Regulating physical access to DNA is central to both gene expression, genome topology, and genome integrity across eukaryotes. Decades of data strongly suggest that more physically accessible chromatin is also more transcriptionally permissive (Klemm et al., 2019; Maeshima et al., 2019 landmark papers from Weintraub and Groudine, 1976; Wu et al., 1979). In contrast, compacted chromatin or heterochromatin has been correlated with transcriptional restriction (Allshire and Madhani, 2018; Flamm et al., 1969; Janssen et al., 2018; Schultz, 1936). These chromatin states are dynamic and subject to tight regulation. Chromatin-binding proteins dictate many of these dynamics in part driven by the presence and deposition of specific post-translational modifications (PTMs) of nucleosomes (Chung et al., 2023; Rothbart and Strahl, 2014; Taverna et al., 2007; Tolsma and Hansen, 2019). For instance, HP1 binds to H3K9me2/3 nucleosomes (Bannister et al., 2001; Nakayama et al., 2001; Lachner et al., 2001; Sanulli et al., 2019), ultimately resulting in transcriptionally repressive chromatin (Bannister et al., 2001; Escobar et al., 2021; Hwang et al., 2001; Nakayama et al., 2001; Lachner et al., 2001). Interestingly, upon binding of HP1 to H3K9me3 nucleosomes, these nucleosomes’ internal residues become more exposed to hydrogen/deuterium exchange (Sanulli et al., 2019). This seminal finding suggests that chromatin-binding proteins not only serve as a recruitment platform for other binding proteins but can rapidly alter the physical properties of individual nucleosomes. The logical extension of this concept is examining how altering innate physical properties of nucleosomes modulates the local chromatin state’s structure and function.

To study how nucleosome dynamics is altered by chromatin binding factors, single molecule techniques have been developed, ranging from groundbreaking in vitro techniques such as optical and magnetic tweezers (Bustamante et al., 2021; Chien and van Noort, 2009; Killian et al., 2018; Neuman and Nagy, 2008) and in vivo single molecule tracking (Iida et al., 2022; Izeddin et al., 2014; Mueller et al., 2013; Shen et al., 2017; van Staalduinen et al., 2023; Wang et al., 2021). The former two techniques rely on precisely designed DNA sequences and constructs to guarantee precise measurements. By manipulating an optical trap or magnetic tweezer, tension and torsion forces can be exerted on the associated DNA molecule, which in turn alters the forces exerted on nucleosomes (Bustamante et al., 2021; Chien and van Noort, 2009; Killian et al., 2018; Neuman and Nagy, 2008). In contrast, single molecule tracking in cells is made possible by photostable fluorophores covalently bound to a target protein. These tagged proteins are introduced into cells at low concentration to allow the tracking of single molecules, with limited control where the tagged proteins will go (Iida et al., 2022; Mueller et al., 2013; Shen et al., 2017; Wang et al., 2021). Both systems are powerful and have distinct advantages, ranging from bp-precision of nucleosome sliding, folding-unfolding dynamics to determining residency time of transcription factors. However, a gap exists connecting these two technical approaches, namely assessing, and quantifying the dynamics of individual nucleosomes and correlating them with global chromatin dynamics. High-speed atomic force microscopy (HS-AFM) has the capability to span this gap. It is an in vitro based technique that permits real time tracking of single molecules in the context of interacting macromolecular complexes (e.g. myosin tracking on actin filaments, Ando, 2018; chromatin techniques reviewed in Melters and Dalal, 2021). By imaging nucleosome arrays in buffer over time, it is possible not just to track, but also quantify the motions of individual nucleosomes within an array.

In addition to PTMs, the chromatin landscape is also marked by the local enrichment of histone variants (Buschbeck and Hake, 2017; Jamge et al., 2023; Martire and Banaszynski, 2020; Melters et al., 2015), such as the centromere-specific H3 histone variant CENP-A/CENH3. CENP-A nucleosomes recruit several centromeric proteins (Walstein et al., 2021; Mendiburo et al., 2011; Régnier et al., 2005), including CENP-C. CENP-C in turn functions as the blueprint for the formation of the kinetochore (Cheeseman et al., 2006; DeLuca and Musacchio, 2012; Hara et al., 2023; Walstein et al., 2021; Przewloka et al., 2007; Weir et al., 2016; Yatskevich et al., 2023). Recently, we reported that the central domain of CENP-C (CENP-C^CD) induces loss of CENP-A nucleosomal elasticity in silico and in vitro (Melters et al., 2019). This finding correlates with decreased hydrogen/deuterium exchange of CENP-A nucleosomes when bound by CENP-C^CD (Falk et al., 2016; Falk et al., 2015; Guo et al., 2017). Interestingly, CENP-C knock-down resulted in downregulation of centromeric transcription (Bury et al., 2020), whereas CENP-C overexpression resulted in reduced RNA polymerase 2 (RNAP2) levels at the centromere and clustering of centromeric chromatin (Melters et al., 2019). We were curious about how CENP-A alone, and in combination with CENP-C mechanistically impacts centromeric chromatin fiber mobility. To address this question, we employed HS-AFM to track thousands of canonical or centromeric nucleosomes in arrays in real time. We report that HS-AFM imaging is free of tip-induced artifacts and CENP-A chromatin responds predictably to various control conditions. Next, we find that the essential kinetochore protein CENP-C, which is CENP-A chromatin’s closest binding partner, directly impacts nucleosome mobility and, surprisingly, also chromatin fiber motion in vitro. These data represent a technological advance in imaging and analyzing chromatin dynamics by HS-AFM. We extended these findings in vivo using immunofluorescence imaging and biochemical approaches, reporting that overexpressing CENP-C alters centromeric chromatin transcription and the ability to load new CENP-A molecules. Cumulatively, these data support the notion that local transcriptional competency depends on innate properties and local homeostasis of histone variants within the chromatin fiber in vivo.

We are interested in understanding how nucleosomes ‘behave’ in biologically relevant conditions. Elegant single-molecule techniques have enabled us to understand details about the movement of transcription factors inside the nucleus (Iida et al., 2022; Mueller et al., 2013; Shen et al., 2017; Wang et al., 2021) and how torsion and pulling/pushing forces influence nucleosomes (Bustamante et al., 2021; Chien and van Noort, 2009; Killian et al., 2018; Neuman and Nagy, 2008). Although the behavior of a single trajectory might be stochastic, the statistical behavior from many trajectories may reveal additional physical properties, such as diffusion and folding-unfolding dynamics.

By directly observing topographic characteristics and dynamics of chromatin using HS-AFM, we were able to assess the motions of individual nucleosomes in real-time (Figure 1). This emerging single-molecule technique is powerful, and shares similarities with live cell imaging (Ashwin et al., 2019; Specht et al., 2017), magnetic tweezers, and optical tweezers (Bustamante et al., 2021; Chien and van Noort, 2009; Killian et al., 2018; Neuman and Nagy, 2008). Whereas, live cell imaging and single-molecule force spectroscopy methods rely on fluorophore-tags and tethering, HS-AFM can be done on both unmodified and modified protein all while requiring minimal sample preparation (Ando, 2018).

Figure 1 with 2 supplements see all

Download asset Open asset

Recently, we showed by HS-AFM that, at a qualitative level, H3 nucleosomes are mobile and make intermittent contact with other H3 nucleosomes (Melters and Dalal, 2021). Here, we set out to quantify the dynamics of individual nucleosomes in the context of chromatin. This means that we observed and quantified global nucleosome movement on mica surface, which can be one-dimenstional motion of nucleosome sliding along DNA events and two-dimensional whole chromatin fiber movements (Figure 1). Nucleosomes that move away from the mica surface into the buffer solution could not be tracked. We predict that these global nucleosome motions reflect the complex dynamics nucleosomes display in the nucleus (Ide et al., 2022). To assay CENP-A chromatin, we reconstituted CENP-A nucleosomes on a~3.5 kbp plasmid containing four copies of α-satellite repeats or a~8.5 kbp plasmid containing three copies of the PCAT2 lncRNA gene. After verifying successful in vitro chromatin reconstitution (Figure 1—figure supplement 1, Figure 1—figure supplement 1—source data 1 and 2), we imaged CENP-A chromatin using the Cypher VRS system (see Materials and methods for details). Videos were obtained at one second per frame. When the AFM tip scans the mica surface, it first goes from the top to the bottom, followed by from the bottom to the top. To avoid discontinuous scanning of the same regions of the mica surface, we limited our analysis to every other frame, resulting in an effective scanning rate of two seconds per frame. Next, the videos were converted to TIFF sequences. Using MATLAB’s single particle tracking MatlabTrack package, we obtained a total of 6052 individual nucleosome trajectories from 13 different samples (see Materials and methods for details). To obtain significant mean square displacement (MSD) values, individual trajectories needed to be of sufficient duration. Therefore, we selected individual trajectories that were at least 20 s, with a maximum of 120 s (10–60 points). In addition, we wanted to make sure that we were tracking the same nucleosomes, so we estimated the maximum single step size at twice the nucleosome width or 24 nm. Furthermore, we were also careful about potential nucleosomes sticking to the mica surface, which led us to reject trajectories with an average R-step size (the displacement between two successive images) smaller than 1 nm and a maximum R range smaller than 8 nm. Next, we corrected each video for drift (Figure 1—figure supplement 2). Finally, we analyzed 3835 trajectories to obtain the step angle, step size, mean square displacement (MSD), and diffusion constant of individual nucleosomes (Figure 1).

HS-AFM imaging is free of tip-induced artifacts

AFM is a topographical imaging technique that creates a sub-nanometer scale topological map of biological samples. A common concern is that the AFM tip may alter the sample during scanning, as the AFM tip moves in a zig-zag manner across the sample, potentially moving biological material in its path. To determine whether there is indeed a tip effect, we performed several controls. We reasoned that if the AFM tip altered samples during scanning, it would create a distinctive signature in single particle trajectories. We imaged CENP-A chromatin under multiple conditions (Figure 2—videos 1–6). First, we assessed the angle between successive steps of the nucleosomes (Figure 2A). If there was tip-induced drift, there would be a bias in the distribution of angles. We did not observe a bias in the angle distributions (Figure 2B; Figure 2—figure supplement 1A, Figure 2—source data 1). Second, we looked at the diffusion constants over the x-axis alone or the y-axis alone (Figure 2C) in the absence or presence of 1-(3-aminopropyl) silane (APS). APS functionalizes the mica surface with positively charged amino groups, binding nucleic acid molecules under physiological conditions, allowing for the ability to image in air, in fluid, as well as perform force spectroscopy (Lyubchenko et al., 2014; McAllister et al., 2005; Melters et al., 2019; Melters and Dalal, 2021; Rakshit et al., 2020; Shlyakhtenko et al., 2003). We predicted that adding an excess of APS (333 nM APS is 2 x APS) would lower the diffusion constant compared to not adding APS (no APS). Indeed, we measured a lower diffusion constant for 2 x APS than no APS (Figure 2D, Figure 2—source data 1 and 2). If there were a tip effect, we reasoned there would be a difference in the diffusion constants between the x-axis and y-axis, which are parallel and perpendicular to the direction of tip scanning, respectively. We did not observe a bias in the diffusion constants between the two axes (Figure 2D; Figure 2—figure supplement 1B, Figure 2—source data 1 and 2). Third, to test for the potential impact of prolonged imaging on manipulation of trajectories, we assessed the step size distribution over time. We did not observe a significant effect of the video length on the step size (Figure 2—figure supplement 2, Figure 2—source data 1 and 2), indicating that extended imaging did not alter the trajectory dynamics. We are therefore confident that HS-AFM does not introduce a tip effect on chromatin and that drift is adequately corrected.

Figure 2 with 8 supplements see all

Download asset Open asset

Figure 2—source data 1

Trajectory statistics for all tracked nucleosomes.

X and Y coordinates, distance between two steps, accumulated distance per trajectory, and angle between two steps are reported.

https://cdn.elifesciences.org/articles/86709/elife-86709-fig2-data1-v1.zip

Download elife-86709-fig2-data1-v1.zip

Figure 2—source data 2

Basic statistics for all tracked nucleosomes.

This includes diffusion constant (nm²·s^–1), MSD slope (nm²), average MSD slope (nm²), average step size (nm), maximum R-step (nm), and R-step range (nm).

https://cdn.elifesciences.org/articles/86709/elife-86709-fig2-data2-v1.zip

Download elife-86709-fig2-data2-v1.zip

Salt and APS concentrations impact chromatin dynamics in an anticipated manner

Next, we set out to determine how different buffer conditions impact CENP-A chromatin dynamics. Different salt concentrations are known to impact chromatin compaction and dynamics (Allahverdi et al., 2015; Brasch et al., 1971; Yager et al., 1989; Yager and van Holde, 1984). At lower salt concentrations (below 50 mM NaCl), nucleosomes are stabilized, whereas at higher salt concentrations (above 100 mM NaCl) nucleosomes become unstable. Here, we tested the effect of low salt concentrations (5 mM NaCl) versus high salt concentration (150 mM NaCl) on nucleosome dynamics by HS-AFM. For each condition we tracked 124 and 161 nucleosome trajectories, respectively (Table 1, Figure 3—videos 1 and 2). As expected, the MSD curve for high salt had a larger slope than for low salt (Figure 3A, Figure 3—figure supplement 1A, Figure 3—source data 1 and 2). This is reflected in the diffusion constant, which was 1.2±0.2 nm²·s^–1 for low salt and 4.1±0.3 nm²·s^–1 for high salt (Figure 3B, Figure 3—figure supplement 1B, Figure 3—source data 1 and 2).

Figure 3 with 17 supplements see all

Download asset Open asset

Figure 3—source data 1

Trajectory statistics for all tracked nucleosomes.

X and Y coordinates, distance between two steps, accumulated distance per trajectory, and angle between two steps are reported.

https://cdn.elifesciences.org/articles/86709/elife-86709-fig3-data1-v1.zip

Download elife-86709-fig3-data1-v1.zip

Figure 3—source data 2

Basic statistics for all tracked nucleosomes.

