Single-Cell and Spatial Multiomics: Applications for Diseases.

Area of Science:

• Biomedical research
• Genomics
• Molecular biology

Background:

• Human diseases involve complex molecular networks.
• Single-cell and spatial multiomics provide new tools for disease research.
• Integrating these multiomics approaches enhances understanding of cellular characteristics and connections in disease.

Purpose of the Study:

• To review single-cell and spatial multiomics approaches.
• To discuss their application in understanding disease mechanisms (e.g., cancer, neurodegenerative diseases, aging).
• To explore the role of deep learning in analyzing multiomics data for disease insights.

Main Methods:

• Review of single-cell and spatial multiomics techniques.
• Analysis of deep learning applications in multiomics data.
• Case studies on cancer, neurodegenerative diseases, and aging.

Main Results:

• Single-cell and spatial multiomics integration offers deeper insights into disease pathogenesis.
• Deep learning enhances the analysis of upstream pathogenic alterations and downstream molecular effects.
• These approaches are crucial for guiding treatment strategies and risk assessment in precision medicine.

Conclusions:

• Single-cell and spatial multiomics are powerful tools for elucidating disease pathogenesis.
• The integration of these technologies promises significant advancements in understanding and treating human diseases.
• The field is rapidly evolving, with deep learning playing a key role in data analysis.