Single Epigenetic Enzyme Tracking Predicts Dynamic Chromatin Remodeling in Living Cells

Introduction: DNA conformation in the nucleus plays a pivotal role in regulating gene expression in vivo. This steady-state conformation arises from the balance of multiple epigenetic enzymes including histone deacetylase 1 (HDAC) [1]. Traditional methods to measure enzyme activity have used cell-free systems lacking the genomic context necessary for such measurements. Alternatively, diffraction-limited live-cell microscopy has provided valuable insights, though these techniques average measurements across numerous enzymes [2]. To overcome these limitations, this study aims to precisely quantify the catalytic activity of individual enzymes (i.e., HDAC) within the nucleus of living cells by employing single particle tracking (SPT). Using HaloTag labeling, our approach allows for the analysis of molecular trajectory data by delineating tracks into chromatin-bound (Bound) and freely-diffusing (Free) populations to estimate diffusion coefficients and profile enzyme activity levels. Furthermore, we employ a decision tree (DT) machine learning algorithm to classify distinct molecular behaviors. All HDAC tracking data was validated by tracking Histone-H2B (H2B) motion and by super-resolution imaging (i.e., H2B-STORM) on fixed cells to measure genomic conformation.

Materials and

Methods: HeLa cell lines were established via lentiviral transduction, introducing fusion protein constructs containing a HaloTag protein (i.e., HDAC-HaloTag or H2B-HaloTag). These cells were then cultured on chambered coverglass slides and labeled with JF-549 HaloTag ligand (Promega). Chemical inhibitor treatments including Trichostatin A (TSA, histone deacetylase inhibitor, 200ng/mL for 6 hours) and Y-27632 (ROCK inhibitor, 20µM for 1 hour) were administered. Imaging was performed on the Nanoimager (ONi). Spot detection and tracking were performed using the ImageJ plugin TrackMate and kinetic modeling of experimental data was done using SpotOn [3, 4]. Trajectory characterization was achieved via machine learning methodologies in Spyder. STORM imaging was performed on fixed HeLa cells immunostained for H2B (Invitrogen) and analyzed on MATLAB following established protocols [5].

Results, Conclusions, and Discussions:

Results: HeLa cell lines were successfully generated and validated for protein expression of HDAC or H2B fusion constructs. SPT of HDAC enzymes was performed following the workflow schematic (Fig. 1A). STORM imaging of HeLa cell nuclei revealed global chromatin decondensation under TSA treatment (Fig. 1A, C). As a validation of HDAC SPT, TSA treatment produced an elevation in the diffusion coefficient of the chromatin-bound fraction, consistent with Bound H2B tracking data and super-resolution measurements (Fig. 1B-C). DT model analysis of tracks showed 62.33% (± 15.84%) accuracy with 5-fold cross-validation and 100 repetitions when classifying control and TSA groups. To interrogate the impact of cellular contractility, HDAC trajectory analysis with Y-27632 treatment revealed an increase in diffusive motion for both Bound and Free populations, consistent with the super-resolution assessment of chromatin state. Analysis of Bound and Free population abundance revealed a larger proportion of the Free HDAC population with Y-27632, indicating decreased epigenetic activity (Fig. 2A-B).

Discussion: This super-resolution imaging technique provides novel insight into the dynamic behavior of epigenetic remodeling enzymes within living cells. Unlike cell-free methods, SPT captures epigenetic enzyme activity depending on local genome binding site accessibility, as variations in enzyme behavior correlate with chromatin conformation within distinct nuclear compartments. This method shows a high predictive potential of dynamic chromatin rearrangements with machine learning assessment. ROCK inhibition reveals insights into the role of cell contractility in HDAC activity regulation, impacting mechanobiological sensing and cell identity maintenance. Taken together, SPT-based super-resolution imaging advances our understanding of epigenetic regulation and dynamic chromatin rearrangements, offering therapeutic potential in areas of cellular reprogramming and disease treatment.

Acknowledgements (Optional): Acknowledgements: This work is supported by the NIH (P50 AR080581).

References: [1] Magaril+ (2023), Cell Dev Biol; [2] Zotter+ (2017), JBC; [3] Tinevez+ (2017), Sci Direct Methods; [4] Hansen+ (2018), eLife; [5] Heo+ (2022), Sci Adv.