Impact of crowding on the diversity of expanding populations

Abstract

Crowding effects critically impact the self-organization of densely packed cellular assemblies, such as biofilms, solid tumors, and developing tissues. When cells grow and divide, they push each other apart, remodeling the structure and extent of the population’s range. Recent work has shown that crowding has a strong impact on the strength of natural selection. However, the impact of crowding on neutral processes, which controls the fate of new variants as long as they are rare, remains unclear. Here, we quantify the genetic diversity of expanding microbial colonies and uncover signatures of crowding in the site frequency spectrum. By combining Luria–Delbrück fluctuation tests, lineage tracing in a novel microfluidic incubator, cell-based simulations, and theoretical modeling, we find that the majority of mutations arise behind the expanding frontier, giving rise to clones that are mechanically “pushed out” of the growing region by the proliferating cells in front. These excluded-volume interactions result in a clone-size distribution that solely depends on where the mutation first arose relative to the front and is characterized by a simple power law for low-frequency clones. Our model predicts that the distribution depends on a single parameter—the characteristic growth layer thickness—and hence allows estimation of the mutation rate in a variety of crowded cellular populations. Combined with previous studies on high-frequency mutations, our finding provides a unified picture of the genetic diversity in expanding populations over the whole frequency range and suggests a practical method to assess growth dynamics by sequencing populations across spatial scales.

Proliferating active matter

“…The ensuing back-and-forth of growth-induced jamming and unjamming can be readily observed, for instance, when yeast cells grow in partially confined microfluidic incubators 74 , 116 . Similar dynamic arrangements, with additional contact dynamics due to dynamic changes in cell shape, have been modelled and observed in growing tissues and tumours over longer timescales 110 , 117 , 118 .…”

“…Whereas the macroscopic structure of proliferating active matter clearly reflects past growth (Fig. 1 ), it is an interesting general question whether and how the statistical properties of dense ensembles of self-replicating cells differ from the properties of disordered granular packings 116 , 125 , 126 . A topological study of 2D colonies of rod-shaped bacteria growing at a constant rate observed that, although +1/2 and –1/2 defects were both produced at the same rate, +1/2 defects tended to move to the periphery 127 , in contrast to the defect dynamics in non-growing active nematics.…”

“…Recent years have revealed that large fluctuations are more ubiquitous than previously thought, because mutations can produce many descendants by chance even if they do not arise early in an exponential growth process. One such mechanism is ‘gene surfing’, which refers to mutations growing to high abundance when they arise at the edge of a spatially expanding population, where organisms and their offspring benefit from elevated growth rates 116 , 149 – 152 . A similar phenomenon occurs when beneficial mutations arise in exceptionally fit individuals, with which they hitchhike to high frequency 153 .…”

Quantifying stochastic establishment of mutants in microbial adaptation

“…Elsewhere, authors have noted that 'bubbles' of mutant cells trapped within the colony could be relevant if they remain viable and could re-grow following clearance of the surrounding wild-type (e.g. by antimicrobial treatment) [37,38].…”

Trends in the Use of Proper Methods for Estimating Mutation Rates in Fluctuation Experiments

“…For instance, methods can now be applied to situations where final population sizes vary among experiments, where mutants grow differently than the wild type and where only a sub-sample of the culture is screened for mutants [22,24,27,28]. Moreover, methods have been developed to deal with specific ecological complications such as growth in colonies and biofilms or in the presence of killing agents [29][30][31]. More details about the major tools are provided in Supplementary Table S1.…”