Covariance distributions in single particle tracking

Bailey, Mary Lou P.; Yan, Hao; Surovtsev, Ivan V.; Williams, Jessica F.; King, Megan C.; Mochrie, S. G. J.

doi:10.1103/physreve.103.032405

Cited by 6 publications

(5 citation statements)

References 39 publications

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“…The position of each individual locus at each time step was determined by fitting a Gaussian to the diffraction-limited lacO /GFP-LacI focus; the motion of the locus was subsequently tracked using a single particle tracking (SPT) algorithm (Crocker and Grier, 1996). Thousands of individual loci tracks were then subjected to motion analysis by calculating and fitting the mean squared displacement (MSD) versus time to theoretical profiles that take into account localization noise and motion blur (Bailey et al, 2021). Covariance analysis of the particle tracks further revealed that the motion of gene loci in fission yeast can be described by a single diffusive state (Bailey et al, 2021).…”

Section: Resultsmentioning

confidence: 99%

“…Thousands of individual loci tracks were then subjected to motion analysis by calculating and fitting the mean squared displacement (MSD) versus time to theoretical profiles that take into account localization noise and motion blur (Bailey et al, 2021). Covariance analysis of the particle tracks further revealed that the motion of gene loci in fission yeast can be described by a single diffusive state (Bailey et al, 2021). On the seconds timescale, we observe a characteristic sub-diffusive behavior in fission yeast that is nearly identical at six different genomic positions (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…( a ) Cells labeled with a lacO array inserted adjacent to the gene of interest or at random are visualized using GFP-LacI fluorescence. DNA loci are tracked using a custom 2D single particle tracking algorithm (Bailey et al, 2021). (b) Chromatin diffusivity is nearly identical across six different genomic locations as shown by the mean squared displacement (MSD) of each genetic locus as a function of the time.…”

Section: Resultsmentioning

confidence: 99%

“…Then, individual displacements were averaged across all sub-trajectories and different time-lapse series for a given experiment. The calculated experimental MSDs were fit by the theoretical MSD for fractional Brownian motion (fBm) with static and dynamic localization errors taken into account, as described (Bailey et al, 2021): where D is an effective diffusivity, Δ t E = 10 ms is exposure time, Δ t is the time step between frames, and α is an exponent (α < 1 corresponds to sub-diffusive motion and α > 1 to super-diffusive motion), n is the number of frames, and σ is the static localization error. Initial MSD fitting, in which D , α, and σ were varied, showed that the values of α grouped around a mean of 0.44 for most measured loci in unperturbed, wild type cells (Supplemental Table S1).…”

Section: Methodsmentioning

confidence: 99%

“…Then, individual displacements were averaged across all sub-trajectories and different timelapse series for a given experiment. The calculated experimental MSDs were fit by the theoretical MSD for fractional Brownian motion (fBm) with static and dynamic localization errors taken into account, as described (Bailey et al, 2021):…”

Section: Loci Tracking and Trajectory Analysismentioning

confidence: 99%

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Loops and the activity of loop extrusion factors constrain chromatin dynamics

Bailey

Surovtsev

Williams

et al. 2020

Preprint

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Chromosome conformation capture techniques (e.g Hi-C) reveal that intermediate-scale chromatin organization is comprised of “topologically associating domains” (TADs) on the tens to thousands of kb scale.1–5 The loop extrusion factor (LEF) model6–10 provides a framework for how TADs arise: cohesin or condensin extrude DNA loops, until they encounter boundary elements. Despite recent in vitro studies demonstrating that cohesin and condensin can drive loop formation on (largely) naked DNA11–13, evidence supporting the LEF model in living cells is lacking. Here, we combine experimental measurements of chromatin dynamics in vivo with simulations to further develop the LEF model. We show that the activity of the INO80 nucleosome remodeler enhances chromatin mobility, while cohesin and condensin restrain chromatin mobility. Motivated by these findings and the observations that cohesin is loaded preferentially at nucleosome-depleted transcriptional start sites14–18 and its efficient translocation requires nucleosome remodeling19–23 we propose a new LEF model in which LEF loading and loop extrusion direction depend on the underlying architecture of transcriptional units. Using solely genome annotation without imposing boundary elements, the model predicts TADs that reproduce experimental Hi-C data, including boundaries that are CTCF-poor. Furthermore, polymer simulations based on the model show that LEF-catalyzed loops reduce chromatin mobility, consistent with our experimental measurements. Overall, this work reveals new tenets for the origins of TADs in eukaryotes, driven by transcription-coupled nucleosome remodeling.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Loci Tracking and Trajectory Analysismentioning

confidence: 99%

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Loops and the activity of loop extrusion factors constrain chromatin dynamics

Bailey

Surovtsev

Williams

et al. 2020

Preprint

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Loops and the activity of loop extrusion factors constrain chromatin dynamics

Bailey

Surovtsev

Williams

et al. 2023

MBoC

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The chromosomes - DNA polymers and their binding proteins - are compacted into a spatially organized, yet dynamic, three-dimensional structure. Recent genome-wide chromatin conformation capture experiments reveal a hierarchical organization of the DNA structure that is imposed, at least in part, by looping interactions arising from the activity of loop extrusion factors. The dynamics of chromatin reflects the response of the polymer to a combination of thermal fluctuations and active processes. However, how chromosome structure and enzymes acting on chromatin together define its dynamics remains poorly understood. To gain insight into the structure-dynamics relationship of chromatin, we combine high-precision microscopy in living Schizosaccharomyces pombe cells with systematic genetic perturbations and Rouse-model polymer simulations. We first investigated how the activity of two loop extrusion factors, the cohesin and condensin complexes, influences chromatin dynamics. We observed that deactivating cohesin, or to a lesser extent condensin, increased chromatin mobility, suggesting that loop extrusion constrains rather than agitates chromatin motion. Our corresponding simulations reveal that the introduction of loops is sufficient to explain the constraining activity of loop extrusion factors, highlighting that the conformation adopted by the polymer plays a key role in defining its dynamics. Moreover, we find that the number loops or residence times of loop extrusion factors influences the dynamic behavior of the chromatin polymer. Last, we observe that the activity of the INO80 chromatin remodeler, but not the SWI/SNF or RSC complexes, is critical for ATP-dependent chromatin mobility in fission yeast. Taken together we suggest that thermal and INO80-dependent activities exert forces that drive chromatin fluctuations, which are constrained by the organization of the chromosome into loops.

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