The paper introduces COAST (Consecutive multi-Omics Alignment of Spatial Tissues), a novel computational method for accurately aligning consecutive tissue sections analyzed by different spatial omics technologies. COAST exclusively utilizes paired staining images (like H&E or nuclear stains) to create a shared feature space for alignment, eliminating the need for matching molecular profiles or prior annotations common in other methods. The paper rigorously benchmarks COAST's performance against existing uni-modal alignment tools using spatial transcriptomics data, demonstrating comparable or superior accuracy in preserving tissue structure and achieving high gene expression similarity in matched spots. Finally, the authors illustrate COAST's significant utility by applying it to align spatial transcriptomics and MALDI-MSI data from a mouse kidney injury model, enabling integrated multi-modal analysis that identifies biologically coherent lipid and metabolite features related to specific cell types.

References:

• Image-guided alignment of consecutive multi-modal tissue slides