Daniel Baugh Institute for Functional Genomics and Computational Biology, Department of Pathology, Anatomy and Cell Biology, Jefferson Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania 19107, USA; Department of Chemical and Biochemical Engineering, University of Delaware, Newark, Delaware 19716, USA;
Daniel Baugh Institute for Functional Genomics and Computational Biology, Department of Pathology, Anatomy and Cell Biology, Jefferson Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania 19107, USA;
Genome Res. 2014 Jun;24(6):930-41. doi: 10.1101/gr.161802.113. Epub 2014 Mar 26.
What is the significance of the extensive variability observed in individual members of a single-cell phenotype? This question is particularly relevant to the highly differentiated organization of the brain. In this study, for the first time, we analyze the in vivo variability within a neuronal phenotype in terms of input type. We developed a large-scale gene-expression data set from several hundred single brainstem neurons selected on the basis of their specific synaptic input types. The results show a surprising organizational structure in which neuronal variability aligned with input type along a continuum of sub-phenotypes and corresponding gene regulatory modules. Correlations between these regulatory modules and specific cellular states were stratified by synaptic input type. Moreover, we found that the phenotype gradient and correlated regulatory modules were maintained across subjects. As these specific cellular states are a function of the inputs received, the stability of these states represents "attractor"-like states along a dynamic landscape that is influenced and shaped by inputs, enabling distinct state-dependent functional responses. We interpret the phenotype gradient as arising from analog tuning of underlying regulatory networks driven by distinct inputs to individual cells. Our results change the way we understand how a phenotypic population supports robust biological function by integrating the environmental experience of individual cells. Our results provide an explanation of the functional significance of the pervasive variability observed within a cell type and are broadly applicable to understanding the relationship between cellular input history and cell phenotype within all tissues.
个体单细胞表型中观察到的广泛可变性有什么意义？这个问题对于大脑的高度分化组织尤其重要。在这项研究中，我们首次根据输入类型分析了神经元表型中的体内可变性。我们从数百个基于特定突触输入类型选择的单个脑干神经元中开发了一个大规模的基因表达数据集。结果显示出一种惊人的组织结构，其中神经元的可变性沿着亚表型和相应的基因调控模块的连续体与输入类型对齐。这些调控模块与特定细胞状态之间的相关性按突触输入类型分层。此外，我们发现，表型梯度和相关的调控模块在不同的研究对象之间是保持一致的。由于这些特定的细胞状态是其接收的输入的函数，这些状态的稳定性代表了沿着受输入影响和塑造的动态景观的“吸引子”样状态，从而实现了不同状态依赖的功能响应。我们将表型梯度解释为由个体细胞的不同输入驱动的基础调控网络的模拟调谐引起的。我们的研究结果改变了我们理解表型群体如何通过整合个体细胞的环境经验来支持稳健的生物学功能的方式。我们的研究结果解释了在细胞类型中观察到的普遍可变性的功能意义，并广泛适用于理解所有组织中细胞输入历史和细胞表型之间的关系。
