Unravelling tumour spatiotemporal heterogeneity using spatial multimodal data.

Area of Science:

• Biomedical research
• Genomics
• Computational biology

Background:

• Spatial multi-omics integrates genome, epigenome, transcriptome, proteome, and metabolome data within cellular context.
• Understanding tumour spatiotemporal heterogeneity is crucial for disease progression insights.

Purpose of the Study:

• To review key technologies and computational methods in spatial multi-omics.
• To discuss challenges and strategies for data analysis.
• To highlight the role of spatial multi-omics in advancing precision medicine.

Main Methods:

• Analysis of spatial multi-omics data using advanced computational tools.
• Development of AI-driven multimodal models for molecular interaction inference.
• Integration of multi-omics technologies with AI-enabled bioinformatics.

Main Results:

• Spatial multi-omics technologies reveal complex molecular interactions shaping cellular behavior and tissue dynamics.
• AI models uncover intra- and inter-cellular networks driving disease progression.
• Identification of spatial domains and their relationships.

Conclusions:

• Spatial multi-omics provides novel insights into disease mechanisms.
• This approach advances precision medicine and informs targeted therapeutic strategies.
• Predicting critical disease stages like pre-cancer is enabled by these combined technologies.