PO.BCS01.05 · 生物信息与计算
在空间转录组学中跨空间尺度识别细胞间相互作用
Identifying cell-cell interactions across spatial scales in spatial transcriptomics
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
引言：单细胞空间转录组学为细胞分辨率下的基因表达谱提供了丰富的数据。这一层次的数据使我们能够估计细胞间的相互作用，并将这些相互作用与临床信息（如生存或对免疫治疗的应答）相关联。此前，我们已表明，在暴露于免疫治疗（IO）后，原发性透明细胞肾细胞癌（ccRCC）肿瘤中COL4A1和ITGAV在空间上显著富集，尤其是在恶性细胞和成纤维细胞/肌成纤维细胞中呈高表达。我们使用K=3来评估这种相互作用，但空间邻近性也是需要探索的重要考量。
方法：为解决真实的空间背景问题，我们开发了一种功能数据分析方法，以在不同空间尺度上刻画细胞间相互作用。我们使用了来自Soupir等人（2024）的14个基质区室视野（FOV）（8个IO初治，6个IO暴露）。使用来自高斯变换距离的行标准化权重，为COL4A1和ITGAV计算双变量Moran's I。核函数的带宽从0变化到250，以计算作为带宽函数的Moran's I，即I(h)，并使用100次置换来确定完全空间随机性（CSR）。从I(h)中减去CSR（I(h)的度）以使各样本间的数值具有可比性。我们使用功能主成分（FPC）分析来分析完整的I(h)度曲线。FPC评分用于比较IO初治和IO暴露的肿瘤。我们将本方法的结果与另一种基于Moran's I的方法SpatialDM（h=75）进行了比较。
结果：暴露于IO的肿瘤的FOV显示出一条独特的、正的I(h)度曲线，在带宽25-50之间出现峰值，而IO初治肿瘤的FOV在I(h)度的形状或符号上各不相同。从所有I(h)度曲线计算得到的FPC显示，FPC1描述了空间关系的整体强度（正评分表示整体升高，负评分表示整体降低的相互作用），而FPC2描述了相互作用是发生在近/远尺度上。将FPC2对FPC1作图，FPC1完美地将IO初治与IO暴露的FOV区分开（Wilcox检验p=0.00067），其中IO暴露的FOV具有正的FPC1评分和约为0的FPC2评分（近/远无变化）。SpatialDM的全局I未显示IO暴露间的显著差异（Wilcox检验p=0.1079）。
结论：将我们的方法应用于ccRCC表明，来自IO暴露的原发性ccRCC肿瘤这些基质FOV中的COL4A1和ITGAV，跨空间尺度上的空间关联比来自IO初治原发性ccRCC肿瘤的基质FOV更强。我们的方法还显示出比SpatialDM显著更强的空间关联。需要进一步研究以更好地理解这一转变的潜在原因，这可能带来新的药物靶点。
查看英文原文 English abstract
Introduction: Single-cell spatial transcriptomics provides rich data for the gene expression profiles at cellular resolutions. This level of data offers the ability to estimate interactions between cells and associate those interactions with clinical information such as survival or response to immunotherapy. Previously, we had shown that COL4A1 and ITGAV are significantly more spatially enriched in primary clear cell renal cell carcinoma (ccRCC) tumors after exposure to immunotherapy (IO), specifically showing high expression in malignant cells and fibroblasts/myofibroblasts. We assessed this interaction by using K=3, but spatial proximity is also an important consideration to explore.
Methods: To address true spatial context, we developed a functional data analysis approach to profile cell-cell interactions at varying spatial scales. We used 14 stromal compartment FOVs (8x IO naïve, 6x IO exposed) from Soupir et. al. (2024). Bivariate Moran's I was calculated for COL4A1 and ITGAV using row standardized weights from Gaussian transformed distances. The bandwidth of the kernel was varied from 0 to 250 to calculate Moran's I as a function of bandwidth, I(h), using 100 permutations to determine complete spatial randomness (CSR). CSR was subtracted from I(h) (Degree of I(h)) to make values comparable across samples. We used functional principal component (FPC) analysis to analyze the full Degree of I(h) curves. FPC scores were used to compare the IO naïve and IO exposed tumors. Results from our approach were compared to SpatialDM, another approach based on Moran's I, with h=75.
Results: FOVs from tumors exposed to IO showed a distinct, positive Degree of I(h) curve with a peak at a bandwidth between 25-50 while FOVs from IO naïve tumors were each unique in either shape or sign of Degree of I(h). Calculating (FPCs) from all Degree of I(h) curves showed that FPC1 describes the overall strength of the spatial relationship (positive scores indicate overall elevated and negative scores indicate overall decreased interaction) while FPC2 describes whether the interaction occurs at a near/far scale. Plotting FPC2 vs FPC1, FPC1 perfectly separates IO naïve from IO exposed FOVs (Wilcox Test p=0.00067, where IO exposed FOVs have a positive FPC1 score and FPC2 scores around 0 (no change in near/far). SpatialDM's global I didn't show significant differences between IO exposures (Wilcox Test p=0.1079)
Conclusion: The application of our approach to ccRCC indicates that COL4A1 and ITGAV in these stroma FOVs from IO exposed primary ccRCC tumors are more strongly spatially related across spatial scales than stroma FOVs from IO naïve primary ccRCC tumors. Our approach also showed a significantly stronger spatial association than SpatialDM. Further research is needed to better understand the underlying cause of this shift, which may lead to new drug targets.
利益披露 Disclosure
A. C. Soupir, None.