Organization of the mitotic chromosome

Abstract

Mitotic chromosomes are among the most recognizable structures in the cell, yet for over a century their internal organization remains largely unsolved. We applied chromosome conformation capture methods, 5C and Hi-C, across the cell cycle and revealed two distinct three-dimensional folding states of the human genome. We show that the highly compartmentalized and cell type-specific organization described previously for nonsynchronous cells is restricted to interphase. In metaphase, we identified a homogenous folding state that is locus-independent, common to all chromosomes, and consistent among cell types, suggesting a general principle of metaphase chromosome organization. Using polymer simulations, we found that metaphase Hi-C data are inconsistent with classic hierarchical models and are instead best described by a linearly organized longitudinally compressed array of consecutive chromatin loops.

Figure 1. Organization of chromosome 21 through the cell cycle

(A). FACS profiles and microscopy images of cell populations analyzed in this study. Images show DAPI-stained DNA (blue) and alpha-tubulin (green), scale bars are one micron. Image under M shows cells arrested in metaphase (12 hours nocodazole); top left inset shows cells with intact spindle; right half shows nocodazole-arrested metaphase chromosomes with disrupted spindles; bottom inset shows arrested chromosomes stained for SMC2, showing separated sister chromatids. (Right) Non-synchronous population consists of a mixture of all cell-cycle phases. Circular diagram shows cell cycle, with red markers indicating studied synchronization samples. Inside of cell-cycle circle: correlation matrix between 5C interaction patterns of both non-synchronous cells and all studied stages of the cell cycle (Methods). (B). Corrected 5C matrices of chromosome 21 for these cell populations; raw 5C data were binned to 250 Kb with a 50 Kb sliding window, and corrected using ICE. Grey regions are not interrogated in this study. (C). A/B compartment profile for each data set. (D). TAD signal for each data set.

Figure 2. Hi-C analysis of chromosome organization in G1 and Mitotic cells

(A) Relative Hi-C contact probability maps for chromosome 17 and an equally sized 82 Mb region of chromosome 4, at 1Mb resolution. M-phase arrest: 12 hours nocodazole. (B) A/B compartment profile for these regions. (C) Zoom-in of 4Mb sub-regions. (Top) Region of a contact map at 40Kb resolution. (Bottom) TAD signal for this region. (D) Hi-C contact probability maps for a region of chromosome 14 in interphase and metaphase. Displayed are HeLaS3-G1, HFF1-NS (non-synchronous), and published K562-NS (7) datasets (left) and HeLaS3-M, HFF1-M, and K562-M datasets (right).

Figure 3. Contact probability as a function of genomic distance

To compare experiments with different numbers of reads, here and below all P(s) plots are normalized to integrate to one. (A). Contact probability for interphase and mitotic cells averaged over all chromosomes; datasets as in Fig 2D. Arrows indicate fold-change from interphase to metaphase. (B). Contact probability for individual HeLa S3 mitotic chromosomes, compared with P(s)~s^−0.5. Diagrams on the right illustrate that loci separated by fewer than 10Mb occupy overlapping longitudinal positions, whereas loci separated by more than 10Mb rarely overlap. (C). Mitotic P(s) below 10Mb plotted against schematic P(s) for fractal and equilibrium globule states. Insets show spatial organization of simulated polymer fibers for each state, where fibers (here and below) are colored from blue to red along their lengths. Observed P(s) for mitotic chromosomes falls in-between that of an equilibrium globule, where regions of the polymer are highly mixed, and a fractal globule, where different regions are spatially segregated.

Figure 4. Polymer models of mitotic chromosome organization (left) and their corresponding P(s) (right)

Experimental P(s) in metaphase (grey shaded area) is bounded by minimum and maximum P(s) calculated from six independent Hi-C datasets (three cell lines). (A) Linear organization model: each monomer is constrained to have reproducible mean longitudinal positions with 120nm standard deviation (illustrated in the diagram, next to an example of a polymer conformation for this model). (B) Hierarchical model formed by successively folding the fiber into a next level fiber, here using loops with average length of 9kb, 240kb and 4.8Mb; conformation colored from blue to red at each level of magnification (Figures S13, S14). (C) Models with consecutive loops, cylindrical geometry, and linear organization. Bases of the loops (red) are either free (left) or attracted to a central scaffold (middle). For optimal loop sizes, P(s) curves for these models approach experimental P(s). (D) Models with non-consecutive loops, cylindrical geometry and linear organization, either free (left) or attracted to a central scaffold (middle). Non-consecutive loops are obtained by randomizing positions of consecutive loop bases, while maintaining loop lengths. Models with non-consecutive loops have worse agreement with metaphase P(s) than models with consecutive loops (Figure S15).

Figure 5. A two-stage process of mitotic chromosome folding

(A). Stage I: linear compaction by formation of consecutive chromosomal loops leads to the formation of a fiber of loop bases. Stage II: homogeneous axial compression of the fiber’s backbone leads to formation of a dense chromosome. This two-stage process produces a chromosome with the appropriate cylindrical geometry and linear organization (genomic position is indicated by the coloring from blue to red). (B) Contact probability P(s) for the two-stage process compared with observed P(s) (grey shaded area as in Figure 4). (C). Average contact map for chromosomes folded by two-stage process.