Determining cell population size from cell fraction in cell plasticity models

TL;DR

This paper introduces two stochastic model-based computational methods, FOM and SOM, to estimate absolute cell population sizes from fraction data, addressing a key challenge in biological quantification.

Contribution

The paper presents novel FOM and SOM methods that map cell fractions to population size using stochastic models, with SOM not requiring initial population knowledge.

Findings

SOM method eliminates the need for initial population size.

Both methods are robust across different biological mechanisms.

Methods provide new insights into cell population interactions.

Abstract

Quantifying the size of cell populations is crucial for understanding biological processes such as growth, injury repair, and disease progression. Often, experimental data offer information in the form of relative frequencies of distinct cell types, rather than absolute cell counts. This emphasizes the need to devise effective strategies for estimating absolute cell quantities from fraction data. In response to this challenge, we present two computational approaches grounded in stochastic cell population models: the first-order moment method (FOM) and the second-order moment method (SOM). These methods explicitly establish mathematical mappings from cell fraction to cell population size using moment equations of the stochastic models. Notably, our investigation demonstrates that the SOM method obviates the requirement for a priori knowledge of the initial population size, highlighting the utility of incorporating variance details from cell proportions. The robustness of both the FOM and SOM methods was analyzed from different perspectives. Additionally, we extended the application of the FOM and SOM methods to various biological mechanisms within the context of cell plasticity models. Our methodologies not only assist in mitigating the inherent limitations of experimental techniques when only fraction data is available for detecting cell population size, but they also offer new insights into utilizing the stochastic characteristics of cell population dynamics to quantify interactions between different biomasses within the system.