Identification of adult spinal Shox2 neuronal subpopulations based on unbiased computational clustering of electrophysiological properties.
Identification of adult spinal Shox2 neuronal subpopulations based on unbiased computational clustering of electrophysiological properties.
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DOI：
10.3389/fncir.2022.957084
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发表时间：
2022
影响因子：
3.5
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中科院分区：
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--
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脊髓神经元整合感觉和下行信息以产生运动输出。转录因子的表达已被用来解剖出电路的神经元组成部分的行为。然而，大多数典型的中间神经元群体是异质的，需要额外的标准来确定功能亚群。表达转录因子Shox2的神经元可以基于转录因子Chx10的共表达进行亚分类，并且每个亚群被提出具有不同的连接性和在运动中的不同作用。成年Shox2神经元最近被证明是基于其放电特性的多样性。在这里，为了对成年小鼠Shox 2神经元进行亚分类，我们对从腰椎切片中视觉识别的Shox 2神经元的全细胞膜片钳记录收集的数据进行了多次分析。在分析中包括一组较小的Chx10神经元用于验证。我们进行了k均值和分层无偏聚类方法，考虑电生理变量。与按放电类型分类不同，这些簇显示出可以区分Shox 2神经元簇的电生理特性。在两种聚类技术中，都存在完全由Shox 2神经元组成的簇，这表明可以单独通过电生理特性区分Shox 2 + Chx 10 −神经元和Shox 2 + Chx 10+神经元。通过免疫组织化学进一步验证计算簇在一小部分神经元中的准确性。因此，使用电生理特性的无偏聚类分析是一种工具，可以增强当前的神经元间亚分类，并可以补充基于转录因子和分子表达的分组。
Spinal cord neurons integrate sensory and descending information to produce motor output. The expression of transcription factors has been used to dissect out the neuronal components of circuits underlying behaviors. However, most of the canonical populations of interneurons are heterogeneous and require additional criteria to determine functional subpopulations. Neurons expressing the transcription factor Shox2 can be subclassified based on the co-expression of the transcription factor Chx10 and each subpopulation is proposed to have a distinct connectivity and different role in locomotion. Adult Shox2 neurons have recently been shown to be diverse based on their firing properties. Here, in order to subclassify adult mouse Shox2 neurons, we performed multiple analyses of data collected from whole-cell patch clamp recordings of visually-identified Shox2 neurons from lumbar spinal slices. A smaller set of Chx10 neurons was included in the analyses for validation. We performed k-means and hierarchical unbiased clustering approaches, considering electrophysiological variables. Unlike the categorizations by firing type, the clusters displayed electrophysiological properties that could differentiate between clusters of Shox2 neurons. The presence of clusters consisting exclusively of Shox2 neurons in both clustering techniques suggests that it is possible to distinguish Shox2+Chx10− neurons from Shox2+Chx10+ neurons by electrophysiological properties alone. Computational clusters were further validated by immunohistochemistry with accuracy in a small subset of neurons. Thus, unbiased cluster analysis using electrophysiological properties is a tool that can enhance current interneuronal subclassifications and can complement groupings based on transcription factor and molecular expression.
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