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结合机器学习和多重复合,现场分析来设计细胞类型和行为特异性.

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概括

我们开发了ESCargoT,这是一个使用机器学习和多重选的平台,用于发现精确的神经电路控制的增强剂,成功地准了小鼠的疼痛和的途径.

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科学领域:

  • 神经科学是一个神经科学.
  • 遗传学 是一个遗传学.
  • 生物工程是生物工程.

背景情况:

  • 精确控制神经回路对于理解和治疗神经系统疾病至关重要.
  • 发现基因表达的细胞特异增强剂是具有挑战性的,因为体内成功率低和物种特异活性.
  • 目前的方法缺乏空间细节,并与像腺相关病毒 (AAVs) 这样的复杂病毒载体作斗争.

研究的目的:

  • 为了加速发现细胞向增强剂的背脊髓,一个关键区域的疼痛和处理.
  • 开发一个集成的平台,结合机器学习,模块化AAV组装和现场查以发现增强器.
  • 为了实现增强剂的空间分辨,多重体的体内选.

主要方法:

  • 开发了ESCargoT (货物转录的工程特异性),这是一个端到端的增强器发现平台.
  • 利用跨物种染色质可访问性数据来训练机器学习模型,以预测特定细胞类型中的增强剂活性.
  • 创建了一个空间并行报告员测试 (SPRA),集成金门组件与多重现场选,用于增强器-AAV库的并行分析.

主要成果:

  • 成功识别和验证了向激发性背部角神经元的增强剂,从而逆转了机械代.
  • 证明能够在体内同时选27种增强剂的库,准包括寡细胞和背角神经元亚型在内的多种细胞类型.
  • 经过验证的增强剂准Exc-LMO3和Exc-SKOR2神经元,其中一种被证明可以阻止化学感.
  • 展示了跨物种的适用性,在小鼠中发挥作用的类衍生增强剂.

结论:

  • ESCargoT平台显著加速了用于神经电路操纵的细胞特异增强剂的发现.
  • 空间分辨,多重化体内查对于识别功能增强剂是有效的.
  • 这种方法有助于开发新的细胞向工具和治疗疼痛和症的基因疗法.