Preibisch Stephan, Innerberger Michael, León-Periñán Daniel, Karaiskos Nikos, Rajewsky Nikolaus
Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
Cell Syst. 2025 May 21;16(5):101264. doi: 10.1016/j.cels.2025.101264. Epub 2025 Apr 22.
We present the "spatial transcriptomics imaging framework" (STIM), an imaging-based computational framework focused on visualizing and aligning high-throughput spatial sequencing datasets. STIM is built on the powerful, scalable ImgLib2 and BigDataViewer (BDV) image data frameworks and thus enables novel development or transfer of existing computer vision techniques to the sequencing domain characterized by datasets with irregular measurement-spacing and arbitrary spatial resolution, such as spatial transcriptomics data generated by multiplexed targeted hybridization or spatial sequencing technologies. We illustrate STIM's capabilities by representing, interactively visualizing, 3D rendering, automatically registering, and segmenting publicly available spatial sequencing data from 13 serial sections of mouse brain tissue and from 19 sections of a human metastatic lymph node. We demonstrate that the simplest alignment mode of STIM achieves human-level accuracy.
我们提出了“空间转录组学成像框架”（STIM），这是一个基于成像的计算框架，专注于可视化和对齐高通量空间测序数据集。STIM建立在强大、可扩展的ImgLib2和大数据查看器（BDV）图像数据框架之上，因此能够将现有的计算机视觉技术进行新的开发或转移到以具有不规则测量间距和任意空间分辨率的数据集为特征的测序领域，例如通过多重靶向杂交或空间测序技术生成的空间转录组学数据。我们通过对来自小鼠脑组织13个连续切片和人类转移性淋巴结19个切片的公开可用空间测序数据进行表示、交互式可视化、三维渲染、自动配准和分割，来说明STIM的能力。我们证明，STIM最简单的对齐模式可达到人类水平的准确性。
