Zhang Pengzhi, Chen Weiqing, Tran Tu Nhi, Zhou Minghao, Carter Kaylee N, Kandel Ibrahem, Li Shengyu, Hoi Xen Ping, Youker Keith, Lai Li, Song Qianqian, Yang Yu, Nikolos Fotis, Chan Keith Syson, Wang Guangyu
Center for Bioinformatics and Computational Biology, Houston Methodist Research Institute, Houston, TX, 77030, USA.
Center for Cardiovascular Regeneration, Houston Methodist Research Institute, Houston, TX, 77030, USA.
Res Sq. 2025 Mar 10:rs.3.rs-4909620. doi: 10.21203/rs.3.rs-4909620/v1.
Spatial transcriptomics integrates transcriptomics data with histological tissue images, offering deeper insights into cellular organization and molecular functions. However, existing computational platforms mainly focus on genomic analysis, leaving a gap in the seamless integration of genomic and image analysis. To address this, we introduce Thor, a comprehensive computational platform for multi-modal analysis of spatial transcriptomics and histological images. Thor utilizes an anti-shrinking Markov diffusion method to infer single-cell spatial transcriptomes from spot-level data, effectively integrating cell morphology with spatial transcriptomics. The platform features 10 modules designed for cell-level genomic and image analysis. Additionally, we present Mjolnir, a web-based tool for interactive tissue analysis using vivid gigapixel images that display information on histology, gene expression, pathway enrichment, and immune response. Thor's accuracy was validated through simulations and ISH, MERFISH, Xenium, and Stereo-seq datasets. To demonstrate its versatility, we applied Thor for joint genomic-histology analysis across various datasets. In in-house heart failure patient samples, Thor identified a regenerative signature in heart failure, with protein presence confirmed in blood vessels through immunofluorescence staining. Thor also revealed the layered structure of the mouse olfactory bulb, performed unbiased screening of breast cancer hallmarks, elucidated the heterogeneity of immune responses, and annotated fibrotic regions in multiple heart failure zones using a semi-supervised approach. Furthermore, Thor imputed high-resolution spatial transcriptomics data in an in-house bladder cancer sample sequenced using Visium HD, uncovering stronger spatial patterns that align more closely with histology. Bridging the gap between genomic and image analysis in spatial biology, Thor offers a powerful tool for comprehensive cellular and molecular analysis.
空间转录组学将转录组学数据与组织学组织图像相结合，能够更深入地洞察细胞组织和分子功能。然而，现有的计算平台主要侧重于基因组分析，在基因组和图像分析的无缝整合方面存在差距。为了解决这一问题，我们引入了Thor，这是一个用于空间转录组学和组织学图像多模态分析的综合计算平台。Thor利用抗收缩马尔可夫扩散方法从斑点水平数据推断单细胞空间转录组，有效地将细胞形态与空间转录组学整合在一起。该平台具有10个用于细胞水平基因组和图像分析的模块。此外，我们还展示了Mjolnir，这是一个基于网络的工具，用于使用生动的千兆像素图像进行交互式组织分析，这些图像显示了组织学、基因表达、通路富集和免疫反应等信息。Thor的准确性通过模拟以及ISH、MERFISH、Xenium和Stereo-seq数据集得到了验证。为了证明其通用性，我们将Thor应用于跨各种数据集的联合基因组-组织学分析。在内部心力衰竭患者样本中，Thor在心力衰竭中识别出一种再生特征，并通过免疫荧光染色在血管中证实了蛋白质的存在。Thor还揭示了小鼠嗅球的分层结构，对乳腺癌特征进行了无偏筛选，阐明了免疫反应的异质性，并使用半监督方法对多个心力衰竭区域的纤维化区域进行了注释。此外，Thor在使用Visium HD测序的内部膀胱癌样本中估算了高分辨率空间转录组学数据，发现了与组织学更紧密对齐的更强空间模式。Thor弥合了空间生物学中基因组和图像分析之间的差距，为全面的细胞和分子分析提供了一个强大的工具。
