Integrating Spatial Omics and Deep Learning: Toward Predictive Models of Cardiomyocyte Differentiation Efficiency

Area of Science:

• Cardiovascular Research
• Regenerative Medicine
• Computational Biology

Background:

• Cardiac regenerative medicine is rapidly advancing.
• Understanding cardiomyocyte differentiation is crucial for cardiac repair.
• Spatial multi-omics and AI are emerging as key technologies in this field.

Purpose of the Study:

• To systematically review the integration of artificial intelligence (AI) with spatial multi-omics technologies in cardiac regenerative medicine.
• To explore the application of deep learning architectures, such as Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs), in analyzing cardiac single-cell and spatial omics data.
• To establish a foundation for AI-enabled cardiac regeneration by synthesizing methodologies and innovations from recent studies.

Main Methods:

• Systematic literature review of 88 PRISMA-selected studies published between 2015 and 2025.
• Analysis of deep learning implementations in spatiotemporal genomics and spatial multi-omics applications within cardiac tissues.
• Synthesis of insights on cardiomyocyte differentiation, predictive modeling, and AI applications in precision cardiology.

Main Results:

• Spatial omics technologies have significantly enhanced the understanding of cardiac tissue organization, revealing novel cellular communities and metabolic landscapes.
• Deep learning models, particularly GNNs and RNNs, effectively synergize with multi-modal single-cell and spatially resolved omics datasets.
• AI integration accelerates the mechanistic understanding and therapeutic prediction in cardiac regeneration.

Conclusions:

• The synergy between AI and spatial multi-omics provides a powerful foundation for advancing cardiac regenerative medicine.
• These integrated approaches are crucial for deciphering complex cellular dynamics in cardiomyocyte differentiation and cardiovascular disease.
• This review highlights the potential to accelerate clinical translation of regenerative treatments through improved AI-driven predictive models.