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，并启用了高通量 跨大脑区域和物种的分子细胞类型的表征。我们和其他人都开创了多个 模态技术，例如斑块序列和空间转录组学，以将分子类型的细胞类型与其 生理学，细胞形态和解剖学特征，但我们仍然缺乏高通量，具有成本效益的方法 可以提供整个哺乳动物大脑的全面突触分辨率接线图并整合 这些与分子定义的细胞类型的连接组。 我们建议进一步发展和验证狂犬病条形码相互作用检测，然后进行测序 （狂犬病）以实现大脑范围的高通量，可扩展和成本效益的映射 映射神经元的良好和转录组纤维填充。我们已经优化了狂犬病病毒的产生， 包装以实现包含超过170万个独特条形码的条形码图书馆 与先前的研究相比，比例更高，足以将输入映射到A中数千个突触后神经元 单动物。但是，这项技术仍然面临着一些实验和计算挑战以实现 它的全部潜力。在AIM 1中，我们将解决将Rabid-seq扩展到的三个潜在挑战 研究大脑范围的，垂直标记的电路：初始感染和扩散的随机性，毒性和潜力 对于多突触事件，当许多创始人细胞被标记时。此外，我们将开发一个新的Ra- 在可以消除单突触与多突触的差异的情况下，其均具有可转化的条形码 密集的标签。在AIM 2中，我们将针对其他黄金标准技术基准基准rabid-seq连接。 使用多重序列和空间转录组学测量连通性。在AIM 3中，我们将开发新算法 使用图形神经网络从条形码病毒数据集中重建单突触连接，评估 这些算法在计算机中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电路重建。如果成功的话，这些研究将建立狂热的赛克 作为可扩展的，具有成本效益的工具，用于整个大脑连通性映射，可以整合转录组细胞类型 并以单细胞分辨率的突触级接线图。 通过将突触连接性问题减少到条形码测序问题中，我们的方法具有 显着增加吞吐量，降低成本并提供直接链接到转录组的潜力 每个映射的单元格。狂热的seq将将范围范围的电路映射到一个常规实验中，可以是 在任何具有适度资源的实验室中执行 疾病状态，性别之间以及整个寿命之间的条件。我们还将在这两个中生成试点数据 小鼠和人类切片培养物，以证明该工具跨物种的实用性。