Imaging-anchored multiomics in cardiovascular disease: integrating cardiac imaging, bulk, single-cell, and spatial...

Area of Science:

• Cardiovascular research
• Medical imaging
• Genomics and transcriptomics

Background:

• Cardiovascular disease (CVD) involves complex interactions between genetic risk, molecular processes, and tissue changes.
• Clinical imaging (echocardiography, cardiac MRI, CT) and molecular profiling (RNA sequencing, spatial transcriptomics) are crucial for CVD diagnosis and research.
• Current analysis pipelines for imaging and molecular data are largely separate, limiting integrated insights.

Purpose of the Study:

• To review joint representations linking cardiac imaging phenotypes to transcriptomic and spatially resolved molecular states.
• To adopt an imaging-anchored perspective, integrating echocardiography, CMR, and CT with molecular data for spatial context.
• To explore integrative pipelines for radiogenomics and image-based gene-expression prediction.

Main Methods:

• Examining representation requirements for cardiac imaging and transcriptomic modalities.
• Comparing multimodal fusion strategies for integrating diverse data types.
• Synthesizing integrative pipelines for radiogenomics, spatial alignment, and gene-expression prediction.

Main Results:

• Development of joint representations linking imaging phenotypes to molecular states.
• Advancement in spatial multiomic maps and multimodal medical foundation models for cardiac research.
• Identification of integrative pipelines for radiogenomics and image-based gene-expression prediction.

Conclusions:

• Integrating cardiac imaging and molecular data through joint representations enhances understanding of CVD.
• Spatial multiomics and advanced models are progressing imaging-anchored multiomics in cardiology.
• Cost, scalability, and tissue availability remain significant challenges for widespread clinical translation.