Stochastic gene expression in proliferating cells: Differing noise intensity in single-cell and population perspectives

Random fluctuations (noise) in gene expression can be studied from two complementary perspectives: following expression in a single cell over time or comparing expression between cells in a proliferating population at a given time. Here, we systematically investigated scenarios where both perspectives can lead to different levels of noise in a given gene product. We first consider a stable protein, whose concentration is diluted by cellular growth. This protein inhibits growth at high concentrations, establishing a positive feedback loop. Using a stochastic model with molecular bursting of gene products, we analytically predict and contrast the steady-state distributions of protein concentration in both frameworks. Although positive feedback amplifies the noise in expression, this amplification is much higher in the population framework compared to following a single cell over time. We also study other processes that lead to different noise levels even in the absence of such dilution-based feedback. When considering randomness in the partitioning of molecules between daughters during mitosis, we find that in the single-cell perspective, the noise in protein concentration is independent of noise in the cell cycle duration. In contrast, partitioning noise is amplified in the population perspective by increasing randomness in cell-cycle time. Overall, our results show that the single-cell framework that does not account for proliferating cells can, in some cases, underestimate the noise in gene product levels. These results have important implications for studying the inter-cellular variation of different stress-related expression programs across cell types that are known to inhibit cellular growth.Author summary: Expression levels of gene products can exhibit cell-to-cell variation within an otherwise genetically identical cell population. Such random variation, often referred to as expression noise, has been reported in all organisms, from bacterial to human cells. This contribution quantifies the degree of random fluctuations in the concentration of a specific protein from two complementary perspectives: following its concentration in a single cell over time (single-cell perspective) and across a cell population (population perspective). When are the statistical fluctuations in expression levels different between these two perspectives? Analytical results combined with agent-based models that track protein concentration in each cell of a growing colony identify two such scenarios. The first scenario corresponds to high levels of a specific protein reducing cellular growth. In the second scenario, the random segregation of molecules between daughters (partitioning) dominates expression noise. In both these cases, the classical single-cell approach underestimates the degree of concentration fluctuations seen across a cell population. These results have important implications for regulating stochastic variations in diverse stress-related expression programs that promote drug-tolerant states in microbial and cancer cells.