Asymmetric Binomial Statistics Explains Organelle Partitioning Variance in Cancer Cell Proliferation

ABSTRACT

Asymmetric inheritance of organelle and cellular compounds between daughter cells impacts on the phenotypic variability and was found to be a hallmark for differentiation and rejuvenation in stem-like cells as much as a mechanism for enhancing resistance in bacteria populations. Whether the same processes take place in the context of cancer cell lines is still poorly investigated. Here, we present a method that allows the measurement of asymmetric organelle partitioning, and we use it to simultaneously measure the partitioning of three kinds of cellular elements, i.e. cytoplasm, membrane, and mitochondria in a proliferating population of human Jurkat T-cells. For this porpoise, we use multiple live cell markers which permit us both to follow the partitioning process for multiple generations and to investigate the correlations between the partitioning of different cellular constituents. Assuming a minimal model of asymmetric partitioning where cell sub-components are divided according to a biased binomial statistics, we derived exact analytical relationships for the average fluorescence intensity and its fluctuations as a function of the generation, obtaining an excellent agreement with the experimental measurements.

We found that although cell cytoplasm is divided symmetrically, mitochondria and membrane lipids are asymmetrically distributed between the two daughter cells and present a stable positive correlation with cytoplasm apportioning, which is incompatible with an independent division mechanism. Therefore, our findings show that asymmetric segregation mechanisms can also arise in cancer cell populations, and that, in this case, membrane lipids and mitochondria do not respectively segregate independently from the cytoplasm. This helps us understand the high phenotypic variability reported in these cancer cell lines. In perspective, this could be particularly relevant in the case of tumor micro-environment diversity, where comprehension of the non-genetic cell heterogeneity could pave the way to novel and more targeted therapies. Moreover, the developed experimental and theoretical apparatus can be easily generalized to different cell kinds and different cell sub-components providing a powerful tool for understanding partitioning-driven heterogeneity.