The Origin of Phenotypic Heterogeneity in a Clonal Cell Population In Vitro

Abstract

BackgroundThe spontaneous emergence of phenotypic heterogeneity in clonal populations of mammalian cells in vitro is a rule rather than an exception. We consider two simple, mutually non-exclusive models that explain the generation of diverse cell types in a homogeneous population. In the first model, the phenotypic switch is the consequence of extrinsic factors. Initially identical cells may become different because they encounter different local environments that induce adaptive responses. According to the second model, the phenotypic switch is intrinsic to the cells that may occur even in homogeneous environments.Principal FindingsWe have investigated the “extrinsic” and the “intrinsic” mechanisms using computer simulations and experimentation. First, we simulated in silico the emergence of two cell types in a clonal cell population using a multiagent model. Both mechanisms produced stable phenotypic heterogeneity, but the distribution of the cell types was different. The “intrinsic” model predicted an even distribution of the rare phenotype cells, while in the “extrinsic” model these cells formed small clusters. The key predictions of the two models were confronted with the results obtained experimentally using a myogenic cell line.ConclusionsThe observations emphasize the importance of the “ecological” context and suggest that, consistently with the “extrinsic” model, local stochastic interactions between phenotypically identical cells play a key role in the initiation of phenotypic switch. Nevertheless, the “intrinsic” model also shows some other aspects of reality: The phenotypic switch is not triggered exclusively by the local environmental variations, but also depends to some extent on the phenotypic intrinsic robustness of the cells.

Single cell metabolomics

“…The two prerequisites for stochasticity-induced phenotypic heterogeneity [20,21] are met in practically every biomolecular network. Thus it is very likely that the currently known cases of such heterogeneity merely represent the tip of the iceberg of all of these cases [22].…”

“…The two prerequisites for stochasticity-induced phenotypic heterogeneity [20,21] are met in practically every biomolecular network. Thus it is very likely that the currently known cases of such heterogeneity merely represent the tip of the iceberg of all of these cases [22].In fact, this type of heterogeneity was also found to occur in metabolic systems: the well-known lactose utilization system in Escherichia coli was found to display an ''all or none' Overall, a technology to measure metabolite levels in single cells would be an excellent tool for firstly discovery of metabolic differences in individual cells (for which likely semi-quantitative methods will be sufficient) and secondly system biology endeavours that aim at generating an understanding about the emergence of such phenotypic or genetic heterogeneity (for which likely more quantitative methods will be required). …”

Non-genetic heterogeneity of cells in development: more than just noise

“…Heterogeneity was more recently shown in singlecell analysis using fluorescently labeled probes for hybridization (Levsky et al, 2002) or visual gene-expression reporters (Sigal et al, 2006;Takasuka et al, 1998), as well as in single-cell PCR (Hayashi et al, 2008;Warren et al, 2006;Diercks et al, 2009). Apart from a few exceptions (Aird, 2004;Grundel and Rubin, 1988;Rubin, 1992), the biological significance of non-genetic cell population heterogeneity in mammals has not been explicitly articulated until recently, when it has been described in the context of stem cells and fate decision (Stockholm et al, 2007;Chambers et al, 2007;Chang et al, 2008;Dietrich and Hiiragi, 2007;Kalmar et al, 2009;Singh et al, 2007;Spencer et al, 2009;Kobayashi et al, 2009).…”

Small-Volume Analysis of Cell–Cell Signaling Molecules in the Brain

“…Because of the increased ability to study individual cells, a clearer understanding of the effects of the cellular surroundings has emerged, with the awareness that the cellular microenvironment contributes more to cell-tocell variability than stochastic mechanisms can account for (Swain et al, 2002;Stockholm et al, 2007;Raj and van Oudenaarden, 2008;Snijder and Pelkmans, 2011 (Figure 2) greatly benefits from prior genomic information because limited sample amounts often are not sufficient for de novo structural characterization . By knowing which genes are expressed in a cell of interest, a library of peptides encoded by the prohormones can be compiled and used as a reference for interpretation of spectra by peptide mass fingerprinting.…”