用深度学习同时分析细胞空间位置和基因关系，发现组织内隐藏的细胞类型和功能区
Uncovering spatial tissue domains and cell types in spatial omics through cross-scale profiling of cellular and genomic interactions
- 联合建模细胞空间分布与基因调控关系
- 在多个数据集上提升区域分割准确率
- 适合研究组织微环境与细胞功能的科研人员
细胞的身份与功能既取决于其内在基因组特征，也受组织微环境中空间位置的影响。空间转录组学（ST）在单细胞分辨率下提供原位基因表达谱，揭示了细胞在组织中的空间与功能组织结构。然而，ST 数据本身噪声大、规模高且结构复杂，现有计算方法难以有效捕捉空间相互作用与基因组关系之间的关联，限制了关键生物模式的识别。本文提出 CellScape，一种深度学习框架，旨在克服这些局限，实现高性能的空间转录组数据分析与模式发现。该方法联合建模组织空间中的细胞相互作用与细胞间的基因组关系，生成融合空间信号与基因调控机制的综合表征。这一技术揭示了具有生物学意义的模式，提升了空间域分割效果，并支持跨多种转录组数据集的全面空间细胞分析，为深入解析与解读空间转录组数据提供了一种准确且通用的框架。
原文摘要 · Abstract (English)
Cellular identity and function are linked to both their intrinsic genomic makeup and extrinsic spatial context within the tissue microenvironment. Spatial transcriptomics (ST) offers an unprecedented opportunity to study this, providing in situ gene expression profiles at single-cell resolution and illuminating the spatial and functional organization of cells within tissues. However, a significant hurdle remains: ST data is inherently noisy, large, and structurally complex. This complexity makes it intractable for existing computational methods to effectively capture the interplay between spatial interactions and intrinsic genomic relationships, thus limiting our ability to discern critical biological patterns. Here, we present CellScape, a deep learning framework designed to overcome these limitations for high-performance ST data analysis and pattern discovery. CellScape jointly models cellular interactions in tissue space and genomic relationships among cells, producing comprehensive representations that seamlessly integrate spatial signals with underlying gene regulatory mechanisms. This technique uncovers biologically informative patterns that improve spatial domain segmentation and supports comprehensive spatial cellular analyses across diverse transcriptomics datasets, offering an accurate and versatile framework for deep analysis and interpretation of ST data.w
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。