Abstract
Biological functions are typically performed by groups of cells that express predominantly the same genes, yet display a continuum of phenotypes. While it is known how one genotype can generate such non‐genetic diversity, it remains unclear how different phenotypes contribute to the performance of biological function at the population level. We developed a microfluidic device to simultaneously measure the phenotype and chemotactic performance of tens of thousands of individual, freely swimming Escherichia coli as they climbed a gradient of attractant. We discovered that spatial structure spontaneously emerged from initially well‐mixed wild‐type populations due to non‐genetic diversity. By manipulating the expression of key chemotaxis proteins, we established a causal relationship between protein expression, non‐genetic diversity, and performance that was theoretically predicted. This approach generated a complete phenotype‐to‐performance map, in which we found a nonlinear regime. We used this map to demonstrate how changing the shape of a phenotypic distribution can have as large of an effect on collective performance as changing the mean phenotype, suggesting that selection could act on both during the process of adaptation.
Highlights
Biological functions are not typically carried out by isolated cells, but rather by populations of clonal or near-clonal cells that display a continuous distribution of phenotypes
We determined the tumble bias of each trajectory using a probabilistic model that classified every time point along a trajectory as either a run or a tumble based on information about cell velocity, acceleration, and angular acceleration (Materials and Methods; Dufour et al, 2016)
We verified that cells in the device exhibited a distribution of tumble bias similar to that measured using tethered cells (Park et al, 2010) and remained stable for at least an hour (Appendix Fig S1)
Summary
Biological functions are not typically carried out by isolated cells, but rather by populations of clonal or near-clonal cells that display a continuous distribution of phenotypes. The difficulty is that the ability of a clonal population to perform a biological function emerges as a convolution of the distribution of phenotypes in the population, P(X) (Fig 1A, left), with the function that relates individual phenotype to performance, φ(X) (Fig 1A, right), where X is a random variable describing the phenotype. An important consequence of this convolution is that if φ(X) is nonlinear, the population performance can become very sensitive to non-genetic diversity, since outliers in the distribution of P(X) (Fig 1A, left, bright pink region) that display nonlinear performance characteristics (Fig 1A, right, bright green region) may have disproportionate effects on population performance (Golowasch et al, 2002). It is possible to accurately determine P(X) using flow cytometry, microscopy, and microfluidics, determining φ(X) is difficult because it requires simultaneous measurement of both phenotype and performance in the same individual
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