Computations of transcriptomic neuron types in cortex

皮层转录组神经元类型的计算

• 批准号: EP/Y028295/1
• 负责人: Kenneth Harris
• 金额: $ 269.67万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY028295%2F1
• 关键词: Computations; transcriptomic; neuron; types; cortex

The cerebral cortex contains a rich diversity of neuronal types, particularly among inhibitory cells. Transcriptomic methods, which measure the expression of many genes simultaneously, have identified nearly 100 fine subtypes of cortical neuron. Understanding how these subtypes underlie cortical computation requires measuring the activity of large cortical populations in vivo, identifying each cell's transcriptomic subtype.We have developed a method to identify the transcriptomic subtypes of all cells in a population whose activity has been measured in vivo by 2-photon calcium imaging. We will use this together with tools such as 2-photon mesoscopy, cell-type-specific viruses and mathematical data analysis, to reveal for the first time how the cortex's cell types are coordinated during information processing. Our working hypothesis is that an animal's cognitive state controls the joint activity of inhibitory neurons, which in turn sculpt information coding in excitatory populations. We will test this hypothesis and characterize in detail of each cell type's role in the circuit.By recording during spontaneous behavior, we will ask if inhibitory population activity is constrained to a manifold reflecting fixed interactions between inhibitory subtypes, and how this inhibitory activity correlates with ongoing behavior and sensory coding in excitatory neurons. By recording transcriptomically-identified populations while mice perform a behavioral task, we will reveal how inhibitory activity and excitatory coding vary according to cognitive context such as stimulus expectation and omission, engagement, impulsiveness, and reward. By combining cell-type-specific optogenetics with transcriptomically identified recordings, we will reveal the causal role of each inhibitory subclass and of neuromodulators. Together, these experiments will provide foundational information on how information is processed by the diverse cell types that make up cortical circuits.

大脑皮层包含丰富多样的神经元类型，特别是抑制细胞。转录组学方法可同时测量许多基因的表达，已鉴定出近 100 种皮质神经元的精细亚型。了解这些亚型如何构成皮质计算的基础，需要测量体内大量皮质群体的活动，识别每个细胞的转录组亚型。我们开发了一种方法来识别群体中所有细胞的转录组亚型，其活性已通过 2-光子钙成像。我们将把它与 2 光子介观镜、细胞类型特异性病毒和数学数据分析等工具结合使用，首次揭示皮层细胞类型在信息处理过程中是如何协调的。我们的工作假设是，动物的认知状态控制抑制性神经元的联合活动，而抑制性神经元反过来又在兴奋性群体中塑造信息编码。我们将测试这个假设并详细描述每种细胞类型在回路中的作用。通过记录自发行为，我们将询问抑制群体活动是否被限制在反映抑制亚型之间固定相互作用的流形，以及这种抑制活动如何与兴奋性神经元的持续行为和感觉编码。通过在小鼠执行行为任务时记录转录组识别的群体，我们将揭示抑制活动和兴奋性编码如何根据认知环境（例如刺激预期和遗漏、参与、冲动和奖励）而变化。通过将细胞类型特异性光遗传学与转录组鉴定的记录相结合，我们将揭示每个抑制亚类和神经调节剂的因果作用。这些实验将共同提供关于构成皮质回路的不同细胞类型如何处理信息的基础信息。