This includes diffusion constant (nm²·s^–1), MSD slope (nm²), average MSD slope (nm²), average step size (nm), maximum R-step (nm), and R-step range (nm).

https://cdn.elifesciences.org/articles/86709/elife-86709-fig3-data2-v1.zip

Download elife-86709-fig3-data2-v1.zip

Table 1

Sample	n	Number of steps	Average Diffusion constant (nm2/s)	Average step size (nm)	Maximum R-step (nm)	R-step range (nm2)
CENP-A nucleosomes	498	13,989	2.3±0.2	4.2±0.1	11.2±0.2	23.2±0.6
+1 x CENP-CCD	368	7790	2.1±0.1	3.1±0.2	12.6±0.4	23.7±0.7
+2 x CENP-CCD	310	9063	0.78±0.06	2.8±0.1	8.0±0.2	14.3±0.4
+4 x CENP-CCD	166	7034	0.61±0.05	2.3±0.2	8.6±0.4	15.4±0.7
CENP-A controls
Low salt	124	3783	1.2±0.2	2.8±0.1	9.6±0.4	18.3±0.9
High salt	161	4887	4.1±0.3	5.5±0.2	14.6±0.4	40±2
No APS	244	5186	7.5±0.5	6.0±2.0	15.9±0.4	43±2
2 x APS	120	2520	2.5±0.3	3.8±0.2	11.4±0.5	21±1
Tween-20	587	16,126	5.9±0.2	6.0±0.1	14.7±0.2	43±1
PCAT2 DNA	710	15,497	5.7±0.4	5.8±0.2	16.0±0.2	33.7±0.6
H3 controls
H3 nucleosomes	66	1109	2.5±0.3	4.2±0.2	9.9±0.5	19±1
+H1.5	391	8492	3.2±0.3	4.7±0.1	12.3±0.3	26.9±0.8
H3 mononucleosome	90	1344	9.3±0.9	7.9±0.3	17.4±0.5	47±2

To better understand the step dynamics, we first assessed the average single-frame step size and found that the average step size of CENP-A nucleosomes for high salt is double the length of that of low salt (5.5±0.2 nm vs 2.8±0.1 nm, respectively, Table 1, Figure 3—figure supplement 1C, Figure 3—source data 2). Furthermore, we calculated the R-step. The R-step is the single-frame displacement in the plane of the trajectory and is defined as the square root of the sum of the squares of the displacement in the x and y directions. The maximum R-step was only slightly different between the low and high salt conditions (9.6±0.4 nm vs 14.5±0.4 nm, respectively, Table 1, Figure 3—figure supplement 1D, Figure 3—source data 2). When we looked at the total range of a trajectory by calculating the R-step range, we observed a much larger variance in the distribution for the high salt compared to the low salt (40±2 nm² vs 18.3±0.9 nm², respectively, Table 1, Figure 2—figure supplement 1E, Figure 3—source data 2). These results are in line with how salt concentration is known to impact chromatin dynamics (Allahverdi et al., 2015; Brasch et al., 1971; Yager et al., 1989; Yager and van Holde, 1984).

Next, we plotted the distributions of single step displacements in the x-axis and y-axis and fitted them to a single Gaussian distribution, which is expected for a diffusive process. Although the x- and y- single-step distributions were poorly fit by single Gaussian distributions (D₀), they were well-fit by a sum of two Gaussian distributions (D₁ and D₂) (Figure 3D, Figure 3—figure supplements 2 and 3, Figure 3—source data 1 and 2). The relative fraction of the two Gaussian distributions differed between low and high salt conditions (Figure 3E, Figure 3—figure supplements 2 and 3, Figure 3—source data 1 and 2). These data indicate that there are two distinct populations of nucleosomes in both low- and high-salt conditions. The D₁ Gaussian population represents slower diffusing nucleosomes, potentially reflecting transient pausing, whereas the D₂ Gaussian population represents faster diffusing nucleosomes. As AFM can only detect samples that are on the mica surface, one concern might be that nucleosomes stick to the surface. To test for this possibility, we first manually verified whether individual nucleosomes could switch from what appears to be the smaller D₁ diffusion constant to the larger D₂ diffusion constant or vice versa. Indeed, we found a myriad of such examples across different conditions (Figure 3—figure supplement 4A). This was further reflected by the broad range of individual step sizes of each particle trajectory (Figure 3—figure supplement 4B, Figure 3—source data 1). Second, we tested whether the slower diffusing nucleosomes correspond to ‘sticking’ nucleosomes. To do this, we analyzed the rejected ‘stuck’ nucleosome trajectories with an average R-step of less than 1 nm and an R-step range of less than 8 nm. Next, we compared the effective diffusion constant of these ‘stuck’ nucleosomes for each video with the smaller diffusion constant obtained from the fit of a sum of two Gaussians. With the exception of the 2 x APS and low-salt conditions, we found that the effective diffusion constant of the ‘stuck’ nucleosome trajectories was significantly smaller than that of the smaller diffusion constant from the fit of a sum of two Gaussians (Figure 3—figure supplement 5, Figure 3—figure supplement 5—source data 1). In other words, these data would exclude the possibility of nucleosomes being ‘stuck’ to the mica surface.

Furthermore, the average step distribution (Figure 3—figure supplement 1C), maximum R-step (Figure 3—figure supplement 1D) and R-step range (Figure 3—figure supplement 1E) display a continuum of data points, instead of a bimodal distribution. Altogether, our data suggests that individual nucleosomes may have the capacity to move back and forth between the D₁ and the D₂ Gaussian distributions, indicating the possibility of switching between two diffusive modes.

Next, we expanded our analyses of the HS-AFM videos of the no APS and 2 x APS conditions (Figure 3—videos 3 and 4) to obtain the MSDs, and Gaussian fitting of the single step distributions. As APS functionalization positively charges the mica surface on which chromatin is deposited, we predicted that CENP-A nucleosomes trajectories would display faster dynamics in the no APS condition vs 2 x APS condition. Indeed, the slope of the MSD curve of in the absence of APS was larger compared to 2 x APS (Figure 3A, Figure 3—figure supplement 1A). The average diffusion constant was also higher in the absence of APS (7.5±0.5 nm²·s^–1) than 2 x APS (2.5±0.3 nm²·s^–1, Figure 3B, Table 1, Figure 3—figure supplement 1B, Figure 3—source data 2). A similar pattern was observed for the average step size (6.0±2.0 nm vs 3.8±0.2 nm, respectively), maximum R-step (15.9±0.4 nm vs 11.4±0.5 nm, respectively), and R-step variance (43±2 nm² vs 21±1 nm², respectively, Table 1, Figure 3—figure supplement 1C–E, Figure 3—source data 2), with higher values for no APS than 2 x APS. The single-step displacement distributions were well-fit by a sum of two Gaussians (Figure 3—figure supplements 2 and 3).

As an additional control, we used a very low concentration of Tween-20 (0.01%), a polysorbate surfactant that both stabilizes proteins and reduces non-specific hydrophobic interactions with the surface. We were interested to learn whether CENP-A chromatin in the presence of Tween-20 would display either more restricted nucleosomes mobility due to protein stabilization, or less restricted nucleosome mobility due to reduced non-specific interactions. HS-AFM videos of CENP-A chromatin in physiological buffer (0.5 x PBS, 2 mM MgCl₂) with 0.01% Tween-20 displayed the most amount of drift (Figure 3—video 5). After drift correction and filtering, we analyzed 587 trajectories to obtain single step distributions, MSD curves, and diffusion constants. We found that CENP-A chromatin in the presence of Tween-20 behaved more like no APS and high salt conditions with a steep MSD curve, a very broad distribution of average step sizes, and a large R-step range distribution (Figure 3, Figure 3—figure supplements 1–3, Figure 3—source data 1 and 2). We interpret this to mean that Tween-20 in the context of imaging CENP-A chromatin by HS-AFM primarily reduces non-specific hydrophobic interactions resulting in less restricted nucleosome mobility.

Furthermore, we wondered if our 3.5 kbp plasmid, which contains four copies of human centromere-derived α-satellite DNA, might induce nucleosome phasing or positioning (Luger et al., 1997; Stormberg and Lyubchenko, 2022). Therefore, we in vitro reconstituted CENP-A nucleosomes on an 8.5 kbp plasmid containing the lncRNA PCAT2 gene. In human cancer cell lines, ectopic CENP-A can be found at the PCAT2 locus, whereas, PCAT2 DNA is not known to position nucleosomes, in contrast to α-satellite DNA or the Widom 601-sequence (Lowary and Widom, 1998; Luger et al., 1997; Stormberg and Lyubchenko, 2022; Thåström et al., 1999). We observed an MSD curve similar to the high salt conditions (Figure 3A, Figure 3—video 6, Figure 3—source data 1 and 2), with an average diffusion constant of 5.7±0.4 nm²·s^–1 (Figure 3B, Table 1, Figure 3—source data 1 and 2). A similar pattern was observed for the average step size (5.8±0.2 nm), maximum R-step (16.0±0.2 nm), and R-step variance (33.7±0.6 nm², Table 1, Figure 3—figure supplement 1C-E, Figure 3—source data 2). The single step displacement distributions were well-fit by a sum of two Gaussians (Figure 3—figure supplement 2, Figure 3—source data 1 and 2). Overall, it appears that CENP-A nucleosomes reconstituted on the 8.5 kbp plasmid without known positioning sequences behaves similar to CENP-A nucleosomes reconstituted on 3.5 kbp plasmid with known positioning sequences, thereby suggesting that nucleosome mobility measured in these experiments may be independent of DNA sequence specificity.

When we analyzed previously published HS-AFM videos of H3 chromatin with or without linker histone H1.5 (Melters and Dalal, 2021) as well as H3 mononucleosomes, we observed a bias in both the angle between successive steps and Gaussian fitting of single step displacements (Figure 3—figure supplements 6 and 7, Figure 3—videos 7–9, Figure 3—source data 1 and 2). These data provide evidence that bias in HS-AFM trajectories is a possibility and that it can be detected. Furthermore, there was no difference in fitting either a single or double Gaussian distributions for H3 mononucleosomes (Figure 3—figure supplement 7F). Mononucleosomes are not associated with other nucleosomes and a priori mononucleosomes cannot display whole chromatin fiber motions, allowing mononucleosomes to move freely independent of the DNA strand. We observed that the diffusion constant of mononucleosomes is about threefold larger than that of chromatin arrays (Table 1) and 10- to 185-fold larger than the D₁ diffusion constants observed under various conditions (Figure 3—figure supplements 2 and 7). In addition, the R-step range of mononucleosomes is roughly twofold larger than chromatin arrays (Table 1). Therefore, the latter observations imply that the unconstrained motion of mononucleosomes results in a single Gaussian distribution of step displacements.

Altogether, when we analyzed HS-AFM videos, CENP-A chromatin responded to varying salt and APS concentrations in agreement with previous reports (Allahverdi et al., 2015; Brasch et al., 1971; Lyubchenko et al., 2014; Shlyakhtenko et al., 2003; Yager et al., 1989; Yager and van Holde, 1984). Low salt and 2 x APS concentrations reduced CENP-A nucleosome mobility, whereas high salt and no APS concentrations increased CENP-A nucleosome mobility (Figure 3—figure supplement 8). For the remainder of the HS-AFM experiments, we used near physiological relevant salt concentrations (67.5 mM NaCl, 2 mM MgCl₂) and standardized APS concentrations (167 nM [Lyubchenko et al., 2014]).

CENP-C^CD represses CENP-A nucleosome mobility in vitro

Previously, we showed that a central domain fragment of CENP-C (Figure 4A) rigidified CENP-A nucleosomes and induced CENP-A nucleosome clustering (Melters et al., 2019). Based on these observations, we hypothesized that CENP-C^CD would reduce CENP-A nucleosome mobility. To test this hypothesis, we imaged CENP-A chromatin by HS-AFM under near physiological conditions. Subsequently, nucleosome tracks were extracted in either the absence (Figure 4B, Figure 4—figure supplements 1–3, Figure 4—video 1, Figure 4—source data 1 and 2) or presence of 1, 2, or 4 CENP-C^CD molecules (1 x, 2 x, or 4 x, respectively) per CENP-A nucleosome (Figure 4C, Figure 4—figure supplements 1–3, Figure 4—videos 2–4, Figure 4—source data 1 and 2). From at least 3 experiments per sample, we obtained 498, 368, 310, and 166 trajectories, respectively (Table 1). First, we verified that there were no motion artifacts associated with the tip scanning (Figure 4—figure supplement 1). Next, we calculated the MSD of CENP-A nucleosomes alone or in the presence of 1 x, 2 x, or 4 x CENP-C^CD and found that individual MSD curves of CENP-A nucleosomes were broadly distributed (Figure 4D, Figure 4—figure supplement 2A, B), with an average MSD curve that reached a plateau after ~25 s (Figure 4—figure supplement 2A), implying confined motion (Kapanidis et al., 2018; Zhong and Wang, 2020).

Figure 4 with 7 supplements see all

Download asset Open asset

Figure 4—source data 1

Trajectory statistics for all tracked nucleosomes.

X and Y coordinates, distance between two steps, accumulated distance per trajectory, and angle between two steps are reported.

https://cdn.elifesciences.org/articles/86709/elife-86709-fig4-data1-v1.zip

Download elife-86709-fig4-data1-v1.zip

Figure 4—source data 2

Basic statistics for all tracked nucleosomes.

This includes diffusion constant (nm²·s^–1), MSD slope (nm²), average MSD slope (nm²), average step size (nm), maximum R-step (nm), and R-step range (nm).

https://cdn.elifesciences.org/articles/86709/elife-86709-fig4-data2-v1.zip

Download elife-86709-fig4-data2-v1.zip

When CENP-C^CD was added at either 2 x or 4 x molar excess to CENP-A nucleosomes, CENP-A nucleosome mobility was strongly restricted (Figure 4D, Figure 4—figure supplement 2A, Figure 4—videos 2–4, Figure 4—source data 1 and 2). Indeed, the MSD curve was much lower for 2 x and 4 x CENP-C^CD compared to CENP-A alone, and the 2 x and 4 x CENP-C^CD MSD curve maintained a shallow slope (Figure 4D, Figure 4—figure supplement 2A). In contrast, the 1 x CENP-C^CD MSD curve was similar to CENP-A alone (Figure 4D, Figure 4—figure supplement 2B, Figure 4—source data 1 and 2). The diffusion constant of CENP-A alone was 2.3±0.2 nm²·s^–1, whereas the addition of 2 x and 4 x CENP-C^CD reduced the diffusion constant 2.9-fold to 0.78±0.06 nm²·s^–1 and 3.8-fold to 0.61±0.05 nm²·s^–1, respectively (Table 1, Figure 4E, Figure 4—figure supplement 2B, Figure 4—source data 1 and 2). This difference is reflected in the smaller single frame step size when CENP-C^CD is added to CENP-A chromatin compared to CENP-A chromatin alone (Figure 4—figure supplement 2C, Figure 4—source data 2). The maximum R-step and R-step range were also larger for CENP-A nucleosomes without CENP-C^CD (Figure 4—figure supplement 2D, E, Figure 4—source data 2). Next, we fitted the single step displacement distributions with Gaussian distributions and found that a sum of two Gaussian distributions provided better fits (Figure 4—figure supplement 3, Figure 4—source data 1 and 2). Overall, by HS-AFM we observed that CENP-C^CD restricts CENP-A nucleosome mobility in a switch-like manner. We hypothesized that this switch, as we recently observed (Melters et al., 2019), could be the mechanism by which overexpression of CENP-C in living cells results in CENP-A chromatin clustering.

Excess CENP-C suppresses centromeric RNAP2 levels and centromeric transcription in vivo

Next, we asked what the functional consequences are of CENP-C on CENP-A chromatin, beyond the formation of kinetochores (Cheeseman et al., 2006; Walstein et al., 2021; Mendiburo et al., 2011; Régnier et al., 2005). Previously, we showed that overexpressing CENP-C in HeLa cells resulted in increased clustering of centromeric chromatin and loss of centromeric RNAP2 (Melters et al., 2019). These results, combined with our ‘decrease of motion’ observations above, suggest that centromeric non-coding α-satellite transcription might be impaired in the background of CENP-C overexpression. To examine this facet of CENP-C:CENP-A homeostasis, we overexpressed CENP-C in HeLa cells to assess if centromeric transcription is altered.

