
==== Front
Nat Commun
Nat Commun
Nature Communications
2041-1723
Nature Publishing Group UK London

39223123
52040
10.1038/s41467-024-52040-y
Article
Single-molecule imaging of SWI/SNF chromatin remodelers reveals bromodomain-mediated and cancer-mutants-specific landscape of multi-modal DNA-binding dynamics
Engl Wilfried 12
Kunstar-Thomas Aliz 12
Chen Siyi 12
http://orcid.org/0000-0002-1062-1770
Ng Woei Shyuan 12
http://orcid.org/0000-0001-5231-7286
Sielaff Hendrik 12
http://orcid.org/0000-0002-1233-9867
Zhao Ziqing Winston zhaozw@nus.edu.sg

1234
1 https://ror.org/01tgyzw49 grid.4280.e 0000 0001 2180 6431 Department of Chemistry, Faculty of Science, National University of Singapore, Singapore, 119543 Singapore
2 https://ror.org/01tgyzw49 grid.4280.e 0000 0001 2180 6431 Centre for BioImaging Sciences, Faculty of Science, National University of Singapore, Singapore, 117557 Singapore
3 https://ror.org/01tgyzw49 grid.4280.e 0000 0001 2180 6431 Mechanobiology Institute, National University of Singapore, Singapore, 117411 Singapore
4 https://ror.org/01tgyzw49 grid.4280.e 0000 0001 2180 6431 Integrative Sciences and Engineering Programme, National University of Singapore, Singapore, 119077 Singapore
2 9 2024
2 9 2024
2024
15 764630 12 2023
22 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Despite their prevalent cancer implications, the in vivo dynamics of SWI/SNF chromatin remodelers and how misregulation of such dynamics underpins cancer remain poorly understood. Using live-cell single-molecule tracking, we quantify the intranuclear diffusion and chromatin-binding of three key subunits common to all major human SWI/SNF remodeler complexes (BAF57, BAF155 and BRG1), and resolve two temporally distinct stable binding modes for the fully assembled complex. Super-resolved density mapping reveals heterogeneous, nanoscale remodeler binding “hotspots” across the nucleoplasm where multiple binding events (especially longer-lived ones) preferentially cluster. Importantly, we uncover distinct roles of the bromodomain in modulating chromatin binding/targeting in a DNA-accessibility-dependent manner, pointing to a model where successive longer-lived binding within “hotspots” leads to sustained productive remodeling. Finally, systematic comparison of six common BRG1 mutants implicated in various cancers unveils alterations in chromatin-binding dynamics unique to each mutant, shedding insight into a multi-modal landscape regulating the spatio-temporal organizational dynamics of SWI/SNF remodelers.

Live-cell single-molecule imaging of human SWI/SNF chromatin remodeler complex reveals nanoscale binding “hotspots” in cell nucleus, and uncovers multi-modal aberrations in DNA binding dynamics associated with mutants implicated in various cancers.

Subject terms

Single-molecule biophysics
Chromatin remodelling
Single-molecule biophysics
Nanoscale biophysics
https://doi.org/10.13039/501100001459 Ministry of Education - Singapore (MOE) A-0008484-00-00 T2EP30222-0038 MOET32020-0001 Zhao Ziqing Winston https://doi.org/10.13039/501100001349 MOH | National Medical Research Council (NMRC) MOH-000227-00 Zhao Ziqing Winston issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

In the eukaryotic cell nucleus, the packaging of the genome in the form of nucleosomes1 poses a topological challenge when access to the underlying naked DNA is required for various chromatin-based molecular transactions (e.g. transcription, DNA repair, replication), and can be alleviated via the process of chromatin remodeling carried out by ATP-dependent, multi-subunit chromatin remodeler complexes2. Among the different subfamilies of chromatin remodelers, the SWI/SNF (switch/sucrose non-fermentable) remodelers provide DNA access by repositioning or ejecting nucleosomes or evicting histone dimers3,4. In mammalian cells, the homolog mSWI/SNF remodeler complexes can be further divided into BAF (canonical BRG1/BRM-associated factors), PBAF (polybromo-associated BAF) and ncBAF (non-canonical BAF) subtypes, each consisting of 8–15 subunits with a total molecular weight of ~1–1.5 MDa5. Structurally, the nucleosome is clamped from both sides in nucleosome-bound SWI/SNF remodeler complexes, with BRG1 being the core ATPase/translocase subunit that targets, binds and anchors the nucleosome with its nucleosome-facing C-terminus, while the N-terminus is anchored in the complex in combination with scaffold subunits such as BAF57 and BAF1556–9. Such multi-subunit composition also allows the cell to flexibly assemble remodeler complexes with different tissue- and developmental-stage-specific compositions to regulate gene expression and other related processes where and when needed4,10. Importantly, mutations in the 29 genes encoding these remodeler subunits11,12 have been associated with ~20% of all human cancers across a wide range of tumor types13,14, which is not surprising given that the capability to alter genome organization, accessibility and expression has long been known as a fundamental “enabling characteristic” of cancer15.

In contrast to our detailed knowledge about the biochemistry, structure and genetics of chromatin remodelers, their spatio-temporal organization and dynamics in live cells are much less understood from a quantitative perspective. Among the few imaging-based studies conducted previously, fluorescence correlation spectroscopy (FCS) and fluorescence recovery after photobleaching (FRAP) on the human ISWI remodeler complex have shown that the majority of ISWI remodelers constantly samples nucleosomes via transient binding, while a small fraction stably binds to chromatin to carry out remodeling16,17. Such a dynamic model was further corroborated and extended to six different remodeler subfamilies (including SWI/SNF) in live yeast cells using single-molecule tracking (SMT)18. More recently, the dynamic targeting to chromatin of BAF180, a subunit unique to the PBAF subtype of SWI/SNF remodeler complexes, was probed in human cells with SMT19. However, to date no systematic quantification of the intranuclear dynamics of the human SWI/SNF remodeler subfamily as a whole has been undertaken, nor do we know how misregulation of such dynamics in both space and time might underpin the various types of cancer in which they are implicated.

Herein, we combine SMT with a strategy for super-resolved density mapping of intranuclear binding events to quantify the live-cell diffusion and chromatin-binding dynamics of three key human SWI/SNF remodeler subunits, namely BAF57, BAF155 and BRG1 (encoded by the SMARCC1, SMARCE1 and SMARCA4 genes, respectively). All three subunits not only serve key catalytic/structural roles (BRG1 is the core ATPase/translocase that directly interacts with the nucleosome, while BAF57 and BAF155 form the backbone of the complex and are critically involved in the complex assembly process), they are also common to all major subtypes of the human SWI/SNF complexes, hence allowing us to reveal dynamic properties that are generic to the entire SWI/SNF remodeler subfamily. These three subunits are also incorporated into the SWI/SNF complex across different stages of the assembly process, with BAF155 being the first, followed by BAF57, and BRG1 being the final subunit to be added5, thereby allowing us to pinpoint the dynamics that are specific to the fully assembled remodeler complex (as opposed to partially assembled complexes or unincorporated individual subunits). Moreover, given that some of the most frequent mutations in human cancers are found in genes encoding the SWI/SNF remodeler subunits (with BRG1 being one of the most frequently mutated)13,20, we further probe the chromatin-binding dynamics of six common mutants of BRG1 implicated in various cancers across tumor types. Our systematic characterizations across a diverse range of spatio-temporal parameters uncover the existence of heterogeneously distributed intranuclear “hotspots” where the remodeler binds preferentially and repeatedly as a potential strategy to promote sustained remodeling at these loci, and reveal DNA-accessibility-dependent enhancement in the chromatin-binding dynamics of SWI/SNF remodelers mediated by the bromodomain. Together with the characteristic alterations we discover for each of the cancer mutants, we propose an integrated structure-dynamics model that sheds critical insight into a multi-modal landscape at work for the regulation of SWI/SNF-mediated remodeling dynamics, and establishes the biophysical basis for cancer-associated aberrations in remodeler–chromatin interactions in vivo.

