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

Area of Science:

• Biotechnology
• Molecular Biology
• Bioinformatics

Background:

• Spatial omics enables localized measurement of biomolecules within tissues.
• Integrating multiple spatial omics modalities presents computational and experimental challenges.

Purpose of the Study:

• To present MIMIC, a reproducible workflow for integrating Mass Spectrometry Imaging and Imaging Mass Cytometry.
• To address computational difficulties in multi-modal spatial omics data analysis.

Main Methods:

• Developed MIMIC, a semi-automated workflow for joint downstream analysis of Mass Spectrometry Imaging and Imaging Mass Cytometry data.
• Incorporated rigorous quality control, including registration error assessment.
• Implemented pixel-level modeling for analyte-cell type association.

Main Results:

• Demonstrated MIMIC's utility on artificial tissue and human liver tissue with metabolic dysfunction-associated steatotic liver disease.
• Successfully recovered known molecular associations across modalities.
• Identified novel spatial relationships between molecules and cell types.

Conclusions:

• MIMIC provides a robust framework for integrating multi-modal spatial omics data.
• The workflow overcomes integration challenges to enable deeper understanding of tissue microenvironments.
• This approach facilitates discovery of new biological insights in complex diseases.