Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics.
Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics.
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DOI：
10.1371/journal.pbio.2006387
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发表时间：
2018-06
期刊：
影响因子：
9.8
通讯作者：
Hjerling-Leffler J
中科院分区：
文献类型：
--
作者：
Harris KD;Hochgerner H;Skene NG;Magno L;Katona L;Bengtsson Gonzales C;Somogyi P;Kessaris N;Linnarsson S;Hjerling-Leffler J
理解任何大脑回路都需要对其组成神经元进行分类。到目前为止，在海马CA1区，至少有23类GABA能神经元被提出。然而，这份清单可能是不完整的；此外，还不清楚离散的类别是否足以描述皮质抑制神经元的多样性，或者是否也需要连续的变异性模式。我们研究了3,663个CA1抑制细胞的转录，揭示了10个主要的GABA能群，分为49个细小的簇。确定了所有先前描述的和几个新的细胞类，其中三个先前描述的类出人意料地被发现是相同的。然而，划分成离散的类并不足以描述这些细胞的多样性，因为类之间和类内也发生了连续的变化。潜因子分析显示，一个单一的连续变量可以预测几个基因的表达水平，这些基因与它在多种细胞类型中的表达水平具有类似的相关性。对与这个变量相关的基因的分析表明，它反映了一系列的变化，从以近端锥体细胞为靶点的代谢高度活跃的快尖峰细胞，到以远端树突或中间神经元为靶点的慢尖峰细胞。这些结果阐明了最简单的皮质结构之一中抑制神经元的复杂性，并表明表征这些细胞需要连续的变异模式和离散的细胞类别。单细胞RNA测序使科学家能够计算在多个单独分离的细胞中表达的每个基因的拷贝数。由于不同的细胞类型表达的基因数量不同，具有相似表达模式的细胞群很可能对应于不同的细胞类型。然而，除了离散的类别外，细胞在基因表达上也表现出持续的变化。为了研究一个众所周知的大脑系统中细胞类别和连续性之间的关系，我们应用了新的分析方法来研究来自小鼠海马区CA1区的抑制性中间神经元的数据集。由于几十年的密集工作，至少23类CA1中间神经元已经被确定。我们能够用我们的转录簇将它们全部识别出来，但出人意料地发现其中三个是相同的。由于这些细胞的连通性已经建立，我们也能够识别这些细胞中持续变异的主要模式，这与它们的轴突靶点位置有关。这种对CA1相对简单的皮质环路的深入了解，不仅可以澄清这一重要大脑结构的细胞组成，还将为理解更复杂的结构，如等皮质，奠定坚实的基础。
Understanding any brain circuit will require a categorization of its constituent neurons. In hippocampal area CA1, at least 23 classes of GABAergic neuron have been proposed to date. However, this list may be incomplete; additionally, it is unclear whether discrete classes are sufficient to describe the diversity of cortical inhibitory neurons or whether continuous modes of variability are also required. We studied the transcriptomes of 3,663 CA1 inhibitory cells, revealing 10 major GABAergic groups that divided into 49 fine-scale clusters. All previously described and several novel cell classes were identified, with three previously described classes unexpectedly found to be identical. A division into discrete classes, however, was not sufficient to describe the diversity of these cells, as continuous variation also occurred between and within classes. Latent factor analysis revealed that a single continuous variable could predict the expression levels of several genes, which correlated similarly with it across multiple cell types. Analysis of the genes correlating with this variable suggested it reflects a range from metabolically highly active faster-spiking cells that proximally target pyramidal cells to slower-spiking cells targeting distal dendrites or interneurons. These results elucidate the complexity of inhibitory neurons in one of the simplest cortical structures and show that characterizing these cells requires continuous modes of variation as well as discrete cell classes. Single-cell RNA sequencing allows scientists to count the number of copies of each gene expressed in multiple individually isolated cells. Because different cell types express genes in different amounts, “clusters” of cells with similar expression patterns are likely to correspond to different cell types. As well as discrete classes, however, cells also show continuous variation in gene expression. To study the relationship between cell classes and continua in a well-understood brain system, we applied new analysis methods to a dataset of inhibitory interneurons from area CA1 of the mouse hippocampus. Thanks to decades of intensive work, at least 23 classes of CA1 interneurons have been previously defined. We were able to identify them all with our transcriptomic clusters but unexpectedly found three to be identical. Because the connectivity of these cells has already been established, we were also able to identify the primary mode of continuous variation in these cells, which related to their axon target location. This in-depth understanding of the relatively simple cortical circuit of CA1 not only clarifies the cellular composition of this important brain structure but also will form a solid foundation for understanding more complex structures, such as the isocortex.
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DOI：
10.1126/science.aab3415
发表时间：
2015-09-11
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Dehorter N;Ciceri G;Bartolini G;Lim L;del Pino I;Marín O
通讯作者：
Marín O
影响因子：
46.9
作者：
Cadwell CR;Palasantza A;Jiang X;Berens P;Deng Q;Yilmaz M;Reimer J;Shen S;Bethge M;Tolias KF;Sandberg R;Tolias AS
通讯作者：
Tolias AS
影响因子：
5.3
作者：
Ferraguti, F;Klausberger, T;Dalezios, Y
通讯作者：
Dalezios, Y
影响因子：
25
作者：
Dudok, Barna;Barna, Laszlo;Ledri, Marco;Szabo, Szilard I.;Szabadits, Eszter;Pinter, Balazs;Woodhams, Stephen G.;Henstridge, Christopher M.;Balla, Gyula Y.;Nyilas, Rita;Varga, Csaba;Lee, Sang-Hun;Matolcsi, Mate;Cervenak, Judit;Kacskovics, Imre;Watanabe, Masahiko;Sagheddu, Claudia;Melis, Miriam;Pistis, Marco;Soltesz, Ivan;Katona, Istvan
通讯作者：
Katona, Istvan
影响因子：
25
作者：
Chittajallu, Ramesh;Craig, Michael T.;McFarland, Ashley;Yuan, Xiaoqing;Geffen, Scott;Tricoire, Ludovic;Erkkila, Brian;Barron, Sean C.;Lopez, Carla M.;Liang, Barry J.;Jeffries, Brian W.;Pelkey, Kenneth A.;McBain, Chris J.
通讯作者：
McBain, Chris J.