ). Upon cohesin depletion, these rapid, distance-independent searches collapse into a diffusive regime reflected as a loss of interactions in both population-based and single-cell analysis ( Natsume et al., 2016 ). Our finding that loss of RAD21 has a stronger effect on TAD boundary distance than depletion of the bona fide boundary factor CTCF is consistent with this interpretation. These observations point to a more passive role of TADs in gene regulation, such as limiting inter-TAD enhancer-promoter interactions ( Sood and Misteli, 2022 ). In support of a modulatory role rather than a stringent regulatory function, intra-TAD interactions are only enriched ~2-fold compared to inter-TAD interactions ( Finn et al., 2019 ). This modulatory behavior does, however, not exclude the possibility of significant effects on gene expression, as has been observed upon deletion of some boundary regions ( Chakraborty et al., 2025 ; Mateo et al., 2019 ). Our study has several limitations. First, our observations are restricted to the relationship of transcription and TAD boundary distances rather than that of structure of the TAD boundary or the TAD as a whole. The behavior of the boundaries may not be representative of the internal TAD architecture. Boundaries may move without affecting the internal compaction or regulatory organization of the domain, and, conversely, the internal structure may change while the boundary distance remains constant. This behavior is in line with the dynamic properties of TADs observed in living cells and highlights the variability of TAD structure at the single-allele level ( Finn et al., 2019 ; Gabriele et al., 2022 ). Future work that maps TAD boundaries and internal domain contacts at high resolution in single cells, in parallel with transcriptional state, should provide deeper insight into the relationship between TAD chromatin and transcription. Second, our analysis is limited by the resolution of optical imaging and the size of the FISH probes used. We deliberately use relatively large BAC probes to generate robust, highly reproducible signals and to eliminate effects arising from local chromatin behavior. While the use of larger probes enhances the robustness of measurements, it limits resolution, and subtle changes in boundary architecture may not be detected, although we find very good correlation between Micro-C/Hi-C interaction frequency and distance measurements. In sum, our observations support the view that the structural and transcriptional layers of genome organization can be partially uncoupled. These insights have implications for interpreting chromatin conformation maps and for understanding the scale at which genome architecture influences transcriptional regulation. Materials and methods Key resources table. Reagent type (species) or resource Designation Source or reference Identifiers Additional information Cell line ( Homo sapiens ) Cell line HBEC3-KT (HBEC) Ramirez et al., 2004 RRID: CVCL_X491 Human bronchial epithelial cells immortalized with hTERT and CDK4 Cell line ( Homo sapiens ) Cell line HFF-hTERT clone 6 (HFFc6; HFF) Cellosaurus RRID: CVCL_VC41 hTERT-immortalized human foreskin fibroblasts (clone 6) Cell line ( Homo sapiens ) Cell line HCT 116 Cellosaurus RRID: CVCL_0291 Human colorectal carcinoma line; used as parent for degron derivatives Cell line ( Homo sapiens ) Cell line HCT-116 RAD21-mAID-mClover (RAD21-mAC) Natsume et al., 2016 Human colorectal carcinoma line (HCT116) derivative used for AID1-mediated RAD21 depletion Cell line ( Homo sapiens ) Cell line HCT116-CTCF-AID2 Yesbolatova et al., 2020 HCT116 derivative used for AID2-mediated CTCF depletion Commercial assay or kit Airway Epithelial Cell Basal Medium ATCC ATCC:PCS-300-030 Commercial assay or kit Bronchial Epithelial Cell Growth Kit ATCC ATCC:PCS-300-040 Chemical compound, drug DRB Sigma-Aldrich Sigma:D1916 Transcription inhibition Chemical compound, drug Dexamethasone (Dex) Sigma-Aldrich Sigma:D4902 Glucocorticoid receptor agonist Chemical compound, drug Auxin Sigma-Aldrich Sigma:I3750 Used for RAD21 depletion in AID1 degron system Chemical compound, drug 5-Ph-IAA GLPBio GLPBio:GC46061 AID2 ligand used in AID2 degron system Recombinant DNA reagent BAC probe RP11-112A3 (EGFR upstream control) BACPAC Resources Center BACPAC:RP11-112A3 Recombinant DNA reagent BAC probe RP11-117I14 (EGFR 5′ TAD) BACPAC Resources Center BACPAC:RP11-117I14 Recombinant DNA reagent BAC probe RP11-98C17 (EGFR 3′ TAD) BACPAC Resources Center BACPAC:RP11-98C17 recombinant DNA reagent BAC probe RP11-788I22 (MYC upstream control) BACPAC Resources Center BACPAC:RP11-788I22 Recombinant DNA reagent BAC probe RP11-765K23 (MYC 5′ TAD) BACPAC Resources Center BACPAC:RP11-765K23 Recombinant DNA reagent BAC probe RP11-717D13 (MYC 3′ TAD) BACPAC Resources Center BACPAC:RP11-717D13 – Recombinant DNA reagent BAC probe RP11-279H6 (ERRFI1 upstream control) BACPAC Resources Center BACPAC:RP11-279H6 Recombinant DNA reagent BAC probe RP11-338N10 (ERRFI1 5′ TAD) BACPAC Resources Center BACPAC:RP11-338N10 Recombinant DNA reagent BAC probe RP11-366K21 (ERRFI1 3′ TAD) BACPAC Resources Center BACPAC:RP11-366K21 Recombinant DNA reagent BAC probe RP11-192H11 (VARS2 upstream control) BACPAC