Image-guided alignment of consecutive multi-modal tissue slides

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Authors: Benedetta Manzato, Claudio Novella-Rausell, Gangqi Wang, Nina Ogrinc, Rosalie G. J. Rietjens, Marleen E Jacobs, Botos Csaba, Sébastien J. Dumas, Ton J. Rabelink, Ahmed Essam Mahfouz
Cited by: 0
References: 44

Abstract

We present 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 from different technologies, tissues, and resolutions, in which 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.

Fields of Study & Subjects

Single-cell and spatial transcriptomics Cell Image Analysis Techniques Gene expression and cancer classification

Publication details

Journal / source
Cell Reports Methods
Pages
101575–101575
Publication date
1 September 2026
Language
EN