Image-guided alignment of consecutive multi-modal tissue slides
Benedetta Manzato (Leiden University Medical Center)
Claudio Novella Rausell (Leiden University Medical Center)
Gangqi Wang (Children’s Hospital of Fudan University, Leiden University Medical Center)
Nina Ogrinc (Leiden University Medical Center)
Rosalie G.J. Rietjens (Leiden University Medical Center)
Marleen E. Jacobs (Leiden University Medical Center)
Christos Botos (Leiden University Medical Center)
Sebastien J. Dumas (Leiden University Medical Center, Université de Toulouse)
Ton J. Rabelink (Leiden University Medical Center)
Ahmed Mahfouz (TU Delft - Electrical Engineering, Mathematics and Computer Science, Leiden University Medical Center)
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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.
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