Resources Center BACPAC:RP11-192H11 Recombinant DNA reagent BAC probe RP11-159K11 (VARS2 5′ TAD) BACPAC Resources Center BACPAC:RP11-159K11 Recombinant DNA reagent BAC probe RP11-803D22 (VARS2 3′ TAD) BACPAC Resources Center BACPAC:RP11-803D22 Recombinant DNA reagent BAC probe RP11-107C8 (FKBP5 upstream control) BACPAC Resources Center BACPAC:RP11-107C8 Recombinant DNA reagent BAC probe RP11-78C20 (FKBP5 5′ TAD) BACPAC Resources Center BACPAC:RP11-78C20 Recombinant DNA reagent BAC probe RP11-828B18 (FKBP5 3′ TAD) BACPAC Resources Center BACPAC:RP11-828B18 Sequence-based reagent Stellaris RNA probe set: EGFR (Atto647N) LGC Biosearch Technologies Sequence-based reagent Stellaris RNA probe set: MYC (Atto647N) LGC Biosearch Technologies Sequence-based reagent Stellaris RNA probe set: ERRFI1 (Quasar 670) LGC Biosearch Technologies Sequence-based reagent Stellaris RNA probe set: VARS2 (Atto647N) LGC Biosearch Technologies Sequence-based reagent Stellaris RNA probe set: FKBP5 (Atto647N) LGC Biosearch Technologies LGC:ISMF-2059-5 Software, algorithm HiTIPS Keikhosravi et al., 2024 ; Keikhosravi, 2025 https://github.com/CBIIT/HiTIPS High-throughput segmentation/detection and quantification pipeline Software, algorithm CellPose PMID: 33318659 RRID: SCR_021716 Nucleus segmentation within HiTIPS workflow Software, algorithm DNA/RNA registration (cross-correlation) Keikhosravi, 2024 : Almansour et al., 2024 https://github.com/CBIIT/DNA_RNA_registration Used for sequential DNA/RNA image registration; implemented in Python 3.8 Software, algorithm Analysis scripts for DNA/RNA HiFISH quantification Other https://github.com/CBIIT/mistelilab-tad-ge R scripts used for boundary-distance and single-cell gene-expression calculations Software, algorithm UCSC Genome Browser Nassar et al., 2023 RRID: SCR_005780 Software, algorithm 4DN Data Portal Dekker et al., 2017 RRID: SCR_016925 Other Charcoal-stripped fetal bovine serum R&D Systems R&D:S11650H Other 384-Well imaging plates (PhenoPlate) Revvity Revvity:6057500 High-throughput imaging format Other DY549P1-dUTP Dyomics Used for BAC probe labeling by nick translation Other DY488-dUTP Dyomics Used for BAC probe labeling by nick translation Other THE RNA Storage Solution Thermo Fisher Scientific Thermo Fisher:AM7001 Included in hybridization buffer Other Yokogawa CV8000 spinning-disk confocal microscope Yokogawa 60× water objective (NA 1.2); 4 laser lines (405/488/561/640 nm) Open in a new tab Cell culture The identity of cell lines was authenticated by sequencing, and cell lines were tested periodically for absence of mycoplasma. Cell lines used were: HBEC3-KT, HBEC-MYC-MS2, HFF-hTERT clone 6, HCT116, HCT116-RAD21-mAID-mClover, HCT116-CTCF-AID2. Details on cell lines are provided below. HBECs (HBEC3-KT) are derived from normal human bronchial tissue and immortalized by stable transduction with hTERT and CDK4 ( Ramirez et al., 2004 ). HBEC3-KT cells were maintained in keratinocyte serum-free medium (Thermo Fisher Scientific, cat. no. 17005042) supplemented with bovine pituitary extract (Thermo Fisher Scientific, cat. no. 13028014), human growth hormone (Thermo Fisher Scientific, cat. no. 1045013), and 50 U/ml penicillin/streptomycin (Thermo Fisher Scientific, cat. no. 15070063). HBEC- MYC -MS2, a derivative of HBEC with 12xMS2 inserted at the 3′ end of MYC on both alleles, constitutively expressing a GFP-MS2 coat protein fusion, was maintained under the same conditions. Human foreskin fibroblasts (HFFs), immortalized with hTERT ( Benanti and Galloway, 2004 ), were cultured in DMEM (Thermo Fisher Scientific, cat. no. 10569010) with 10% fetal bovine serum (Thermo Fisher Scientific, cat. no. 10082147) and penicillin/streptomycin. HCT-116 cells ( Natsume et al., 2016 ) were cultured in McCoy’s 5A medium (Thermo Fisher Scientific, cat. no. 16600082) supplemented with charcoal-stripped fetal bovine serum ( Yesbolatova et al., 2020 ) (R&D Systems, cat. no. S11650H) or with 10% FBS, 2 mM L-glutamine, and 100 U/ml penicillin, and 100 µg/ml streptomycin (Thermo Fisher Scientific, cat. no. 15070063). All cells were grown at 37°C in 5% CO 2 and split 1:4 twice weekly. Cells were seeded into 384-well imaging plates (PhenoPlate, Revvity, cat. no. 6057500) and allowed to grow to ~80% confluency prior to experiments ( Finn and Misteli, 2021 ). Cell treatments For transcription inhibition, HBECs and HFF cells were treated with 100 μM DRB (Sigma-Aldrich, cat. no. D1916) in culture medium for 2 hr, then fixed as described below. For glucocorticoid-mediated transcriptional stimulation, HBECs were cultured in Airway Epithelial Cell Basal Medium (ATCC, cat. no. PCS-300-030) supplemented with the Bronchial Epithelial Cell Growth Kit (ATCC, cat. no. PCS-300-040) and penicillin/streptomycin. To eliminate background glucocorticoid activity, cells were transferred 24 hr before induction to hormone-free medium, prepared by supplementing the basal ATCC Airway Epithelial Medium with HLL Supplement, L-glutamine, and penicillin/streptomycin while omitting both the P-extract and the Airway Epithelial Cell Supplement, as these components contain glucocorticoids. Dex (Sigma-Aldrich, cat. no. D4902) was prepared as a 100 μM stock solution in ethanol, aliquoted, protected