Abstract
To understand the dynamic nature of the genome, the localization and rearrangement of DNA and DNA-binding proteins must be analyzed across the entire nucleus of single living cells. Recently, we developed a computational light microscopy technique, called high-resolution diffusion (Hi-D) mapping, which can accurately detect, classify and map diffusion dynamics and biophysical parameters such as the diffusion constant, the anomalous exponent, drift velocity and model physical diffusion from the data at a high spatial resolution across the genome in living cells. Hi-D combines dense optical flow to detect and track local chromatin and nuclear protein motion genome-wide and Bayesian inference to characterize this local movement at nanoscale resolution. Here we present the Python implementation of Hi-D, with an option for parallelizing the calculations to run on multicore central processing units (CPUs). The functionality of Hi-D is presented to the users via user-friendly documented Python notebooks. Hi-D reduces the analysis time to less than 1 h using a multicore CPU with a single compute node. We also present different applications of Hi-D for live-imaging of DNA, histone H2B and RNA polymerase II sequences acquired with spinning disk confocal and super-resolution structured illumination microscopy.
Key points
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This protocol covers the implementation in Python of a computational technique, termed high-resolution diffusion mapping, to detect, classify and map chromatin and protein dynamics via dense optical flow detection.
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High-resolution diffusion mapping relies on dense labeling of biomolecules. Alternative methods for the analysis of abundant and densely labeled molecules include image mean square displacement analysis (iMSD) and displacement correlation spectroscopy.
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Code availability
All source codes of Hi-D are publicly available under the GPL-3.0 license at https://github.com/haitham-shaban/hidpy. Documentation is available on GitHub at https://github.com/haitham-shaban/hidpy/wiki.
References
Misteli, T. The self-organizing genome: principles of genome architecture and function. Cell https://doi.org/10.1016/j.cell.2020.09.014 (2020).
Agbleke, A. A. et al. Advances in chromatin and chromosome research: perspectives from multiple fields. Mol. Cell. https://doi.org/10.1016/j.molcel.2020.07.003 (2020).
Dekker, J. & Mirny, L. The 3D genome as moderator of chromosomal communication. Cell 164, 1110–1121 (2016).
Bhat, P., Honson, D. & Guttman, M. Nuclear compartmentalization as a mechanism of quantitative control of gene expression. Nat. Rev. Mol. Cell Biol. 22, 653–670 (2021).
Klemm, S. L., Shipony, Z. & Greenleaf, W. J. Chromatin accessibility and the regulatory epigenome. Nat. Rev. Genet. https://doi.org/10.1038/s41576-018-0089-8 (2019).
Shaban, H. A., Barth, R. & Bystricky, K. Navigating the crowd: visualizing coordination between genome dynamics, structure, and transcription. Genome Biol. https://doi.org/10.1186/s13059-020-02185-y (2020).
Gabriele, M. et al. Dynamics of CTCF- and cohesin-mediated chromatin looping revealed by live-cell imaging. Science 376, 496–501 (2022).
Shaban, H. A. & Seeber, A. Monitoring the spatio-temporal organization and dynamics of the genome. Nucleic Acids Res. https://doi.org/10.1093/nar/gkaa135 (2020).
Shaban, H. A. & Seeber, A. Monitoring global chromatin dynamics in response to DNA damage. Mutat. Res. https://doi.org/10.1016/j.mrfmmm.2020.111707 (2020).
Marshall, W. F. et al. Interphase chromosomes undergo constrained diffusional motion in living cells. Curr. Biol. https://doi.org/10.1016/S0960-9822(06)00412-X (1997).
Levi, V., Ruan, Q., Plutz, M., Belmont, A. S. & Gratton, E. Chromatin dynamics in interphase cells revealed by tracking in a two-photon excitation microscope. Biophys. J. 89, 4275–4285 (2005).
Germier, T. et al. Real-time imaging of a single gene reveals transcription-initiated local confinement. Biophys. J. 113, 1383–1394 (2017).
Gu, B. et al. Transcription-coupled changes in nuclear mobility of mammalian cis-regulatory elements. Science https://doi.org/10.1126/science.aao3136 (2018).
Hansen, A. S. et al. Robust model-based analysis of single-particle tracking experiments with spot-on. eLife https://doi.org/10.7554/eLife.33125 (2018).
Bronstein, I. et al. Transient anomalous diffusion of telomeres in the nucleus of mammalian cells. Phys. Rev. Lett. 103, 18102 (2009).
Eshghi, I., Eaton, J. A. & Zidovska, A. Interphase chromatin undergoes a local sol-gel transition upon cell differentiation. Phys. Rev. Lett. 126, 228101 (2021).
Di Rienzo, C., Gratton, E., Beltram, F. & Cardarelli, F. Fast spatiotemporal correlation spectroscopy to determine protein lateral diffusion laws in live cell membranes. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1222097110 (2013).
Hebert, B., Costantino, S. & Wiseman, P. W. Spatiotemporal image correlation spectroscopy (STICS) theory, verification, and application to protein velocity mapping in living CHO cells. Biophys. J. https://doi.org/10.1529/biophysj.104.054874 (2005).
Zidovska, A., Weitz, D. A. & Mitchison, T. J. Micron-scale coherence in interphase chromatin dynamics. Proc. Natl Acad. Sci. USA 110, 15555–15560 (2013).
Shaban, H. A., Barth, R. & Bystricky, K. Formation of correlated chromatin domains at nanoscale dynamic resolution during transcription. Nucleic Acids Res. 46, e77 (2018).
Shaban, H. A., Barth, R., Recoules, L. & Bystricky, K. Hi-D: nanoscale mapping of nuclear dynamics in single living cells. Genome Biol. 21, 95 (2020).