Results

Single-molecule tracking of SWI/SNF remodelers resolves distinct modes of transient and stable binding

To quantify the intranuclear dynamics of the SWI/SNF remodeler complex, we performed SMT measurements on three key remodeler subunits, each labeled with JF54921 via a fused HaloTag in live HeLa cells, while simultaneously monitoring the DNA background stained with Hoechst 33342 (Fig. 1a). In order to avoid expression-level-associated artifacts and ensure consistency, we optimized the intranuclear expression levels of the remodelers to exhibit comparable distributions to their endogenous levels in a cell population, and only selected cells within a narrowly controlled range of expression levels for SMT measurements (Supplementary Fig. 1). We first performed fast tracking at 5.5 ms per frame (Supplementary Movie 1). Analysis of the observed trajectories with the Spot-On framework22 yielded displacement distributions that can only be satisfactorily fitted with a three-state model for all three subunits (Fig. 1b and Supplementary Fig. 2). In addition to a faster population corresponding to the diffusion of the unincorporated individual subunits with mean diffusion coefficients in the range of 2.8–3.8 µm2 s−1, we further resolved two distinct slower modes with mean diffusion coefficients in the ranges of 0.50–0.57 µm2 s−1 and 0.06–0.08 µm2 s−1, respectively (Fig. 1d), likely corresponding to the diffusion and binding of the whole complex, as evidenced by mean squared displacement (MSD) analysis (Supplementary Fig. 2d). Importantly, the distributions of the diffusion coefficient and frequency for both modes overlap excellently between the three subunits, suggesting that they both correspond to the fully assembled remodeler complex in which all three subunits have been incorporated, as opposed to partially assembled complexes in which the various subunits will likely exhibit different dynamics depending on whether each of them has been incorporated.Fig. 1 Live-cell single-molecule tracking quantifies intranuclear diffusion and binding of SWI/SNF remodelers.

a Representative frames from SMT movies of a SWI/SNF remodeler subunit (BRG1 is shown here as example) in fast- (left, 5.5 ms per frame) or slow- (right, 300 ms per frame) tracking mode; individual trajectories of diffusing (left) and bound (right) remodeler molecules are superimposed on each frame. b A typical displacement histogram derived from fast-tracking trajectories obtained across multiple cells, satisfactorily fitted with a three-state model. c A typical survival probability histogram derived from slow-tracking trajectories in a single cell, fitted with a two-state or three-state model or analyzed by GRID. d Scatter plots of diffusion coefficient (D) and frequency associated with each mode resolved from fast-tracking trajectories for BAF57, BAF155, BRG1 and an overlay of the three plots. e Scatter plots of residence time (τ) and frequency associated with each mode resolved from slow-tracking trajectories for BAF57, BAF155, BRG1 and an overlay of the three plots. f Box-and-whisker plot of the relative fraction of time bound under each of the three modes resolved in (e) for BAF57, BAF155 and BRG1. Median, 25th/75th percentiles (box) and 5th/95th percentiles (whiskers) are shown. g Longer-lived stable binding of both BRG1 and BAF155 is abrogated in cells treated with PFI-3. In (d–g), each dot denotes a single cell, and squares in (d, e, g) denote mean values. n = 41 (BAF57), 38 (BAF155) and 39 (BRG1) cells for (d), 85 (BAF57), 80 (BAF155) and 92 (BRG1) cells for (e, f), and 92 (BRG1, −PFI-3), 35 (BRG1, +PFI-3), 80 (BAF155, −PFI-3) and 29 (BAF155, +PFI-3) cells for (g). Source data are provided as a Source Data file.

In addition, we also performed slow tracking (at 300 ms per frame) in order to blur out the rapidly diffusing remodeler molecules and facilitate the probing of binding events that take place on the second timescale (Fig. 1a and Supplementary Movie 2). Given that many of the trajectories observed still consisted of a mixture of diffusion and binding, we implemented a strategy to unambiguously discriminate binding from diffusion by scanning through all possible sub-trajectories and selecting only those that are spatially circumscribed within a confined area with an optimized size (Supplementary Fig. 3a). We also quantified the effect of photobleaching during acquisition to ensure that it minimally impacts the accuracy of our measurements (Supplementary Fig. 4). The survival time distributions23 computed from the binding trajectories were best fitted with a three-state model (Fig. 1c), consisting of a transient binding fraction with a mean residence time in the range of 0.84–0.96 s, as well as two stable binding fractions: a shorter-lived one with mean residence time in the range of 3.6–4.1 s, and a longer-lived one with mean residence time in the range of 15.7–17.8 s (Fig. 1e and Table 1). As validation, we analyzed the data with the genuine rate identification method (GRID), a recently developed analytical framework capable of robustly decomposing multi-component reaction systems in an unbiased fashion, which has been validated on DNA-binding proteins24. The spectrum of residence times obtained by GRID analysis showed three distinct peaks with residence times that agree excellently with those obtained from the three-state model we used (Fig. 1c and Supplementary Fig. 3b), indicating that three distinct modes are indeed needed to fully account for the binding dynamics observed. Similar three-mode dynamics was also observed in a CRISPR knock-in cell line expressing BRG1 at endogenous level (Supplementary Fig. 5), further demonstrating that our measurements faithfully recapitulate remodeler dynamics in its native context. Moreover, we computed the fraction of time the remodelers were bound under each mode (Fig. 1f), defined as the normalized product of residence time and binding frequency associated with each mode shown in Fig. 1e (see “Methods” for details). We found that all three subunits exhibited similar fractions of time under each mode (with a mean fraction in the range of 22–26% for transient binding, 37–40% for shorter-lived stable binding and 34–39% for longer-lived stable binding). Finally, treating the cells with 50 µM of PFI-3 (an inhibitor of the BRG1 subunit via its bromodomain (BD)25) led to the abrogation of longer-lived stable binding for BRG1 (Fig. 1g and Supplementary Fig. 3c), suggesting that this mode likely corresponds to ATPase-associated activities critical for the functioning of the remodeler complex, and is mediated by the BD. Similar PFI-3-induced abrogation of the longer-lived stable mode was also observed for BAF155 (which has no BD) (Fig. 1g), indicating that this mode arises from a complex that has incorporated both subunits. This, together with the fact that all three subunits showed similarly distributed residence times and binding frequencies (Fig. 1e), indicates that we are detecting the binding of the fully assembled remodeler complex. While such binding dynamics most likely arises from interactions with chromatin, at this stage we cannot, however, rule out the possibility of the remodeler complex binding to other intranuclear structures (e.g. nuclear speckles, PML bodies, etc.).Table 1 Binding parameters of key SWI/SNF remodeler subunits determined from intranuclear SMT measurements

Subunit	Residence time τ (s)	Binding frequency f	Fraction of time bound F	
Transient binding	Stable binding	Transient binding	Stable binding	Transient binding	Stable binding	
Shorter-lived	Longer-lived	Shorter-lived	Longer-lived	Shorter-lived	Longer-lived	
BAF57	0.96 ± 0.28	4.1 ± 1.6	17.8 ± 7.8	0.67 ± 0.09	0.26 ± 0.07	0.07 ± 0.04	0.25 ± 0.09	0.37 ± 0.09	0.37 ± 0.15	
BAF155	0.84 ± 0.29	3.6 ± 1.5	15.7 ± 7.0	0.69 ± 0.08	0.26 ± 0.07	0.06 ± 0.03	0.27 ± 0.09	0.40 ± 0.08	0.33 ± 0.13	
BRG1	0.91 ± 0.33	4.1 ± 1.6	16.7 ± 6.7	0.66 ± 0.08	0.27 ± 0.06	0.07 ± 0.04	0.23 ± 0.07	0.40 ± 0.10	0.37 ± 0.13	
Reported values are mean ± S.D.