from light, and stored at –20°C. For induction, HBECs were treated with 100 nM Dex (final concentration) for 2 or 4 hr. For RAD21 depletion, HCT116-RAD21-AID1 cells were treated with 0.17 μM auxin (Sigma-Aldrich, cat. no. I3750) or DMSO vehicle control for 3 hr ( Rao et al., 2017 ; Natsume et al., 2016 ). Cells were fixed with 4% PFA (Electron Microscopy Sciences, cat. no. 15710) in PBS (Millipore Sigma, cat. no. D8537) for 10 min, washed, and stored in PBS at 4°C. For CTCF depletion, HCT116-CTCF-AID2 cells were treated with 1 µM 5-Ph-IAA (GLPBio, cat. no. GC46061) or DMSO vehicle control. The 5-Ph-IAA (AID2 ligand) working solution was freshly prepared from a 1 mM intermediate stock immediately before use. Cells were cultured for approximately 36 hr to reach 80–90% confluence before treatment. The medium was then replaced with an equal volume of medium containing 1 µM 5-Ph-IAA or an equivalent volume of DMSO control, and cells were incubated for 3 hr at 37°C. Following treatment, cells were directly fixed without washing in 4% paraformaldehyde (Electron Microscopy Sciences, cat. no. 15710) in PBS (Millipore Sigma, cat. no. D8537) for 10 min, washed, and stored in PBS at 4°C until further processing. FISH probes BAC probes targeting MYC or EGFR TAD boundaries (RP11-765K23, RP11-717D13 for MYC ; RP11-366D3, RP11-98C17 for EGFR ) were obtained from the BACPAC Resources Center (BACPAC Resources Center). Negative controls targeting equidistant upstream regions were RP11-788I22 and RP11-112A3 (see Supplementary file 1 for a complete list of BAC probes). BAC probes were labeled by nick translation at 14°C for 80 min using DY549P1-dUTP or DY488-dUTP (Dyomics) as previously described ( Finn and Misteli, 2021 ). Labeled probes were ethanol-precipitated with 38 ng/μl Cot-1 DNA (Millipore Sigma), 256 ng/μl yeast tRNA, and 0.1 M sodium acetate, washed, and resuspended in hybridization buffer, which is made up of 30% formamide (pH 7.0), 10% dextran sulfate, 0.5% Tween-20, 2× SSC, 0.5× RNAsecure RNAse inhibitor, and 3% THE RNA Storage Solution (Thermo Fisher Scientific, cat. no. AM7001) dissolved entirely in molecular H 2 O. Stellaris RNA probes (LGC Biosearch Technologies) targeting intron 1 of MYC and EGFR consisted of 48 20-mer oligonucleotides labeled with Atto647N (see Figure 2—figure supplement 2 for details). DNA/RNA HiFISH in 384-well plates HiFISH was performed as described ( Almansour et al., 2024 ). In brief, cells were permeabilized in 0.5% saponin/0.5% Triton X-100/1× RNAsecure in PBS for 20 min, deproteinated in 0.1 N HCl for 15 min, neutralized in 2× SSC, and equilibrated in 50% formamide/2× SSC overnight at 4°C. Hybridization mixtures (0.4 μg DNA probe+12.5 μM RNA probe+hybridization buffer) were denatured at 85°C for 7 min and applied to cells, followed by 48 hr incubation at 37°C. Post-hybridization washes included 50% formamide/2× SSC at 37°C (2×1 hr), 2× SSC (twice), and prewarmed 1× SSC and 0.1× SSC at 45°C (three washes each). Nuclei were stained with 3 μg/ml of 4′,6-diamidino-2-phenylindole (DAPI) in 2× SSC. High-throughput image acquisition Images were acquired using a Yokogawa CV8000 spinning-disk confocal microscope as described in Almansour et al., 2024 , with a 60× water objective (NA 1.2), four laser lines (405, 488, 561, 640 nm), and appropriate emission filters. Z-stacks spanning 7 μm (1 μm steps) were collected in four channels. Image pre-processing For simultaneous FISH, images were analyzed directly. For sequential FISH, DNA and RNA images were registered as described ( Almansour et al., 2024 ) using cross-correlation algorithms implemented in Python 3.8 to align DAPI signals (GitHub: https://github.com/CBIIT/DNA_RNA_registration ). High-throughput image analysis HiTIPS software ( Keikhosravi et al., 2024 ) was used for segmentation and detection of FISH signals. Nucleus segmentation used CellPose ( Stringer et al., 2021 ), and segmentation parameters were adjusted per plate, with quality-control overlays to confirm accuracy. Data from each well were consolidated into experiment-wide datasets in R (R Core Team, 2024) using tidyverse ( Wickham et al., 2019 ) and other packages ( Barrett, 2024 ; Hester, 2023 ). For diploid cells, only cells with two DNA signals and ≤2 RNA signals were included for analysis. For analysis of triploid MYC HCT116, only cells with two and three MYC DNA signals and ≤3 RNA signals were included for analysis. TAD boundary pairs were identified as closest neighbors of a 5’ and 3’ signal. An active allele was defined based on the presence of an RNA signal within 1 μm of either boundary signal, based on the analysis of RNA-DNA distances in pilot experiments, demonstrating that >95% RNA signals were located within 1 μm. Micro-C analysis Published Micro-C XL data from H1-hESC and HFFc6 cells ( Krietenstein et al., 2020 ) were visualized using UCSC Genome Browser ( Nassar et al., 2023 ) and 4DN Data Portal ( Dekker et al., 2017 ; Reiff et al., 2022 ). Heatmaps were displayed with UCSC Track Settings: Display mode Full, Score Maximum Auto-scale, Draw mode triangle, Color HEX (#000000), and no interaction distance filter. ChromHMM analysis Chromatin state annotation for the HCT116 cell line was obtained from ENCODE (file