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Updated: Jun 8, 2026

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Published on: March 25, 2020

Spatial integration of multi-omics data from serial sections using the novel Multi-Omics Imaging Integration Toolset.

Maximilian Wess^1,2, Maria K Andersen^1,3, Elise Midtbust^1,3

¹Department of Circulation and Medical Imaging, NTNU-Norwegian University of Science and Technology, Trondheim, 7491, Norway.

|May 14, 2025

Summary

This summary is machine-generated.

A new Python framework, MIIT, integrates spatial omics data from different tissue sections. This toolset enables comprehensive cancer biology understanding by combining mass spectrometry imaging and spatial transcriptomics for precision medicine.

Keywords:

image registration mass spectrometry imaging spatial transcriptomics

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Area of Science:

Biomedical research
Computational biology
Genomics and proteomics

Background:

Understanding cancer biology in heterogeneous tumors requires multi-omics data and spatial resolution.
Spatial transcriptomics (ST) and mass spectrometry imaging (MSI) capture spatial omics data but are often applied to serial sections.
Integrating data from serial sections is crucial for leveraging multi-omics insights.

Purpose of the Study:

To develop a computational framework for integrating spatially resolved multi-omics data.
To enable the combination of data from different omics technologies performed on serial tissue sections.

Main Methods:

Development of the Multi-Omics Imaging Integration Toolset (MIIT), a Python framework.
Implementation of a nonrigid registration algorithm (GreedyFHist) for aligning serial sections.
Validation of GreedyFHist on 244 images from fresh-frozen serial sections.

Main Results:

MIIT successfully integrates spatially resolved multi-omics data.
GreedyFHist achieved state-of-the-art performance in serial section registration.
Proof-of-concept integration of ST and MSI data from prostate cancer, correlating gene signatures with metabolic measurements.

Conclusions:

MIIT is an accurate, customizable, and open-source framework for integrating spatial omics data.
The framework facilitates the combination of data from different spatial omics technologies.
MIIT enhances the understanding of cancer biology by enabling multi-modal spatial data integration.