Super-resolved density mapping of SMT trajectories reveals intranuclear remodeler binding “hotspots”

To furnish spatial contexts to the intranuclear dynamics observed above, we reasoned that the density of displacements (or steps) detected in SMT trajectories at a particular intranuclear location serves as a measure of the frequency with which that location is visited or resided upon by the remodeler. Hence, we devised a strategy, termed STep Accumulation Reconstruction (STAR), to map the distribution of local displacement density in SMT trajectories in a similar way as localization coordinates are used in the reconstruction of super-resolution microscopy images (Supplementary Fig. 6 and “Methods”). While our approach is conceptually akin to methods such as sptPALM26 and other variant techniques27–30, STAR expands the scope and utility of previous methods that focus only on diffusion by enabling the mapping of binding events across the cell nucleus. The resulting super-resolved density maps for each of the three binding modes revealed distinctly and heterogeneously distributed clusters across the nucleoplasm, with each cluster corresponding to a binding “hotspot” where the remodeler binds preferentially and repeatedly; in contrast, the corresponding density map for diffusion exhibited a more homogeneous distribution (Fig. 2a). Ripley’s K-function analysis, a commonly used statistical method for quantifying spatial patterns, indicated that these hotspots have a characteristic radius of rc ~ 140 nm (regardless of whether all binding modes or only stable binding modes are considered, Fig. 2b), and consist of an average of 6.3 binding events per cluster (Fig. 2c). Furthermore, to probe the temporal correlation between binding events within a hotspot, we compared the probabilities of observing two consecutive (P(A∩B)) vs. two independent (P(A)·P(B)) binding events of the remodeler complex (Fig. 2d). Interestingly, consecutive longer-lived stable binding events within a hotspot are statistically more correlated as compared to other sequential combinations of longer-lived and shorter-lived binding events (all of which are largely independent of each other). Such trend is independent of the gap time that separates consecutive binding events, and is unlikely to be a consequence of a binding trajectory being artificially split into two due to the slow out-of-focus drifting of the bound remodeler molecule (Supplementary Fig. 7). The fact that a longer-lived binding event is more likely to be followed by another longer-lived binding event suggests that the genomic binding sites within these hotspots could be predisposed or induced upon longer-lived binding to favor further longer-lived binding, as a potential strategy to promote continuous remodeling activity at these sites.Fig. 2 Super-resolved STAR mapping reveals nanoscale binding hotspots for SWI/SNF remodelers.

a A live HeLa cell nucleus showing Hoechst-stained DNA background (top) and the corresponding STAR maps for diffusion (middle) and binding (bottom, color-coded according to binding mode). Gray line delineates nuclear boundary. BRG1 is shown here as example; similar maps can be constructed for other subunits. b Plots of Ripley’s K-function for either all binding events or stable binding events only from (a) reveal the existence of nanoscale clusters indicative of remodeler binding hotspots; dotted line indicates typical cluster radius (rc) corresponding to the peak of the K-function. c Histogram of the number of binding events per cluster, color-coded according to binding mode; dotted line indicates mean value. d Box-and-whisker plot of the ratio between the probability of observing two consecutive binding events and that of observing two independent binding events within a cluster, PA∩BP(A)⋅P(B) (where A and B denotes either a shorter-lived or a longer-lived stable binding event). Median, 25th/75th percentiles (box) and 5th/95th percentiles (whiskers) are shown. Each dot denotes a single cell; n = 84 cells. e Normalized number of binding hotspots for BRG1 as a function of minimum binding duration in the absence or presence of PFI-3. Source data are provided as a Source Data file.

In line with this, the frequencies of the stable binding modes were found to be significantly higher inside the hotspots as compared to outside (Supplementary Fig. 8), further suggesting that these hotspots indeed correspond to intranuclear sites where remodeling is facilitated. Expectedly, most of the hotspots disappeared in cells expressing only the HaloTag (which does not bind to DNA) (Supplementary Fig. 9), thereby validating the veracity of these binding hotspots. Furthermore, the normalized number of binding hotspots for BRG1 as a function of the minimum duration that defines a binding event declined much more rapidly in the presence of PFI-3 (Fig. 2e), indicating that PFI-3 treatment impacts not only the temporal dynamics of remodeler binding (by abrogating the longer-lived stable mode, as shown in Fig. 1g) but also the spatial organization of such binding dynamics (in the form of hotspots), and further points to the functional significance of the longer-lived binding mode for remodeling activity.

Bromodomain modulates DNA-accessibility-dependent enhancement of remodeler binding dynamics

In order to ascertain that the binding dynamics we observed indeed corresponds to chromatin-binding of the fully assembled SWI/SNF remodelers, we treated the cells with trichostatin A (TSA), a histone deacetylase inhibitor known to decondense chromatin and enhance DNA accessibility by promoting histone acetylation31 (Supplementary Fig. 10a). Indeed, enhancing DNA accessibility with TSA decreased the residence times (by an average of 18–22% for shorter-lived and 25–30% for longer-lived) and increased the frequencies (by an average of 23–34% for shorter-lived and 54–64% for longer-lived) of the stable binding modes for all three remodeler subunits (Supplementary Fig. 10b, c). The residence time for transient binding, however, was less affected. Moreover, enhancing DNA accessibility did not significantly alter the fraction of time the remodeler stayed bound under each mode (Supplementary Fig. 10d), thereby suggesting that DNA accessibility modulates remodeler–chromatin interactions by making binding more dynamic, but without shifting the overall partitioning of remodeler molecules among the different bound states. Moreover, the binding maps for all three remodeler subunits exhibited a marked reduction in the density of diffusive trajectories and transient binding events relative to that of stable binding events (Supplementary Fig. 10e). To better quantify this trend, we defined a targeting efficiency (TE) for each remodeler as the ratio between the intranuclear space explored by stable binding versus that by diffusion and transient binding combined (see “Methods” for details), which provides a convenient metric for how efficiently each remodeler can specifically target the desired genomic sites for stable binding. As expected, TSA treatment significantly enhanced the TE for all three subunits (Supplementary Fig. 10f) and increased the number of stable binding hotspots detected (Supplementary Fig. 10g), thereby suggesting that enhancing DNA accessibility can either expose more genomic spaces for remodelers to bind or generate new binding hotspots by virtue of the higher frequency for sampling genomic target sites as a result of enhanced binding dynamics.

Furthermore, the recognition of acetylated histone tails on nucleosomes is known to be mediated through the BD in various chromatin remodelers32,33; in particular, BDs in the BAF180 subunit unique to the PBAF remodeler subtype have recently been shown to regulate its targeting and binding to chromatin19. Among the three SWI/SNF remodeler subunits under our examination, BRG1 harbors a BD that recognizes and binds acetylated lysines on histone tails32–34. Hence, in order to probe if BD plays a generic role in modulating SWI/SNF remodeler dynamics, we compared the chromatin-binding of two naturally existing mutants of BRG1 that truncate either the BD (E1449X35, thereafter denoted as ∆BD) or the BD plus the preceding AT-hook (a short arginine/lysine-rich motif that binds to the minor DNA groove formed by AT-rich DNA elements36) (R1415X37, thereafter denoted as ∆BD + AT) with that of wildtype BRG1 (Fig. 3). Both mutants showed expression levels and intranuclear localization patterns comparable to wildtype BRG1, and exhibited chromatin-binding dynamics similar to wildtype BRG1 in the absence of TSA (Supplementary Fig. 11), in line with the previous in vitro finding that deleting the BD does not affect the remodeling ability of BRG138. However, the enhanced binding dynamics induced by TSA was abrogated upon BD deletion (Fig. 3a, b), demonstrating that BD indeed plays a generic role in modulating the binding dynamics of SWI/SNF remodelers in a DNA-accessibility-dependent manner. AT-hook deletion did not further contribute to this effect, in line with the fact that it is not involved in binding acetylated histone tails39. Moreover, STAR mapping (Fig. 3d) revealed that deleting the BD did not abrogate the enhanced TE for BRG1 (Fig. 3e), thereby suggesting that BD does not provide additional targeting capability to the hyperacetylated and more accessible chromatin. On the other hand, deleting the BD did abrogate the increased number of stable binding hotspots induced by TSA (Fig. 3f), indicating that these additional binding hotspots are a consequence of the enhanced binding dynamics (particularly the higher binding frequency), as opposed to the expanded genomic spaces that can be sampled by the remodeler upon TSA treatment.Fig. 3 DNA-accessibility-dependent enhancement of BRG1 binding dynamics is modulated by the bromodomain.