accession ENCFF993RQV, annotation ID ENCSR448SWW, hg38) using the ChromHMM algorithm ( Ernst and Kellis, 2012 ). Each genomic interval was assigned to 1 of 15 functional states (ENCODE Project Consortium, 2020; https://www.encodeproject.org/ ). Data analysis Distance calculations were performed in R using SpatialTools ( French, 2023 ). Statistical comparisons used Kolmogorov-Smirnov ( Massey, 1951 ), Wilcoxon rank-sum ( Wilcoxon, 1945 ), and Dunn’s test with Bonferroni correction ( Dunn, 1964 ; Shaffer, 1995 ). p-Values were categorized as ***p<0.001, **p<0.01, *p<0.05, ns p≥0.05. Statistical tests KS tests compared distributions of radial distances; Wilcoxon tests compared medians of two groups; Dunn’s test followed Kruskal-Wallis ANOVA for multi-group comparisons ( Kruskal and Wallis, 1952 ). Statistical thresholds and exact p-values are reported in figure legends. Figure generation BioRender was used to generate Figures 1A–3A — 3 ( https://biorender.com/vjsbft3 ). Acknowledgements We thank members of the Misteli lab for input throughout the study. RNA-seq data was kindly provided by Thomas Johnson, NCI. Computation was performed on the NIH HPC Biowulf cluster. FA was supported by a graduate fellowship from the Ministry of Education of Saudi Arabia. This research was supported by the Intramural Research Program of the National Institutes of Health (NIH), National Cancer Institute NCI, Center for Cancer Research through grant 1-ZIA-BC010309 to TM, grant 1-ZIC-BC-011567 to HiTIF, and grant 1-ZIA-BC-011383 to DL. The contributions of the NIH author(s) were made as part of their official duties as NIH federal employees, are in compliance with agency policy requirements, and are considered Works of the United States Government. However, the findings and conclusions presented in this paper are those of the author(s) and do not necessarily reflect the views of the NIH or the U.S. Department of Health and Human Services. Funding Statement The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. Contributor Information Tom Misteli, Email: [email protected]. Timothy J Stasevich, Colorado State University, United States. Adèle L Marston, University of Edinburgh, United Kingdom. Funding Information This paper was supported by the following grants: National Institutes of Health

1-ZIA-BC010309 to Tom Misteli. National Institutes of Health

1-ZIC-BC-011567 to Gianluca Pegoraro. National Institutes of Health

1-ZIA-BC-011383 to Daniel R Larson. Ministry of Education of Saudi Arabia

PHD fellowship to Faisal Almansour. Additional information Competing interests No competing interests declared. Author contributions Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review and editing. Resources, Writing – review and editing. Software. Software, Writing – review and editing. Resources, Writing – review and editing. Software, Supervision, Funding acquisition, Writing – review and editing. Conceptualization, Formal analysis, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing. Additional files MDAR checklist elife-110197-mdarchecklist1.pdf (287.1KB, pdf) Supplementary file 1. RNA probe sequences. elife-110197-supp1.xlsx (18.5KB, xlsx) Data availability Code for DNA/RNA image registration is available at https://github.com/CBIIT/DNA_RNA_registration ( Almansour et al., 2024 ). The HiTIPS source code is available at https://github.com/CBIIT/HiTIPS , ( Keikhosravi, 2025 ) with full documentation-—including package structure, functions, installation instructions, user guidance, output table descriptions, and developer resources-—accessible at https://hitips.readthedocs.io/en/latest/ ( Almansour et al., 2024 ). All R scripts used for image quantification, including calculations of TAD boundary distances and single-cell gene-expression measurements from DNA/RNA HiFISH data, are publicly available at https://github.com/CBIIT/mistelilab-tad-ge (copy archived at Pegoraro, 2026 ). Uncropped ImageJ-generated TIFF composites underlying the microscopy panels, along with the DNA/RNA HiFISH datasets used in this study, have been deposited in Figshare (DOI: https://doi.org/10.6084/m9.figshare.31190179 , https://doi.org/10.6084/m9.figshare.31399215 ). 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[ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] eLife. doi: 10.7554/eLife.110197.3.sa0 eLife Assessment Timothy J Stasevich Timothy J Stasevich 1 Colorado State University, United States Reviewing Editor Find articles by Timothy J Stasevich 1 Author information Article notes Copyright and License information 1 Colorado State University, United States Roles Timothy J Stasevich : Reviewing Editor Keywords: Convincing Keywords: Important PMC Copyright notice In this important study, DNA and RNA are co-imaged in single cells to show that the proximity of topologically associated domain (TAD) boundaries is uncoupled from the transcriptional activity of nearby genes. The evidence supporting these conclusions is convincing for the regions examined, with high-throughput imaging providing robust statistics. This work will be of interest to researchers studying genome architecture and its relationship to gene regulation. eLife. doi: 10.7554/eLife.110197.3.sa1 Reviewer #2 (Public