Robust assessment of asymmetric division in colon cancer cells
- Center for Life Nano and Neuro Science, Istituto Italiano di Tecnologia, Italy
- Department of Physics, Sapienza University, Italy
- Department of Medical Biotechnology and Translational Medicine, University of Milan, Italy
- Istituto di Scienze del Patrimonio Culturale, Consiglio Nazionale delle Ricerche, Italy
Figures
Figure 1 with 3 supplements
Modeling partitioning noise at cell division.
(a) Schematic representation of the growth and division process of a mother cell. The cell undergoes an initial phase of duplication, where its internal elements are multiplied, followed by a division phase, where these elements are partitioned between the two daughter cells. One daughter inherits a fraction of the mother’s elements, while the other receives the remaining fraction . (b) (Blue) Idealized behavior of the components number of a cell element over time, considering three different noise terms: fluctuations in compound counts during growth, uncertainty in the timing of division, and noise in the compound’s partition fraction. (Red) Same description, but considering only production and degradation processes. (c) Microscopy images of a single-cell division event for an HCT116 cell, whose cytoplasm is stained with Celltrace Yellow. (d) Time evolution of the distribution of the components’ number of a cellular element for a population of cells subjected only to partitioning noise. The distribution used for the simulation is a Gaussian distribution with mean and standard deviation . (e) Examples of partition distributions , with increasing coefficient of variation. (f) Mean () and variance () of the number of components as a function of the generations, g, for the proliferation of a population subject to different partition noise distributions. Different colors correspond to distributions with varying coefficients of variation, as represented in the top panels. The dashed line represents the theoretical behavior obtained from the model. Dots are colored according to the distributions shown in panel (e). (g) Behavior of the mean (left) and standard deviation (right) of the simulated dynamics compared to the expected theoretical behavior for a proliferating population assuming a sizer division strategy (see Appendix).
Figure 1—figure supplement 1
Mean (left) and variance (right) of the distribution of fluorescence intensity as a function of the generations for simulations with cell-cycle length variability.
The division time of each cell is extracted from an Erlang distribution (mean = 18 hr and k = 4). Partitioning noise and variability in division time are considered independent. The results are compatible with the theoretical framework with no dependency on the coefficient of variation (CV).
Figure 1—figure supplement 2
Stability to coupled noises.
Comparison with the analytical expression of the mean (left) and variance (right) of the component distribution as a function of generation and the properties of the partitioning distribution in a coupled noise system. Different colors correspond to different coefficients of variation () of the partitioning distribution.
Figure 1—figure supplement 3
Fit of the coupled noise simulations.
(Left) Fit of the simulated experiments for the variance of the distribution of components as a function of the generation (Equation 11) for multiple values of the coefficient of variation . The dashed line is the fitted curves, while points are the simulated results. (Right) Measured CVs compared to expected values. The greater the partitioning noise, the larger the error in estimating the variance from fitting the dynamics. The blue area represents the lowest uncertainty level observed in experimental measurements (20%). This curve demonstrates the robustness of our method, as the estimates remain within the pre-established experimental error margin.
Figure 2 with 11 supplements
Quantification of partitioning noise via population-level measurements.
(a) Time evolution of CellTrace-Violet fluorescence intensity distribution measured in a flow cytometry time course experiment for a population of HCT116 cells. Time progresses from the darkest shade of blue to the lightest, spanning from [0, 84] hr (bottom line to top). (b) Snapshots of the evolution of the distribution of CellTrace-Violet fluorescence intensity measured in a flow cytometry time course experiment for a population of HCT116 cells. Experimental data are represented by the light blue histogram, while the best-fit Gaussian mixture model is displayed as lines, with different colors representing different generations. (c) Mean (left) and variance (right) of the intensity of the fluorescent markers as a function of generations, normalized to the initial population values. Each replica of the experiment is identified by a different color and a point marker’s shape. The experiments are conducted on Caco2 cells, and the points correspond to the mean values for each generation. Dashed lines represent the best fit according to Equations 6 and 10, respectively. (d) Division asymmetry of the obtained by fitting Equation 10 to data for all experiments and cell lines. The division asymmetry is measured via the percentage of the coefficient of variation.
Figure 2—figure supplement 1
Gate strategy for isolation of CTV-positive HCT-116 cells.
Representative dot plots of HCT-116 cells showing the gate strategy for isolation of CTV highly positive cells. Cells first gated based on forward scatter versus side scatter area parameters (FSC-A and SSC-A) were then selected for doublets exclusion (heights, H versus area, A for both FSC and side scatter; upper panels) and finally collected based on CTV highest expression level (left lower panels). CTV-positive cells sorting efficiency is shown in the right lower panel. To show the sorted cells (blue), backgating strategy is used in all plots to identify cell population and to confirm the gate strategy.
Figure 2—figure supplement 2
Components distribution at varying coefficient of variations (CVs) of initial components and partitioning distributions.
Starting from a condition in which both division asymmetry and wideness of the initial components distribution are low and different generations are clearly separable, increasing either the CVs leads to distribution mixing and greater reconstruction difficulty.
