Single-Cell Transcriptional Analysis Reveals Novel Neuronal Phenotypes and Interaction Networks Involved in the Central Circadian Clock.
Single-Cell Transcriptional Analysis Reveals Novel Neuronal Phenotypes and Interaction Networks Involved in the Central Circadian Clock.
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单细胞转录分析揭示了中央昼夜节律涉及的新型神经元表型和相互作用网络。
DOI：
10.3389/fnins.2016.00481
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发表时间：
2016
影响因子：
4.3
通讯作者：
Vadigepalli R
中科院分区：
文献类型：
--
作者：
Park J;Zhu H;O'Sullivan S;Ogunnaike BA;Weaver DR;Schwaber JS;Vadigepalli R
单细胞异质性阻碍了理解细胞群如何组织成组织水平功能基础的细胞网络的努力。这种复杂性在哺乳动物的视交叉上核（SCN）中尤为突出。在这里，单个神经元表现出大量的异步行为和转录异质性。然而，SCN 神经元能够产生精确协调的突触和分子输出，通过组织成细胞网络使身体与常见的昼夜节律周期同步。为了理解这种新兴的细胞网络特性，协调单神经元异质性与网络组织非常重要。鉴于最近的研究表明转录异质细胞组织成不同的细胞表型，我们对来自经历昼夜节律周期相移的小鼠的 352 个 SCN 神经元的转录、空间和功能组织进行了表征。使用群落结构检测方法和多变量分析技术，我们鉴定了以前未描述的神经元表型，这些神经元表型可能参与已知 SCN 细胞类型的调节网络。基于新发现的神经元表型，我们开发了一种数据驱动的神经元网络结构，其中多种细胞类型通过已知的突触和旁分泌信号机制相互作用。这些结果为解释 SCN 神经元的功能变异性提供了基础，并描述了理解异质单细胞群如何组织成组织水平功能基础的细胞网络的方法。
Single-cell heterogeneity confounds efforts to understand how a population of cells organizes into cellular networks that underlie tissue-level function. This complexity is prominent in the mammalian suprachiasmatic nucleus (SCN). Here, individual neurons exhibit a remarkable amount of asynchronous behavior and transcriptional heterogeneity. However, SCN neurons are able to generate precisely coordinated synaptic and molecular outputs that synchronize the body to a common circadian cycle by organizing into cellular networks. To understand this emergent cellular network property, it is important to reconcile single-neuron heterogeneity with network organization. In light of recent studies suggesting that transcriptionally heterogeneous cells organize into distinct cellular phenotypes, we characterized the transcriptional, spatial, and functional organization of 352 SCN neurons from mice experiencing phase-shifts in their circadian cycle. Using the community structure detection method and multivariate analytical techniques, we identified previously undescribed neuronal phenotypes that are likely to participate in regulatory networks with known SCN cell types. Based on the newly discovered neuronal phenotypes, we developed a data-driven neuronal network structure in which multiple cell types interact through known synaptic and paracrine signaling mechanisms. These results provide a basis from which to interpret the functional variability of SCN neurons and describe methodologies toward understanding how a population of heterogeneous single cells organizes into cellular networks that underlie tissue-level function.
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影响因子：
64.8
作者：
Cheng, MY;Bullock, CM;Zhou, QY
通讯作者：
Zhou, QY
影响因子：
16.2
作者：
Evans JA;Leise TL;Castanon-Cervantes O;Davidson AJ
通讯作者：
Davidson AJ
影响因子：
--
作者：
Gossan, Nicole;Zeef, Leo;Hensman, James;Hughes, Alun;Bateman, John F.;Rowley, Lynn;Little, Christopher B.;Piggins, Hugh D.;Rattray, Magnus;Boot-Handford, Raymond P.;Meng, Qing-Jun
通讯作者：
Meng, Qing-Jun
影响因子：
64.5
作者：
Durruthy-Durruthy R;Gottlieb A;Hartman BH;Waldhaus J;Laske RD;Altman R;Heller S
通讯作者：
Heller S
DOI：
10.1073/pnas.1507125112
发表时间：
2015-06-09
影响因子：
11.1
作者：
Darmanis S;Sloan SA;Zhang Y;Enge M;Caneda C;Shuer LM;Hayden Gephart MG;Barres BA;Quake SR
通讯作者：
Quake SR