Perspectives on integrating artificial intelligence and single-cell omics for cellular plasticity research

Area of Science:

• Cell Biology
• Genomics
• Computational Biology

Background:

• Cellular plasticity is key to tissue repair, regeneration, and diseases like cancer metastasis.
• Single-cell omics technologies have revolutionized the study of cellular states and transitions.

Purpose of the Study:

• To highlight the integration of chromatin accessibility, microenvironmental cues, and single-cell transcriptomics for holistic models of cellular plasticity.
• To emphasize the potential of artificial intelligence (AI) coupled with single-cell omics for identifying and characterizing plastic cells.
• To propose the development of a universal plasticity metric for standardized quantification.

Main Methods:

• Integrating multi-omics data (chromatin accessibility, transcriptomics) with microenvironmental factors.
• Applying artificial intelligence algorithms to single-cell omics data.
• Developing a standardized metric for quantifying cellular plasticity.

Main Results:

• Holistic models integrating diverse data types can identify plastic cell states.
• AI-driven analysis of single-cell omics data can overcome current challenges in plasticity research.
• A universal plasticity metric will enable consistent quantification across studies.

Conclusions:

• Integrating multi-omics data and AI is crucial for understanding cellular plasticity.
• A universal plasticity metric will standardize research and bridge developmental biology, cancer research, and regenerative medicine.
• Advancing the study of cellular plasticity will accelerate therapeutic development for complex diseases.