review): Anonymous Anonymous Reviewer Find articles by Anonymous Author information Copyright and License information Roles Anonymous : Reviewer PMC Copyright notice Summary: Almansour et al., investigate whether the proximity of TAD boundaries is directly linked to gene activity. The authors use high-throughput imaging to simultaneously measure the gene activity and physical distances between boundary regions in an allele-specific manner. Using transcriptional inhibitors, expression induction, and acute depletion of CTCF and cohesin, they test whether proximity of boundaries affects, or is affected by, gene activity. Strengths: The combined use of DNA and RNA imaging enabled simultaneous measurement of boundary proximity and transcriptional status at individual alleles. This allows single-allele correlation between boundary proximity and gene activity at multiple loci across thousands of alleles. The use of both transcription inhibitors and transcription stimulation provides compelling and consistent evidence that boundary proximity can be disconnected from a gene's activity. The data convincingly support the conclusion that stable proximity between boundary regions is not required for ongoing transcription at the loci and timescales examined. This work strengthens the emerging view that genome organization at the level of domain boundaries does not impose a deterministic control over transcription. Strong disruption of boundary distances is only observed upon depletion of cohesin. Notably, this corresponds with the largest changes in gene activity. In contrast, depletion of CTCF actually had minimal impact on boundary distances and also had minimal impact on gene activity. This makes sense in light of previous work, where live cell imaging demonstrated that cohesin is more important for domain-structure, whereas CTCF is only important for blocking cohesin from continuing on, such that the fully formed loop occurs in a very small percentage of cells. Therefore, the fact that disruption of cohesin (more important for internal domain structure) affects gene activity while disruption of CTCF does not is exceptionally interesting. Weaknesses: In untreated cells, the distribution of distance measurements between boundary probes is exceptionally narrow. While depletion of RAD21 clearly demonstrates an ability to detect changes in this distribution, this tight baseline distribution may limit sensitivity to more subtle changes (like those one might expect from transcriptional influences). This approach primarily tests the role of boundary interactions rather than domain organization as a whole. eLife. doi: 10.7554/eLife.110197.3.sa2 Reviewer #3 (Public review): Anonymous Anonymous Reviewer Find articles by Anonymous Author information Copyright and License information Roles Anonymous : Reviewer PMC Copyright notice Summary: This study addresses a central question in genome organization: whether the positions of chromosomal domain boundaries are functionally coupled to gene activity. The authors use high-throughput imaging to simultaneously measure distances between boundary markers and nascent RNA production in thousands of individual cells, enabling direct comparison of boundary positions and transcriptional status at single chromosomal copies. This approach is applied across multiple loci, genes, and cell types, and is combined with acute transcriptional perturbations and depletion of architectural proteins to test the relationship between chromosome structure and gene activity in both directions. This work makes a meaningful contribution by providing direct, single-cell evidence that domain boundary positions and gene activity are largely uncoupled in this system. Strengths: A major strength of the work is its single-cell, single-allele resolution, which overcomes the averaging inherent to population-based assays. The authors consistently find that boundary proximity is largely independent of transcriptional status: active and inactive alleles have similar boundary distances, transcriptional perturbations do not shift boundary distributions, and depletion of the boundary factor CTCF does not alter gene expression, whereas cohesin depletion affects both boundary organization and transcription. These conclusions are supported by large numbers of alleles, multiple loci and cell types, and internal controls that distinguish boundary-specific effects from broader chromatin influences. The study offers a robust, scalable imaging pipeline that will be valuable for future studies linking genome organization and transcription at single-cell resolution. Weaknesses: The study has important limitations that are acknowledged by the authors. Measurements are restricted to distances between flanking boundaries and do not capture internal domain architecture, sub-domain structure, or finer-scale regulatory contacts. Resolution is limited by probe size and imaging, potentially masking subtle positional changes, and only a small set of loci is examined, leaving open how broadly the uncoupling generalizes. Some perturbation effects, particularly for RAD21, may involve mechanisms beyond boundary disruption. eLife. 