Massively Parallel Single Nucleus Transcriptional Profiling Defines Spinal Cord Neurons and Their Activity during Behavior.
Massively Parallel Single Nucleus Transcriptional Profiling Defines Spinal Cord Neurons and Their Activity during Behavior.
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DOI：
10.1016/j.celrep.2018.02.003
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发表时间：
2018-02-20
期刊：
影响因子：
8.8
通讯作者：
Levine AJ
中科院分区：
文献类型：
--
作者：
Sathyamurthy A;Johnson KR;Matson KJE;Dobrott CI;Li L;Ryba AR;Bergman TB;Kelly MC;Kelley MW;Levine AJ
为了了解行为的细胞基础，有必要了解神经系统中存在的细胞类型及其对功能的贡献。脊髓网络对于感觉处理和运动行为是必不可少的，并且为识别行为的细胞相关性提供了强大的系统。在这里，我们使用大规模并行单核RNA测序（snRNA-seq）来创建成年小鼠腰髓的图谱。我们确定了43个神经元群体的分子特征。接下来，我们利用snRNA-seq方法，使用神经元活动的转录特征，提供在感觉和运动行为后活跃的神经元群体的无偏鉴定。这种方法可以在未来用于将单核基因表达数据与对行为，损伤和疾病的动态生物反应联系起来。Sathyamurthy等人使用大规模平行单核RNA-seq来探测脊髓细胞类型，并展示了43个神经元群体的图谱。通过在感觉和运动行为之后使用这种方法，他们能够检测和分子识别与每个功能相关的激活神经元。
To understand the cellular basis of behavior, it is necessary to know the cell types that exist in the nervous system and their contributions to function. Spinal networks are essential for sensory processing and motor behavior and provide a powerful system for identifying the cellular correlates of behavior. Here, we used massively parallel single nucleus RNA sequencing (snRNA-seq) to create an atlas of the adult mouse lumbar spinal cord. We identified and molecularly characterized 43 neuronal populations. Next, we leveraged the snRNA-seq approach to provide unbiased identification of neuronal populations that were active following a sensory and a motor behavior, using a transcriptional signature of neuronal activity. This approach can be used in the future to link single nucleus gene expression data with dynamic biological responses to behavior, injury, and disease. Sathyamurthy et al. use massively parallel single nucleus RNA-seq to probe spinal cord cell types and present an atlas of 43 neuronal populations. By using this approach after a sensory and a motor behavior, they were able to detect and molecularly identify activated neurons associated with each function.
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影响因子：
64.5
作者：
Duan B;Cheng L;Bourane S;Britz O;Padilla C;Garcia-Campmany L;Krashes M;Knowlton W;Velasquez T;Ren X;Ross S;Lowell BB;Wang Y;Goulding M;Ma Q
通讯作者：
Ma Q
DOI：
10.1126/science.1247651
发表时间：
2014-02-14
期刊：
Science (New York, N.Y.)
影响因子：
--
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影响因子：
25
作者：
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影响因子：
48
作者：
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通讯作者：
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影响因子：
48
作者：
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通讯作者：
Regev A