Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
Department of Genetics, Harvard Medical School, Boston, MA, USA.
Nat Methods. 2024 Oct;21(10):1843-1854. doi: 10.1038/s41592-024-02415-2. Epub 2024 Sep 12.
Spatial transcriptomics (ST) technologies have advanced to enable transcriptome-wide gene expression analysis at submicron resolution over large areas. However, analysis of high-resolution ST is often challenged by complex tissue structure, where existing cell segmentation methods struggle due to the irregular cell sizes and shapes, and by the absence of segmentation-free methods scalable to whole-transcriptome analysis. Here we present FICTURE (Factor Inference of Cartographic Transcriptome at Ultra-high REsolution), a segmentation-free spatial factorization method that can handle transcriptome-wide data labeled with billions of submicron-resolution spatial coordinates and is compatible with both sequencing-based and imaging-based ST data. FICTURE uses the multilayered Dirichlet model for stochastic variational inference of pixel-level spatial factors, and is orders of magnitude more efficient than existing methods. FICTURE reveals the microscopic ST architecture for challenging tissues, such as vascular, fibrotic, muscular and lipid-laden areas in real data where previous methods failed. FICTURE's cross-platform generality, scalability and precision make it a powerful tool for exploring high-resolution ST.
空间转录组学 (ST) 技术已经取得了进展，能够在亚微米分辨率的大面积范围内进行全转录组基因表达分析。然而，高分辨率 ST 的分析常常受到复杂组织结构的挑战，由于细胞大小和形状不规则，现有的细胞分割方法存在困难，并且缺乏可扩展到全转录组分析的无分割方法。在这里，我们提出了 FICTURE（超高分辨率地图转录组的因子推断），这是一种无分割的空间因子化方法，可以处理标记有数十亿个亚微米分辨率空间坐标的全转录组数据，并且与基于测序和基于成像的 ST 数据兼容。FICTURE 使用多层狄利克雷模型进行像素级空间因子的随机变分推断，效率比现有方法高出几个数量级。FICTURE 揭示了具有挑战性的组织的微观 ST 结构，例如真实数据中血管、纤维化、肌肉和富含脂质的区域，而以前的方法在此类区域无法使用。FICTURE 的跨平台通用性、可扩展性和精度使其成为探索高分辨率 ST 的强大工具。
