A unified framework for measuring selection on cellular lineages and traits.

Area of Science:

• Cellular and Molecular Biology
• Evolutionary Biology
• Population Dynamics

Background:

• Cellular states exhibit intrinsic noise, leading to phenotypic variations among cell lineages.
• Understanding the adaptive and evolutionary significance of these variations requires linking them to population growth rates.

Purpose of the Study:

• To extend a cell lineage statistics framework to quantify the contribution of fitness landscape cumulants to population growth rates.
• To reveal the relationship between fitness heterogeneity and population growth rate responses to perturbations.
• To introduce a comprehensive function for cell lineage statistics.

Main Methods:

• Mathematical framework extending cell lineage statistics.
• Expansion of population growth rate by fitness landscape cumulants (mean, variance, skewness, etc.).
• Application to experimental cell lineage data from bacteria to mammalian cells.

Main Results:

• Population growth rate can be quantified by fitness landscape cumulants.
• Fitness heterogeneity influences population growth rate responses to perturbations.
• Growth rate gains from fitness heterogeneity vary across environments and organisms.
• Higher-order cumulants are negligible in constant growth but significant in regrowth from arrested states.
• Selection can increase fitness variance in retrospective compared to chronological statistics.

Conclusions:

• The developed framework provides a universal method to link cellular phenotypic heterogeneity to population growth.
• It quantifies the contribution of different fitness distribution moments to population growth.
• The findings are applicable to diverse biological systems where phenotypic variation and proliferation are key.