Work

Scalable image-based visualization and alignment of spatial transcriptomics datasets

Year: 2025

Type: article

Abstract: 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.

Source: Cell Systems

DOI: 10.1016/j.cels.2025.101264

Authors: Stephan Preibisch, Michael Innerberger, Daniel León-Periñán, Nikos Karaiskos, Nikolaus Rajewsky