Daijun Zhang1, Ren Qi1, Xun Lan2 and Bin Liu1,3,4 1School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China; 2School of Medicine, Tsinghua University, Beijing 100084, China; 3SMBU-MSU-BIT Joint Laboratory on Bioinformatics and Engineering Biology, Shenzhen MSU-BIT University, Shenzhen, Guangdong 518172, China; 4Zhongguancun Academy, Beijing 100094, China Corresponding author: bliubliulab.net Abstract

The development of spatial transcriptomics (ST) technologies has revolutionized the way we map the complex organization and functions of tissues. These technologies offer valuable insights into the organization and function of complex biological systems. However, existing methods often focus too narrowly on single modalities or resolutions, thereby hindering the comprehensive capture of multilayered biological heterogeneity. Here, STMSC is proposed as a multislice joint analysis framework featuring a precorrection mechanism that enables the precise identification of complex spatial domains, advancing disease pathology insights. STMSC assumes that precise three-dimensional (3D) reconstruction is essential for an in-depth investigation of tissue components and mechanisms. Incorporating hematoxylin and eosin (H&E) imaging data, STMSC enhances slice alignment accuracy in 3D reconstruction. By deconstructing microenvironments, it reconstructs fine-grained cellular landscapes and emphasizes collective cellular behavior in defining spatial domains. Its graph attention autoencoder with precorrection balances biological information at different levels, improving the accuracy of ST analyses. By analyzing consecutive tissue slices and pathological data sets, STMSC accurately reconstructs 3D structures and provides deeper insights into complex cancer environments. Specifically, STMSC captures intra- and interstage heterogeneity in cancer development, offering novel insights into the complexity of pathological tissue structures.

Received November 26, 2024. Accepted June 12, 2025.