Box-and-whisker plots of fold changes (on a log2 scale) upon TSA treatment in a residence time (τ), b binding frequency (f) and c fraction of time bound (F) associated with each of the three binding modes for BRG1 (both wildtype and two mutants with either BD or BD plus AT-hook truncated). d STAR maps for diffusion and transient binding combined and stable binding (both shorter-lived and longer-lived) for BRG1 (both wildtype and the two mutants) before (top row) and after (bottom row) TSA treatment. Box-and-whisker plots of fold changes (on a log2 scale) upon TSA treatment in e targeting efficiency (TE) and f number of stable binding hotspots for BRG1 (both wildtype and the two mutants). All box-and-whisker plots show median, 25th/75th percentiles (box) and 5th/95th percentiles (whiskers). p values are determined using two-sided Student’s t test: *p < 0.05; **p < 0.01; ***p < 0.001; NS not significant. In (a–c, e, f), each dot denotes a single cell. n = 38 (BRG1WT), 35 (BRG1∆BD) and 37 (BRG1∆BD+AT) cells for (a–c), and 45 (BRG1WT), 39 (BRG1∆BD) and 38 (BRG1∆BD+AT) cells for (e, f). Source data are provided as a Source Data file.

Distinct, multi-modal alterations in chromatin-binding dynamics are associated with various cancer-implicated BRG1 mutants

Despite the fact that BRG1 is one of the most frequently mutated SWI/SNF subunits in human cancers13, how these mutations impact the chromatin-binding dynamics of the remodeler complex in relation to various cancers remains poorly understood. To address this key deficiency, we examined six common BRG1 mutants implicated in a variety of cancers across tumor types, including those that affect the skin, lung, liver, colon, kidney, ovary and placenta (Table 2); their respective locations are mapped onto the domain organization and structure of BRG1 in complex with nucleosome (Fig. 4a, b and Supplementary Fig. 12)6. Among the two point mutations, P1456L occurs in the linker between the AT-hook and BD, while R1502H occurs within the BD34. Among the four truncation mutants, Δ530–80740 has parts of the ATPase domain (which consists of two conserved RecA-like lobes known as DExx and HELICc domains and is the catalytically active domain involved in ATP binding/hydrolysis and DNA translocation during remodeling41) and the HSA domain (which regulates remodeling activity in tandem with its neighboring post-HSA domain9,42 and the actin related protein (ARP) module4), as well as the entire BRK domain (consisting of a potential protein-binding motif with unknown function43) deleted, while Δ851–113840 has a part of the ATPase domain deleted. Finally, Q1304X44 harbors a stop codon mutation that leads to the C-terminal truncation of the BD, the AT hook and the SnAC/post-SnAC domains (which regulate ATPase activity and nucleosome mobilization45,46 and facilitate remodeler anchoring to the nucleosome core during remodeling6), while Q164X40 harbors a stop codon mutation that leads to the truncation of all functional domains in BRG1.Table 2 Mutant-specific signatures in chromatin-binding dynamics associated with six cancer-implicated BRG1 mutants

Mutant	Domain(s) affected	Type(s) of cancer implicated	No. of stable binding mode(s)	% Change (compared to wildtype)	
Residence time τ	Binding frequency f	Fraction of time bound F	Targeting efficiency TE	Hotspots #	
Shorter-lived	Longer-lived	Shorter-lived	Longer-lived	Shorter-lived	Longer-lived	
Point mutants	
P1456L	Linker between AT-hook and BD	Malignant melanoma	2	−8 ± 33

NS

	−8 ± 29

NS

	−1 ± 28

NS

	+21 ± 62

NS

	−4 ± 21

NS

	+14 ± 36

NS

	+1 ± 28

NS

	+17 ± 56

NS

	
R1502H	BD	Colon adenocarcinoma	2	−31 ± 23

***

	−30 ± 31

**

	0 ± 18

NS

	+28 ± 87

NS

	−3 ± 18

NS

	+6 ± 35

NS

	−7 ± 29

NS

	+15 ± 58

NS

	
Truncation mutants	
Δ530−807	HSA, BRK, ATPase (DExx only)	Ovarian cancer	2	+8 ± 42

NS

	+23 ± 39

NS

	−3 ± 33

NS

	−34 ± 48*	+13 ± 34

NS

	−22 ± 37

NS

	−17 ± 35

**

	−41 ± 52

***

	
Δ851−1138	ATPase (DExx and HELICc)	Colon adenocarcinoma, ovarian cancer	2	−8 ± 36

NS

	+1 ± 43

NS

	−1 ± 29

NS

	−39 ± 59

**

	+15 ± 27

NS

	−36 ± 39

**

	−21 ± 30

***

	−33 ± 40

***

	
Q1304X	SnAC, AT-hook, BD	Liver carcinoma, lung adenocarcinoma, placenta cancer	2	−3 ± 43

NS

	+5 ± 45

NS

	−18 ± 24

**

	−30 ± 34

**

	−3 ± 19

NS

	−16 ± 28

NS

	−46 ± 17

***

	−69 ± 12

***

	
Q164X	QLQ, HSA, BRK, ATPase, SnAC, AT-hook, BD	Adrenal cortex adenocarcinoma	1	Not comparable	Not comparable	Not comparable	−49 ± 15

***

	−62 ± 32

***

	
Reported values are mean ± S.D. “+” and “−” denote increase and decrease in value, respectively. The values of τ, f and F for mutant Q164X (which shows only one stable binding mode) are not comparable to those of other mutants (which show two distinct stable binding modes).

p values are determined using two-sided Student’s t test: *p < 0.05; **p < 0.01; ***p < 0.001. NS not significant.

