Molecular signature of parafacial expiratory neurons

项目总结 呼吸系统的一个关键特征是它有能力通过招募 呼气肌促进肺泡通风，这一过程称为主动呼气。有效到期时间 被认为是由位于外侧副面区域(PFL)的一群神经元介导的。然而，尽管 这种生理意义，呼气性PFL神经元的身份尚不为人所知，而这些是唯一的标志物 细胞尚未确定。为了解决这一需求，拟议的实验将确定分子 呼气性PFL神经元的特征，并确定这一群体中可选择性用于 对这个群体进行标记和操作，以进行详细的功能分析。我们将使用RNA-seq来执行 对PFL区和附近呼吸中枢神经元转录表达的无偏见评估 称为后梯形核(RTN)。这项分析将集中在预期的谷氨酸能种群 包括呼气性PFL神经元和RTN神经元。我们将使用已知的RTN神经元的分子图谱 作为识别独特PFL标记的试金石。为了将基因表达与功能联系起来，我们将 反标记面旁呼气神经元的基线活动和CO2/H敏感性的特征 呼气控制中心，我们将手动采集这些细胞进行靶向qPCR，以识别常见的和 RTN和PFL群体之间的基因差异。鉴定PFL神经元的转录组将 代表着呼吸控制领域的重大技术进步，因为它将使开发成为可能 有选择地操纵这一人群的目标策略。这项工作也将提供对基因的洞察 控制呼吸的神经元和回路的组织，并在此过程中帮助理解这些元素是如何 饱受疾病困扰。

PROJECT SUMMARY A critical feature of the respiratory system is its ability to adapt to increased metabolic demand by recruitment of expiratory muscles to facilitate alveolar ventilation, a process referred to as active expiration. Active expiration is thought to be mediated by a cluster of neurons located in the lateral parafacial region (pFL). However, despite this physiological significance, the identity of expiratory pFL neurons is not known and unique markers of these cells have not been determined. To address this need, proposed experiments will determine the molecular profile of expiratory pFL neurons and identify unique markers of this population that can be used to selectively label and manipulate this population for detailed functional analysis. We will use RNA-seq to perform an unbiased evaluation of transcript expression of neurons from the pFL region and a nearby respiratory center called the retrotrapezoid nucleus (RTN). This analysis will focus on glutamatergic populations that are expected to include expiratory pFL neurons and RTN neurons. We will use the known molecular profile of RTN neurons as a touchstone for identification of unique pFL markers. To correlate gene expression with function, we will characterize baseline activity and CO2/H+ sensitivity of putative expiratory parafacial neurons back-labelled from an expiratory control center, and we will manually harvest these cells for targeted qPCR to identify common and differentiating genes between RTN and pFL populations. Identifying the transcriptome of pFL neurons will represent a major technical advance in the field of respiratory control as it would make possible the development of targeting strategies to selectively manipulate this population. This work will also provide insight into the genetic organization of neurons and circuits that control breathing, and in doing so help understand how these elements are perturbed in disease.