First, we measured the effects of CENP-C overexpression on the level of RNAP2 on CENP-A chromatin. We overexpressed CENP-C 2.8-fold (Figure 5—figure supplement 1; Figure 5—figure supplement 1—source data 1 and 2) in HeLa cells for 72 hr and subsequently purified CENP-A chromatin associated with CENP-C by CENP-C native ChIP (nChIP), and the unbound CENP-A chromatin was pulled-down by sequential ACA nChIP. We found that RNAP2 levels were reduced upon CENP-C overexpression (Figure 5A, Figure 5—figure supplement 1, Figure 5—figure supplement 1—source data 1 and 2). In addition, we observed that, upon CENP-C overexpression, total CENP-A levels were also reduced (Figure 5B, Figure 5—figure supplement 1, Figure 5—figure supplement 1—source data 1 and 2). Previously (Melters et al., 2019), we showed that the addition of CENP-C^CD or overexpression of CENP-C results in compaction of CENP-A chromatin. We therefore wondered if CENP-C overexpression impacted centromeric transcription. By quantitative PCR, we observed a ~60% reduction in α-satellite transcripts in cells overexpressing CENP-C (Figure 5C, Figure 5—source data 1).

Figure 5 with 1 supplement see all

Download asset Open asset

Figure 5—source data 1

Quantification of RT-PCR of α-satellite transcripts for Figure 5C.

https://cdn.elifesciences.org/articles/86709/elife-86709-fig5-data1-v1.zip

Download elife-86709-fig5-data1-v1.zip

These results indicate that CENP-A levels are reduced and that centromeric transcription is indeed impaired upon CENP-C overexpression. Previous reports showed that new CENP-A loading is transcriptionally regulated (Jansen et al., 2007; Quénet and Dalal, 2014). Therefore, we hypothesized that CENP-C overexpression leads to defective de novo CENP-A loading.

CENP-C overexpression limits de novo CENP-A loading

To test this hypothesis, we turned to the well-established SNAP-tagged CENP-A system combined with quench pulse-chase immunofluorescence (Bodor et al., 2012). Using this system in cells synchronized to mid-G1, one can distinguish between older CENP-A (TMR-block) and newly incorporated CENP-A (TMR-Star; Figure 6A and B). Strikingly, in an CENP-C overexpression background, we observed a 1.3-fold reduction of de novo incorporation of CENP-A (Figure 6C and D, Figure 6—figure supplement 1—source data 1).

Figure 6 with 1 supplement see all

Download asset Open asset

Thus, the functional consequences of CENP-C overexpression are suppression of α-satellite transcription (Figure 5C) and subsequent impairment of new CENP-A loading (Figure 6C and D).

Here, we demonstrate that HS-AFM is a reliable technique for studying single nucleosome dynamics by directly visualizing their motion in real time. Importantly, we show that the AFM tip scanning motion does not generate observable artifacts in nucleosome dynamics (Figure 2, Figure 2—figure supplements 1 and 2). Various critical scanning conditions were tested to assess their impact on CENP-A chromatin dynamics. Salt is a well-known factor that can either stabilize or destabilize chromatin (Allahverdi et al., 2015; Brasch et al., 1971; Lyubchenko et al., 2014; Shlyakhtenko et al., 2003; Yager et al., 1989; Yager and van Holde, 1984). Case in point, salt dialysis is a common method for in vitro nucleosome reconstitution (Cruz-Becerra and Kadonaga, 2021; Peterson, 2008; Walkiewicz et al., 2014). Indeed, low salt restricted CENP-A nucleosome motion, whereas high salt did the opposite (Figure 3, Figure 3—figure supplements 1–3). In AFM studies, APS is used to functionalize the mica surface with positive charges facilitating the interaction of DNA or chromatin with the mica surface. Not functionalizing the mica surface resulted in more mobile CENP-A nucleosomes, whereas 2 x APS functionalized mica resulted in increased levels of immobile CENP-A nucleosomes (Figure 3, Figure 3—figure supplements 1–3). Furthermore, we used a plasmid containing a non-nucleosome positioning sequence PCAT2 (Figure 3, Figure 3—figure supplements 1–3). Interestingly, the diffusion constant for CENP-A nucleosomes reconstituted on PCAT2 containing plasmid was higher than that of CENP-A nucleosomes reconstituted on α-satellite containing plasmid imaged under the same conditions (Table 1).There are various parameters that could account for this difference in diffusion constant. Only 20% of the α-satellite containing plasmid is comprised of nucleosome positioning sequences. Although it cannot be excluded, it is unlikely that the nucleosome positioning sequence reduces the global diffusion constants. Alternatively, the two plasmids differed substantially in length (8.5 kbp PCAT2 plasmid versus 3.5 kbp α-satellite plasmid). This means that PCAT2 plasmid possibly contained a much larger number of nucleosomes, potentially impacting local nucleosome density and thus chromatin compaction. The potential role of nucleosome crowding on nucleosome mobility should be investigated. In addition, H3 mononucleosomes were the only nucleosomes for which the single-frame step size distribution was well-fit by a single Gaussian (Figure 3—figure supplement 7F), this is in stark contrast with CENP-A chromatin, for which the step size distribution is well-fit by the sum of two Gaussians (Figure 3—figure supplement 2, Figure 4—figure supplement 2). This implies that nucleosome arrays might drive the two mobility states we observed. Looking more closely at individual tracks, we noticed that nucleosomes can switch between the D₁ and D₂ state (Figure 3—figure supplement 3). The slower D₁ diffusion constant did not appear to simply reflect nucleosomes being stuck to the mica surface (Figure 3—figure supplement 5). It will be interesting to learn what precisely causes the two Gaussian distributions, including whether oncohistones (Nacev et al., 2019), PTMs, or nucleosome binding factors (Zhou et al., 2019) can alter the relative probability of the two Gaussian distributions. Altogether, these data provide evidence that CENP-A nucleosomes respond to various control conditions in a predictable manner, but also that their dynamics are complex and include at least two mobility states.

CENP-C modulates both CENP-A nucleosome accessibility (Ali-Ahmad et al., 2019; Ariyoshi et al., 2021; Falk et al., 2016; Falk et al., 2015; Guo et al., 2017) and its elasticity (Melters et al., 2019). Here, we show that CENP-C^CD regulates CENP-A nucleosomes mobility in a switch-like manner (Figure 4, Figure 4—figure supplements 1–3). This might imply that CENP-C compacts CENP-A chromatin once a critical mass is reached. The data presented here correlates with CENP-C^CD rigidification of CENP-A nucleosomes (Melters et al., 2019). Additionally, we show that in vivo, CENP-C overexpression results in reduced levels of centromeric RNAP2 (Melters et al., 2019), impaired centromeric transcription (Figure 5C), and subsequent decreased loading of new CENP-A at the centromere (Figure 6). These findings combined provide evidence for a speculative link between the physical properties of nucleosomes, their mobility along DNA, and how these material properties might regulate chromatin accessibility and transcriptional potential.

In the nucleus, nucleosomes move in different dimensions, either in a single dimension along the DNA strand, or three dimensions where the DNA strand moves and the nucleosomes follow as passengers (Babokhov et al., 2020; Ide et al., 2022; Melters and Dalal, 2021). In addition, various events, ranging from transcription to DNA repair and replication, involve chromatin remodeling (Nodelman and Bowman, 2021; Zaret, 2020). Several in vivo studies have utilized tagged H2B combined with high-resolution live cell imaging to probe nucleosome dynamics. Nucleosomes within heterochromatin were less dynamic compared to euchromatin regions (Ashwin et al., 2019; Maeshima et al., 2023; Nozaki et al., 2017; Ricci et al., 2015), transcriptional inhibition diminished constraints of local chromatin movements (Nagashima et al., 2019), and local levels of H1 correlated with reduced nucleosome dynamics (Gómez-García et al., 2021). Indeed, chromatin-associated H1-eGFP mobility was two orders of magnitude smaller than free H1-eGFP (Bernas et al., 2014; Wachsmuth et al., 2016). ATP-driven effects also impact DNA motion (Bhattacharya et al., 2006; Levi et al., 2005). Tagging DNA is an effective method to test how mobile chromosome loci are in vivo. Using fluorescence recovery spectroscopy, GFP-tagged loci in CHO cells showed a slow and fast diffusion coefficient of 0.24·10^–3 and 3.13·10^–3 µm²·s^–1 (Levi et al., 2005) and TetO tagged-DNA D_HJ_H loci in B cells had a diffusion coefficient of ~2.0·10³ µm²·s^0.5 (Lucas et al., 2014). The change in diffusion coefficient of V_HD_H loci is interpreted to results in a fourfold increase in interaction frequency (Lucas et al., 2014). In these cases, specific loci were tagged and tracked, but the behavior of individual nucleosomes within these domains remain unaccounted for. Therefore, in parallel, studies using optical tweezers probed the one dimensional diffusive behavior of nucleosomes (Chen et al., 2019; Rudnizky et al., 2019). A recent study using optical tweezers showed that the diffusion constant of H3 mononucleosomes sliding along a DNA strand is ~1.3 bp²·s^–1 (~0.15 nm²·s^–1; Rudnizky et al., 2019). This value is roughly similar to the D₁ diffusion constants we observed (Figure 3—figure supplements 2 and 7, Figure 4—figure supplements 1 and 2), implying that the D₁ diffusion constant might reflect sliding nucleosomes. Binding of transcription factors to nucleosomal DNA biases nucleosome motion, potentially through manipulation of the folding and unfolding of nucleosomal DNA around the nucleosome core particle (Donovan et al., 2023; Rudnizky et al., 2019). Here, we show that CENP-A nucleosomes have an average diffusion constant of 2.3±0.2 nm²·s^–1 and their single-step distribution was well-fit by a sum of two Gaussian distributions (Figure 4, Figure 4—figure supplement 2). When CENP-C^CD was added at a two- or fourfold molar ratio to CENP-A nucleosomes, we observed a diffusion constant that is about three times lower than CENP-A nucleosomes alone (0.78±0.06 nm²·s^–1 and 0.61±0.05 nm²·s^–1, respectively). This difference in diffusion constants for nucleosomes between the Rudnizky study (~0.15 nm²·s^–1, [Rudnizky et al., 2019]) and our results (0.61–2.3 nm²·s^–1) could be due to technical differences of their experimental set-up compared to our experimental approach (optical tweezers vs HS-AFM). We used nucleosome arrays which permit both one-dimensional (nucleosome sliding) and two-dimensional motions (whole chromatin fiber movement) of nucleosomes compared to mononucleosomes that were fixed to polystyrene beads, which only permit one-dimensional motion (Rudnizky et al., 2019). In contrast, single molecule tracking in cultured cells captures nucleosome dynamics within the nucleus (Iida et al., 2022; Kimura and Cook, 2001; Morisaki et al., 2014; Wagh et al., 2023), but requires fluorophore-tags. Fluorophore-tags are photosensitive (Jradi and Lavis, 2019) and have the potential of altering the function of the protein it is bound to Maheshwari et al., 2015; Ravi et al., 2010. Calculated diffusion constants for H2B-eGFP range from 0.0019 µm²·s^–1 to 7.3 µm²·s^–1 (Bhattacharya et al., 2006; Mazza et al., 2012) and 0.03 µm²·s^–1 to 1.39 µm²·s^–1 for HALO-H2B (Lovely et al., 2020; Ranjan et al., 2020). These in vivo H2B diffusion constants are several orders of magnitudes larger than we observed (0.61–2.3 nm²·s^–1). This could be due to the many activities within the nucleus of a living cell compared to the steady-state nature of an in vitro experiment. As such, the potential of single molecule analysis by HS-AFM to contribute to elegant studies in the field is exciting, as it can link single-molecule force spectroscopy analysis to in vivo single molecule tracking experiments.

Looking at another histone variant nucleosome, the diffusion constant of H2A.Z nucleosomes is larger than H3 nucleosomes (Rudnizky et al., 2019), which correlates with the transcriptional buffering function of H2A.Z (Chen et al., 2019; Giaimo et al., 2019). A logical prediction from our results would be that transcription through CENP-A chromatin would be more efficient compared to H3 chromatin. Indeed, a recent single-molecule study showed that CENP-A creates an open chromatin structure (Nagpal and Fierz, 2022). We found that proteins that exert their activity by binding to the outer surface of nucleosomes, rather than changing the internal constituents of nucleosomes, have the opposite effect on transcription. CENP-C^CD rigidifies CENP-A nucleosomes (Melters et al., 2019), and CENP-C^CD-CENP-A nucleosomes have a significantly reduced diffusion constant compared to CENP-A nucleosomes alone (Table 1). In vivo, overexpression of CENP-C resulted in decreased centromeric transcription (Figure 6). Thus, this study significantly extends the existing paradigm that suggests nucleosome dynamics are highly tunable, with a surprising twist – tuners can dampen or exaggerate nucleosome motion, which correlates with higher order chromatin folding and accessibility.

Taking these and prior findings in context, our data suggest a model where chromatin effector partners modify the material properties of histone variant nucleosomes at the local level, supporting the formation of functional nucleosome clutches (Portillo-Ledesma et al., 2021; Ricci et al., 2015; Figure 7). Here, we specifically probed CENP-A chromatin and based on our results we propose the following working model. CENP-A nucleosomes recruit CENP-C to form a kinetochore-promoting CENP-A chromatin clutch (Kale et al., 2023). CENP-C functions as the template for the recruitment of additional kinetochore proteins (Klare et al., 2015; Yatskevich et al., 2023). When CENP-C binds, CENP-A nucleosomes become rigidified (Melters et al., 2019) and restrict CENP-A nucleosomes mobility (Figure 4). This unique clutch facilitates the recruitment of other inner kinetochore components by locally immobilizing CENP-A chromatin (Yatskevich et al., 2023). Yet, to load new CENP-A molecules, transcription must happen (Arunkumar and Melters, 2020; Quénet and Dalal, 2014). The repressive chromatin state that CENP-C induces (Figure 5) contradicts this functional necessity. One speculative manner in which cells can work around the juxtaposition of the dual functions of CENP-A chromatin is by maintaining a pool of free centromeric CENP-A nucleosomes (Figure 7). Indeed, ChIP-seq and FISH data have established that centromeric CENP-C levels are lower compared to CENP-A levels (Henikoff et al., 2015; Kyriacou and Heun, 2018). We propose that unbound elastic and mobile CENP-A chromatin clutches create an intrinsically accessible chromatin state, allowing for the recruitment of transcriptional machinery that maintains centromeric CENP-A levels to facilitate both opposing functions (Figure 7). Succinctly, the epigenetic fate of a locus may be tightly, possibly causally, linked to the mechanical state of the chromatin fiber; concomitantly, the reinstatement of an epigenetic signature by de novo loading of a particular variant, reinforces the mechanical state of the fiber.