Fig. 4 BRG1 mutants implicated in diverse cancers exhibit distinct and multi-modal alterations in chromatin-binding dynamics.

a Overview of domain organization of BRG1 and the locations of six point/truncation mutants examined. b Cryo-EM structure of BRG1 (in cartoon representation) in complex with nucleosome (in surface representation), generated according to ref. 6 (PDB-Dev ID: PDBDEV_00000056). Point mutations P1456L and R1502H are shown in red, and truncation in mutants Δ530–807, Δ851–1138 and Q1304X are shown in blue, green and pink, respectively. Residues 1–335 (which is largely intrinsically disordered and contains the truncation mutant Q164X), 532–679 (part of the truncation in mutant Δ530–807) and 1419–1647 (containing the BD), however, were not resolved in the original structure. The structure of the BD (pink) was separately resolved in complex with DNA34 (PDB ID: 3UVD), and is placed here (shaded region) to illustrate its approximate orientation in the overall complex. Box-and-whisker plots of fold changes (on a log2 scale) in c residence time (τ), d binding frequency (f) and e fraction of time bound (F) associated with shorter-lived and longer-lived stable binding for each of the six mutants (except Q164X), relative to that for wildtype BRG1. f STAR maps for diffusion and transient binding combined and stable binding (both shorter-lived and longer-lived) for each of the six mutants, together with wildtype BRG1 as reference. Gray line delineates nuclear boundary. Box-and-whisker plots of fold changes (on a log2 scale) in g targeting efficiency (TE) and h number of stable binding hotspots for each of the six mutants, relative to that for wildtype BRG1. All box-and-whisker plots show median, 25th/75th percentiles (box) and 5th/95th percentiles (whiskers). p values are determined using two-sided Student’s t test: *p < 0.05; **p < 0.01; ***p < 0.001; NS not significant. In (c–e, g, h), each dot denotes a single cell. n = 25 (P1456L), 26 (R1502H), 34 (Δ530–807), 30 (Δ851–1138) and 30 (Q1304X) cells for (c–e); and 34 (P1456L), 35 (R1502H), 39 (Δ530–807), 38 (Δ851–1138), 41 (Q1304X) and 39 (Q164X) cells for (g, h). Source data are provided as a Source Data file.

We first validated that all mutants were expressed at comparable levels and exhibited similar intranuclear localization patterns as wildtype BRG1 (Supplementary Fig. 13). Performing SMT measurements, quantifications and STAR mapping as described above (Fig. 4c–h and Table 2), we found that point mutation R1502H in the BD resulted in significantly reduced residence times for both modes of stable binding, likely as a result of replacing the positively charged arginine finger that interacts with the negatively charged DNA backbone with the more neutral and shorter histidine residue, thereby destabilizing remodeler–DNA binding. In contrast, point mutant P1456L exhibited no significant change in any of the parameters measured, despite the fact that it falls within the linker region between the AT-hook and the BD that positions both domains into contact with DNA47.

In contrast to the point mutants, the C-terminal truncation mutant Q1304X showed significant reduction in the frequencies of both stable binding modes, targeting efficiency and the number of stable binding hotspots. This is in agreement with the roles of BD in binding DNA and acetylated histone tails as well as of the post-SnAC domain in binding the nucleosome core and facilitating its anchoring during remodeling6. The impaired nucleosome-binding ability of this mutant, as manifested by the drop in stable binding frequencies, increases the time spent in diffusing and nonspecifically sampling the genomic space, hence leading to reductions in both TE and number of binding hotspots. Moreover, truncation mutants ∆530–807 and Δ851–1138 showed reductions in both targeting efficiency and number of stable binding hotspots, as well as in the frequency and fraction of time bound under the longer-lived stable mode (Δ851–1138 only). This can be attributed to the fact that the proper orientation and positioning of all domains C-terminal to the truncated region (especially the SnAC/post-SnAC domains and the BD) in relation to the nucleosome are likely disturbed in both mutants, potentially leading to misalignment or even conformations that could sterically hinder the remodeler from establishing stable contacts with the nucleosome. Finally, mutant Q164X exhibited not only drastically reduced chromatin-binding, TE and number of stable binding hotspots similar to the other three truncation mutants, but also showed only a single stable binding mode (with a residence time of 6.4 ± 1.6 s) distinct from the two stable binding modes for wildtype BRG1. In particular, the marked (close to 3-fold) reduction in the number of stable binding hotspots is in line with the previous finding that removing the BRG1/BRM subunit reduces the chromatin affinity of mammalian SWI/SNF complexes to a residual level48. As such, the impaired chromatin-binding/targeting is likely a consequence of such residual affinity exhibited by the remodeler complex that has incorporated this mutant. Even though binding can still be achieved (albeit to a much lower extent) via this single stable mode through the interactions of other remodeler subunits with the nucleosome, no subsequent remodeling can be carried out due to the deletion of all functional domains.

Collectively, these findings revealed a multi-modal spatio-temporal landscape used by SWI/SNF remodelers via the dynamic modulation of the efficiency of targeting/binding chromatin, as well as the differential partitioning of the remodeler molecules among the different binding modes to regulate remodeler–chromatin interactions in vivo.

Discussion

In this study, we performed a systematic single-molecule quantification of the live-cell dynamics of the fully assembled human SWI/SNF remodeler complex by correlating three of its key common subunits. We first performed fast-tracking to resolve distinct modes of intranuclear dynamics for these subunits, revealing a faster fraction corresponding to the diffusion of unincorporated individual subunits, and two slower fractions that correspond to the diffusion and chromatin-binding of the assembled remodeler complexes (both partially and fully). Quantifying the residence time with slow-tracking revealed one transient binding and two stable binding modes for all three remodeler subunits. The fact that all three subunits exhibited similarly distributed residence times, binding frequencies and fractions of time bound, together with the PFI-3-induced abrogation of longer-lived stable binding of both BAF155 and BRG1 (which are incorporated into the complex at the beginning and the end of the assembly pathway, respectively), indicates that the three binding modes we resolved correspond to the binding of the fully assembled remodeler complex. Our findings are in line with an FCS-based model previously proposed for the human ISWI remodeler subfamily, in which the majority of the remodeler complexes continuously sample the nucleosomes via transient binding while a small fraction stably binds to chromatin for extended durations to carry out remodeling16, although our SMT measurements have enabled us to further resolve the stable binding fraction of the SWI/SNF subfamily with much greater detail. In addition, a recent study on six different yeast remodeler subfamilies (including SWI/SNF) uncovered only one stable binding mode for all remodelers measured (with a mean residence time of 4.4 s for the SWI/SNF subfamily)18, as opposed to the two distinct stable modes we found for human SWI/SNF remodelers. This difference is likely a consequence of the fact that the yeast genome is less complex in architecture, with much of the genome being constitutively open for transcription49, and hence requires less sophisticated mechanisms to remodel.

Adding onto the temporal aspects of remodeler binding dynamics, the STAR mapping strategy we devised offers a powerful way for directly visualizing the spatial landscape of remodeler binding in a mode-specific manner. With this unique capability, we revealed numerous nanoscale and heterogeneously distributed binding hotspots across the nucleoplasm for the SWI/SNF remodeler complex, in which multiple binding events (for both transient and stable binding) preferentially cluster. The typical size we observed for these hotspots (~140 nm in radius) agrees excellently with that previously found for BAF180 hubs (~250 nm in diameter)19. Moreover, the higher frequency observed for the stable binding modes within the hotspots, as compared to outside the hotspots, indicates that the chromatin microenvironment within the hotspots is more accessible. This, coupled with the enrichment of sequential longer-lived stable binding events in the hotspots, suggests that these hotspots are intranuclear foci of enhanced chromatin accessibility and likely correspond to genomic sites that are predisposed or induced upon longer-lived stable binding to promote sustained remodeling at these sites, as a potential strategy used by SWI/SNF remodelers to selectively engage genomic targets to provide DNA access for other chromatin-dependent processes. Similar hotspots have also been previously observed for ISWI remodelers, which are bound for durations similar to those we observed for SWI/SNF remodelers (both in the range of seconds to minutes)16.

Empowered by these capabilities, we further showed that enhancing DNA accessibility makes remodeler binding (especially the stable modes) more dynamic, manifested in terms of shortened residence times but higher binding frequencies. The fact that deleting the BD abrogates such enhanced binding dynamics points to a generic role played by the BD in modulating the chromatin-binding of SWI/SNF remodelers in a DNA-accessibility-dependent manner. However, deleting the BD does not significantly impact the enhanced targeting efficiency associated with TSA treatment, thereby suggesting that one of the previously proposed roles of BD in targeting the hyperacetylated and more accessible chromatin50 needs to be reconsidered. Moreover, the fact that deleting the BD does abrogate the higher number of stable binding hotspots associated with TSA treatment indicates that enhanced binding dynamics plays a more prominent role in the generation of new binding hotspots as compared to the additional genomic space exposed to the remodelers upon enhancing DNA accessibility. These observations are further in line with our findings that inhibiting the BD in BRG1 with PFI-3 treatment both abrogates longer-lived stable binding as well as disrupts the clustering of such binding events into intranuclear hotspots, since the inhibited BD is now unable to recognize the acetylated histone marks due to steric clash introduced by PFI-3 (which competes for the same binding site on the BD25).

