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## Results: 11

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2.

Cell imaging and segmentation. (

*A*) Automatically segmented*C. reinhardtii*cell. The cell boundary is indicated in red, and the locations of the organelles, DNA-micronucleoids in this case, are indicated with white circles. (*B*) Transmitted light micrograph of the same cell. (*C*) Florescence in the DAPI channel, indicating the presence of DNA, in the same cell.3.

Simulated results demonstrate applicability of the method in both two and three dimensions. (

*A*) Network diagram of the distance measurements in*B*. (*B*) Comparison of distances obtained using different organizational parameters of cells with full 3D simulations and those obtained using the 2D projections of the same 3D data. max, maximum; min, minimum; rand, random; sim, simulation.4.

Statistical method for quantifying cell organization. (

*A*) Voronoi tessellation of a single cell is computed and used to calculate the variance of the Voronoi facets within the cell. (*B*) Monte Carlo simulation is used calculate the distribution of variances for the null model in a given cell. (*C*)*P*values for each cell in the null model are calculated using the variance of the real cells and the distribution of variances found by Monte Carlo simulation.5.

Analysis of organization in mutants with centrioles defects. (

*A*) Comparison of*B*and*C*. (*B*) K-S statistical distance from populations of cc125 (WT), asq2 (mutant), and bld10 (mutant) cells to a population of cc124 cells (blue). (*C*) Same statistical distance measure applied to simulated versions of the four strains with randomly placed organelles. These simulated cells had the same parameters as the WT cells, but the locations of their organelles were drawn from a uniform random distribution within an ellipsoid. The real distance between the two WT populations, cc125 and cc124, is indistinguishable from statistically identical populations in the same cells. In contrast, both mutants (asq2 and bld10) are significantly distant from WT cc124, indicating a different underlying organizational distribution.6.

Schematic description of simulations that test the effect of differences in real cell size and shape on outcomes of organizational difference tests. (

*A*) Segmented data from real cells defining the number of organelles and the boundary of the cell are the starting point for creating the real cell simulations. (*B*) For each cell, the number of organelle points in the real cell is replaced based on the type of organizational distribution desired. (*C*) Each real cell in the dataset is replaced with a simulated cell, with the only difference being that the placement of the organelles is now in the simulated pattern (placed in pairs in the case of this example).7.

Examples of different kinds of organization and the distribution of

*P*values that each type of organization creates with repeated simulations in a single example cell. (*A*) Uniform distribution of points within the cell. The cell with the minimum interorganelle distance from among 1,000 random cells (*B*), the cell with the maximum interorganelle distance from among 1,000 random cells (*C*), and the cell in which points are placed in pairs, with the position of the pairs being uniformly distributed (*D*), are illustrated. (*E*) Frequency vs. Ln (*P*value) curves for each type of simulation. Ln, natural logarithm; NRClumpsPvals, distribution of cells placed in a nonrandom clumped pattern; NRMaxPvals, distribution of cells placed in a nonrandom distance maximized pattern; NRMinPVals, distribution of cells placed in a nonrandom distance minimized pattern; NRRandomPvals, distribution of cells placed in a random pattern.8.

Diagram of the methodology used to compare the organization between two populations of cells. (

*A*) Each separate population of cells is fixed, stained, and placed on a slide. (*B*) Each separate slide is imaged using standard fluorescence microscopy at 20× magnification. (*C*) Software is used to segment each cell in each population. (*D*) Consensus binning scheme based on volume and organelle number is determined for the populations to be compared; an equal number of cells from each bin are randomly selected from each population. (*E*)*P*values of each the selected cells are calculated as is done in Fig. 1. (*F*) KG statistical distance between the*P*values of the populations is calculated.*D*,*E*, and*F*are iterated to bootstrap the variance and mean of the statistical distance.9.

Analysis of organization in mutants with defects in centrioles. (

*A*) Network diagram illustrates the comparisons shown in*B*and*C*. (*B*) KS statistical distance from populations of cc125 (WT), asq2 (mutant), and bld10 (mutant) cells to a population of cc124 cells (in blue). Identically sized samples were taken from several size and area bins for each cell line. (*C*) Same statistical distance measure is applied to simulated versions of the same four strains with randomly placed organelles. These simulated cells had the same parameters as the WT cells, but the locations of their organelles were drawn from a uniform random distribution within an ellipsoid. This result indicates that the real distance between the two WT populations, cc125 and cc124, is indistinguishable from that of statistically identical populations in the same cells. In contrast, it appears that both mutants,*asq2*and*bld10*, are significantly distant from WT cc124, indicating a different underlying organizational distribution.10.

Organizational differences can be detected despite differences in cell size and shape between different populations. (

*A*) Network diagram of the comparisons used in generating the graph to the right. Every simulation is compared with the “Rand sim” point placement in cc124. (*B*) Spatial bias can be detected despite variation in cell size, shape, and organelle number between mutant and WT strains. Statistically significant differences are seen between all compared groups, with the exception of the random-to-random comparisons, which are not statistically significant. The ellipse configuration and number of organelles from each of the real cells were used to generate synthetic datasets (corresponding directly to each real population) using the nonbiased and biased simulation methods of Fig. 3. The distance between the synthetic cc124 dataset generated with a random distribution of points and each of the synthetic datasets was calculated. Simulations of uniform point distribution in both population pairs (dark red) contrast strongly with simulations using uniform distribution in one population and biased distribution in the other, revealing sensitivity to changes in organizational type despite variation in cell size, shape, or organelle number. Dist, distance; Max, maximum; Min, minimum; Rand, random; Sim, simulation.11.

Cell size and shape differences between mutant and WT cells do not create the observed organizational differences. (

*A*) Network diagram illustrates the comparisons used in generating the graph to the right. Each simulation is conducted using cell size, shape, and nucleoid numbers drawn from real cells but with the placement of the organelles simulated according to the four hypothetical organization schemes from Fig. 3. (*B*) Cell shape and organelle differences do not cause significant differences in the apparent degree of organization between mutant and WT cells. The ellipse configuration and number of organelles from real cells were used to generate synthetic datasets (corresponding directly to each real population) using the nonbiased and biased simulation methods of Fig. 3. The distance between each of the different synthetic datasets (for cc125,*asq2*, and*bld10*) was measured against the synthetic dataset generated using cell shape and organelle number from WT strain cc124 simulated with the same spatial model (random and three different bias models). Results show that when comparing these populations with similar underlying distributions using different cell populations for cell shape and organelle number, the resulting KS distance is not statistically significant for all simulations. Dist, distance; Max, maximum; Min, minimum; Rand, random; Sim, simulation.