Loom：利用局部邻域和全局轨迹对空间转录组学进行多区域分析
Loom: Multi-Region Analysis of Spatial Transcriptomics with Local Neighborhoods and Global Trajectories
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中文总结 AI 辅助
Loom是用于空间转录组学分析的视觉计算系统，利用新颖图形符号和计算框架，应对多模态整合挑战，经案例研究和可用性研究评估，有效支持细胞转变及时空表达动态发现。
中文摘要 AI 辅助
我们展示了Loom，一个空间转录组学（ST）视觉计算系统，以支持对伪时间轨迹的分析、跨样本和感兴趣区域的比较研究，以及对局部微环境内空间结构过程的检查。ST是一种分子分析技术，能在保留组织空间组织的同时直接在薄组织切片内测量基因表达。对于实际应用驱动的分析，ST局部微环境数据需与细胞参考数据集及细胞行为的时间模拟相结合。由于多模态配准问题以及伪时间模式、空间富集数据和基因表达动态的复杂性，这种整合具有挑战性。Loom利用一种新颖的图形符号和计算框架，便于对局部微环境进行详细的伪时间探索、跨样本比较以及时空生物学机制研究。我们通过与组织病理学专家和肿瘤学家合作开展的两个案例研究以及一项外部可用性研究对Loom进行评估。结果表明，Loom有效地支持了细胞转变和时空表达动态的发现。
英文摘要
We present Loom, a spatial transcriptomics (ST) visual computing system to support the analysis of pseudo-temporal trajectories, comparative investigation across samples and regions of interest, and the examination of spatially structured processes within local microenvironments. ST is a molecular profiling technology that measures gene expression directly within a thin tissue section while preserving its spatial organization. For practical application-driven analyses, the ST local microenvironment data needs to be integrated with cell reference datasets and temporal simulations of cell behavior. This integration is challenging due to multi-modal registration issues and the complexity of the pseudo-temporal patterns, spatial enrichment data, and gene expression dynamics. Loom leverages a novel glyph coupled with a computational backbone to facilitate the detailed pseudo-temporal exploration of local microenvironments, cross-sample comparisons, and investigation of spatiotemporal biological mechanisms. We evaluate Loom through two case studies developed with experts in tissue pathology and oncologists and through an external usability study. The results demonstrate that Loom supports effectively the discovery of cellular transitions and spatiotemporal expression dynamics.
