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Review
. 2009 Dec;13(5-6):556-61.
doi: 10.1016/j.cbpa.2009.09.015. Epub 2009 Oct 14.

Non-genetic cell-to-cell variability and the consequences for pharmacology

Mario Niepel  1 , Sabrina L Spencer, Peter K Sorger
Affiliations
Review

Non-genetic cell-to-cell variability and the consequences for pharmacology

Mario Niepel et al. Curr Opin Chem Biol. 2009 Dec.

Abstract

Recent advances in single-cell assays have focused attention on the fact that even members of a genetically identical group of cells or organisms in identical environments can exhibit variability in drug sensitivity, cellular response, and phenotype. Underlying much of this variability is stochasticity in gene expression, which can produce unique proteomes even in genetically identical cells. Here we discuss the consequences of non-genetic cell-to-cell variability in the cellular response to drugs and its potential impact for the treatment of human disease.

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Figures

Figure 1
Figure 1. Cell-to-cell variability of the proteome gives rise to phenotypic heterogeneity
(a) Interaction among factors that determine average cellular phenotype and variance around the average. Combinations of environmental (blue), genomic (red) and proteomic (green) variation can cause heterogeneity in an initially homogenous population. (b) Probability density functions of three lognormal distributions with different coefficients of variation (CV; standard deviation divided by the mean). Red bars show the increasing disparity in protein levels between cells in the bottom and the top 5th percentiles. Protein numbers for the top and bottom 5th percentile and their ratio are shown for each distribution, assuming a mean of 100,000 proteins per cell. (c) Three hypothetical cases illustrating how the mean phenotype of a population (top row) can arise from different underlying phenotypic distributions (bottom row) at the single cell level. (d) Illustration of the effects of various transfer functions linking protein levels to phenotypes. The histograms (red) represent the distribution of phenotypes in a population generated by applying the univariate transfer functions (blue) to the protein level probability density functions (green).
See this image and copyright information in PMC

References

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