IDENTIFYING EPIGENOMICS AND CONNECTOMICS OF PROTRACTED OPIOID WITHDRAWAL AND RELAPSE USING CELLULAR BARCODING

摘要 阿片成瘾的惊人发生率继续在流行的水平上，扰乱和摧毁了 数百万美国人，每年的财务损失接近800亿美元。治疗阿片成瘾的一个主要障碍是 戒断和复发的慢性循环。啮齿动物研究已经确定了基因表达的长期变化， 表观遗传学，以及阿片类药物暴露和戒断后的电路连接，但很少有研究调查 这些变化在不同医疗模式之间的相互作用。我们最近开发了一类新的基于 关于细胞条形码，它可以将单细胞和实验中的三种模式联系起来。这些工具，它们 包括MAPseq和BARseq，每个实验使用RNA条形码唯一地标记数千个神经元，并 绘制它们的区域间联系图。在每个标记的神经元中，条形码被输送到轴突，在那里我们 可以通过高通量测序来检测它们。跨潜在目标区域匹配条形码序列 然后产生所有条形码神经元的单细胞投影矩阵。因为条形码是mRNA，所以它们在 与内源性转录组相同的模式，使我们能够天然地桥接单细胞连接 用单细胞测量同一细胞中的基因表达和基因组可获得性。最后， 将这些技术与空间转录方法相结合，包括Starmap和BARseq2，让 US在3D大脑空间以高分辨率绘制观察到的变化。 在这里，我们应用我们的多组学工具来测量内侧眼眶额叶皮质(Mofc)引起的变化。 在两种阿片类药物戒断和复发的啮齿动物模型中。我们在沙汉姆实验室的合作者最近发现 电屏障诱导的自愿戒断后大鼠mOFC中相反的功能连接变化。在 同样的大鼠模型使用我们的工具，我们将定义哪些细胞类型负责这些连接变化， 单细胞连接模式如何变化，以及基因表达、表观遗传学和连接性如何变化 在单细胞水平上相互作用。关键的是，我们通过转录组、表观基因组组、 以及连接方式。然后，我们将这些变化与在第二只啮齿动物的mOFC中观察到的变化进行比较 模型，一种经典的阿片戒断小鼠条件性位置厌恶(CPA)模型。我们希望能找到一套 对这两种行为具有健壮性并在物种之间保持保守的变化，强调它们是 阿片类药物的戒断和复发。我们将因果性地操纵与这些核心相关的表观遗传因素 通过特定细胞类型的基因敲除和过度表达的变化，并评估它们对行为的影响。公众 这项工作对健康的影响在于对以下因素引起的长期电路变化的基本了解 阿片类药物戒断和复发在前所未有的多组学分辨率和一套潜在的鉴定 保守的核心改变作为治疗的候选。

SUMMARY The staggering incidence of opioid addiction continues at epidemic levels, disrupting and destroying the lives of millions of Americans with a yearly financial toll nearing $80 billion. A major hurdle in treating opioid addiction is a chronic cycle of withdrawal and relapse. Rodent studies have identified long-term changes in gene expression, epigenetics, and circuit connectivity after opioid exposure and withdrawal, but few studies have investigated the interaction of these changes across modalities. We recently developed a new class of brain mapping tools based on cellular barcoding that can relate the three modalities in single cells and experiments. These tools, which include MAPseq and BARseq, use RNA barcodes to uniquely label thousands of neurons per experiment and map their inter-regional connections. In each labeled neuron, barcodes are trafficked into the axons, where we can detect them by high-throughput sequencing. Matching up barcode sequences across potential target regions then produces the single-cell projection matrix for all barcoded neurons. As barcodes are mRNAs, they are in the same modality as the endogenous transcriptome, allowing us to natively bridge single-cell connectomic measurements with single-cell measures of gene expression and genome accessibility in the same cells. Finally, combination of these technologies with spatial transcriptomics methods, including STARmap and BARseq2, lets us map the observed changes with high resolution in 3D brain space. Here we apply our multi-omic tools to measure the changes induced in the medial orbitofrontal cortex (mOFC) in two rodent models of opioid withdrawal and relapse. Our collaborators in the Shaham lab recently identified opposing functional connectivity changes in rat mOFC after electric barrier-induced voluntary abstinence. In the same rat model using our tools, we will define which cell types are responsible for these connectional changes, how single-cell connection patterns change, and how gene expression, epigenetics, and connectivity changes interact at the level of single cells. Critically, we define cell types holistically across transcriptomic, epigenomic, and connectomic modalities. We then compare these changes to those observed in mOFC in a second rodent model, a classical opioid withdrawal conditioned place aversion (CPA) model in mice. We hope to identify a set of changes robust to the two behaviors and conserved across species, highlighting them as core features of opioid withdrawal and relapse. We will causally manipulate epigenetic factors associated with these core changes by cell-type-specific knockouts and overexpression, and assess their effects on behavior. The public health impact of this work lies in the fundamental understanding of the long-term circuit changes induced by opioid withdrawal and relapse at unprecedented multi-omic resolution and the potential identification of a set of conserved core changes as candidates for treatment.