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Updated: Jul 2, 2026

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

Published on: June 6, 2025

SpaMFG: a spatial multi-omics integration method based on feature grouping.

Zilin Li¹, Litian Ma¹, Jingtao Liu¹

¹School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China.

Bioinformatics (Oxford, England)

|June 30, 2026

Summary

SpaMFG is a new framework for spatial multi-omics integration. It improves biological interpretation by accurately grouping and matching features across different omics layers using spatial data.

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DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

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Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Published on: May 16, 2025

Area of Science:

Computational Biology
Bioinformatics
Genomics
Proteomics

Background:

Spatial multi-omics technologies allow simultaneous measurement of gene and protein expression with spatial context.
Challenges in spatial multi-omics include low spatial resolution and high feature dimensionality, hindering data integration and interpretation.
Integrating multi-omics data is crucial for understanding tissue heterogeneity.

Purpose of the Study:

To develop an interpretable framework for spatial multi-omics integration.
To address challenges in feature grouping and cross-omics relationship interpretation.
To enhance biological insights from spatial multi-omics data.

Main Methods:

Proposed SpaMFG, a feature-group-level framework for spatial multi-omics integration.
Implemented spatial proximity weighting for improved feature grouping.
Developed a cross-omics feature group matching method using spatial location and Jaccard similarity, optimized with the Hungarian algorithm.

Main Results:

SpaMFG demonstrated effectiveness in enhancing biological interpretability of cross-omics feature relationships.
Comparative analysis on human lymph node dataset confirmed SpaMFG's performance.
Robustness validated across human tonsils, mouse spleens, and mouse thymus datasets.

Conclusions:

SpaMFG provides an innovative and interpretable approach to spatial multi-omics integration.
The framework effectively leverages spatial information for accurate feature analysis.
SpaMFG offers a robust solution for diverse biological contexts.