Figure 7

Download asset Open asset

In summary, we report the MSDs and diffusion constants for CENP-A nucleosomes under a variety of conditions, and we show that single-step distributions are well-fit by the sum of two Gaussians. This implies that nucleosomes exist in at least two distinct mobility states. CENP-C^CD modulates centromeric chromatin by restricting the motions of CENP-A nucleosomes. As we observed a switch-like behavior of restricting CENP-A nucleosome motions, instead of a linear dose-response, we speculate that CENP-C limits CENP-A mobility once a critical concentration is reached. In vivo, we observed diminished transcriptional competence when CENP-C is overexpressed, resulting in suppression of de novo CENP-A loading. These results strongly imply that there is a mechanistic link between modulating the material properties of nucleosomes and chromatin accessibility. Nucleosome dynamics play an important role in genome compaction and regulating DNA access by DNA binding factors. These dynamics are driven by only a few interactions between the interfaces of DNA and nucleosomes (Fierz and Poirier, 2019; Polach and Widom, 1995; Widom, 1998). An exciting line of investigation is to examine how at the nanoscale level the interaction between CENP-A nucleosomes and CENP-C protein co-evolved to repress CENP-A’s mobile and elastic nature. It will also be crucial to examine how CENP-A:CENP-C homeostasis, along with those of other key inner kinetochore proteins such as CENP-N, T, S, X, and W regulate centromeric transcription and thereby de novo assembly of CENP-A to maintain the epigenetic identity of centromeres in other species. At a more global scale, it will be exciting to ask how different H1 variants modulate the chromatin fiber at the local level to promote or limit transcriptional competency, and how H1 variants dictate the mechanical motions of individual nucleosomes within the local 10 nm chromatin fiber.

All data generated and analyzed during this study are included in the manuscript and supporting files; Source data files have been provided for Figures 1-6 and accompanied Figure 1-6 - figure supplements. Entire western blot shown in Figure 1 - figure supplement 1 - source data 1 and Figure 5 - figure supplement 1 - source data 1 are included.

1. 1. Ali-Ahmad A
   2. Bilokapić S
   3. Schäfer IB
   4. Halić M
   5. Sekulić N

   (2019) CENP-C unwraps the human CENP-A nucleosome through the H2A C-terminal tail

   EMBO Reports 20:e48913.

   https://doi.org/10.15252/embr.201948913

   • PubMed
   • Google Scholar
2. 1. Allahverdi A
   2. Chen Q
   3. Korolev N
   4. Nordenskiöld L

   (2015) Chromatin compaction under mixed salt conditions: opposite effects of sodium and potassium ions on nucleosome array folding

   Scientific Reports 5:8512.

   https://doi.org/10.1038/srep08512

   • PubMed
   • Google Scholar
3. 1. Allshire RC
   2. Madhani HD

   (2018) Ten principles of heterochromatin formation and function

   Nature Reviews. Molecular Cell Biology 19:229–244.

   https://doi.org/10.1038/nrm.2017.119

   • PubMed
   • Google Scholar
4. 1. Ando T

   (2018) High-speed atomic force microscopy and its future prospects

   Biophysical Reviews 10:285–292.

   https://doi.org/10.1007/s12551-017-0356-5

   • PubMed
   • Google Scholar
5. 1. Ariyoshi M
   2. Makino F
   3. Watanabe R
   4. Nakagawa R
   5. Kato T
   6. Namba K
   7. Arimura Y
   8. Fujita R
   9. Kurumizaka H
   10. Okumura E-I
   11. Hara M
   12. Fukagawa T

   (2021) Cryo-EM structure of the CENP-A nucleosome in complex with phosphorylated CENP-C

   The EMBO Journal 40:e105671.

   https://doi.org/10.15252/embj.2020105671

   • PubMed
   • Google Scholar
6. 1. Arunkumar G
   2. Melters DP

   (2020) Centromeric transcription: a conserved swiss-army knife

   Genes 11:911.

   https://doi.org/10.3390/genes11080911

   • PubMed
   • Google Scholar
7. 1. Arunkumar G
   2. Baek S
   3. Sturgill D
   4. Bui M
   5. Dalal Y

   (2022) Oncogenic lncRNAs alter epigenetic memory at a fragile chromosomal site in human cancer cells

   Science Advances 8:eabl5621.

   https://doi.org/10.1126/sciadv.abl5621

   • PubMed
   • Google Scholar
8. 1. Ashwin SS
   2. Nozaki T
   3. Maeshima K
   4. Sasai M

   (2019) Organization of fast and slow chromatin revealed by single-nucleosome dynamics

   PNAS 116:19939–19944.

   https://doi.org/10.1073/pnas.1907342116

   • Google Scholar
9. 1. Babokhov M
   2. Hibino K
   3. Itoh Y
   4. Maeshima K

   (2020) Local chromatin motion and transcription

   Journal of Molecular Biology 432:694–700.

   https://doi.org/10.1016/j.jmb.2019.10.018

   • PubMed
   • Google Scholar
10. 1. Bannister AJ
    2. Zegerman P
    3. Partridge JF
    4. Miska EA
    5. Thomas JO
    6. Allshire RC
    7. Kouzarides T

    (2001) Selective recognition of methylated lysine 9 on histone H3 by the HP1 chromo domain

    Nature 410:120–124.

    https://doi.org/10.1038/35065138

    • PubMed
    • Google Scholar
11. 1. Bernas T
    2. Brutkowski W
    3. Zarębski M
    4. Dobrucki J

    (2014) Spatial heterogeneity of dynamics of H1 linker histone

    European Biophysics Journal 43:287–300.

    https://doi.org/10.1007/s00249-014-0962-0

    • PubMed
    • Google Scholar
12. 1. Bhattacharya D
    2. Mazumder A
    3. Miriam SA
    4. Shivashankar GV

    (2006) EGFP-tagged core and linker histones diffuse via distinct mechanisms within living cells

    Biophysical Journal 91:2326–2336.

    https://doi.org/10.1529/biophysj.105.079343

    • PubMed
    • Google Scholar
13. 1. Bodor DL
    2. Rodríguez MG
    3. Moreno N
    4. Jansen LET

    (2012)

    Current Protocols in Cell Biology

    Analysis of protein turnover by quantitative SNAP-based pulse-chase imaging, Current Protocols in Cell Biology, United states, John Wiley and Sons, 10.1002/0471143030.cb0808s55, 23129118.

    • Google Scholar
14. 1. Brasch K
    2. Seligy VL
    3. Setterfield G

    (1971) Effects of low salt concentration on structural organization and template activity of chromatin in chicken erythrocyte nuclei

    Experimental Cell Research 65:61–72.

    https://doi.org/10.1016/s0014-4827(71)80050-2

    • PubMed
    • Google Scholar
15. 1. Bui M
    2. Dimitriadis EK
    3. Hoischen C
    4. An E
    5. Quénet D
    6. Giebe S
    7. Nita-Lazar A
    8. Diekmann S
    9. Dalal Y

    (2012) Cell-cycle-dependent structural transitions in the human CENP-A nucleosome in vivo

    Cell 150:317–326.

    https://doi.org/10.1016/j.cell.2012.05.035

    • PubMed
    • Google Scholar
16. 1. Bui M
    2. Pitman M
    3. Nuccio A
    4. Roque S
    5. Donlin-Asp PG
    6. Nita-Lazar A
    7. Papoian GA
    8. Dalal Y

    (2017) Internal modifications in the CENP-A nucleosome modulate centromeric dynamics

    Epigenetics & Chromatin 10:17.

    https://doi.org/10.1186/s13072-017-0124-6

    • Google Scholar
17. 1. Bury L
    2. Moodie B
    3. Ly J
    4. McKay LS
    5. Miga KH
    6. Cheeseman IM

    (2020) Alpha-satellite RNA transcripts are repressed by centromere-nucleolus associations

    eLife 9:e59770.

    https://doi.org/10.7554/eLife.59770

    • PubMed
    • Google Scholar
18. 1. Buschbeck M
    2. Hake SB

    (2017) Variants of core histones and their roles in cell fate decisions, development and cancer

    Nature Reviews. Molecular Cell Biology 18:299–314.

    https://doi.org/10.1038/nrm.2016.166

    • PubMed
    • Google Scholar
19. 1. Bustamante CJ
    2. Chemla YR
    3. Liu S
    4. Wang MD

    (2021) Optical tweezers in single-molecule biophysics

    Nature Reviews. Methods Primers 1:25.

    https://doi.org/10.1038/s43586-021-00021-6

    • PubMed
    • Google Scholar
20. 1. Cheeseman IM
    2. Chappie JS
    3. Wilson-Kubalek EM
    4. Desai A

    (2006) The conserved KMN network constitutes the core microtubule-binding site of the kinetochore

    Cell 127:983–997.

    https://doi.org/10.1016/j.cell.2006.09.039

    • PubMed
    • Google Scholar
21. 1. Chen Z
    2. Gabizon R
    3. Brown AI
    4. Lee A
    5. Song A
    6. Díaz-Celis C
    7. Kaplan CD
    8. Koslover EF
    9. Yao T
    10. Bustamante C

    (2019) High-resolution and high-accuracy topographic and transcriptional maps of the nucleosome barrier

    eLife 8:e48281.

    https://doi.org/10.7554/eLife.48281

    • PubMed
    • Google Scholar
22. 1. Chien F-T
    2. van Noort J

    (2009) 10 years of tension on chromatin: results from single molecule force spectroscopy

    Current Pharmaceutical Biotechnology 10:474–485.

    https://doi.org/10.2174/138920109788922128

    • PubMed
    • Google Scholar
23. 1. Chung YC
    2. Bisht M
    3. Thuma J
    4. Tu LC

    (2023) Single-chromosome dynamics reveals locus-dependent dynamics and chromosome territory orientation

    Journal of Cell Science 136:jcs260137.

    https://doi.org/10.1242/jcs.260137

    • PubMed
    • Google Scholar
24. 1. Cruz-Becerra G
    2. Kadonaga JT

    (2021) Reconstitution of chromatin by Stepwise salt dialysis

    Bio-Protocol 11:e3977.

    https://doi.org/10.21769/BioProtoc.3977

    • PubMed
    • Google Scholar
25. 1. DeLuca JG
    2. Musacchio A

    (2012) Structural organization of the kinetochore-microtubule interface

    Current Opinion in Cell Biology 24:48–56.

    https://doi.org/10.1016/j.ceb.2011.11.003

    • PubMed
    • Google Scholar
26. 1. Dimitriadis EK
    2. Weber C
    3. Gill RK
    4. Diekmann S
    5. Dalal Y

    (2010) Tetrameric organization of vertebrate centromeric nucleosomes

    PNAS 107:20317–20322.

    https://doi.org/10.1073/pnas.1009563107

    • PubMed
    • Google Scholar
27. 1. Donovan BT
    2. Luo Y
    3. Meng Z
    4. Poirier MG

    (2023) The nucleosome unwrapping free energy landscape defines distinct regions of transcription factor accessibility and kinetics

    Nucleic Acids Research 51:1139–1153.

    https://doi.org/10.1093/nar/gkac1267

    • PubMed
    • Google Scholar
28. 1. Escobar TM
    2. Loyola A
    3. Reinberg D

    (2021) Parental nucleosome segregation and the inheritance of cellular identity

    Nature Reviews. Genetics 22:379–392.

    https://doi.org/10.1038/s41576-020-00312-w

    • PubMed
    • Google Scholar
29. 1. Falk SJ
    2. Guo LY
    3. Sekulic N
    4. Smoak EM
    5. Mani T
    6. Logsdon GA
    7. Gupta K
    8. Jansen LET
    9. Van Duyne GD
    10. Vinogradov SA
    11. Lampson MA
    12. Black BE

    (2015) CENP-C reshapes and stabilizes CENP-A nucleosomes at the centromere

    Science 348:699–703.

    https://doi.org/10.1126/science.1259308

    • PubMed
    • Google Scholar
30. 1. Falk S.J
    2. Lee J
    3. Sekulic N
    4. Sennett MA
    5. Lee TH
    6. Black BE

    (2016) CENP-C directs A structural transition of CENP-A nucleosomes mainly through sliding of DNA gyres

    Nature Structural & Molecular Biology 23:204–208.

    https://doi.org/10.1038/nsmb.3175

    • Google Scholar
31. 1. Fierz B
    2. Poirier MG

    (2019) Biophysics of chromatin dynamics

    Annual Review of Biophysics 48:321–345.

    https://doi.org/10.1146/annurev-biophys-070317-032847

    • PubMed
    • Google Scholar
32. 1. Flamm WG
    2. Walker PMB
    3. McCallum M

    (1969) Some properties of the single strands isolated from the DNA of the nuclear satellite of the mouse (Mus musculus)

    Journal of Molecular Biology 40:423–443.

    https://doi.org/10.1016/0022-2836(69)90163-6

    • PubMed
    • Google Scholar
33. 1. Giaimo BD
    2. Ferrante F
    3. Herchenröther A
    4. Hake SB
    5. Borggrefe T

    (2019) The histone variant H2A.Z in gene regulation

    Epigenetics & Chromatin 12:37.

    https://doi.org/10.1186/s13072-019-0274-9

    • Google Scholar
34. 1. Gómez-García PA
    2. Portillo-Ledesma S
    3. Neguembor MV
    4. Pesaresi M
    5. Oweis W
    6. Rohrlich T
    7. Wieser S
    8. Meshorer E
    9. Schlick T
    10. Cosma MP
    11. Lakadamyali M

    (2021) Mesoscale modeling and single-nucleosome tracking reveal remodeling of clutch folding and dynamics in stem cell differentiation

    Cell Reports 34:108614.

    https://doi.org/10.1016/j.celrep.2020.108614

    • PubMed
    • Google Scholar
35. 1. Guo LY
    2. Allu PK
    3. Zandarashvili L
    4. McKinley KL
    5. Sekulic N
    6. Dawicki-McKenna JM
    7. Fachinetti D
    8. Logsdon GA
    9. Jamiolkowski RM
    10. Cleveland DW
    11. Cheeseman IM
    12. Black BE

    (2017) Centromeres are maintained by fastening CENP-A to DNA and directing an arginine anchor-dependent nucleosome transition

    Nature Communications 8:15775.

    https://doi.org/10.1038/ncomms15775

    • PubMed
    • Google Scholar
36. 1. Hara M
    2. Ariyoshi M
    3. Sano T
    4. Nozawa RS
    5. Shinkai S
    6. Onami S
    7. Jansen I
    8. Hirota T
    9. Fukagawa T

    (2023) Centromere/kinetochore is assembled through CENP-C oligomerization

    Molecular Cell 83:2188–2205.

    https://doi.org/10.1016/j.molcel.2023.05.023

    • Google Scholar
37. 1. Henikoff JG
    2. Thakur J
    3. Kasinathan S
    4. Henikoff S

    (2015) A unique chromatin complex occupies young α-satellite arrays of human centromeres

    Science Advances 1:e1400234.

    https://doi.org/10.1126/sciadv.1400234

    • PubMed
    • Google Scholar
38. 1. Hwang KK
    2. Eissenberg JC
    3. Worman HJ

    (2001) Transcriptional repression of euchromatic genes by Drosophila heterochromatin protein 1 and histone modifiers

    PNAS 98:11423–11427.

    https://doi.org/10.1073/pnas.211303598

    • PubMed
    • Google Scholar
39. 1. Ide S
    2. Tamura S
    3. Maeshima K

    (2022) Chromatin behavior in living cells: Lessons from single-nucleosome imaging and tracking

    BioEssays 44:e2200043.

    https://doi.org/10.1002/bies.202200043

    • PubMed
    • Google Scholar
40. 1. Iida S
    2. Shinkai S
    3. Itoh Y
    4. Tamura S
    5. Kanemaki MT
    6. Onami S
    7. Maeshima K

    (2022) Single-nucleosome imaging reveals steady-state motion of interphase chromatin in living human cells

    Science Advances 8:eabn5626.

