MIMIC: a flexible pipeline to register and summarize IMC-MSI experiments

Area of Science:

• Biotechnology
• Computational Biology
• Molecular Pathology

Background:

• Spatial omics enables detailed analysis of tissue microenvironments by measuring biomolecules like transcripts and proteins.
• Integrating multiple spatial omics modalities presents significant experimental and computational challenges.

Purpose of the Study:

• To present MIMIC, a reproducible workflow for integrating Mass Spectrometry Imaging (MSI) and Imaging Mass Cytometry (IMC) data.
• To address computational difficulties arising from differences in resolution, noise, and data channels in multi-modal spatial omics.

Main Methods:

• MIMIC is a semi-automated workflow for joint downstream analysis of MSI and IMC data.
• The workflow includes quality control steps like registration error assessment.
• It supports pixel-level modeling to link analytes with cell types.

Main Results:

• The MIMIC workflow was validated on artificial tissue and applied to human liver tissue with metabolic dysfunction-associated steatotic liver disease.
• The approach successfully identified known molecular associations.
• Novel spatial relationships between molecules and cell types were uncovered across modalities.

Conclusions:

• MIMIC offers a robust framework for integrating multi-modal spatial omics data, overcoming common computational challenges.
• The workflow facilitates a deeper understanding of tissue microenvironments and disease pathology.
• This integration enhances the discovery of spatial molecular associations in complex biological systems.