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Cell Lineage Analyses and Gene Function Studies Using Twin-spot MARCM

Cell Lineage Analyses and Gene Function Studies Using Twin-spot MARCM

Published on: March 2, 2017

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

Shunpei Yamauchi¹, Takashi Nozoe¹, Reiko Okura¹

¹Department of Basic Science, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan.

|December 6, 2022

Summary

Cellular variations arise from intrinsic noise, impacting population growth. This study quantifies how fitness distribution, including variance and skewness, affects growth rates across diverse organisms and environments.

Keywords:

E. coli S. pombe cell lineage statistics computational biology cumulant fitness phenotypic heterogeneity population growth rate selection strength systems biology

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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.