Orthocoding for Spatial Sequencing

项目摘要 细胞的3D空间环境决定了它表达哪些基因和RNA亚型，从而使 专门化的细胞功能是多细胞生命的基础。在典型的单细胞RNA序列(scRNA-seq)中，第一个 细胞分离的步骤擦除了细胞的空间背景。这种缺陷产生了对一种技术的迫切需求 它具有与scRNA-seq相同的吞吐量，但还编码了细胞的空间上下文。虽然是新一波浪潮 最近出现了基于测序的空间转录技术，都患有严重的 限制：效率低(约为降序效率的1-2%)，仅提供2D分辨率，无法 区分单元格边界，需要专门或昂贵的设备。这些限制是固有的 并由它们共同依赖于固相载体的原位合成而产生。基于图像的 技术具有更高的空间分辨率，但需要更多的设备、协议执行的时间， 基因测量吞吐量有限，不能分析RNA异构体或其他序列变体。 为了克服最先进的空间转录方法中的这些限制，我们建议开发 Orthocode，一种基于原理证明的统计驱动空间转录分析的创新范式 分子实验和前沿统计理论。正交码实现了50倍或更高的敏感度 与当前通过编码和恢复简单、廉价和 高效的分子生物学方案。实验中的Orthocode协议有两个步骤：1)两个人的池 位置编码寡核苷酸的类型(A)条形码发射体寡核苷酸产生自身的副本，并在局部扩散 和(B)“接收器”记录附近发射器的条形码耦合到单元；2)耦合到位置的单元- 共同记录细胞空间位置的编码寡核苷酸被分离并输入到scRNA-seq 工作流程，例如Drop-Seq并已排序。然后，Orthocode对条形码进行严格的统计分析 位置编码寡核苷酸的简档，以三角测量每个测序细胞的位置。这一严谨的理由 实验设计和原型开发构建了从最简单的测试系统到原型的Orthocode 这将使组织中史无前例的空间转录分辨率能够解决 生物医学。Orthocode范例可以从RNA分析推广到空间测量 蛋白质、DNA和表观遗传修饰，是深度测序中潜在的突破性创新 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.