    https://doi.org/10.1126/sciadv.abn5626

    • PubMed
    • Google Scholar
41. 1. Izeddin I
    2. Récamier V
    3. Bosanac L
    4. Cissé II
    5. Boudarene L
    6. Dugast-Darzacq C
    7. Proux F
    8. Bénichou O
    9. Voituriez R
    10. Bensaude O
    11. Dahan M
    12. Darzacq X

    (2014) Single-molecule tracking in live cells reveals distinct target-search strategies of transcription factors in the nucleus

    eLife 3:e02230.

    https://doi.org/10.7554/eLife.02230

    • PubMed
    • Google Scholar
42. 1. Jamge B
    2. Lorković ZJ
    3. Axelsson E
    4. Osakabe A
    5. Shukla V
    6. Yelagandula R
    7. Akimcheva S
    8. Kuehn AL
    9. Berger F

    (2023) Histone variants shape chromatin states in Arabidopsis

    eLife 12:RP87714.

    https://doi.org/10.7554/eLife.87714

    • PubMed
    • Google Scholar
43. 1. Jansen LET
    2. Black BE
    3. Foltz DR
    4. Cleveland DW

    (2007) Propagation of centromeric chromatin requires exit from mitosis

    The Journal of Cell Biology 176:795–805.

    https://doi.org/10.1083/jcb.200701066

    • PubMed
    • Google Scholar
44. 1. Janssen A
    2. Colmenares SU
    3. Karpen GH

    (2018) Heterochromatin: guardian of the genome

    Annual Review of Cell and Developmental Biology 34:265–288.

    https://doi.org/10.1146/annurev-cellbio-100617-062653

    • PubMed
    • Google Scholar
45. 1. Jradi FM
    2. Lavis LD

    (2019) Chemistry of photosensitive fluorophores for single-molecule localization microscopy

    ACS Chemical Biology 14:1077–1090.

    https://doi.org/10.1021/acschembio.9b00197

    • PubMed
    • Google Scholar
46. 1. Kale S
    2. Boopathi R
    3. Belotti E
    4. Lone IN
    5. Graies M
    6. Schröder M
    7. Petrova M
    8. Papin C
    9. Bednar J
    10. Ugrinova I
    11. Hamiche A
    12. Dimitrov S

    (2023) The CENP-A nucleosome: where and when it happens during the inner kinetochore’s assembly

    Trends in Biochemical Sciences S0968-0004(23)00176-7.

    https://doi.org/10.1016/j.tibs.2023.07.010

    • PubMed
    • Google Scholar
47. 1. Kapanidis AN
    2. Uphoff S
    3. Stracy M

    (2018) Understanding protein mobility in Bacteria by tracking single molecules

    Journal of Molecular Biology 430:4443–4455.

    https://doi.org/10.1016/j.jmb.2018.05.002

    • PubMed
    • Google Scholar
48. 1. Killian JL
    2. Ye F
    3. Wang MD

    (2018) Optical tweezers: a force to be reckoned with

    Cell 175:1445–1448.

    https://doi.org/10.1016/j.cell.2018.11.019

    • PubMed
    • Google Scholar
49. 1. Kimura H
    2. Cook PR

    (2001) Kinetics of core histones in living human cells: little exchange of H3 and H4 and some rapid exchange of H2B

    The Journal of Cell Biology 153:1341–1353.

    https://doi.org/10.1083/jcb.153.7.1341

    • PubMed
    • Google Scholar
50. 1. Klare K
    2. Weir JR
    3. Basilico F
    4. Zimniak T
    5. Massimiliano L
    6. Ludwigs N
    7. Herzog F
    8. Musacchio A

    (2015) CENP-C is a blueprint for constitutive centromere–associated network assembly within human kinetochores

    Journal of Cell Biology 210:11–22.

    https://doi.org/10.1083/jcb.201412028

    • Google Scholar
51. 1. Klemm SL
    2. Shipony Z
    3. Greenleaf WJ

    (2019) Chromatin accessibility and the regulatory epigenome

    Nature Reviews Genetics 20:207–220.

    https://doi.org/10.1038/s41576-018-0089-8

    • Google Scholar
52. 1. Kyriacou E
    2. Heun P

    (2018) High-resolution mapping of centromeric protein association using APEX-chromatin fibers

    Epigenetics & Chromatin 11:68.

    https://doi.org/10.1186/s13072-018-0237-6

    • Google Scholar
53. 1. Lachner M
    2. O’Carroll D
    3. Rea S
    4. Mechtler K
    5. Jenuwein T

    (2001) Methylation of histone H3 lysine 9 creates a binding site for HP1 proteins

    Nature 410:116–120.

    https://doi.org/10.1038/35065132

    • PubMed
    • Google Scholar
54. 1. Levi V
    2. Ruan Q
    3. Plutz M
    4. Belmont AS
    5. Gratton E

    (2005) Chromatin dynamics in interphase cells revealed by tracking in a two-photon excitation microscope

    Biophysical Journal 89:4275–4285.

    https://doi.org/10.1529/biophysj.105.066670

    • PubMed
    • Google Scholar
55. Preprint

    1. Lovely GA
    2. Braikia FZ
    3. Singh A
    4. Schatz DG
    5. Murre C
    6. Liu Z
    7. Sen R

    (2020) Direct Observation of RAG Recombinase Recruitment to Chromatin and the IgH Locus in Live pro-B Cells

    bioRxiv.

    https://doi.org/10.1101/2020.09.07.286484

    • Google Scholar
56. 1. Lowary PT
    2. Widom J

    (1998) New DNA sequence rules for high affinity binding to histone octamer and sequence-directed nucleosome positioning

    Journal of Molecular Biology 276:19–42.

    https://doi.org/10.1006/jmbi.1997.1494

    • PubMed
    • Google Scholar
57. 1. Lucas JS
    2. Zhang Y
    3. Dudko OK
    4. Murre C

    (2014) 3D trajectories adopted by coding and regulatory DNA elements: first-passage times for genomic interactions

    Cell 158:339–352.

    https://doi.org/10.1016/j.cell.2014.05.036

    • PubMed
    • Google Scholar
58. 1. Luger K
    2. Mäder AW
    3. Richmond RK
    4. Sargent DF
    5. Richmond TJ

    (1997) Crystal structure of the nucleosome core particle at 2.8 A resolution

    Nature 389:251–260.

    https://doi.org/10.1038/38444

    • PubMed
    • Google Scholar
59. Book

    1. Lyubchenko YL
    2. Gall AA
    3. Shlyakhtenko LS

    (2014) Visualization of DNA and protein–DNA complexes with atomic force microscopy in

    In: Kuo J, editors. Electron Microscopy: Methods and Protocols. Totowa, NJ: Humana Press. pp. 367–384.

    https://doi.org/10.1007/978-1-62703-776-1

    • Google Scholar
60. 1. Maeshima K
    2. Ide S
    3. Babokhov M

    (2019) Dynamic chromatin organization without the 30-nm fiber

    Current Opinion in Cell Biology 58:95–104.

    https://doi.org/10.1016/j.ceb.2019.02.003

    • PubMed
    • Google Scholar
61. 1. Maeshima K
    2. Iida S
    3. Shimazoe MA
    4. Tamura S
    5. Ide S

    (2023) Is euchromatin really open in the cell? Trends in Cell Biology

    Trends Cell Biol pp. S0962–S8924.

    https://doi.org/10.1016/j.tcb.2023.05.007

    • Google Scholar
62. 1. Maheshwari S
    2. Tan EH
    3. West A
    4. Franklin FCH
    5. Comai L
    6. Chan SWL
    7. Bomblies K

    (2015) Naturally occurring differences in CENH3 affect chromosome segregation in zygotic mitosis of hybrids

    PLOS Genetics 11:e1004970.

    https://doi.org/10.1371/journal.pgen.1004970

    • PubMed
    • Google Scholar
63. 1. Martire S
    2. Banaszynski LA

    (2020) The roles of histone variants in fine-tuning chromatin organization and function

    Nature Reviews. Molecular Cell Biology 21:522–541.

    https://doi.org/10.1038/s41580-020-0262-8

    • PubMed
    • Google Scholar
64. 1. Mazza D
    2. Abernathy A
    3. Golob N
    4. Morisaki T
    5. McNally JG

    (2012) A benchmark for chromatin binding measurements in live cells

    Nucleic Acids Research 40:e119.

    https://doi.org/10.1093/nar/gks701

    • PubMed
    • Google Scholar
65. 1. McAllister C
    2. Karymov MA
    3. Kawano Y
    4. Lushnikov AY
    5. Mikheikin A
    6. Uversky VN
    7. Lyubchenko YL

    (2005) Protein interactions and misfolding analyzed by AFM force spectroscopy

    Journal of Molecular Biology 354:1028–1042.

    https://doi.org/10.1016/j.jmb.2005.10.012

    • PubMed
    • Google Scholar
66. 1. McNulty SM
    2. Sullivan LL
    3. Sullivan BA

    (2017) Human Centromeres produce chromosome-specific and array-specific Alpha Satellite transcripts that are complexed with CENP-A and CENP-C

    Developmental Cell 42:226–240.

    https://doi.org/10.1016/j.devcel.2017.07.001

    • PubMed
    • Google Scholar
67. 1. Melters DP
    2. Nye J
    3. Zhao H
    4. Dalal Y

    (2015) Chromatin dynamics in Vivo: a game of musical chairs

    Genes 6:751–776.

    https://doi.org/10.3390/genes6030751

    • PubMed
    • Google Scholar
68. 1. Melters DP
    2. Pitman M
    3. Rakshit T
    4. Dimitriadis EK
    5. Bui M
    6. Papoian GA
    7. Dalal Y

    (2019) Intrinsic elasticity of nucleosomes is encoded by histone variants and calibrated by their binding partners

    PNAS 116:24066–24074.

    https://doi.org/10.1073/pnas.1911880116

    • Google Scholar
69. 1. Melters DP
    2. Dalal Y

    (2021) Nano-Surveillance: tracking individual molecules in a Sea of Chromatin

    Journal of Molecular Biology 433:166720.

    https://doi.org/10.1016/j.jmb.2020.11.019

    • PubMed
    • Google Scholar
70. 1. Mendiburo MJ
    2. Padeken J
    3. Fülöp S
    4. Schepers A
    5. Heun P

    (2011) Drosophila CENH3 is sufficient for centromere formation

    Science 334:686–690.

    https://doi.org/10.1126/science.1206880

    • PubMed
    • Google Scholar
71. 1. Morisaki T
    2. Müller WG
    3. Golob N
    4. Mazza D
    5. McNally JG

    (2014) Single-molecule analysis of transcription factor binding at transcription sites in live cells

    Nature Communications 5:4456.

    https://doi.org/10.1038/ncomms5456

    • PubMed
    • Google Scholar
72. 1. Mueller F
    2. Stasevich TJ
    3. Mazza D
    4. McNally JG

    (2013) Quantifying transcription factor kinetics: at work or at play?

    Critical Reviews in Biochemistry and Molecular Biology 48:492–514.

    https://doi.org/10.3109/10409238.2013.833891

    • PubMed
    • Google Scholar
73. 1. Nacev BA
    2. Feng L
    3. Bagert JD
    4. Lemiesz AE
    5. Gao JJ
    6. Soshnev AA
    7. Kundra R
    8. Schultz N
    9. Muir TW
    10. Allis CD

    (2019) The expanding landscape of “oncohistone” mutations in human cancers

    Nature 567:473–478.

    https://doi.org/10.1038/s41586-019-1038-1

    • PubMed
    • Google Scholar
74. 1. Nagashima R
    2. Hibino K
    3. Ashwin SS
    4. Babokhov M
    5. Fujishiro S
    6. Imai R
    7. Nozaki T
    8. Tamura S
    9. Tani T
    10. Kimura H
    11. Shribak M
    12. Kanemaki MT
    13. Sasai M
    14. Maeshima K

    (2019) Single nucleosome imaging reveals loose genome chromatin networks via active RNA polymerase II

    The Journal of Cell Biology 218:1511–1530.

    https://doi.org/10.1083/jcb.201811090

    • PubMed
    • Google Scholar
75. Preprint

    1. Nagpal H
    2. Fierz B

    (2022) CENP-A and CENP-B Collaborate to Create an Open Centromeric Chromatin State

    bioRxiv.

    https://doi.org/10.1101/2022.07.08.499316

    • Google Scholar
76. 1. Nakayama J
    2. Rice JC
    3. Strahl BD
    4. Allis CD
    5. Grewal SIS

    (2001) Role of Histone H3 Lysine 9 Methylation in epigenetic control of heterochromatin assembly

    Science 292:110–113.

    https://doi.org/10.1126/science.1060118

    • Google Scholar
77. 1. Neuman KC
    2. Nagy A

    (2008) Single-molecule force spectroscopy: optical tweezers, magnetic tweezers and atomic force microscopy

    Nature Methods 5:491–505.

    https://doi.org/10.1038/nmeth.1218

    • PubMed
    • Google Scholar
78. 1. Nodelman IM
    2. Bowman GD

    (2021) Biophysics of Chromatin Remodeling

    Annual Review of Biophysics 50:73–93.

    https://doi.org/10.1146/annurev-biophys-082520-080201

    • PubMed
    • Google Scholar
79. 1. Nozaki T
    2. Imai R
    3. Tanbo M
    4. Nagashima R
    5. Tamura S
    6. Tani T
    7. Joti Y
    8. Tomita M
    9. Hibino K
    10. Kanemaki MT
    11. Wendt KS
    12. Okada Y
    13. Nagai T
    14. Maeshima K

    (2017) Dynamic organization of chromatin domains revealed by super-resolution live-cell imaging

    Molecular Cell 67:282–293.

    https://doi.org/10.1016/j.molcel.2017.06.018

    • PubMed
    • Google Scholar
80. 1. Peterson CL

    (2008) Salt gradient dialysis reconstitution of nucleosomes

    CSH Protocols 2008:db.

    https://doi.org/10.1101/pdb.prot5113

    • PubMed
    • Google Scholar
81. 1. Polach KJ
    2. Widom J

    (1995) Mechanism of protein access to specific DNA sequences in Chromatin: a dynamic equilibrium model for Gene Regulation

    Journal of Molecular Biology 254:130–149.

    https://doi.org/10.1006/jmbi.1995.0606

    • Google Scholar
82. 1. Portillo-Ledesma S
    2. Tsao LH
    3. Wagley M
    4. Lakadamyali M
    5. Cosma MP
    6. Schlick T

    (2021) Nucleosome clutches are regulated by Chromatin internal parameters

    Journal of Molecular Biology 433:166701.

    https://doi.org/10.1016/j.jmb.2020.11.001

    • PubMed
    • Google Scholar
83. 1. Przewloka MR
    2. Zhang W
    3. Costa P
    4. Archambault V
    5. D’Avino PP
    6. Lilley KS
    7. Laue ED
    8. McAinsh AD
    9. Glover DM

    (2007) Molecular analysis of core kinetochore composition and assembly in Drosophila melanogaster

    PLOS ONE 2:e478.

    https://doi.org/10.1371/journal.pone.0000478

    • PubMed
    • Google Scholar
84. 1. Quénet D
    2. Dalal Y

    (2014) A long non-coding RNA is required for targeting centromeric protein A to the human centromere

    eLife 3:e03254.

    https://doi.org/10.7554/eLife.03254

    • PubMed
    • Google Scholar
85. 1. Rakshit T
    2. Melters DP
    3. Dimitriadis EK
    4. Dalal Y

