Abstract
Noisy gene expression is of fundamental importance to single cells, and is therefore widely studied in single-celled organisms. Extending these studies to multicellular organisms is challenging since their cells are generally not isolated, but individuals in a tissue. Cell–cell coupling via signalling, active transport or pure diffusion, ensures that tissue-bound cells are neither fully independent of each other, nor an entirely homogeneous population. In this article, we show that increasing the strength of coupling between cells can either increase or decrease the single-cell variability (and, therefore, the heterogeneity of the tissue), depending on the statistical properties of the underlying genetic network. We confirm these predictions using spatial stochastic simulations of simple genetic networks, and experimental data from animal and plant tissues. The results suggest that cell–cell coupling may be one of several noise-control strategies employed by multicellular organisms, and highlight the need for a deeper understanding of multicellular behaviour.
Highlights
Noisy gene expression is of fundamental importance to single cells, and is widely studied in single-celled organisms
The differences between a population of identical independent cells and a tissue of identical connected cells can be seen with stochastic simulations of a simple genetic network
As an attempt to address this question, we introduced a measure of single-cell variability, L, which is a non-dimensional statistical coefficient which determines whether the cells in a tissue are more or less heterogeneous than an equivalent population of independent cells
Summary
Noisy gene expression is of fundamental importance to single cells, and is widely studied in single-celled organisms Extending these studies to multicellular organisms is challenging since their cells are generally not isolated, but individuals in a tissue. We show that increasing the strength of coupling between cells can either increase or decrease the single-cell variability (and, the heterogeneity of the tissue), depending on the statistical properties of the underlying genetic network. We confirm these predictions using spatial stochastic simulations of simple genetic networks, and experimental data from animal and plant tissues. A single cell in a tissue is partially dependent on its neighbour cells, and partially independent of them, and so mathematical models of cells within multicellular organisms must take account of this coupling
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