BRAIN CONNECTS: Synaptic resolution whole-brain circuit mapping of molecularly defined cell types using a barcoded rabies virus

大脑连接：使用条形码狂犬病病毒对分子定义的细胞类型进行突触分辨率全脑电路图谱

• 批准号: 10672786
• 负责人: Andreas Tolias
• 金额: $ 218.9万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10672786
• 关键词: Acceleration; Algorithms; Anatomy; Animals; Area; Bar Codes; Behavior; Benchmarking; Brain; Brain Mapping; Cells; Cognition; DNA sequencing; Data; Data Set; Detection; Disease; Event; Face; Human; In Vitro; Infection; Label; Libraries; Link; Location; Longevity; Maps; Measurement; Measures; Methods; Molecular; Molecular Profiling; Mus; Neurons; Perception; Physiological; Physiology; Production; Publishing; Rabies; Rabies virus; Resolution; Resources; Slice; Specificity; Synapses; Techniques; Technology; Testing; Toxic effect; Variant; Viral; area striata; cell type; computerized tools; connectome; cost; cost effective; experimental study; graph neural network; improved; in silico; in vivo; motor control; multimodality; new technology; patch clamp; patch sequencing; postsynaptic neurons; presynaptic neurons; reconstruction; sex; single-cell RNA sequencing; tool; transcriptome; transcriptomic profiling; transcriptomics

ABSTRACT Single-cell transcriptomics has revolutionized our understanding of neuronal diversity and enabled high-throughput characterization of molecular cell types across brain areas and species. We and others have pioneered multi- modal technologies such as Patch-seq and spatial transcriptomics to link molecularly-defined cell types with their physiology, cytomorphology, and anatomical features, but we still lack high-throughput, cost-effective methods that can provide comprehensive synaptic resolution wiring diagrams of entire mammalian brains and integrate these connectomes with molecularly defined cell types. We propose to further develop and validate Rabies Barcode Interaction Detection followed by sequencing (RaBID-seq) to enable high-throughput, scalable, and cost-effective mapping of brain-wide synaptic-level con- nectivity and transcriptomic profiling of the mapped neurons. We have optimized rabies virus production and packaging to achieve barcoded libraries containing more than 1.7 million unique barcodes, two orders of mag- nitude higher compared to prior studies, enough to map the inputs to thousands of post-synaptic neurons in a single animal. However, this technology still faces several experimental and computational challenges to realize its full potential. In Aim 1, we will address three potential challenges that may arise when scaling RaBID-seq to study brain-wide, densely labeled circuits: stochasticity of initial infection and spread, toxicity, and the potential for polysynaptic events when many founder cells are labeled. In addition, we will develop a new variant of Ra- bies featuring an evolvable barcode that can disambiguate monosynaptic vs polysynaptic spread in the setting of dense labeling. In Aim 2, we will benchmark RaBID-seq connectomes against other gold standard techniques measuring connectivity using multipatch-seq and spatial transcriptomics. In Aim 3, we will develop new algorithms using graph neural networks to reconstruct monosynaptic connectomes from barcoded viral datasets, assess the robustness of these algorithms under different experimental parameters in silico, and test whether an evolvable barcode can improve monosynaptic circuit reconstruction. If successful, these studies will establish RaBID-seq as a scalable, cost-effective tool for brain-wide connectivity mapping that can integrate transcriptomic cell types with their synaptic-level wiring diagram at single-cell resolution. By reducing the problem of synaptic connectivity into a problem of barcode sequencing, our approach has the potential to dramatically increase throughput, decrease costs and provide a direct link to the transcriptome of each mapped cell. RaBID-seq will transform brain-wide circuit mapping into a routine experiment that can be performed in any lab with modest resources, making it possible to explore how circuits differ between treatment conditions, in disease states, between the sexes, and across the lifespan. We will also generate pilot data in both mice and human slice cultures to demonstrate the utility of this tool across species.

抽象的 单细胞转录组学彻底改变了我们对神经元多样性的理解并实现了高通量 我们和其他人开创了跨大脑区域和物种的分子细胞类型的表征。 Patch-seq 和空间转录组学等模态技术将分子定义的细胞类型与其 生理学、细胞形态学和解剖学特征，但我们仍然缺乏高通量、经济有效的方法 可以提供整个哺乳动物大脑的全面突触分辨率接线图并集成 这些连接体具有分子定义的细胞类型。 我们建议进一步开发和验证狂犬病条形码相互作用检测，然后进行测序 (RaBID-seq) 能够以高通量、可扩展且经济高效的方式绘制全脑突触级 con- 我们优化了狂犬病病毒的生产和映射神经元的活性和转录组分析。 包装以实现包含超过 170 万个独特条形码的条形码库，两个订单的 mag- 与之前的研究相比，数量更高，足以将输入映射到一个神经元中的数千个突触后神经元。 然而，这项技术的实现仍面临一些实验和计算挑战。 在目标 1 中，我们将解决 RaBID-seq 扩展时可能出现的三个潜在挑战。 研究全脑、密集标记的回路：初始感染和传播的随机性、毒性和潜力 当许多创始细胞被标记时，我们将开发一种新的 Ra- 变体。 bis 具有可进化的条形码，可以在以下情况下消除单突触与多突触传播的歧义 在目标 2 中，我们将针对其他黄金标准技术对 RaBID-seq 连接组进行基准测试。 使用 multipatch-seq 和空间转录组学测量连接性 在目标 3 中，我们将开发新算法。 使用图神经网络从条形码病毒数据集中重建单突触连接体，评估 这些算法在不同实验参数下的鲁棒性，并测试是否可进化 条形码可以改善单突触回路重建如果成功，这些研究将建立RaBID-seq。 作为一种可扩展、经济高效的全脑连接图谱工具，可以整合转录组细胞类型 及其单细胞分辨率的突触级接线图。 通过将突触连接问题简化为条形码测序问题，我们的方法 显着提高通量、降低成本并提供转录组直接链接的潜力 每个映射细胞的 RaBID-seq 会将全脑回路映射转变为可以进行的常规实验。 在任何资源有限的实验室中进行，使得探索治疗之间的回路有何不同成为可能 我们还将生成两个方面的试点数据。 小鼠和人类切片培养物展示了该工具在跨物种中的实用性。