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

摘要： 阿片类药物成瘾对社会造成了巨大的损失，但对重复暴露如何影响的机械理解 阿片类药物如海洛因最终导致某些个体的强迫性吸毒和寻求药物行为， 而不是其他人，仍然未知。一个由来已久的观点是，神经回路功能的持久变化 因为药物诱导的某些脑细胞基因表达的变化。这有利于随后的吸毒 和寻求行为的人。不幸的是，识别细胞类型特异性改变后， 药物使用（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测定的， 异质大脑区域，以先验假设驱动的方式（即，先前的知识预测 可能涉及特定基因）。这极大地限制了数据收集的吞吐量，并可能使 随后解释为基因表达模式数据通常被捕获数千到数百万 均质化的细胞。鉴于神经系统是由高度异质性的组织组成的，重新评估 细胞类型特异性基因表达的变化，以一种不偏不倚的方式从1000的个别细胞是绝望的 needed.在这里，我们建议联合收割机结合我们的专业知识，以生成全面的数据集，旨在 了解单细胞基因表达，电路连接和神经活动模式如何受到 以前的吸毒行为这些数据将提供急需的细胞图谱和资源， 成瘾神经科学界，并可能导致许多新的细胞类型，基因， 表达变化，以及可以用于未来研究的集合。

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.