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).

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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.

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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.

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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).

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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).

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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.

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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).

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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).

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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.

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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).

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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).

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Figure 5—source data 1

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

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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).

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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

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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.

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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

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