Spatial multi-omics in precision medicine: Integrating biological insights through multidisciplinary collaboration

Area of Science:

• Biomedical Research
• Genomics
• Proteomics
• Metabolomics

Background:

• Spatial multi-omics integrates molecular data with spatial context.
• It enables analysis of tissue architecture and cellular interactions.
• This approach is vital for understanding complex biological systems.

Purpose of the Study:

• To provide an overview of spatial multi-omics technologies.
• To discuss data acquisition, quality management, and integration strategies.
• To highlight applications in cancer biology and clinical translation.

Main Methods:

• Review of spatial multi-omics technologies across multiple molecular layers.
• Focus on spatial transcriptomics as a foundational framework.
• Discussion of data integration, challenges, and optimization strategies.

Main Results:

• Spatial transcriptomics aids in aligning multi-omics data with cellular architecture.
• Applications include elucidating tumor heterogeneity and microenvironment dynamics.
• Key challenges involve technical noise, batch effects, and data integration complexity.

Conclusions:

• Spatial multi-omics offers transformative potential in biomedical research.
• Addressing challenges is crucial for clinical translation and precision medicine.
• Ethical considerations and AI implementation require careful examination.