Figure 2—figure supplement 3
Resume of experimental data and fit.
In this figure, we resume the results of all the experiments we have performed on the different studied cell lines. The fitting procedure returns the value of the variance of the partitioning distribution function as presented in the general formulation of the model (Equation 11). In (A) and (B) is shown the obtained fit for and , respectively, on HCT-116 cells. In (C) and (D) on CCD18Co cells. In (E) and (F) on Caco2 cells.
Figure 2—figure supplement 4
Replica 1 of Caco2 cells experiments.
In the figure are shown the raw data and the fit of the different generations with the Gaussian mixture model (GMM) for a single experiment of Caco2 cells.
Figure 2—figure supplement 5
Replica 2 of Caco2 cells experiments.
In the figure are shown the raw data and the fit of the different generations with the Gaussian mixture model (GMM) for a single experiment of Caco2 cells.
Figure 2—figure supplement 6
Replica 3 of Caco2 cells experiments.
In the figure are shown the raw data and the fit of the different generations with the Gaussian mixture model (GMM) for a single experiment of Caco2 cells.
Figure 2—figure supplement 7
Replica 1 of CCD18Co cells experiments.
In the figure are shown the raw data and the fit of the different generations with the Gaussian mixture model (GMM) for a single experiment of CCD18Co cells.
Figure 2—figure supplement 8
Replica 2 of CCD18Co cells experiments.
In the figure are shown the raw data and the fit of the different generations with the Gaussian mixture model (GMM) for a single experiment of CCD18Co cells.
Figure 2—figure supplement 9
Replica 1 of HCT-116 cells experiments.
In the figure are shown the raw data and the fit of the different generations with the Gaussian mixture model (GMM) for a single experiment of HCT-116 cells.
Figure 2—figure supplement 10
Replica 2 of HCT-116 cells experiments.
In the figure are shown the raw data and the fit of the different generations with the Gaussian mixture model (GMM) for a single experiment of HCT-116 cells.
Figure 2—figure supplement 11
Replica 3 of HCT-116 cells experiments.
In the figure are shown the raw data and the fit of the different generations with the Gaussian mixture model (GMM) for a single experiment of HCT-116 cells.
Figure 3 with 2 supplements
Quantification of partitioning noise via single-cell measurements.
(a) Example of a recorded cell colony of HCT116 cells in brightfield (left) and on CTFR fluorescence (right). (b) Cell cytoplasm fluorescence intensity as a function of time for a cell before and after division. Dark green circles correspond to the fluorescence intensity of the mother cell up to the division frame, and then to the sum of the daughters’ fluorescence. Lighter green triangles and squares represent the fluorescence intensity of the daughter cells. Solid lines are the linear fit of the points. The intercepts of the linear fit are used to compute the fraction of tagged components inherited by the daughter cells. The time is counted from the start of the experiment. (c) (bottom) Strip plot of the distribution of the inherited fraction of cytoplasm for the different cell lines. The points are randomly spread on the y-axis to avoid overlay. (Top) Fit of the inherited fraction distribution with the sum of two Gaussians with a mean symmetric to 1/2. (d) Comparison of division asymmetry obtained with time-lapse fluorescent microscopy measures (striped bars) with the ones obtained from flow cytometry experiments (plain bars). The flow cytometry bars are obtained as the mean over the multiple conducted experiments.
Figure 3—figure supplement 1
Simulated noisy dynamics of the fluorescence intensity.
The gray circles are the true extracted fraction, while the red and blue dots are the noisy dynamics, respectively, for the daughters that inherited a fraction and . The dashed lines are the result of the fitting procedure with the constraint of an equal slope.
Figure 3—figure supplement 2
Comparison between the standard deviation of the partitioning distribution for the noisy and noise-free dynamics, shown in red and blue, respectively.
Each histogram is the distribution of variances measured in the noisy (and noise-free) fluorescence dynamics. Each distribution contains 200 points, and the vertical lines indicate the means of the two distributions, with colors matching the data. The difference between the means of the two distributions is 1.3.
Figure 4
Cytoplasm partition fluctuations versus cell size.
(a) Behavior of the integral term (Equation 17) as a function of the number of dividing elements, , at fixed . Vertical lines mark typical values of cellular elements, like mitochondria. (b) Theoretical value of the variance of in the binomial assumption for different levels of asymmetries and increasing values of . (c) Asymmetry of the partitioning distribution in the binomial limit as measured by the binomial bias, p. (d) Sample cases of volume asymmetric division for different cell lines. Images show the overlay of consecutive times during the division dynamic. Time flows from the bottom left to the top right.
Additional files
-
MDAR checklist
- https://cdn.elifesciences.org/articles/104528/elife-104528-mdarchecklist1-v1.pdf
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Source data 1
Data to reproduce the results shown in the Figures.
- https://cdn.elifesciences.org/articles/104528/elife-104528-data1-v1.zip
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Robust assessment of asymmetric division in colon cancer cells
eLife 14:RP104528.
https://doi.org/10.7554/eLife.104528.4
