Inferring cell state tumor microenvironment maps by integrating single-cell and spatial transcriptomics

总结 单细胞RNA测序（scRNA-Seq）已被证明是癌症生物学的变革性技术， 能够对单个肿瘤细胞进行无偏的转录组学分析，并揭示了大量的 恶性细胞中的转录异质性。近年来的许多报告已经确定了一系列癌症 不同癌症类型中的细胞状态表明这些是稳定和功能性肿瘤单位， 肿瘤的维持和发展。然而，scRNA-Seq分析的一个主要缺点是失去了DNA序列。 在测序之前从肿瘤解离得到的空间信息。缺乏对...的了解 每个细胞在组织内的一般位置，以及其局部邻域，scRNA-Seq不能单独 告诉我们癌细胞状态之间的复杂关系，以及它们与 肿瘤微环境的元素。空间转录组学是一项颠覆性的新技术， 第一次能够在整个组织中以稳健的方式测量整个转录组。而空间 转录组学同时绘制所有基因的表达图谱-实现系统性和公正性 转录组分析-它本身不是一种单细胞技术，因此也不能单独告诉我们关于转录组的信息。 癌细胞状态的模式化以及肿瘤微环境的状态。灵敏而坚固 因此需要算法来利用这些技术的集成中隐含的全部功率。这里我们 我建议开发一种新的计算方法，称为SNAP（单细胞邻域图），它使用 匹配来自同一肿瘤的scRNA-Seq和空间转录组学数据，以推断每个肿瘤的空间位置。 scRNA-Seq-通过参考空间转录组学数据鉴定细胞，并产生邻近区域 每个scRNA-Seq细胞的转录组。为了分析这些新的邻里转录组，我们提出了一个 该方法将具有共同邻居模式的小区聚类，从而识别共定位小区状态的集合。 SNAP有望利用单细胞和空间转录组学的互补方面，将共转录基因与转录组学联系起来。 定位癌细胞状态和肿瘤微环境的状态。这里介绍的方法 包括几种新颖的算法，所有这些算法都将免费提供给社区，我们希望 它们将广泛应用于癌症生物学。

SUMMARY Single-cell RNA-Sequencing (scRNA-Seq) has proved to be a transformative technology for cancer biology, enabling the unbiased transcriptomic profiling of individual tumor cells and revealing a striking amount of transcriptional heterogeneity in malignant cells. Many reports in recent years have identified a range of cancer cell states in diverse cancer types suggesting that these are stable and functional tumor units, with roles in tumor maintenance and progression. However, a major shortcoming of scRNA-Seq analysis is the loss of spatial information which follows from the dissociation of the tumor prior to sequencing. Lacking knowledge of the general location of each cell within the tissue, as well as its local neighborhood, scRNA-Seq cannot alone inform us about the complex set of relationships among cancer cell states, together with their interactions with the elements of the tumor microenvironment. Spatial transcriptomics is a disruptive new technology that for the first time is able to measure whole transcriptomes in a robust fashion throughout a tissue. While spatial transcriptomics maps the expression of all genes simultaneously – enabling systematic and unbiased transcriptome analysis – it is not itself a single-cell technology and thus also cannot alone inform us on the patterning of cancer cell states together with states of the tumor microenvironment. Sensitive and robust algorithms are thus required to harness the full power implicit in an integration of these technologies. Here we propose to develop a new computational method called SNAP (Single-cell Neighborhood Map) which uses matched scRNA-Seq and spatial transcriptomics data from the same tumor to infer the spatial location of each scRNA-Seq-identified cell by reference to the spatial transcriptomics data, and produces a neighborhood transcriptome for each scRNA-Seq cell. To analyze these novel neighborhood transcriptomes we propose an approach to cluster cells with common patterns of neighbors, thereby identifying sets of colocalizing cell states. SNAP promises to exploit the complementary aspects of single-cell and spatial transcriptomics to link co- localizing cancer cell states and states of the tumor microenvironment. The methodology presented here includes several novel algorithms, all of which will be made freely available to the community, where we expect them to be broadly applicable across cancer biology.