In light of the functional significance of the longer-lived binding mode (particularly those clustered into hotspots) for remodeling activity as well as the role of the BD in modulating the organization and dynamics of remodeler binding in both space and time, we propose a mechanistic model for SWI/SNF remodeler–chromatin interactions that integrates our dynamic SMT measurements and STAR mapping results with structural features of the remodeler complex in the context of nucleosome binding (Fig. 5). In this model, the assembled remodeler complex nonspecifically samples the genome for potential binding sites via transient binding events, similar to those observed for other DNA-interacting proteins51,52. Upon locating potential binding sites on chromatin, the remodeler complex can undergo two distinct modes of stable binding, modulated by the BD upon histone tail acetylation: either shorter-lived binding (lasting a few seconds), or longer-lived binding (lasting tens of seconds) that is likely accompanied by the recruitment of additional factors (e.g. transcription factors (TFs), RNA polymerase II (Pol II), histone acetyltransferases (HATs), ARP module, etc.) known to work in synergy with SWI/SNF remodelers4,53. These factors can trigger cues that predispose genomic sites for enhanced remodeler binding and promote longer-lived binding events to take place successively at the same genomic loci, thereby leading to sustained productive remodeling (both translocation and/or eviction) within these hotspots. In contrast, shorter-lived binding events not accompanied by these facilitatory factors or cues will likely result in unsuccessful engagement with chromatin (i.e. abortive remodeling), for which the remodeler will dissociate from the genomic site after binding for a short time.Fig. 5 An integrated structure-dynamics model for SWI/SNF remodeler–chromatin binding interactions.

In addition to nonspecific sampling of the genome via transient binding events, shorter-lived stable binding of the SWI/SNF remodeler complex to chromatin (with residence time (τ) on the order of a few seconds) likely leads to abortive remodeling (left), while longer-lived stable binding (with τ on the order of 10 s of seconds), likely accompanied by the actions of additional factors (such as TFs, Pol II and HATs) that work in synergy with SWI/SNF remodelers and could triggers cues to promote successive longer-lived binding events at the same genomic loci, leads to sustained productive remodeling inside the binding hotspots (right). Such longer-lived stable binding is reduced (in terms of binding frequency (f), targeting efficiency (TE) or number of stable binding hotspots) or abrogated altogether for cancer-associated remodeler mutants in which the BD is either mutated, truncated or misaligned in the remodeler–nucleosome complex (middle).

Our model is in line with the recent finding that the BAF remodeler complex synergizes with both Pol II and DNA-sequence-specific TFs to accomplish chromatin remodeling/nucleosome eviction, while the absence of these factors leads to stalled remodeler complexes at nucleosomes and aborted remodeling53. Additionally, the enhanced remodeler binding dynamics observed within the hotspots (particularly for longer-lived stable binding, see Supplementary Fig. 8), similar to that produced upon TSA treatment (Fig. 3), suggests that the binding events in both cases are similarly associated with enhanced DNA accessibility, hence pointing to the likely involvement of factors that modulate DNA accessibility (such as HATs) in order to facilitate longer-lived stable binding within these hotspots. Moreover, our model is further supported by the multi-modal alterations in binding dynamics we observed for a series of cancer-associated remodeler mutants, in which the BD is either mutated, truncated or misaligned in the remodeler–nucleosome complex. Specifically, the reduced frequency of longer-lived binding or even the abrogation of the longer-lived binding mode altogether observed for these mutants (Fig. 4) suggests aberrant modulation of productive remodeling activity by the BD as a potential mechanism that underpins various cancer-implicated remodeler mutations. In addition, the fact that further truncation beyond the SnAC domain in BRG1 (Q164X) does not lead to further changes in stable binding dynamics (Fig. 4g, h) also suggests that the SnAC domain, AT-hook and BD may collectively constitute a sufficient functional module that synergistically modulates the targeting, positioning and binding of the remodeler complex to nucleosome.

Overall, the approaches developed in our study revealed critical intranuclear dynamics of the SWI/SNF remodeler subfamily, and paved the way for further investigations into the organization and dynamics of chromatin remodeling in general. The super-resolved STAR mapping strategy we devised can also be applied to probe a wide range of intranuclear processes, e.g. transcription54, DNA repair55,56 and chromatin-based phase condensation57. Importantly, our findings establish the biophysical basis for aberrant remodeler–chromatin interactions associated with SWI/SNF remodeler mutants implicated in various cancers, and could potentially serve as a unique set of identifying yardsticks for these mutants. More fundamentally, they also revealed a much broader and multi-modal landscape at work to regulate remodeler dynamics beyond that captured by the conventionally used residence time and binding fraction, and argue for the adoption of other equally revealing but often under-explored dynamic parameters in both space and time in order to comprehensively describe remodeler–chromatin interactions in vivo. At the same time, the lack of observable change for the P1456L mutant, as well as for three other point mutations E861K, T910M and R1192C (all located in the ATPase domain) (Supplementary Fig. 14), underscores the fact that even such a wide coverage of parameter space as ours still cannot fully capture the consequences of cancer-causing remodeler mutations, and calls for a more holistic approach in order to reveal their functional impact. In addition, correlating remodeler binding hotspots with other key nuclear structures (e.g. transcription factories58, DNA replication foci59, DNA loops60 and epigenetic signatures), as well as with molecular players known to synergize with the SWI/SNF remodelers (e.g. TFs, Pol II and HATs)4,53, will also enable us to pinpoint the molecular nature and chromatin microenvironment of these hotspots. Finally, systematic quantifications can be performed across different cell types specifically implicated in each cancer type, both at the level of individual remodeler mutants as well as multiple mutations that co-exist in the same cancer cell, thereby allowing us to reveal potential synergistic effects of remodeler dysregulation that are generally applicable to a wide range of disease contexts.

Methods

Reagents

All restriction enzymes, NEBuilder HIFI DNA Assembly Master Mix (E2621L), KLD Enzyme Mix (M0554S) and the Quick Ligation Kit (M2200L) were purchased from New England Biolabs (Ipswich, USA). All other chemicals were purchased from Merck (Darmstadt, Germany).

