Orthocoding for Spatial Sequencing

项目摘要 细胞的3D空间背景决定了它表达哪些基因和RNA亚型， 特殊的细胞功能是多细胞生命的基础。在典型的单细胞RNA-seq（scRNA-seq）中，第一个 细胞解离的步骤消除了细胞的空间背景。这一缺陷使得我们迫切需要一种技术 它具有与scRNA-seq相同的通量，但也编码细胞的空间背景。虽然新一波 最近出现了基于测序的空间转录组技术，所有这些技术都受到严重的影响。 局限性：效率低（约为Drop-Seq效率的1-2%），仅提供2D分辨率， 区分小区边界并且需要专门的或昂贵的设备。这些限制是内在的 并且是由于它们共同依赖于来自固体支持物的原位cDNA合成。基于成像 技术具有更高的空间分辨率，但需要更多的设备、用于协议执行的时间， 有限的基因测量通量，并且不能分析RNA同种型或其他序列变体。 为了克服这些限制，在国家的最先进的空间转录组学方法，我们建议开发 Orthocode，一种基于原理证明的空间转录组学创新范式 分子实验和尖端统计理论Orthocode实现> 50倍或更高的灵敏度 与当前的方法相比， 高效的分子生物学方案实验性的Orthocode协议有两个步骤：1）两个 “位置编码寡核苷酸”的类型（a）条形码发射寡核苷酸产生自身的拷贝， 和（B）“受体”记录与细胞偶联的附近发射体的条形码; 2）与位置- 将共同记录细胞空间位置的编码寡核苷酸分离并输入scRNA-seq 工作流程，例如Drop-seq和测序。Orthocode然后对条形码进行严格的统计分析 位置编码寡核苷酸的分布图，以三角测量每个测序细胞的位置。这是经过严格推理的 实验设计和原型开发将Orthocode从最简单的测试系统构建为原型 这将使组织中前所未有的空间转录组学分辨率能够解决关键的未满足需求， 生物医药Orthocode范例可以被推广到RNA谱分析之外的空间测量， 蛋白质，DNA和表观遗传修饰，是深度测序的潜在突破性创新 基于空间的组学

Project Summary The 3D spatial context of a cell determines which genes and RNA isoforms it expresses, enabling specialized cell functions fundamental to multicellular life. In typical single-cell RNA-seq (scRNA-seq), the first step of cell dissociation erases the spatial context of the cell. This flaw creates an urgent need for a technology that has the same throughput of scRNA-seq but also encodes the cells’ spatial context. Although a new wave of spatial transcriptomic technologies based on sequencing has emerged recently, all suffer from severe limitations: low efficiency (~1-2% of the Drop-Seq efficiency), providing 2D resolution only, failure to discriminate cell boundaries and requiring specialized or expensive equipment. These limitations are intrinsic and result from their shared reliance on cDNA synthesis in situ by from a solid support. Imaging-based technologies have higher spatial resolution but require more equipment, time for protocol execution, have limited gene measurement throughput, and cannot profile RNA isoforms or other sequence variants. To overcome these limitations in state-of-the-art spatial transcriptomic methods, we propose to develop Orthocode, an innovative paradigm for statistically-driven spatial transcriptomics, grounded in proof-of-principle molecular experiments, and cutting-edge statistical theory. Orthocode achieves > 50x or higher sensitivity compared to current approaches by encoding and recovering spatial information from simple, inexpensive and efficient molecular biology protocols. The experimental Orthocode protocol has two steps: 1) a pool of two types of “location-encoding oligos” (a) barcoded emitter oligos produce copies of themselves that diffuse locally and (b) “receptors” record the barcodes of nearby emitters are coupled to cells; 2) cells coupled to location- encoding oligos that have together record the spatial position of the cell, are isolated and input into scRNA-seq workflows, eg. Drop-seq and sequenced. Orthocode then employs a rigorous statistical analysis of the barcode profiles of location encoding oligos to triangulate the location of each sequenced cell. This rigorously reasoned experimental design and prototype development builds Orthocode from the simplest test systems to prototypes that will allow unprecedented spatial transcriptomic resolution in tissues to address a critical unmet need in biomedicine. The Orthocode paradigm can be generalized beyond RNA profiling to spatial measurements of proteins, DNA and epigenetic modifications and is a potential breakthrough innovation in deep-sequencing based spatial ‘omics.