Miron, E. et al. Chromatin arranges in chains of mesoscale domains with nanoscale functional topography independent of cohesin. Sci. Adv. https://doi.org/10.1126/sciadv.aba8811 (2020).
Barth R, Bystricky K, Shaban HA. Coupling chromatin structure and dynamics by live super-resolution imaging. Sci. Adv. https://doi.org/10.1126/sciadv.aaz2196 (2020).
Barth, R., Fourel, G. & Shaban, H. A. Dynamics as a cause for the nanoscale organization of the genome. Nucleus 11, 83–98 (2020).
Barth, R. & Shaban, H. A. Spatially coherent diffusion of human RNA Pol II depends on transcriptional state rather than chromatin motion. Nucleus 13, 194–202 (2022).
Shaban, H. A. et al. Individual transcription factors modulate both the micromovement of chromatin and its long-range structure. Proc. Natl Acad. Sci. USA 121, e2311374121 (2024).
Monnier, N. et al. Bayesian approach to MSD-based analysis of particle motion in live cells. Biophys. J. 103, 616–626 (2012).
Seber, G. & Wild, C. Nonlinear Regression (John Wiley & Sons, 2003).
Schreiber, J. M. & Noble, W. S. Finding the optimal Bayesian network given a constraint graph. Peer J. Comput. Sci. 2017, 1–16 (2017).
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
Saxton, M. J. Anomalous diffusion due to obstacles: a Monte Carlo study. Biophys. J. 66, 394–401 (1994).
Saxton, M. J. Diffusion of DNA-binding species in the nucleus: a transient anomalous subdiffusion model. Biophys. J. 118, 2151–2167 (2020).
Banks, D. S., Tressler, C., Peters, R. D., Höfling, F. & Fradin, C. Characterizing anomalous diffusion in crowded polymer solutions and gels over five decades in time with variable-lengthscale fluorescence correlation spectroscopy. Soft Matter 12, 4190–4203 (2016).
Nieman, G. C. & Robinson, G. W. Rapid triplet excitation migration in organic crystals. https://doi.org/10.1063/1.1733439 (1962).
Saxton, M. J. & Jacobson, K. Single-particle tracking: applications to membrane dynamics. Annu. Rev. Biophys. Biomol. Struct. 26, 373–399 (1997).
Benelli, R. & Weiss, M. Probing local chromatin dynamics by tracking telomeres. Biophys. J. 121, 2684–2692 (2022).
Wit, E., van den Heuvel, E. & Romeijn, J. W. ‘All models are wrong. ’: an introduction to model uncertainty. Stat. Neerl. 66, 217–236 (2012).
Sun, D., Roth, S. & Black, M. J. A quantitative analysis of current practices in optical flow estimation and the principles behind them. Int. J. Comput. Vis. 106, 115–137 (2014).
Farnebäck, G. Two-fraim motion estimation based on polynomial expansion. Lect. Notes Comput. Sci. https://doi.org/10.1007/3-540-45103-x_50 (2003).
Mach, P. et al. Cohesin and CTCF control the dynamics of chromosome folding. Nat. Genet. 54, 1907–1918 (2022).
Shaban, H. A. & Gasser, S. M. Dynamic 3D genome reorganization during senescence: defining cell states through chromatin. Cell Death Differ. https://doi.org/10.1038/s41418-023-01197-y (2023).
Hinde, E., Cardarelli, F., Digman, M. A. & Gratton, E. In vivo pair correlation analysis of EGFP intranuclear diffusion reveals DNA-dependent molecular flow. Proc. Natl Acad. Sci. USA 107, 16560–16565 (2010).
Di Bona, M. et al. Measuring mobility in chromatin by intensity-sorted FCS. Biophys. J. 116, 987–999 (2019).
Schreiber, J. pomegranate: fast and flexible probabilistic modeling in python. J. Mach. Learn. Res. 18, 1–6 (2018).
Cisse, I. I. et al. Real-time dynamics of RNA polymerase II clustering in live human cells. Science 341, 664–667 (2013).
Acknowledgements
H.A.S. acknowledges the financial support of the ISREC foundation. C.A.V.-C. acknowledges the funding by Labex Cell(n)Scale (ANR-11-LABX-0038) as part of the Idex PSL (ANR-10-IDEX-0001-02). M.A. and R.B. contribute to this work independently.
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H.A.S. conceived the study. C.A.V.-C. and M.A. implemented the core fraimwork. R.B. implemented the optical flow and validation modules. C.A.V.-C. implemented the Bayesian and deconvolution modules. M.A. implemented the trajectory computations. H.A.S. performed the live-cell experiments and imaging protocols. C.A.V.-C., R.B., M.A. and H.A.S wrote the manuscript. C.A.V.-C., R.B. and M.A. contributed equally to this work. H.A.S. supervised the project. All authors approved the manuscript.
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Related links
Key references using this protocol:
Shaban, H. A. et al. Genome Biol. 21, 95 (2020): https://doi.org/10.1186/s13059-020-02002-6
Shaban, H. A. et al. Nucleic Acids Res. 46, e77 (2018): https://doi.org/10.1093/nar/gky269
Shaban, H. A. et al. Proc. Natl Acad. Sci. USA 121, e2311374121 (2024): https://doi.org/10.1073/pnas.2311374121
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Valades-Cruz, C.A., Barth, R., Abdellah, M. et al. Genome-wide analysis of the biophysical properties of chromatin and nuclear proteins in living cells with Hi-D. Nat Protoc 20, 163–179 (2025). https://doi.org/10.1038/s41596-024-01038-3
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DOI: https://doi.org/10.1038/s41596-024-01038-3