Constructs generation

Human BAF57 and BAF155 gene fragments were amplified from plasmids pBS-hBAF57 (Addgene ID #17877) and pBS-hBAF155 (Addgene ID #17876), respectively, while the human BRG1 gene fragment (splicing isoform 2 (1614 aa), which differs from the canonical isoform 1 (1647 aa) by the deletion of 33 amino acids (aa 1259–1291) from exon 28, outside of any functional domain) was amplified from the plasmid pCS2-hBRG1 (a gift of Nicolas Plachta, University of Pennsylvania). The C-terminal HaloTag fusion construct for BAF57 (pBAF57-HTC) was generated by amplifying the HaloTag backbone from the pHTC HaloTag® CMV-neo vector (G7711, Promega) and digesting it with AgeI and KpnI, followed by ligation with the BAF57 gene fragment using the Quick Ligation Kit. The C-terminal HaloTag fusion construct for BAF155 (pBAF155-HTC) was generated by amplifying the HaloTag backbone from a pCS2-HaloTag vector (in-house), followed by Gibson assembly with the BAF155 gene fragment using the NEBuilder HIFI DNA Assembly Master Mix. The N-terminal HaloTag fusion construct for BRG1 (pHTN-BRG1) was generated by digesting the pHTN HaloTag® CMV-neo vector (G7721, Promega) with PvuI and XbaI, followed by ligation with the BRG1 gene fragment using the Quick Ligation Kit. The N-terminal HaloTag fusion constructs for BRG1 point mutants (pHTN-BRG1E861K, pHTN-BRG1T910M, pHTN-BRG1R1192C, pHTN-BRG1P1456L and pHTN-BRG1R1502H) were generated via site-directed mutagenesis by introducing each point mutation via the respective primer during pHTN-BRG1 amplification. The linear fragment obtained was then subjected to the KLD Enzyme Mix to re-circularize the plasmid. The N-terminal HaloTag fusion constructs for BRG1 truncation mutants (pHTN-BRG1Δ530–807, pHTN-BRG1Δ851–1138, pHTN-BRG1Q1304X and pHTN-BRG1Q164X, as well as the BD truncation mutants pHTN-BRG1ΔBD and pHTN-BRG1ΔBD+AT) were generated by amplifying the desired pHTN-BRG1 sequence while omitting the respective truncated region. The linear fragment obtained was then subjected to the KLD Enzyme Mix to re-circularize the plasmid. The location of each mutation was mapped to the corresponding position in BRG1 isoform 1 (UniProt entry: P51532.1). The proper generation of all constructs was confirmed by sequencing. All primers used to generate the constructs are listed in Supplementary Table 1.

Cell culture and transfection

Immortalized HeLa cell line (ATCC CCL-2, a gift from Thorsten Wohland, National University of Singapore) was cultured in a 25-cm2 flask using HyClone Dulbecco’s modified Eagle’s medium (DMEM) with high glucose (SH30022.01, Cytiva) supplemented with 10% (v/v) fetal bovine serum (FBS, 10500064, Life Technologies), 100 units/ml penicillin and 100 µg/ml streptomycin (Pen-Strep, 15140122, Life Technologies), and maintained at 37 °C in a humidified atmosphere with 5% CO2. Prior to transfection, cells were seeded in glass bottom dishes (P35G-1.5-10-C, MatTek Life Sciences) with a 10-mm microwell and no. 1.5 coverglass. Cells were transfected overnight with 0.6 µg of plasmid DNA for each remodeler construct at 60–70% confluency, using Lipofectamine 3000 (L3000-015, Thermo Fisher Scientific) according to manufacturer’s protocol, prior to SMT measurements.

Microscope setup

All live-cell imaging experiments were performed on a customized Eclipse Ti2 inverted microscope (Nikon) equipped for highly inclined and laminated optical sheet (HILO) illumination61. Live-cell samples were placed in an enclosed microscope stage chamber maintained at 37 °C and supplied with 5% CO2. The objective lens was also pre-heated to 37 °C to avoid a temperature sink. The microscope was equipped with four diode lasers with excitation wavelength at 405 nm (OBIS 405 LX, Coherent), 488 nm (OBIS 488 LS, Coherent), 561 nm (OBIS 561 LS, Coherent) and 639 nm (MRL-FN-639-300mW, Changchun New Industries Optoelectronics Tech. Co.). Images were collected with a 100x, NA 1.49 oil objective (SR HP Apo, Nikon), and recorded using an EMCCD camera (Andor iXon Life 897, Oxford Instruments) water-cooled to −70 °C. All images were acquired using NIS-Elements AR software (version 5.21.03, Nikon).

Live-cell labeling, SMT measurements and TSA treatment

Live HeLa cells expressing each Halo-tagged chromatin remodeler were labeled with 5 nM Janelia Fluor® 549 (JF549) Halo-tag® ligand (GA1110, Promega) for 15 min at 37 °C. After rinsing 3 times with phosphate buffered saline (PBS, 17-517Q, Lonza), cells were stained with Hoechst 33342 (NucBlue™, R37605, Life Technologies) in imaging medium consisting of phenol red-free DMEM (21063029, Life Technologies) for 15 min at 37 °C, followed by washing for 10 min in imaging medium. To ensure the consistency and comparability between measurements, only cells within a narrowly controlled range of intranuclear expression levels for each remodeler were selected for SMT measurements (Supplementary Fig. 1); cells in the mitotic phase were also excluded.

For fast-tracking measurements, cells were continuously illuminated at 561 nm under HILO mode at a power density of ~0.4 kW/cm2. Images of single remodeler molecules were acquired for up to 20,000 frames at 5.5 ms per frame with a ROI of 128 × 128 pixels. For slow-tracking measurements, live-cell single-molecule images were instead acquired at a power density of ~0.02 kW/cm2 for up to 2000 frames at 300 ms per frame with a ROI of 256 × 256 pixels. Images of each cell nucleus were acquired under 405-nm excitation before and after each SMT acquisition to monitor the extent of nuclear movement during measurement, as well as to be used as a mask in the subsequent image analysis for isolating intranuclear trajectories only.

When investigating the effect of DNA accessibility on remodeler dynamics, cells were first treated with 400 nM TSA (T1952, Sigma-Aldrich) in imaging medium for 6 h prior to SMT measurements. In order to minimize the potential impact of variations in remodeler expression levels between different days and cell populations, every SMT dataset for TSA treatment and BRG1 mutants was accompanied by a control dataset (i.e. without TSA treatment or using wildtype BRG1) acquired on identically prepared cell populations on the same day. Up to 15 cells per remodeler species or treatment condition were measured per day. The distribution of each dynamic parameter measured was then normalized against the median of the control distribution.

SMT data analysis

The 2D coordinates for individual localizations of labeled chromatin remodelers were extracted from each frame of a SMT acquisition using a custom-written plugin in ImageJ (version 1.53, National Institutes of Health). Each frame was first denoised with a wavelet filter62, followed by segmenting individual localizations and obtaining their coordinates using a radial gradient-based algorithm63. All further analyses were performed in Matlab (version R2020a, MathWorks). Localizations of individual molecules across frames were linked into a trajectory using the Matlab version of the TrackMate plugin in ImageJ64. When constructing trajectories, a maximum displacement of 640 nm (fast tracking) or 280 nm (slow tracking) between consecutive frames was used, while a maximum tolerance of 2 consecutive missed frames in each trajectory was allowed in both cases.

Diffusion coefficients and their corresponding frequencies for each remodeler subunit were obtained from fast-tracking trajectories by first constructing a normalized displacement histogram from all recorded trajectories, and fitting it with a three-state model22:1 P(r,Δt)=Fboundr2(DboundΔt+σ2)exp−r24(DboundΔt+σ2)+Fslowr2(DslowΔt+σ2)exp−r24(DslowΔt+σ2)+(1−Fbound−Fslow)r2(DfastΔt+σ2)exp−r24(DfastΔt+σ2),

where D and F denote the diffusion coefficient and frequency of each of the three states (fast, slow, and bound), respectively, and σ denotes localization uncertainty (15 nm in our case). This model, which is based solely on displacements without referencing any individual trajectory, is capable of describing all possible transitions between the different states of remodeler dynamics. Mean squared displacement (MSD) analysis was performed by first constructing a series of displacement histograms as a function of increasing number of steps (Δt) in the trajectory, and decomposing each distribution using a three-state model; the peak displacement for each state can then be used to compute the root-mean-square displacement at each value of Δt to yield an MSD plot for each mode (Supplementary Fig. 2d).

