haCCA: Multi-module Integrating of spatial transcriptomes and metabolomes

Spatial multiomics techniques, such as spatial transcriptomics and MALDI-MSI, provide detailed molecular profiles across tissue sections, capturing mRNA expression and mass-to-charge (m/z) spectra, respectively. These methods generate matrices with spatial coordinates, offering insights into the biological states of specific tissue spots. However, integrating spatial transcriptomics with MALDI-MSI data is challenging due to the lack of shared features or spots, which limits conventional integration strategies.

We present haCCA, a workflow designed to integrate spatial transcriptomics and metabolomics data using high-correlated feature pairs and modified spatial morphological alignment. This approach ensures high-resolution and accurate spot-to-spot data integration. To evaluate haCCA, we generated benchmark datasets combining spatial transcriptomes with pseudo-spatial metabolomes. Our results show that haCCA outperforms existing morphological alignment methods, such as STUtility.

We applied haCCA to both publicly available 10X Visium and MALDI-MSI datasets from mouse brain tissue and a custom 10X VisiumHD and MALDI-MSI dataset from an intrahepatic cholangiocarcinoma (ICC) model. These applications demonstrated haCCA’s effectiveness in enhancing multi-modular spatial data analysis. To facilitate its use, we developed and published an easy-to-use Python package, providing the research community with a robust tool for spatial multiomics integration.