Leone Michael J, van de Weerd Robert, Brown Ashley R, Noh Myung-Chul, Phan BaDoi N, Wang Andrew Z, Corrigan Kelly A, Yeramosu Deepika, Sestili Heather H, Arokiaraj Cynthia M, Lopes Bettega C, Cherupally Vijay Kiran, Fields Daryl, Babu Sudhagar, Srinivasan Chaitanya, Podder Riya, Gadey Lahari, Headrick Daniel, Chen Ziheng, Franusich Michael E, Dum Richard, Lewis David A, Mathys Hansruedi, Stauffer William R, Seal Rebecca P, Pfenning Andreas R
Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, United States.
Medical Scientist Training Program, University of Pittsburgh, Pittsburgh, United States.
bioRxiv. 2025 Jun 21:2025.06.20.660790. doi: 10.1101/2025.06.20.660790.
A promising strategy for the precise control of neural circuits is to use -regulatory enhancers to drive transgene expression in specific cells. However, enhancer discovery faces key challenges: low success rates, species-specific differences in activity, challenges with multiplexing adeno-associated viruses (AAVs), and the lack of spatial detail from single-cell sequencing. In order to accelerate enhancer discovery for the dorsal spinal cord-a region critical for pain and itch processing-we developed an end-to-end platform, ESCargoT (), combining machine learning (ML)-guided enhancer prioritization, modular AAV assembly, and multiplexed, screening. Using cross-species chromatin accessibility data, we trained ML models to predict enhancer activity in oligodendrocytes and in 15 dorsal horn neuronal subtypes. We first demonstrated that an initial enhancer, Excit-1, targeted excitatory dorsal horn neurons and drove reversal of mechanical allodynia in an inflammatory pain model. To enable parallel profiling of a 27-enhancer-AAV library delivered intraspinally in mice, we developed a Spatial Parallel Reporter Assay (SPRA) by integrating a novel Golden-Gate assembly pipeline with multiplexed, screening. Regression adjustment for spatial confounding enabled specificity comparisons between enhancers, demonstrating the ability to screen enhancers targeting diverse cell types (oligodendrocytes, motoneurons, dorsal neuron subtypes) in one experiment. We then validated two candidates, targeting Exc-LMO3 and Exc-SKOR2 neurons, respectively. In a companion paper by Noh , our colleagues show that the functional specificity of the Exc-SKOR2-targeting enhancer, unlike Excit-1, is capable of blocking the sensation of chemical itch in mice. These enhancers were derived from the macaque genome but displayed functional sensitivity in mice. This platform enables spatially resolved, multiplexed enhancer profiling to accelerate discovery of cell-targeting tools and gene therapy development.
一种精确控制神经回路的有前景的策略是使用调控增强子来驱动特定细胞中的转基因表达。然而，增强子的发现面临着关键挑战：成功率低、活性存在物种特异性差异、腺相关病毒（AAV）复用方面的挑战以及单细胞测序缺乏空间细节。为了加速对脊髓背侧区域（对疼痛和瘙痒处理至关重要的区域）增强子的发现，我们开发了一个端到端平台ESCargoT，它结合了机器学习（ML）引导的增强子优先级排序、模块化AAV组装和多重筛选。利用跨物种染色质可及性数据，我们训练了ML模型来预测少突胶质细胞和15种背角神经元亚型中的增强子活性。我们首先证明，一个初始增强子Excit-1靶向兴奋性背角神经元，并在炎症性疼痛模型中驱动机械性异常性疼痛的逆转。为了能够对在小鼠脊髓内递送的27个增强子-AAV文库进行平行分析，我们通过将一种新型的金门组装管道与多重筛选相结合，开发了一种空间平行报告基因分析（SPRA）。对空间混杂因素进行回归调整能够对增强子之间的特异性进行比较，证明了在一个实验中筛选靶向不同细胞类型（少突胶质细胞、运动神经元、背侧神经元亚型）的增强子的能力。然后我们验证了两个候选增强子，分别靶向Exc-LMO3和Exc-SKOR2神经元。在Noh等人的一篇配套论文中，我们的同事表明，与Excit-1不同，靶向Exc-SKOR2的增强子的功能特异性能够阻断小鼠的化学性瘙痒感觉。这些增强子源自猕猴基因组，但在小鼠中表现出功能敏感性。这个平台能够实现空间分辨的多重增强子分析，以加速细胞靶向工具的发现和基因治疗的发展。
