Image-guided alignment of consecutive multi-modal tissue slides

Abstract

Multi-modal spatial data analysis often requires precise physical alignment of consecutive tissue sections, a process that can be challenging and typically relies on shared molecular markers or image recognition techniques. Here, we introduce COAST (Consecutive multi-Omics Alignment of Spatial Tissues), a method to reliably physically align consecutive tissue sections to produce a unified multi-modal molecular dataset suitable for downstream applications. COAST relies exclusively on the images associated with spatial data, eliminating the need for common molecular features or prior annotations. We demonstrate the effectiveness of COAST using spatial transcriptomics slides, where it achieves performance comparable to established uni-modal alignment tools. Applying COAST to spatial transcriptomics and metabolomics/lipidomics tissue sections from a mouse model of ischemia reperfusion injury allowed the investigation of lipid/metabolite features of transcriptionally-defined cell types. Overall, COAST offers a streamlined and integrative solution for multi-modal spatial data alignment.