In situ Single-Cell Multi-Omic and Morphological Profiling in Mammalian Brains

项目总结 单细胞技术彻底改变了哺乳动物大脑的特征，使无偏见的普查成为可能 细胞类型及其转录和表观基因组学特征。然而，分子签名的映射 对三维大脑结构的研究仍然具有极大的挑战性，因为大多数单细胞方法只能 分析分离的细胞或细胞核。我们建议开发光子索引测序(pi-seq)策略。 用于具有单单元分辨率的原位空间条形码。提出的pi-seq策略具有很高的复杂性。 利用DNA指数的顺序连接将分子条形码插入组织，连接反应受控制 通过高分辨率图案化照明。染色质可及性和胞嘧啶修饰是公认的 表观基因组学标记在正常组织和疾病组织的转录调控中起着关键作用。我们会 将pi-seq与现有的单细胞表观基因组学技术相结合，开发出空间分布的方法。 染色质可及性(pi-ATAC-seq)和甲基胞嘧啶(pi-mc-seq)。我们将进一步开发一种原位方法 PI-MCAT-seq在单个细胞中同时分析RNA、甲基胞嘧啶和染色质的可及性 基于我们的单核多组学方法SnmC2T-seq.空间的特殊性和数据 Pi-seq方法的质量将使用由 BICCN.将分子图谱与脑细胞类型的其他定义属性联系起来，如形态和 突触连接，我们将开发将pi-seq与Morf(带有重复的嵌合体)整合的方法 FrameShift)，一种稀疏的神经元和神经胶质细胞的基因标记方法，以阐明其完整的 形态(树突、轴突、突触)。建议的pi-seq方法将提供工具来构建 哺乳动物脑空间分辨表观基因组图谱及脑内基因调控研究进展 单个细胞的发育、功能和疾病。

PROJECT SUMMARY Single-cell technologies have revolutionized the characterization of mammalian brains allowing unbiased census of cell types and their transcriptomic and epigenomics signatures. However, the mapping of molecular signatures onto three-dimensional brain structures remains highly challenging since most single-cell methods can only analyze disassociated cells or nuclei. We propose to develop photonic-indexing sequencing (pi-seq) strategies for in situ spatial barcoding with single-cell resolution. The proposed pi-seq strategy writes high complexity molecular barcodes into the tissue using sequential ligation of DNA indices with the ligation reaction controlled by high-resolution patterned illumination. Chromatin accessibility and cytosine modifications are well-established epigenomics marks playing critical roles in transcription regulation in normal and disease tissues. We will integrate pi-seq with existing single-cell epigenomics techniques to develop methods for the spatial profiling of chromatin accessibility (pi-ATAC-seq) and methylcytosine (pi-mC-seq). We will further develop an in situ method pi-mCAT-seq to simultaneously profile RNA, methylcytosine, and chromatin accessibility at a single-cell resolution based upon our single-nucleus multi-omics method snmC2T-seq. The spatial specificity and data quality of pi-seq methods will be systematically evaluated using single-cell epigenomic datasets generated by BICCN. To connect molecular profiles with other defining properties of brain cell types such as morphology and synaptic connectivity, we will develop methods to integrate pi-seq with MORF (Mosaicism with Repeat Frameshift), a sparse and genetic labeling approach of neurons and glia to illuminate their complete morphologies (dendrites, axons, synapses). The proposed pi-seq methods will provide the tools to construct spatially resolved epigenomic atlas of mammalian brains and advance the study of gene regulation in brain development, function, and disease at the resolution of single cells.