Transcriptional, functional, and circuit profiling at single cell resolution of neuronal ensembles engaged by heroin relapse

摘要： 鸦片成瘾给社会带来了巨大的损失，但机械地理解了反复接触 对海洛因等阿片类药物的使用最终会导致一些人的强迫吸毒和寻觅行为， 但不是其他人，目前还不清楚。一个由来已久的观点是，神经回路功能发生了持久的变化 因为药物诱导的某些脑细胞的基因表达发生变化。这为随后的吸毒提供了便利。 易受伤害个体的寻觅行为。不幸的是，识别以下特定细胞类型的更改 吸毒(通常在已建立的成瘾动物模型中进行)通常是一个缓慢而乏味的过程 由于体内药物暴露后基因表达的变化通常是连续检测的，在 不同的大脑区域，以先验假设驱动的方式(即先前知识预测 可能涉及特定的基因)。这极大地限制了数据收集的吞吐量，并可能使 随后作为基因表达模式数据的解释通常从数千到数百万被捕获 均质细胞的数量。鉴于神经系统是由高度异质的组织组成的，重新评估 细胞类型特异性基因表达变化无偏从1000‘S个别细胞拼命 需要的。在这里，我们建议结合我们的专业知识，以生成旨在 了解单细胞基因表达、电路连通性和神经活动模式如何受到 既往的吸毒行为。这些数据将提供急需的蜂窝地图集和资源 并将可能导致许多新的细胞类型、基因的鉴定 表情变化，以及可用于未来研究的合奏。

Abstract: Opiate addiction extorts a tremendous toll on society, but a mechanistic understanding of how repeated exposure to opioids such as heroin ultimately results in compulsive drug-taking and -seeking behavior in some individuals, but not others, is still not known. A longstanding idea is that enduring changes in neural circuit function occur because of drug-induced gene expression changes in certain brain cells. This facilitates subsequent drug-taking and -seeking behaviors in vulnerable individuals. Unfortunately, identifying cell-type specific alterations following drug use (typically performed in established animal models of addiction), is generally a slow and tedious process as changes in gene expression following in vivo drug exposure are typically assayed in series, within heterogeneous brain regions, in an a-priori hypothesis driven fashion (i.e. previous knowledge predicting a specific gene may be involved). This dramatically limits the throughput of data collection and likely complicates the subsequent interpretation as gene expression patterns data are typically captured from thousands to millions of homogenized cells. Given that the nervous system is composed of highly heterogeneous tissue, re-assessing cell type specific gene expression changes in an unbiased manner from 1000's of individual cells is desperately needed. Here, we propose to combine our expertise in order to generate comprehensive datasets aimed at understanding how single-cell gene expression, circuit connectivity, and neural activity patterns are impacted by previous drug-taking behavior. These data will provide a much-needed cellular atlas and resource for the addiction neuroscience community and will likely lead to the identification of many novel cell type, gene expression changes, and ensembles that can be leveraged for future study.