2026 Apr 15;15:RP110197. doi: 10.7554/eLife.110197.3.sa3 Author response Faisal Almansour Faisal Almansour 1 National Cancer Institute, Bethesda, MD, United States Author Find articles by Faisal Almansour 1 , Nadezda A Fursova Nadezda A Fursova 2 National Cancer Institute, Bethesda, MD, United States Author Find articles by Nadezda A Fursova 2 , Adib Keikhosravi Adib Keikhosravi 3 National Cancer Institute, Bethesda, United States Author Find articles by Adib Keikhosravi 3 , Kathleen S Metz Reed Kathleen S Metz Reed 4 National Cancer Institute, Bethesda, MD, United States Author Find articles by Kathleen S Metz Reed 4 , Daniel R Larson Daniel R Larson 5 National Cancer Institute, Bethesda, United States Author Find articles by Daniel R Larson 5 , Gianluca Pegoraro Gianluca Pegoraro 6 National Cancer Institute, Bethesda, United States Author Find articles by Gianluca Pegoraro 6 , Tom Misteli Tom Misteli 7 National Cancer Institute, Bethesda, MD, United States Author Find articles by Tom Misteli 7 Author information Article notes Copyright and License information 1 National Cancer Institute, Bethesda, MD, United States 2 National Cancer Institute, Bethesda, MD, United States 3 National Cancer Institute, Bethesda, United States 4 National Cancer Institute, Bethesda, MD, United States 5 National Cancer Institute, Bethesda, United States 6 National Cancer Institute, Bethesda, United States 7 National Cancer Institute, Bethesda, MD, United States Roles Faisal Almansour : Author Nadezda A Fursova : Author Adib Keikhosravi : Author Kathleen S Metz Reed : Author Daniel R Larson : Author Gianluca Pegoraro : Author Tom Misteli : Author Collection date 2026. PMC Copyright notice The following is the authors’ response to the original reviews. Public Reviews: Reviewer #1 (Public review): (1) Conceptual framing and interpretation: The central conclusion may require more precise framing to avoid potential overreach. The authors' interpretation equating "physical distance between TAD boundaries" with overall "TAD boundary architecture," and "transcriptional bursting events" with broader "gene activity," could benefit from clarification. This framing may not fully capture the temporal dynamics of transcription or the regulatory complexity within TADs. Furthermore, the broad conclusion of an uncoupled relationship appears to challenge extensive prior evidence from perturbation studies showing that disrupting TAD boundaries can alter gene expression. The authors' own observation of reduced gene activity upon RAD21 degradation suggests that global TAD disruption can affect transcription. A more precise and limited conclusion, acknowledging that their data demonstrate a lack of detectable correlation between boundary distance and bursting activity in their system, would be more accurate and help reconcile these findings with the existing literature. We have modified statements throughout the manuscript, including in the title, to enhance the precision of our conclusions to avoid overreach. We have also added on p. 16 of our Discussion, a separate section on the limitations of the study, noting that our conclusions are limited to TAD boundary distances and do not reflect the structure of TAD boundaries or of TADs themselves. We have also expanded our Discussion of possible TAD functions on p. 14/15. (2) Technical methods and data presentation: (2.1) Accuracy and dimensionality of distance measurements: The manuscript does not clearly state whether distances are measured in 2D or 3D, nor does it sufficiently address precision limits. The stated Z-step size (1 µm) may be inadequate for accurately measuring sub-micron chromatin distances in 3D. We state in both the Results and Methods that our data represent 2D distances derived from maximal-intensity projections of 3D image stacks. We previously published a detailed analysis of the precision of this measurement approach applied to chromatin interactions and documented the effect of 2D vs 3D analysis on these types of measurements. This study by Finn et al., 2022 is cited in the text. We also show in Figure S3 and mention on p. 6 and 10 that we observe similar results using either 2D or 3D analysis. (2.2) Probe design and systematic error: The genomic coverage size of the BAC probes used for DNA FISH is not explicitly stated. Large probe coverage could inherently blur the precise spatial location of adjacent DNA loci. The reported average distance (~300 nm) may be influenced by the physical size of the probes, as well as systematic expansion or distortion introduced by sample fixation and FISH processing. Although such technical limitations are currently unavoidable, the authors should clarify how these factors might affect their ability to detect subtle distance changes. The genomic location and size of all probes are provided in Supplementary Table 1. We deliberately use relatively large BAC probes both to generate robust, highly reproducible signals and to eliminate effects arising from local chromatin behavior. In line with earlier characterization of BAC probes (Finn et al., Cell, 2019; Finn et al., Methods, 2022), we find a strong correlation between micro-C/Hi_C interaction frequency and distance measurements. Systematic errors such as sample fixation and FISH processing have previously been