    (2020) Mechanical properties of nucleoprotein complexes determined by nanoindentation spectroscopy

    Nucleus 11:264–282.

    https://doi.org/10.1080/19491034.2020.1816053

    • PubMed
    • Google Scholar
86. 1. Ranjan A
    2. Nguyen VQ
    3. Liu S
    4. Wisniewski J
    5. Kim JM
    6. Tang X
    7. Mizuguchi G
    8. Elalaoui E
    9. Nickels TJ
    10. Jou V
    11. English BP
    12. Zheng Q
    13. Luk E
    14. Lavis LD
    15. Lionnet T
    16. Wu C

    (2020) Live-cell single particle imaging reveals the role of RNA polymerase II in histone H2A.Z eviction

    eLife 9:e55667.

    https://doi.org/10.7554/eLife.55667

    • PubMed
    • Google Scholar
87. 1. Ravi M
    2. Kwong PN
    3. Menorca RMG
    4. Valencia JT
    5. Ramahi JS
    6. Stewart JL
    7. Tran RK
    8. Sundaresan V
    9. Comai L
    10. Chan SWL

    (2010) The rapidly evolving centromere-specific histone has stringent functional requirements in Arabidopsis thaliana

    Genetics 186:461–471.

    https://doi.org/10.1534/genetics.110.120337

    • PubMed
    • Google Scholar
88. 1. Régnier V
    2. Vagnarelli P
    3. Fukagawa T
    4. Zerjal T
    5. Burns E
    6. Trouche D
    7. Earnshaw W
    8. Brown W

    (2005) CENP-A is required for accurate chromosome segregation and sustained kinetochore association of BubR1

    Molecular and Cellular Biology 25:3967–3981.

    https://doi.org/10.1128/MCB.25.10.3967-3981.2005

    • PubMed
    • Google Scholar
89. 1. Ricci MA
    2. Manzo C
    3. García-Parajo MF
    4. Lakadamyali M
    5. Cosma MP

    (2015) Chromatin fibers are formed by heterogeneous groups of nucleosomes in vivo

    Cell 160:1145–1158.

    https://doi.org/10.1016/j.cell.2015.01.054

    • PubMed
    • Google Scholar
90. 1. Rothbart SB
    2. Strahl BD

    (2014) Interpreting the language of histone and DNA modifications

    Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms 1839:627–643.

    https://doi.org/10.1016/j.bbagrm.2014.03.001

    • Google Scholar
91. 1. Rudnizky S
    2. Khamis H
    3. Malik O
    4. Melamed P
    5. Kaplan A

    (2019) The base pair-scale diffusion of nucleosomes modulates binding of transcription factors

    PNAS 116:12161–12166.

    https://doi.org/10.1073/pnas.1815424116

    • Google Scholar
92. 1. Sanulli S
    2. Trnka MJ
    3. Dharmarajan V
    4. Tibble RW
    5. Pascal BD
    6. Burlingame AL
    7. Griffin PR
    8. Gross JD
    9. Narlikar GJ

    (2019) HP1 reshapes nucleosome core to promote phase separation of heterochromatin

    Nature 575:390–394.

    https://doi.org/10.1038/s41586-019-1669-2

    • PubMed
    • Google Scholar
93. 1. Schultz J

    (1936) Variegation in Drosophila and the inert chromosome regions

    PNAS 22:27–33.

    https://doi.org/10.1073/pnas.22.1.27

    • Google Scholar
94. 1. Shen H
    2. Tauzin LJ
    3. Baiyasi R
    4. Wang W
    5. Moringo N
    6. Shuang B
    7. Landes CF

    (2017) Single particle tracking: from Theory to Biophysical Applications

    Chemical Reviews 117:7331–7376.

    https://doi.org/10.1021/acs.chemrev.6b00815

    • PubMed
    • Google Scholar
95. 1. Shlyakhtenko LS
    2. Miloseska L
    3. Potaman VN
    4. Sinden RR
    5. Lyubchenko YL

    (2003) Intersegmental interactions in supercoiled DNA: atomic force microscope study

    Ultramicroscopy 97:263–270.

    https://doi.org/10.1016/S0304-3991(03)00051-2

    • PubMed
    • Google Scholar
96. 1. Specht EA
    2. Braselmann E
    3. Palmer AE

    (2017) A critical and comparative review of Fluorescent Tools for live-cell imaging

    Annual Review of Physiology 79:93–117.

    https://doi.org/10.1146/annurev-physiol-022516-034055

    • PubMed
    • Google Scholar
97. 1. Stormberg T
    2. Lyubchenko YL

    (2022) The sequence dependent Nanoscale structure of CENP-A Nucleosomes

    International Journal of Molecular Sciences 23:11385.

    https://doi.org/10.3390/ijms231911385

    • PubMed
    • Google Scholar
98. 1. Taverna SD
    2. Li H
    3. Ruthenburg AJ
    4. Allis CD
    5. Patel DJ

    (2007) How chromatin-binding modules interpret histone modifications: lessons from professional pocket pickers

    Nature Structural & Molecular Biology 14:1025–1040.

    https://doi.org/10.1038/nsmb1338

    • Google Scholar
99. 1. Thåström A
    2. Lowary PT
    3. Widlund HR
    4. Cao H
    5. Kubista M
    6. Widom J

    (1999) Sequence motifs and free energies of selected natural and non-natural nucleosome positioning DNA sequences

    Journal of Molecular Biology 288:213–229.

    https://doi.org/10.1006/jmbi.1999.2686

    • PubMed
    • Google Scholar
100. 1. Tolsma TO
     2. Hansen JC

     (2019) Post-translational modifications and chromatin dynamics

     Essays in Biochemistry 63:89–96.

     https://doi.org/10.1042/EBC20180067

     • PubMed
     • Google Scholar
101. 1. van Staalduinen J
     2. van Staveren T
     3. Grosveld F
     4. Wendt KS

     (2023) Live-cell imaging of chromatin contacts opens a new window into chromatin dynamics

     Epigenetics & Chromatin 16:27.

     https://doi.org/10.1186/s13072-023-00503-9

     • Google Scholar
102. 1. Vestergaard CL
     2. Blainey PC
     3. Flyvbjerg H

     (2014) Optimal estimation of diffusion coefficients from single-particle trajectories

     Physical Review E 89:022726.

     https://doi.org/10.1103/PhysRevE.89.022726

     • Google Scholar
103. 1. Wachsmuth M
     2. Knoch TA
     3. Rippe K

     (2016) Dynamic properties of independent chromatin domains measured by correlation spectroscopy in living cells

     Epigenetics & Chromatin 9:57.

     https://doi.org/10.1186/s13072-016-0093-1

     • Google Scholar
104. 1. Wagh K
     2. Stavreva DA
     3. Jensen RAM
     4. Paakinaho V
     5. Fettweis G
     6. Schiltz RL
     7. Wüstner D
     8. Mandrup S
     9. Presman DM
     10. Upadhyaya A
     11. Hager GL

     (2023) Dynamic switching of transcriptional regulators between two distinct low-mobility chromatin states

     Science Advances 9:eade1122.

     https://doi.org/10.1126/sciadv.ade1122

     • PubMed
     • Google Scholar
105. 1. Walkiewicz MP
     2. Bui M
     3. Quénet D
     4. Dalal Y

     (2014) Tracking histone variant nucleosomes across the human cell cycle using biophysical, biochemical, and cytological analyses

     Methods in Molecular Biology 1170:589–615.

     https://doi.org/10.1007/978-1-4939-0888-2_34

     • PubMed
     • Google Scholar
106. 1. Walstein K
     2. Petrovic A
     3. Pan D
     4. Hagemeier B
     5. Vogt D
     6. Vetter IR
     7. Musacchio A

     (2021) Assembly principles and stoichiometry of a complete human kinetochore module

     Science Advances 7:eabg1037.

     https://doi.org/10.1126/sciadv.abg1037

     • PubMed
     • Google Scholar
107. 1. Wang Z
     2. Wang X
     3. Zhang Y
     4. Xu W
     5. Han X

     (2021) Principles and applications of single particle tracking in cell research

     Small 17:e2005133.

     https://doi.org/10.1002/smll.202005133

     • PubMed
     • Google Scholar
108. 1. Weintraub H
     2. Groudine M

     (1976) Chromosomal subunits in active genes have an altered conformation

     Science 193:848–856.

     https://doi.org/10.1126/science.948749

     • PubMed
     • Google Scholar
109. 1. Weir JR
     2. Faesen AC
     3. Klare K
     4. Petrovic A
     5. Basilico F
     6. Fischböck J
     7. Pentakota S
     8. Keller J
     9. Pesenti ME
     10. Pan D
     11. Vogt D
     12. Wohlgemuth S
     13. Herzog F
     14. Musacchio A

     (2016) Insights from biochemical reconstitution into the architecture of human kinetochores

     Nature 537:249–253.

     https://doi.org/10.1038/nature19333

     • Google Scholar
110. 1. Widom J

     (1998) Structure, dynamics, and function of chromatin in vitro

     Annual Review of Biophysics and Biomolecular Structure 27:285–327.

     https://doi.org/10.1146/annurev.biophys.27.1.285

     • PubMed
     • Google Scholar
111. 1. Wu C
     2. Wong YC
     3. Elgin SCR

     (1979) The chromatin structure of specific genes: II. Disruption of chromatin structure during gene activity

     Cell 16:807–814.

     https://doi.org/10.1016/0092-8674(79)90096-5

     • PubMed
     • Google Scholar
112. 1. Yager TD
     2. van Holde KE

     (1984)

     Dynamics and equilibria of nucleosomes at elevated ionic strength

     The Journal of Biological Chemistry 259:4212–4222.

     • PubMed
     • Google Scholar
113. 1. Yager TD
     2. McMurray CT
     3. van Holde KE

     (1989) Salt-induced release of DNA from nucleosome core particles

     Biochemistry 28:2271–2281.

     https://doi.org/10.1021/bi00431a045

     • PubMed
     • Google Scholar
114. 1. Yatskevich S
     2. Barford D
     3. Muir KW

     (2023) Conserved and divergent mechanisms of inner kinetochore assembly onto centromeric chromatin

     Current Opinion in Structural Biology 81:102638.

     https://doi.org/10.1016/j.sbi.2023.102638

     • PubMed
     • Google Scholar
115. 1. Zaret KS

     (2020) Pioneer transcription factors initiating Gene Network changes

     Annual Review of Genetics 54:367–385.

     https://doi.org/10.1146/annurev-genet-030220-015007

     • PubMed
     • Google Scholar
116. 1. Zhong Y
     2. Wang G

     (2020) Three-Dimensional single particle tracking and its applications in confined environments

     Annual Review of Analytical Chemistry 13:381–403.

     https://doi.org/10.1146/annurev-anchem-091819-100409

     • PubMed
     • Google Scholar
117. 1. Zhou K
     2. Gaullier G
     3. Luger K

     (2019) Nucleosome structure and dynamics are coming of age

     Nature Structural & Molecular Biology 26:3–13.

     https://doi.org/10.1038/s41594-018-0166-x

     • PubMed
     • Google Scholar

Decision letter after peer review:

[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]

Thank you for submitting the paper "Mobility of nucleosomes is regulated by their binding partners" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by a Senior Editor. The reviewers have opted to remain anonymous.

While the reviewers agreed that the work was potentially interesting they had significant concerns with the conclusions of the manuscript. We are sorry to say that, after consultation with the reviewers, we have decided that this work will not be considered further for publication by eLife.

Specifically, overall the study seems somewhat incremental over previously published work. The reviewers felt that characterization of the nucleosome arrays and what is happening to them on the stage was lacking. Do the DNA binding proteins affect interactions of nucleosomes with the stage? It is difficult to interpret the results without this information. In addition, the H1 binding experiments were poorly controlled and should not be included in the manuscript.

Reviewer #1 (Recommendations for the authors):

1. I applaud the authors for applying a novel technique to address the fundamental issue of nucleosome mobility. However, this is a relatively short study that is somewhat incremental from previously published work. For example: "Previously we published HSAFM movies of H3 chromatin and H3 chromatin with linker histone variant H1.5 (Melters and Dalal, 2021). Similar as described above, we tracked individual H3 nucleosomes in real-time without (Figure 2A, Supplemental movie 3, Source data 1) and with H1.5 Figure 2B;" Also see: "Previously we showed that overexpressing CENP-C in HeLa cells resulted in increased clustering of centromeric chromatin and loss of centromeric RNAP2 levels (Melters et al., 2019)…To examine this facet of CENP-C: CENP-A homeostasis, we overexpressed CENP-C to assess if the centromeric transcription is altered" "First, we recapitulated the effects of CENP-C overexpression on the levels of RNAP2 on CENPA chromatin" It appears that some of the data in the manuscript are a repeat of published work or at most an incremental advance.

2. The histone H1 experiments should be removed from the manuscript as they are poorly controlled. There is a long history of reported artifactual and preternatural effects of adding H1 to DNA and nucleosomes. Without evidence that H1 is binding the DNA properly and forming chromatosomes the data cannot be interpreted. Moreover, the fact that the H1 to nucleosome ratio used is 0.2:1 suggest that at most only 1 in 5 nucleosomes could be properly bound by H1and measuring the movement of the entire population is not justified. As noted by the authors the data is very heterogeneous suggesting multiple forms of nucleosomes may be present. And again even this data may be a repeat or incremental (see comment 1).

Overall the manuscript would be improved by focusing on CENP-C and CENP-A nucleosomes for which the most data is available and not make sweeping conclusions about all nucleosome binding proteins (see title).

Reviewer #2 (Recommendations for the authors):

1) If the authors want to discuss nucleosome accessibility based on nucleosome mobility, they need to perform additional experiments that would bring explicit data on nucleosome accessibility.

2) To rule out the possibility that reduced mobility upon CENP-CCD or H1.5 addition may be due to the increased interaction between the protein complexes and the surface, other proteins that are known to have no apparent interaction with chromatin (for example, BSA), or proteins that are known to increase chromatin mobility, should be also tested using HS-AFM as controls. Furthermore, to link the in vitro observation to in vivo results, CENP-Cs with the same construct should be used.

3) The title can be more informative. Since this study is basically on CENP-C and CENP-A nucleosomes, the authors could try to include these key words in the title too.

4) The authors should provide a description (or maybe a figure) of the structural domains of CENP-C in the introduction.

5) Figure 2C: The standard error bars of MSD for H3 and H3+H1.5 overlap, which does not indicate a statistically significant difference. The diffusion constants also show no statistically dominant difference. Therefore, we cannot assume that the conclusion "linker H1.5 protein restricts H3 nucleosome mobility" is entirely supported by experimental data.

6) The authors should include in the manuscript gel pictures of the reconstituted polynucleosomes used in this study to show the quality of the reconstitution.

7) It is unclear how much CENP-CCD and H1.5 are bound to polynucleosomes. The authors should report the percentage of bound CENP-CCD and H1.5, for example by performing EMSA and then gel quantification. The authors could include a section where they compare the structures of the polynucleosomes with CENP-CCD or H1.5 compared to polynucleosomes without CENP-CCD or H1.5. Is there a difference in the compaction of the structures?

[Editors’ note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Single Molecule Analysis of CENP-A Chromatin by High-Speed Atomic Force Microscopy" for further consideration by eLife. Your revised article has been evaluated by Kevin Struhl (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

Essential revision:

1) please comment on whether the use of α satellite repeat DNA influences the results as opposed to other DNA with no nucleosome positioning preference.

