The paper introduces SpatialZ, a computational framework designed to overcome current hardware limitations in spatial transcriptomics by reconstructing dense 3D tissue models from sparse 2D sections. Conventional methods often result in data gaps between tissue layers, but this new tool uses interpolation algorithms to generate virtual slices, enabling researchers to map gene expression across entire organs. The software includes modules for in silico sectioning, which allows users to view biological structures from any angle, and 3D continuous rendering for visualizing complex molecular gradients. Validated on mouse brain and human breast cancer data, the technology demonstrates high fidelity in preserving cell-type distributions and anatomical features. Ultimately, this open-source toolkit bridges the gap between planar imaging and comprehensive volume-based biological analysis.

References:

• Lin S, Wang Z, Cui Y, et al. Bridging the dimensional gap from planar spatial transcriptomics to 3D cell atlases[J]. Nature Methods, 2025: 1-13.