Inferring absolute cell numbers from relative proportion in stochastic models with cell plasticity

Quantifying dynamic changes in cell populations is crucial for a comprehensive understanding of biological processes such as cell proliferation, injury repair, and disease progression. However, compared to directly measuring the absolute cell numbers of specific subpopulations, relative proportion data demonstrate greater reproducibility and yield more stable, reliable outcomes. Therefore, inferring absolute cell numbers from relative proportion data may present a novel approach for effectively predicting changes in cell population sizes. To address this, we establish two mathematical mappings between cell proportions and population sizes using moment equations derived from stochastic cell-plasticity models. Notably, our findings indicate that one of these mappings does not require prior knowledge of the initial population size, highlighting the value of incorporating variance information into cell proportion data. We evaluated the robustness of our methods from multiple perspectives and extended their application to various biological mechanisms within the context of cell plasticity models. These methods help mitigate the limitations associated with the direct measurement of absolute cell counts through experimental techniques. Moreover, they provide new insights into leveraging the stochastic dynamics of cell populations to quantify interactions between different biomasses within the system.