2) What causes the two Gaussian distributions of nucleosomes? This is a potentially interesting result that is not pursued or explained.

3) The authors hint that they are showing potential functions of CENP-C beyond forming kinetochores by over-expressing CENP-C. Are these physiologically relevant or over-expression artifacts?

4) In the authors' description of the experimental workflow they mention that each movie is corrected for drift and they refer readers to Supplemental Figure 2. I would suggest that the legend of supplemental figure 2 be revised to include a brief description of how the drift correction is done, and perhaps add a bit more detail regarding this correction to the Methods section. Also, on page 12 the authors note that "… with Tween-20 displayed the most amount of drift". When the authors introduce the concept of correcting for drift I assumed that they were referring to instrument drift, but I am not certain how to instrument drift would be impacted by Tween-20. Can the authors please clarify?

5) Page 9: "We reasoned that if the AFM tip altered samples during scanning, it would create a distinctive signature in single particle trajectories." For the non-AFM audience, could the authors provide just half a sentence explaining what the data might have looked like if there were systematic issues with the tip altering the sample?

6) The observation of two D1 values (based on the sum of two Gaussian fits) is interesting. Pausing during diffusing seems like a reasonable explanation. I have two questions related to this point. First, is it possible to collect reaction trajectories that are long enough to conclusively say whether any given nucleosome transitions between D1 and D2? Second, is it possible to do a simple computational simulation of 1D diffusion and include pausing within the simulation to verify that the data would yield an apparent sum of two D values?

7) Page 14: The authors state that there is a 4-fold reduction in the diffusion constant with the addition of CENP-C(CD). I think the value is 2.9-fold.

8) How much is CENP-C over-expressed compared to normal expression levels?

9) Have nucleosome diffusion coefficients been reported from smFRET measurements, and if so, how do they compare to the values reported here?

10) Regarding the diffusion measurements themselves: If a nucleosome moves in 1D relative to a DNA strand then the authors "score" this behavior as 1D diffusion and the data is used towards calculating a diffusion coefficient. But, if the DNA moves relative to the nucleosome, then this would not be "scored" as diffusion because it could not be observed in the measurements, correct? If this is true, do the D1 values actually represent apparent values that reflect a lower bound for the actual diffusion coefficients?

11) Nucleosome tracking: Since this paper has a heavy emphasis on methodology, further details on tracking will be helpful. Since the surface is somewhat crowded and the DNA strings are not always clearly observed (based on supplementary movies), how do the authors know if the tracked structure is histone or nucleosome (and within that classification – mono, di, or multi nucleosome)? It is unclear if the quality of the images allows for these distinctions, and nucleosomes in each of these sub-populations are expected to have different dynamics. Did the authors use height or a feature from the surface topography as a criterion for selecting particles for analysis?

(b) It would be helpful to have higher-resolution images of a dilute CENP-A sample acquired at a lower speed than the current 2s/frame to show the composition and features of the nucleosome arrays at higher resolution. It is unclear from the air AFM images shown in Supplement Figure 1B if the sample is largely individual nucleosome strings or a cluster of strings.

12) Please induce height scale with all images in the paper

13) Experiments with CENP-A and CENP-C: The images in Figure 4B are difficult to interpret as the structures do not look homogeneous. Are these nucleosome clusters of different sizes (if yes, how are nucleosomes tracked in these samples?) Again, a higher-resolution image of the sample at a low scan rate will clarify the composition of the sample. How do we know if the changes to dynamics are not due to changes in the surface from the non-specific binding of the CENP-C fragment? One or two controls would be helpful here: (i) a dose-dependent effect of CENP-A nucleosome dynamics at 2-3 different CENP-C concentrations and/or (ii) the addition of a CENP-C mutant that does not bind CENP-A

14) Nucleosome mobility: The authors imply in several places that the observed dynamics have contributions from nucleosome sliding on DNA. For example, in the abstract, the authors state: "CENP-C reduces… along the chromatin fiber"; Pg. 6 "CENP-C, …directly impacts nucleosome mobility and, surprisingly, also chromatin fiber motion in vitro"; Pg. 18 "These findings suggest that physical properties of DNA dictate mobility along DNA"). In response to reviewers, the authors state, "it is tantalizing to hypothesize that the two Gaussian fittings reflect both 1D sliding and 3D chromatin motion". Without direct evidence, the dual Gaussian can arise from any number of reasons (such as collisions, surface adhesion, etc.) and is heavily influenced by the surface.

15) Comment on biological aspects and implications of the study: While the authors indicate that they have reduced the focus on biological implications, a significant section of the paper is still dedicated to it (the final three figures and a large fraction of the discussion). Connecting the mobility of nucleosomes on a mica surface to chromatin accessibility is a big leap (example in the abstract: "changes which alter chromatin accessibility in vitro…."). This is because it is not established from these data how much of the mobility is due to nucleosomes diffusing on the surface (which is heavily influenced by surface properties and buffer conditions, as the authors clearly demonstrate) versus nucleosomes sliding on DNA and altering the access of proteins to different DNA regions. Without direct observation of nucleosome sliding on DNA (which I think is future work and not reasonable to expect here), the biological implications of observed mobility changes in vitro are highly speculative and should be stated as such.

16) The statement that "excess CENP-C suppresses centromeric chromatin accessibility and transcription in vivo" is not well supported by the data presented in this paper. ChIP reports on chromatin occupancy, but not directly on changes in chromatin accessibility and transcription. Please modify the statement.

147 For the western blot in Supplementary Figure 12, there are no loading controls. Can the authors please perform the western blot again and this time include loading controls?

18) Can you please include information on how the error bar was calculated in Figure 2D, Figure 3B, and Figure 4F?

19) Drift correction: were any spots excluded from a correction? In supplementary fig-2, a few spots appear unchanged (x,y ~ 400,30; 10,300; 180,300 etc). These may simply be how the data shifts post drift correction, but if some data was excluded from correction, please include more methodological details.

[Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]

Reviewer #1 (Recommendations for the authors):

1. I applaud the authors for applying a novel technique to address the fundamental issue of nucleosome mobility. However, this is a relatively short study that is somewhat incremental from previously published work. For example: "Previously we published HSAFM movies of H3 chromatin and H3 chromatin with linker histone variant H1.5 (Melters and Dalal, 2021). Similar as described above, we tracked individual H3 nucleosomes in real-time without (Figure 2A, Supplemental movie 3, Source data 1) and with H1.5 Figure 2B;" Also see: "Previously we showed that overexpressing CENP-C in HeLa cells resulted in increased clustering of centromeric chromatin and loss of centromeric RNAP2 levels (Melters et al., 2019)…To examine this facet of CENP-C: CENP-A homeostasis, we overexpressed CENP-C to assess if the centromeric transcription is altered" "First, we recapitulated the effects of CENP-C overexpression on the levels of RNAP2 on CENPA chromatin" It appears that some of the data in the manuscript are a repeat of published work or at most an incremental advance.

We thank the reviewer for appreciating the application of HS-AFM in studying nucleosome mobility. We also thank the reviewer for helping us refocus our attention in this manuscript. We refocused our attention on performing and optimizing the many control experiments required to establish hsAFM as a robust quotative tool to measure nucleosome dynamics. To end, we also improved the implementation of the single particle tracking software and extended our downstream analyses.

2. The histone H1 experiments should be removed from the manuscript as they are poorly controlled. There is a long history of reported artifactual and preternatural effects of adding H1 to DNA and nucleosomes. Without evidence that H1 is binding the DNA properly and forming chromatosomes the data cannot be interpreted. Moreover, the fact that the H1 to nucleosome ratio used is 0.2:1 suggest that at most only 1 in 5 nucleosomes could be properly bound by H1and measuring the movement of the entire population is not justified. As noted by the authors the data is very heterogeneous suggesting multiple forms of nucleosomes may be present. And again even this data may be a repeat or incremental (see comment 1).

We thank the reviewer for noting this limitation and we agree with the reviewer. Therefore, we have redirected our focus away from describing H3 and H3+H1.5 as an example of how chromatin compaction factors impact nucleosome mobility and limit our analysis of the H3 and H3+H1.5 data to demonstrate bias in nucleosome tracking data (Supplemental Figures S7 and S8). In addition, we show that mononucleosome and chromatin fiber single-frame step distributions are distinct (see Supplemental Figures S7 and S8). More details about the updated manuscript can be found above.

Overall the manuscript would be improved by focusing on CENP-C and CENP-A nucleosomes for which the most data is available and not make sweeping conclusions about all nucleosome binding proteins (see title).

We thank the reviewer for this suggestion and indeed, we have significantly revised the manuscript to not only focus on CENP-A nucleosomes but especially focus on the technical aspects of using HS-AFM in imaging chromatin by performing various controls as described above in detail.

Reviewer #2 (Recommendations for the authors):

1) If the authors want to discuss nucleosome accessibility based on nucleosome mobility, they need to perform additional experiments that would bring explicit data on nucleosome accessibility.

We thank the reviewer for this important suggestion, and we can confirm that we are following up on this. A detailed analysis of nucleosome accessibility will be explored in a followup manuscript. For this manuscript, we refocused to the establishment of HS-AFM as a tool to study nucleosome mobility, as described above.

2) To rule out the possibility that reduced mobility upon CENP-CCD or H1.5 addition may be due to the increased interaction between the protein complexes and the surface, other proteins that are known to have no apparent interaction with chromatin (for example, BSA), or proteins that are known to increase chromatin mobility, should be also tested using HS-AFM as controls. Furthermore, to link the in vitro observation to in vivo results, CENP-Cs with the same construct should be used.

We thank the reviewer for this very insightful comment. We have expanded our study to address various conditions that could impact chromatin mobility and dynamics, ranging from salt concentration, APS concentrations, and Tween-20, as described above in detail.

3) The title can be more informative. Since this study is basically on CENP-C and CENP-A nucleosomes, the authors could try to include these key words in the title too.

We agree that the title needed to be more informative. Therefore, we have not only updated the manuscript, but we have also updated the title to reflect the theme of the manuscript more accurately.

4) The authors should provide a description (or maybe a figure) of the structural domains of CENP-C in the introduction.

We thank the reviewer for suggestion. We have altered the focus of the manuscript away from focusing on the effects of CENP-C on CENP-A chromatin to stringently testing CENP-A chromatin under various conditions by HS-AFM. We therefore thought it would not be in line with the current flow of the manuscript to describe the structural domains of CENP-C extensively in the introduction.

5) Figure 2C: The standard error bars of MSD for H3 and H3+H1.5 overlap, which does not indicate a statistically significant difference. The diffusion constants also show no statistically dominant difference. Therefore, we cannot assume that the conclusion "linker H1.5 protein restricts H3 nucleosome mobility" is entirely supported by experimental data.

We have redone the analysis for both the CENP-A, CENP-A+CENP-C^CD, H3, and H3+H1.5 based on more tracked particles. These extended data led us to reanalyze all data and revise our interpretations accordingly. We no longer make global statements but restrict our conclusions to the data presented in this manuscript.

6) The authors should include in the manuscript gel pictures of the reconstituted polynucleosomes used in this study to show the quality of the reconstitution.

We thank the reviewer for this suggestion. We added supplemental Figure S1 which has an image of a gel showing the purity of histones; an AFM image showing in vitro reconstituted nucleosomes confirming reconstituted polynucleosomes; a MNase ladder showing digested chromatin confirming reconstituted polynucleosomes; and AFM nucleosome measurements confirming the expected nucleosomal dimensions. All these data confirm the quality of the chromatin reconstitution.

7) It is unclear how much CENP-CCD and H1.5 are bound to polynucleosomes. The authors should report the percentage of bound CENP-CCD and H1.5, for example by performing EMSA and then gel quantification. The authors could include a section where they compare the structures of the polynucleosomes with CENP-CCD or H1.5 compared to polynucleosomes without CENP-CCD or H1.5. Is there a difference in the compaction of the structures?

We thank the reviewer for this question. Previous work has established how much CENP-C^CD binds to CENP-A nucleosomes. Specifically, in Falk et al. 2015, Falk et al. 2016, Guo et al. 2017, Melters et al. 2019, and Ali-Ahmed et al. 2019. We used the same CENP-A nucleosome to CENP-C^CD ratio of 1: 2.2 as established and subsequently used in these studies.

We are very excited to study how H1.5 might or might not interact with CENP-A chromatin. Indeed, this is an ongoing study in the lab that we will publish in a manuscript that is preparation. In short, we see CENP-A chromatin compaction by H1.5. We are working out the details how this mechanistically happens and if there is competition between CENP-C^CD and H1.5.

[Editors’ note: further revisions were suggested prior to acceptance, as described below.]

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

Essential revision:

1) Please comment on whether the use of α satellite repeat DNA influences the results as opposed to other DNA with no nucleosome positioning preference.

We thank the reviewer for this important question. We are aware that DNA sequence can influence nucleosome dynamics, including nucleosome positioning. Indeed, α-satellite DNA can position nucleosomes well, leading it to be used in the now-iconic crystal structure solved by Luger et al. In our study we used a 3.5 kbp plasmid contains 4 copies of a-satellite DNA, which make up less than 20% of the plasmid. The majority of the plasmid lacks nucleosome positioning sequences. Therefore, we accepted the reviewer’s suggestion to expand our study to also include a plasmid that lacks any known nucleosome positioning sequence. We used a 8.5 kbp plasmid that contains a copy of the PCAT2 gene, which is a lncRNA gene near the MYC gene on the human 8q24 locus. In cancers, CENP-A can go to ectopic loci, and the PCAT2 locus is one such known ectopic sites (Athwal et al. 2015, Nye et al. 2018, Ganesan et al. 2022). When we compared the MSD curves and diffusion constants of CENP-A nucleosomes reconstituted on the PCAT2 plasmid to other controls conditions, we observed that CENP-A nucleosomes have a similar MSD curve and diffusion constant as the high salt control condition (updated Figure 3 and Figure 3 —figure supplement 1-3). We found a higher diffusion constant and MSD curve for CENP-A nucleosome on PCAT2 plasmid versus a-satellite plasmid. One possibility is that DNA sequence indeed can influence nucleosome mobility. Another possibility is that the length difference between the two plasmids influences nucleosome mobility. We will work out these details in follow-up experiments more carefully comparing the effect of various positioning sequences- the senior author of this ms performed her PhD working on this exact issue with Arnie Stein and Minou Bina in the 2000s; so it is certainly of deep interest to the Dalal lab.

2) What causes the two Gaussian distributions of nucleosomes? This is a potentially interesting result that is not pursued or explained.

We agree with the reviewer. The precise cause of the two Gaussian distribution is not fully clear to us. As a first step, we elaborated on this interesting finding by addressing whether the smaller D₁ diffusion constant could simply arise from nucleosome “sticking” to the mica surface. For this, we compared the diffusion constant of nucleosome trajectories that were rejected for being potentially “stuck” with the smaller D₁ diffusion constant. Trajectories were rejected if their average R step was less than 1 nm and their maximum R range was less than 8 nm. Next, we fitted the “stuck” steps in the x and y diffusion constant, as described in Figure 2C, D and Figure 2 —figure supplement 1B. The effective diffusion constant of the “stuck” trajectories were compared with the smaller D₁ diffusion constant. We used the F-test to compare the gaussian variances to determine the probability that the smaller diffusion constant is consistent with the “stuck” nucleosomes. With the exception of the 2x APS and low salt condition, the diffusion constants were significantly different. These results are not consistent with the smaller D₁ diffusion constant being the same as the “stuck” trajectories (see new Figure 3 —figure supplement 5). We are therefore more confident that the D₁ and D₂ Gaussian distributions represent two distinct mobility states. Further work will be necessary to understand the cause of the two states, and is slated for a follow up study.