evaluated by comparison to live cell data (see Finn et al., 2019) and found to be negligible, especially as all our analyses involve pairwise comparisons, which would both be similarly affected by systematic errors. We discuss resolution limits due to probe size in our new section on study limitations on p. 16. (2.3) Data Visualization: The manuscript would benefit from including representative, zoomed-in regions of interest from the raw imaging data. This would allow readers to visually assess measured distance differences against background noise. Raw images for inspection at any magnification are available at here . (2.4) Potential impact of resolution limits: In Figure 5, the micro-C data reveal a clear difference in interaction patterns inside versus outside the VARS2 locus TAD, yet the imaging data show no corresponding distance difference. This strongly suggests that the current imaging system, limited by optical resolution, probe size, and localisation accuracy, may be unable to resolve finer-scale spatial reorganizations associated with specific chromatin conformations (e.g., enhancer-promoter loops). The authors should explicitly discuss that their conclusion of "no coupling observed" may be constrained by the resolution and sensitivity of their method and does not preclude the possibility of detecting such associations with higher-precision measurements or in live-cell dynamics. We generally see good agreement between micro-C/Hi-C data and distance measurements. Specifically, we consistently find closer proximity of boundaries than non-boundaries and larger boundary distances for larger TADs than for smaller ones, as presented throughout the study. Contrary to the reviewer’s statement, this is also true for the VARS2 TAD, where we find statistically significant shorter boundary distances for boundary probes (350 nm) vs the outside control region (390 nm), which correlates with the difference in micro-C interaction score of 5847 vs 2308. These data are shown in Figure 3. Regardless, we mention the issue of resolution due to probe size in the study limitation section on p. 16. Reviewer #2 (Public review): In untreated cells, the distribution of distance measurements between boundary probes is exceptionally narrow. While depletion of RAD21 clearly demonstrates an ability to detect changes in this distribution, this tight baseline distribution may limit sensitivity to more subtle changes (like those one might expect from transcriptional influences). In addition, the correlation analysis is asymmetric, primarily stratifying by transcriptional status and then comparing boundary distances. Given the central claim that boundary architecture does not influence gene activity, the analysis should be done from the opposite perspective (stratifying by boundary distance). We mention the limitations on resolution of our approach in our discussion of study limitations on p. 16. An example of an analysis of stratifying by boundary distance is presented in Figure S3C. The conclusion is the same as stratifying by activity status. Strong disruption of boundary distances is only observed upon depletion of cohesin. Notably, this corresponds with the largest changes in gene activity. In contrast, depletion of CTCF actually had minimal impact on boundary distances and also had minimal impact on gene activity. This makes sense in light of previous work, where live cell imaging demonstrated that cohesin is more important for domain-structure, whereas CTCF is only important for blocking cohesin from continuing on, such that the fully formed loop occurs in a very small percentage of cells. Therefore, the fact that disruption of cohesin (more important for internal domain structure) affects gene activity while disruption of CTCF does not is exceptionally interesting but is lacking from the discussion. We mention the stronger effect of cohesion depletion compared to CTCF loss on gene expression in multiple locations in the Results and Discussion. On a related note, this approach primarily tests the role of boundary interactions rather than domain organization as a whole, and it should be acknowledged that internal domain structures are not directly assessed. We have modified statements throughout the manuscript to clearly indicate that our conclusions relate to boundary interactions rather than domain organization as a whole. We also discuss this in our section on study limitations. The comparison to work in other organisms (particularly the comparisons made to Drosophila) should be handled with care. The mechanisms underlying domain formation differ substantially across these systems, particularly regarding the differences in CTCF's role. We have modified our discussion of the data on Drosophila TADs, particularly as it relates to CTCF. Recommendations for the authors: Reviewer #1 (Recommendations for the authors): I couldn't locate the image data from figshare with the information provided (DOI: 10.6084/m9.figshare.30728354) The link has been updated https://figshare.com/projects/_b_TAD_boundaries_and_gene_activity_are_uncoupled_b_/271078 . Reviewer #2 (Recommendations for the authors): Some of the conclusions overreach. I recommend revising the claims and discussion to focus