Computational approaches for the systematic detection of cell-cell interactions by spatial transcriptomics - Resubmission - 1

总结 许多生物过程不是发生在细胞水平，而是发生在系统水平，细胞与细胞的相互作用 对组织功能至关重要随着单细胞RNA-Seq的引入，我们可以对细胞的生长进行稳健的测量。 类型和细胞状态。然而，在这种方法中，在测序之前必须解离所研究的组织 导致空间背景的丢失。空间转录组学是一个很有前途的新领域，其中几个 已经开发了在细胞的天然环境中对细胞的转录组进行谱分析的方法。不过最 这种技术的广泛使用的实现-基于测序的空间转录组学-尚未达到 单细胞分辨率。因此，迫切需要一种新的计算方法， 转录组学和单细胞RNA-Seq，以推断复杂组织中的细胞-细胞关系。我们的实验室 最近开发的这两个数据源的多模式交叉分析，有效地减轻了 每种技术的局限性。在这里，我们建议应用这一概念来揭示细胞-细胞的模式， 生物系统中的相互作用。在我们的第一个目标中，我们提出了StateMap方法来推断局部细胞-细胞 通过基于空间转录组学的共定位和受体-配体关系的相互作用。一个细胞目录 首先使用单细胞数据描绘细胞类型和细胞状态，然后将空间转录组学数据 用来绘制成对的共定位细胞状态。然后，StateMap系统地推断细胞与细胞之间的相互作用 通过统计测试共定位小区状态中的信号/响应关系， 空间转录组学数据。在我们的第二个目标中，我们提出了ST基序方法来概念化 细胞类型和状态的位置作为一个网络，允许通过大量可用的 方法.因此，我们的方法将寻找细胞-细胞关系的问题重新定义为网络基序问题 在这个图表中。在我们的整个建议中，我们在两个模型系统上开发和测试算法，男性 生殖细胞和胎盘，我们的实验室有相当丰富的经验。从概念上讲，我们的建议 有望产生新的算法，用于映射细胞间的相互作用，这是激活电位所必需的。 两种强大的转录组学技术

SUMMARY Many biological processes occur not at the level of a cell but at the level of a system, and cell-cell interactions are crucial for tissue function. With the introduction of single-cell RNA-Seq, we have robust measures of cell types and cell states. In this approach however, the tissue under study must be dissociated prior to sequencing resulting in the loss of spatial context. Spatial transcriptomics is a promising new field, in which several methods have been developed to profile the transcriptome of cells in their native context. However, the most widely used implementation of this technology – sequencing-based spatial transcriptomics – has not reached single-cell resolution. Thus, there is a critical need for novel computational approaches integrating spatial transcriptomics and single-cell RNA-Seq in order to infer cell-cell relationships in complex tissues. Our lab has recently developed analyses for multimodal intersection of these two data sources that effectively mitigate the limitations of each technology. Here, we propose to apply this concept to uncover patterns of cell-cell interactions in biological systems. In our first Aim, we present the StateMap approach to infer local cell-cell interactions by spatial transcriptomics-based co-localization and receptor-ligand relationships. A catalog of cell types and cell states is first delineated using single-cell data, and the spatial transcriptomics data is then harnessed to map pairs of co-localizing cell states. StateMap then systematically infers the cell-cell interaction mechanisms among co-localizing cell states by statistically testing for signal/response relationships in the spatial transcriptomics data. In our second Aim, we propose the ST-motif method to conceptualize the locations of cell types and states as a network, allowing for systematic analysis by a wealth of available methods. Our approach thus reframes the problem of finding cell-cell relationships as a network motif problem in this graph. Throughout our proposal, we develop and test the algorithms on two model systems, the male germline and the placenta, with which our lab has considerable experience. Conceptually, our proposal promises to yield novel algorithms for mapping cell-cell interactions that are required for actuating the potential of two powerful transcriptomic technologies.