Cellular heterogeneity: do differences make a difference?

Abstract

A central challenge of biology is to understand how individual cells process information and respond to perturbations. Much of our knowledge is based on ensemble measurements. However, cell-to-cell differences are always present to some degree in any cell population, and the ensemble behaviors of a population may not represent the behaviors of any individual cell. Here, we discuss examples of when heterogeneity cannot be ignored and describe practical strategies for analyzing and interpreting cellular heterogeneity.

Figure

A. Ensemble-averaged measurements can mask information contained in heterogeneity. Behaviors of cells in (i) tail of a distribution (shaded area) or (ii) small subpopulation (at right) may differ from the remainder of the population or from the “mean” behavior (dashed line at μ₁). (iii) For bimodal cellular behaviors, a population mean may poorly represent the majority of cells. (iv) Multiple measurements may be required to distinguish different patterns of cellular heterogeneity. Correlated (left) or anticorrelated (right) behaviors of cells may be indistinguishable based on single measurements alone (compare histograms at sides of left and right density plots). Axis labels features f1 and f2 represent single cell measurements (e.g. cell size, division time, or expression of cell surface marker). Dashed lines and triangles indicate population means. B. Decompositions of heterogeneity may be tested for functionally-important information. (i) Single cell measurements allow cells (left) to be represented as points in a (high-dimensional) feature space (right). (ii) Cell populations can be partitioned into distinct regions of feature space. This partition may be determined manually or automatically. Illustrated is a decomposition into two subpopulations, S1 and S2; μ represents the population mean. (iii) The values of a functional readout for individual subpopulations and population mean can be tested for significant differences within a population (left; * indicates significance). Alternatively, different mixtures of heterogeneity observed from populations in different conditions can be tested for correlation with function (right). “Function” refers to the evaluation of a functional readout over a collection of cells, either at the subpopulation- or whole-population level (e.g. drug sensitivity).