Unified integration of spatial transcriptomics across platforms

Abstract

Spatial transcriptomics (ST) has transformed our understanding of tissue architecture and cellular interactions, but integrating ST data across platforms remains challenging due to differences in gene panels, data sparsity, and technical variability. Here, we introduce LO_SCPLOWLOKIC_SCPLOW, a novel framework for integrating imaging-based ST data from diverse platforms without requiring shared gene panels. LO_SCPLOWLOKIC_SCPLOW addresses ST integration through two key alignment tasks: feature alignment across technologies and batch alignment across datasets. Optimal transport-guided feature propagation adjusts data sparsity to match scRNA-seq references through graph-based imputation, enabling single-cell foundation models such as scGPT to generate unified features. Batch alignment then refines scGPT-transformed embeddings, mitigating batch effects while preserving biological variability. Evaluations on mouse brain samples from five different technologies demonstrate that LO_SCPLOWLOKIC_SCPLOW outperforms existing methods and is effective for cross-technology spatial gene program identification and tissue slice alignment. Applying LO_SCPLOWLOKIC_SCPLOW to five ovarian cancer datasets, we identify an integrated gene program indicative of tumor-infiltrating T cells across gene panels. Together, LO_SCPLOWLOKIC_SCPLOW provides a robust foundation for cross-platform ST studies, with the potential to scale to large atlas datasets, enabling deeper insights into cellular organization and tissue environments.