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JOURNAL ARTICLE
RESEARCH SUPPORT, N.I.H., EXTRAMURAL
RESEARCH SUPPORT, NON-U.S. GOV'T
REVIEW
Making lineage decisions with biological noise: Lessons from the early mouse embryo.
Understanding how individual cells make fate decisions that lead to the faithful formation and homeostatic maintenance of tissues is a fundamental goal of contemporary developmental and stem cell biology. Seemingly uniform populations of stem cells and multipotent progenitors display a surprising degree of heterogeneity, primarily originating from the inherent stochastic nature of molecular processes underlying gene expression. Despite this heterogeneity, lineage decisions result in tissues of a defined size and with consistent proportions of differentiated cell types. Using the early mouse embryo as a model we review recent developments that have allowed the quantification of molecular intercellular heterogeneity during cell differentiation. We first discuss the relationship between these heterogeneities and developmental cellular potential. We then review recent theoretical approaches that formalize the mechanisms underlying fate decisions in the inner cell mass of the blastocyst stage embryo. These models build on our extensive knowledge of the genetic control of fate decisions in this system and will become essential tools for a rigorous understanding of the connection between noisy molecular processes and reproducible outcomes at the multicellular level. We conclude by suggesting that cell-to-cell communication provides a mechanism to exploit and buffer intercellular variability in a self-organized process that culminates in the reproducible formation of the mature mammalian blastocyst stage embryo that is ready for implantation into the maternal uterus. This article is categorized under: Gene Expression and Transcriptional Hierarchies > Cellular Differentiation Establishment of Spatial and Temporal Patterns > Regulation of Size, Proportion, and Timing Gene Expression and Transcriptional Hierarchies > Gene Networks and Genomics Gene Expression and Transcriptional Hierarchies > Quantitative Methods and Models.
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