3) The authors hint that they are showing potential functions of CENP-C beyond forming kinetochores by over-expressing CENP-C. Are these physiologically relevant or over-expression artifacts?

We thank the reviewer for this question. CENP-C overexpression is not a common event in cells. In fact, in tumors it was found that CENP-C expression is not commonly altered, whereas the expression of other centromeric and kinetochore proteins is (Zhang et al. 2016 Nat Comm). In a follow-up study we will elaborate on this in more detail. In a preview, we will show (see Author response image 1) that overexpression of CENP-C leads to extensive mitotic defects that can be rescued by co-expressing with mutant histone variants that function as a sink for excess CENP-C (H3^{CpA CTD}) or a mutant histone variant that cannot bind CENP-C (CENP-A^∆CTD).

Author response image 1

Download asset Open asset

4) In the authors' description of the experimental workflow they mention that each movie is corrected for drift and they refer readers to Supplemental Figure 2. I would suggest that the legend of supplemental figure 2 be revised to include a brief description of how the drift correction is done, and perhaps add a bit more detail regarding this correction to the Methods section. Also, on page 12 the authors note that "… with Tween-20 displayed the most amount of drift". When the authors introduce the concept of correcting for drift I assumed that they were referring to instrument drift, but I am not certain how to instrument drift would be impacted by Tween-20. Can the authors please clarify?

We have updated Figure 1 —figure supplement 2 to more clearly show how we corrected for sample drift. In short as shown in Figure 1 —figure supplement 2A, for each movie we visualized the raw tracks. Some of the tracks appear to be immobile. These tracts were rejected for further analysis if they had an average R step smaller than 1nm and a maximum R range of smaller than 8 nm. These immobile tracts display a classic pattern of drift where over time the tract would move in the exact same direction as other immobile tracts. By taking the first and last position of several immobile particles, divided by the number of steps, the correction per step was calculated. Next, we verified the correction by visualizing the steps as we did when we determined the drift. In Figure 1 —figure supplement 2B, we show four examples of drift correction, but please note that this was done for each movie individually.

5) Page 9: "We reasoned that if the AFM tip altered samples during scanning, it would create a distinctive signature in single particle trajectories." For the non-AFM audience, could the authors provide just half a sentence explaining what the data might have looked like if there were systematic issues with the tip altering the sample?

We agree with the reviewer that such a statement would make it more obvious to the non-AFM audience. As such, we have updated the text on page 9.

6) The observation of two D1 values (based on the sum of two Gaussian fits) is interesting. Pausing during diffusing seems like a reasonable explanation. I have two questions related to this point. First, is it possible to collect reaction trajectories that are long enough to conclusively say whether any given nucleosome transitions between D1 and D2? Second, is it possible to do a simple computational simulation of 1D diffusion and include pausing within the simulation to verify that the data would yield an apparent sum of two D values?

We thank the reviewer for these insightful questions! (1) We have collected nucleosome trajectories that show what appears to be transitions from D₁ to D₂ states and vice versa. We show a collage of these trajectories in Figure 3 —figure supplement 4A. Additionally, when we looked at the single step size distribution for each trajectory, we observed that there is a very broad range of step sizes, indicating that it is plausible that individual nucleosomes transition between D₁ and D₂ states (Figure 3 —figure supplement 4B). (2) Instead of developing and running a simulation, we performed a test to determine if the smaller D₁ diffusion constant could be the result of “sticking” nucleosomes. In our response to Point 2, we elaborate in detail how we performed this analysis. Finally, we will try to understand the origin of the two mobility populations in a follow-up study.

7) Page 14: The authors state that there is a 4-fold reduction in the diffusion constant with the addition of CENP-C(CD). I think the value is 2.9-fold.

We thank the reviewer for catching this error, which has been corrected.

8) How much is CENP-C over-expressed compared to normal expression levels?

Based on Western blot data (Source Data 6), CENP-C was modestly over-expressed by 2.8-fold from normal. We have updated the text to reflect the level of overexpression.

9) Have nucleosome diffusion coefficients been reported from smFRET measurements, and if so, how do they compare to the values reported here?

As far as we are aware and have found in the literature, smFRET has not been used for determining nucleosome diffusion coefficients. Beat Fierz’s group has done beautiful work using smFRET to study nucleosome array dynamics, but his work has not determined the diffusion coefficients. On the other hands, other imaging-based technologies have been deployed to determine the diffusion coefficient of molecules, such as FCS. We have updated the Discussion section to include the comparison of our results with published work.

10) Regarding the diffusion measurements themselves: If a nucleosome moves in 1D relative to a DNA strand then the authors "score" this behavior as 1D diffusion and the data is used towards calculating a diffusion coefficient. But, if the DNA moves relative to the nucleosome, then this would not be "scored" as diffusion because it could not be observed in the measurements, correct? If this is true, do the D1 values actually represent apparent values that reflect a lower bound for the actual diffusion coefficients?

We thank the reviewer for this important question. Under ideal conditions, we would be able to track nucleosomes along the DNA (1D motion), as well as the chromatin fiber as a whole (2D motion). As the reviewer can appreciate in the movies, DNA frequently moves out of focus (away from the mica surface), making continuous tracking difficult. This is akin to a particle moving out of focus in live cell imaging, or because DNA moves too fast to be captured during scanning process. This latter is aggerated in movies where we scanned at higher resolution, at the cost of scanning speed (see images in response to Comment 11b). There is an upgrade available for the HS-AFM machine that can scan at higher speed with greater resolution, which might provide the technical tools to gain spatiotemporal resolution to observe different diffusion patterns in greater detail.

11) Nucleosome tracking: Since this paper has a heavy emphasis on methodology, further details on tracking will be helpful. Since the surface is somewhat crowded and the DNA strings are not always clearly observed (based on supplementary movies), how do the authors know if the tracked structure is histone or nucleosome (and within that classification – mono, di, or multi nucleosome)? It is unclear if the quality of the images allows for these distinctions, and nucleosomes in each of these sub-populations are expected to have different dynamics. Did the authors use height or a feature from the surface topography as a criterion for selecting particles for analysis?

(b) It would be helpful to have higher-resolution images of a dilute CENP-A sample acquired at a lower speed than the current 2s/frame to show the composition and features of the nucleosome arrays at higher resolution. It is unclear from the air AFM images shown in Supplement Figure 1B if the sample is largely individual nucleosome strings or a cluster of strings.

We thank the reviewer for these questions. (a) Particles were identified as nucleosomes if their diameter was 10-12 nm. Histone H2A/H2B dimers and CENP-A/H4 tetramers are much smaller (see Author response image 2) and were not tracked. Post-analysis, we did a spot check to verify the presence of entry and exit DNA strands from the tracked particles, as these were not always visible in all frames as the reviewer accurately pointed out. We have updated the methods section, as well as the Results section to more clearly explain how nucleosomes were identified.

Author response image 2

Download asset Open asset

(b) We have also imaged CENP-A chromatin at the highest resolution that the Cypher VRS permits, which is 744 x 1024 points & lines. As you can see from the screenshots of HS-AFM Videos (Author response image 3), there is a marginal increase in observed resolution. The mica and sampels were prepared similar as in the main experiment and the sample was imaged using the same parameters, with the only exeption being resolution. The same 400x400 nm area was first scanned at the highest resolution and subsequently at the settings used in the rest of the manuscript. The topological resolution did not improve noticeable under the highest resolution conditions compared to the settings used in the manuscript. Temporal resolution was unfortunatly lost.

Author response image 3

Download asset Open asset

12) Please induce height scale with all images in the paper

We have added height scale bars next to all AFM images.

13) Experiments with CENP-A and CENP-C: The images in Figure 4B are difficult to interpret as the structures do not look homogeneous. Are these nucleosome clusters of different sizes (if yes, how are nucleosomes tracked in these samples?) Again, a higher-resolution image of the sample at a low scan rate will clarify the composition of the sample. How do we know if the changes to dynamics are not due to changes in the surface from the non-specific binding of the CENP-C fragment? One or two controls would be helpful here: (i) a dose-dependent effect of CENP-A nucleosome dynamics at 2-3 different CENP-C concentrations and/or (ii) the addition of a CENP-C mutant that does not bind CENP-A

We thank the reviewer for this suggestion. How we distinguish nucleosomes from CENP-A/H4 tetramers from H2A/H2B dimers, please see response to Point 11. To address whether CENP-C modulates CENP-A nucleosomes in a dose-dependent manner, we performed additional HS-AFM movies of CENP-A chromatin adding 1x and 4x levels of CENP-C^CD. In other words, we added in total either 1, 2, or 4 CENP-C^CD molecules per CENP-A nucleosome (1x, 2x, or 4x CENP-C^CD). By adding two concentration levels for CENP-C^CD, we observed that 1x CENP-C^CD has no effect on CENP-A nucleosome MSD curves (updated Figure 4D) and the diffusion constant (updated Figure 4E), whereas 4x CENP-C^CD MSD curves (updated Figure 4D) and diffusion constant (updated Figure 4E) was very similar to 2x CENP-C^CD. To further clarify the experimental set-up, we included a graphical representation of the CENP-C protein, which fragment was used (central domain), and at what molar concentration (updated Figure 4A). Furthermore, we updated Figure 3 —figure supplement 1-3 and the text, both in the Results section, discussion, and material and methods. We interpret these results to mean that CENP-C^CD regulates CENP-A nucleosome motions not like a rheostat, but instead more like a switch. The consequence of this would be that CENP-A nucleosome motions will only stop once a critical mass of CENP-C^CD is bound to CENP-A chromatin.

14) Nucleosome mobility: The authors imply in several places that the observed dynamics have contributions from nucleosome sliding on DNA. For example, in the abstract, the authors state: "CENP-C reduces… along the chromatin fiber"; Pg. 6 "CENP-C, …directly impacts nucleosome mobility and, surprisingly, also chromatin fiber motion in vitro"; Pg. 18 "These findings suggest that physical properties of DNA dictate mobility along DNA"). In response to reviewers, the authors state, "it is tantalizing to hypothesize that the two Gaussian fittings reflect both 1D sliding and 3D chromatin motion". Without direct evidence, the dual Gaussian can arise from any number of reasons (such as collisions, surface adhesion, etc.) and is heavily influenced by the surface.

We agree with the reviewer that various conditions may influence the behavior of nucleosomes and nucleosome arrays. For that reason, we tested several conditions, ranging from salt concentrations, how sticky the mica surface is (APS concentration), and Tween-20 to modify the non-specific hydrophobic interactions. In all tested conditions, which may represent the largest data set of nucleosomes observed by HS-AFM, we observed that the sum of two Gaussians best fitted the single step distribution (see Figure 3D and Figure 3 —figure supplement 2). That said, we don’t fully understand the precise source of the diffusion constants derived from the sum of two Gaussians. Excitingly, a recent paper from our department colleagues in Gordon Hager’s lab (Wagh et al. 2023 Sci Adv) independently performed single particle tracking analysis of H2B-HALO in vivo, also report two distinct mobility states. We have included this study in the discussion. Furthermore, we performed an additional control as described in our response to Point 2. Finally, in a follow-up study, we will delve into the origin of the two Gaussians.

15) Comment on biological aspects and implications of the study: While the authors indicate that they have reduced the focus on biological implications, a significant section of the paper is still dedicated to it (the final three figures and a large fraction of the discussion). Connecting the mobility of nucleosomes on a mica surface to chromatin accessibility is a big leap (example in the abstract: "changes which alter chromatin accessibility in vitro…."). This is because it is not established from these data how much of the mobility is due to nucleosomes diffusing on the surface (which is heavily influenced by surface properties and buffer conditions, as the authors clearly demonstrate) versus nucleosomes sliding on DNA and altering the access of proteins to different DNA regions. Without direct observation of nucleosome sliding on DNA (which I think is future work and not reasonable to expect here), the biological implications of observed mobility changes in vitro are highly speculative and should be stated as such.

We thank the reviewer for pointing out the speculative nature of extrapolating HS-AFM data to in vivo events. We accept the advice to tone down our excitement about what is happening in vivo. We have updated the manuscript accordingly in both the abstract, introduction, results, and discussion.

16) The statement that "excess CENP-C suppresses centromeric chromatin accessibility and transcription in vivo" is not well supported by the data presented in this paper. ChIP reports on chromatin occupancy, but not directly on changes in chromatin accessibility and transcription. Please modify the statement.

We have modified the statement to represent the data more accurately: “Excess CENP-C suppresses centromeric RNAP2 levels and centromeric transcription in vivo".

17) For the western blot in Supplementary Figure 12, there are no loading controls. Can the authors please perform the western blot again and this time include loading controls?

We apologize for this oversight. We have added H2A as a loading control to Figure 5 —figure supplement 1 and updated the Figure 5 - figure supplement 1 - source data 1 as well.

18) Can you please include information on how the error bar was calculated in Figure 2D, Figure 3B, and Figure 4F?

The error bar in Figures 2D, 3B, and 4F represent the standard error, as reported in the methods section. For clarity, we have updated the figure legends.

19) Drift correction: were any spots excluded from a correction? In supplementary fig-2, a few spots appear unchanged (x,y ~ 400,30; 10,300; 180,300 etc). These may simply be how the data shifts post drift correction, but if some data was excluded from correction, please include more methodological details.

We thank the reviewer for this comment and please note our response to Point 4. When drift correction was performed, all data points within that movie were corrected. The amount of correction was determined in a movie-specific manner.

Author details

1. Daniël P Melters

   National Cancer Institute, Center for Cancer Research, Laboratory Receptor Biology and Gene Expression, Bethesda, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   daniel.melters@nih.gov

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4809-0562

2. Keir C Neuman

   National Heart, Lung, and Blood Institute, Laboratory of Single Molecule Biophysics, Bethesda, United States

   Contribution

   Conceptualization, Formal analysis, Validation, Investigation, Methodology, Writing – review and editing

   For correspondence

   neumankc@nhlbi.nih.gov

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0863-5671

3. Reda S Bentahar

   National Cancer Institute, Center for Cancer Research, Laboratory Receptor Biology and Gene Expression, Bethesda, United States

   Contribution

   Data curation, Validation, Investigation

   Competing interests

   No competing interests declared

4. Tatini Rakshit

   1. National Cancer Institute, Center for Cancer Research, Laboratory Receptor Biology and Gene Expression, Bethesda, United States
   2. Department of Chemistry, Shiv Nadar University, Dadri, India

   Contribution

   Conceptualization

   Competing interests

   No competing interests declared

5. Yamini Dalal

   National Cancer Institute, Center for Cancer Research, Laboratory Receptor Biology and Gene Expression, Bethesda, United States

   Contribution

   Conceptualization, Supervision, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   dalaly@mail.nih.gov

   Competing interests

   Reviewing editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0002-7655-6182

• 724

  Page views

• 70

  Downloads

• 0

  Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.