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

摘要 单细胞RNA测序(scRNA-Seq)已被证明是癌症生物学的一项革命性技术， 实现了对单个肿瘤细胞的无偏见转录图谱，并揭示了惊人数量的 恶性细胞中转录的异质性。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的和有功能的肿瘤单位，在 肿瘤的维持和发展。然而，scRNA-Seq分析的一个主要缺点是丢失了 测序前肿瘤解离所产生的空间信息。缺乏……的知识 每个细胞在组织内的大致位置以及它的局部邻域，scRNA-Seq不能单独 告诉我们癌细胞状态之间复杂的一组关系，以及它们与 肿瘤微环境的要素。空间转录学是一项颠覆性的新技术，对于 First Time能够以强健的方式测量整个组织的整个转录本。而空间 转录组学绘制了所有基因的同时表达图--使系统和公正成为可能 转录组分析-它本身不是一种单细胞技术，因此也不能单独为我们提供关于 癌细胞状态和肿瘤微环境状态的图案化。敏感和健壮 因此，需要算法来充分利用这些技术的集成所隐含的全部力量。在这里我们 提出了一种新的计算方法SNAP(Single-cell Neighborhood Map)，该方法使用 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-通过参照空间转录数据识别细胞，并产生邻域 每个scRNA-Seq细胞的转录组。为了分析这些新奇的邻里转录本，我们提出了一个 一种利用相邻小区的共同模式对小区进行聚类的方法，从而识别共定位小区状态集。 Snap承诺利用单细胞和空间转录的互补方面将联合- 定位癌细胞状态和肿瘤微环境的状态。这里介绍的方法 包括几个新的算法，所有这些算法都将免费提供给社区，我们希望在那里 它们将广泛适用于癌症生物学。

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.