solely on the proximity of boundaries, instead of TADs themselves. This would match better with your experiments. We have modified statements throughout the manuscript, including in the title, to enhance the precision of our conclusions to avoid overreach. We have also added on p. 16, a separate section on limitations of our study, noting that our conclusions are limited to TAD boundary distances and do not reflect on the structure of the TADs themselves. We have also expanded our Discussion of possible TAD functions on p. 14/15. I do disagree with the interpretation of the data in some parts, particularly at the end, where you state that disruption of TADs does not impact gene activity. For example, "Altogether, these results demonstrate that disruption of TAD boundary architecture is insufficient to alter gene expression" doesn't seem to match the results. Sure, depletion of CTCF minimally impacted gene expression, but it also minimally impacted the boundary distances. I think it is interesting that depletion of RAD21 had a bigger impact on both gene expression and boundary distances, and this should be discussed. We have deleted this statement and now mention on p. 13 that RAD21 depletion affected gene expression, whereas loss of CTCF did not, and on p. 15 that loss of RAD21 had a greater impact on boundary distances than loss of CTCF. We have also expanded our Discussion of possible TAD functions on p. 14/15. Related to this, I also recommend expanding the discussion of prior live-cell imaging work (ref 32) that showed that the fully formed CTCF loop is a rare event. We have expanded the discussion of prior live-cell imaging work in several locations. All the analysis is done from the perspective of the gene expression (e.g. group by expression and then measure distances). It would help to show that the inverse analysis is consistent (e.g. group by distances and measure gene expression). Analysis of data stratified by distance measurements is shown in Figure S3C. The discussion of the Drosophila work is strange, given that CTCF in Drosophila has a very different N-terminus, explaining why it doesn't really form loops. Sure, maybe it contributes to domains in some way, but probably no more than the dozens of other architectural proteins that have been found in that system. This work clearly focuses on CTCF-loop domains, so I would be specific about that. In the introduction, you do a good job of saying "in human cells, TADs are.... marked by binding sites for the CTCF protein". However, then you overgeneralize and state that TADs form via a process of loop extrusion. I think a simple statement before this to say that TADs in human cells have become somewhat synonymous with CTCF loop domains, and that is how you will use the term here. However, other organisms have TADs despite the lack of conservation of the CTCF protein. We have modified the text accordingly. On a related note, in the discussion, you cite two papers in Drosophila to state that "TADs form prior to the establishment of cell-type-specific gene expression programs", but that's not entirely accurate for those papers. They actually show that TADs occur coincident with ZGA, but loops form before that (ref 23: Espinola et al), or that there are indeed a few boundaries that show up before ZGA, but these correspond to RNA Polymerase (ref 24: Ing-Simmons et al.). We have corrected this statement. Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Data Citations Almansour F, Fursova NA, Keikhosravi A, Reed KSM, Larson DR, Pegoraro G, Misteli T. 2026. Confocal Microscopy Raw Images for Figure Generation. figshare. [ DOI ] Almansour F, Pegoraro G. 2026. input_figure_1_c. figshare. [ DOI ] Supplementary Materials MDAR checklist elife-110197-mdarchecklist1.pdf (287.1KB, pdf) Supplementary file 1. RNA probe sequences. elife-110197-supp1.xlsx (18.5KB, xlsx) Data Availability Statement Code for DNA/RNA image registration is available at https://github.com/CBIIT/DNA_RNA_registration ( Almansour et al., 2024 ). The HiTIPS source code is available at https://github.com/CBIIT/HiTIPS , ( Keikhosravi, 2025 ) with full documentation-—including package structure, functions, installation instructions, user guidance, output table descriptions, and developer resources-—accessible at https://hitips.readthedocs.io/en/latest/ ( Almansour et al., 2024 ). All R scripts used for image quantification, including calculations of TAD boundary distances and single-cell gene-expression measurements from DNA/RNA HiFISH data, are publicly available at https://github.com/CBIIT/mistelilab-tad-ge (copy archived at Pegoraro, 2026 ). Uncropped ImageJ-generated TIFF composites underlying the microscopy panels, along with the DNA/RNA HiFISH datasets used in this study, have been deposited in Figshare (DOI: https://doi.org/10.6084/m9.figshare.31190179 , https://doi.org/10.6084/m9.figshare.31399215 ). The following datasets were generated: The following dataset was generated: Almansour F, Fursova NA, Keikhosravi A, Reed KSM, Larson DR, Pegoraro G, Misteli T. 2026. Confocal Microscopy Raw Images for Figure Generation. figshare. Almansour F, Pegoraro G. 2026. input_figure_1_c. figshare. 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