Binding events were extracted from slow-tracking trajectories by scanning through all possible sub-trajectories and selecting those that are spatially confined within a circle with an optimized radius (Supplementary Fig. 3a). Only binding events with at least 4 consecutive displacements were selected to ensure that the selected binding trajectories are minimally contaminated by slowly diffusing trajectories65. To quantify binding dynamics, the survival probability for each molecule still being bound after a time t was first calculated23. Given that chromatin-binding dynamics (such as that of SWI/SNF remodelers) can often comprise multiple superimposed modes, the survival probability distribution was analyzed by GRID24 to infer the number of modes involved and their respective rates and amplitudes in an unbiased manner. Subsequently, the survival probability distribution was fitted with a multi-exponential function in accordance with the number of modes determined by GRID to yield the residence time (τ) and the corresponding frequency (f) of each mode. Finally, the relative fraction of time a remodeler was bound under each mode (F) was determined by normalizing the product of τ and f associated with each mode according to:2 Fi=τifi∑m=1nτmfm.

Quantifying the impact of photobleaching on SMT measurements

To quantify the potential effect of photobleaching on the accuracy of our SMT measurements, we performed GRID analysis on two SMT datasets from a U2OS cell line expressing a stably bound nuclear target, Nup96 (a subunit of the nuclear pore complex)66, labeled with the same JF549-HaloTag ligand and acquired under two illumination settings: continuous exposure at 300 ms per frame or time-lapse illumination with 300 ms per frame separated by a dark interval of 1 s (Supplementary Fig. 4). The photobleaching rate can be estimated using the relation between the off-rate constant koff, effective off-rate constant keff, and photobleaching rate constant kb according to:3 keff=koff+kbtintttot,

where tint and ttot denotes the integration time per frame and total time for the time lapse, respectively67. Since the residence time is given by the inverse of the rate constant (i.e. τoff = 1/koff and τeff = 1/keff), the underestimation factor for the residence time as a consequence of photobleaching is therefore given by:4 1−τeffτoff=1−koffkeff=1−keff−kbkeff=kbkeff=τeffτb,

since tint = ttot for continuous illumination.

STAR mapping and analyses

To reveal the spatial distribution of intranuclear binding events, we devised a super-resolution mapping strategy, termed STep Accumulation Reconstruction (STAR), by utilizing individual displacements from SMT trajectories in the same way as localization coordinates are used in the reconstruction of super-resolution microscopy images (Supplementary Fig. 6). We first defined a grid with a pixel size that can be arbitrarily small, but is restricted in practice by the number of displacements detected and the corresponding localization precision (40 nm in our case). As the occurrence of single or multiple binding events leads to a local accumulation of displacements, the brightness of each pixel corresponds to the number of displacements whose coordinates fall into that pixel. Plotting such a displacement density for each pixel then generates a STAR map for binding events across the cell nucleus, which can be further sorted according to the duration that corresponds to each binding mode, and represented as an RGB map. Finally, a Gaussian filter (with σ = 40 nm) was applied to smoothen the raw map and generate the final super-resolved map. Ripley’s K-function (H(r) ≡ L(r) − r) was then calculated68 to reveal the existence of intranuclear clusters of binding events. To further quantify the spatial organization of binding, a Delaunay triangulation-based cluster analysis69 was performed on the average position of each stable binding event using a maximum distance of 200 nm between binding events and a minimum of 3 binding events per cluster. Similar to binding maps, a STAR density map for diffusion was constructed by making use of sub-trajectories that specifically correspond to diffusion. For a given dataset, the comparison between binding and diffusion maps provided a way to assess the targeting efficiency of the remodeler, defined as the ratio between the intranuclear space explored by stable binding events versus that by diffusion and transient binding combined.

Remodelers expression levels calibration

Rabbit monoclonal primary antibodies for BAF57 (ab131328, Abcam), BAF155 (ab172638, Abcam) or BRG1 (ab110641, Abcam) were labeled with JF549-NHS ester (6147, Bio-techne) in 120 mM Na-bicarbonate for 30 min at room temperature. Unreacted dye was removed from the labeled antibody by gel filtration using a PD MidiTrap G-25 column (28918008, Cytiva). The labeling ratio was spectroscopically determined (NanoDrop 2000c, Thermo Fisher Scientific) after labeling. Non-transfected HeLa cells as well as HeLa cells identically transfected with plasmid for each remodeler as those used for SMT measurements were fixed with a mixture of 3% (w/v) paraformaldehyde and 0.1% (w/v) glutaraldehyde in PBS for 10 min at room temperature. After rinsing with PBS twice, cells were blocked and permeabilized with blocking buffer (3% (w/v) bovine serum albumin (BSA, A3059, Sigma-Aldrich), 0.2% (v/v) Triton X-100 (A16046, Thermo Fisher Scientific)) in PBS for 60 min, and then incubated with 10 μg/ml of JF549-labeled primary antibody in blocking buffer at 4 °C overnight under light protection. After rinsing once and washing twice for 5 min with washing buffer (0.2% (w/v) BSA, 0.05% (v/v) Triton X-100 in PBS) and rinsing once with PBS, cells were co-stained with Hoechst 33342 in PBS for 15 min. Epi-fluorescence images of the labeled cells were acquired under 561-nm (for JF549-labeled remodelers) and 405-nm (for Hoechst-stained cell nucleus) excitations. The intranuclear fluorescence signal was isolated using the Hoechst images as mask, and quantified with a custom-written code in Matlab.

Statistical analysis

All data presented were derived from at least three independent measurements conducted on different days. For box-and-whisker plots, the median, 25th/75th percentiles (box) and 5th/95th percentiles (whiskers) are shown. Statistical significance was assessed using two-sided Student’s t test: *p < 0.05; **p < 0.01; ***p < 0.001; NS not significant.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Supplementary information

Supplementary Information

Peer Review File

Description of Additional Supplementary Files

Supplementary Movie 1

Supplementary Movie 2

Reporting Summary

Source data

Source Data

Supplementary information

The online version contains supplementary material available at 10.1038/s41467-024-52040-y.

Acknowledgements

We thank Nicolas Plachta for the gift of the plasmid pCS2-hBRG1, and Thorsten Wohland for the gift of HeLa cell line and helpful suggestions. This work was supported by the National Medical Research Council Open Fund–Young Individual Research Grant (MOH-000227-00), Ministry of Education Academic Research Fund Tier 1 Grant (A-0008484-00-00), Tier 2 Grant (MOE-T2EP30222-0010) and Tier 3 Grant (MOE-T3-2020-0001), as well as the National University of Singapore Presidential Young Professorship Start-up Fund to Z.W.Z.

Author contributions

Z.W.Z. conceived and supervised the study; Z.W.Z., W.E. and H.S. designed the experiments; A.K. and W.E. performed SMT measurements; W.E. wrote codes for and performed SMT/STAR mapping analyses; S.C. and W.S.N. cloned constructs and generated cell lines; A.K., W.E. and H.S. performed remodelers expression levels calibration; W.S.N. performed Western blots; H.S. performed structural analyses; Z.W.Z., H.S. and W.E. wrote the manuscript with contributions from all other authors.

Peer review

Peer review information

Nature Communications thanks Jerry Workman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Data availability

Source data are provided with this paper, including processed SMT data used in constructing Figs. 1–4. Sample raw SMT data generated in this study are available from GitHub at https://github.com/wilengl/Nat_Comm_Codes/. Due to their large size, additional raw SMT data are available from the corresponding author upon request via email; access will be governed by conditions specified in National University of Singapore Data Use Agreement (https://libguides.nus.edu.sg/rdm/terms_of_use), and usually granted within 4–8 weeks. Structural data used in generating Fig. 4 can be accessed from PDB-Dev (ID: PDBDEV_00000056) and PDB (ID: 3UVD), respectively. Source data are provided with this paper.

Code availability

All codes used in this study are available from GitHub at https://github.com/wilengl/Nat_Comm_Codes/.

Competing interests

The authors declare no competing interests.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Wilfried Engl, Aliz Kunstar-Thomas, Siyi Chen.
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