# Transforming heart transplantation care with multi-omics insights

## TL;DR

This paper reviews how multi-omics technologies can improve heart transplant care by enabling noninvasive monitoring and better understanding of rejection and complications.

## Contribution

The paper synthesizes recent advances in multi-omics and machine learning for precision monitoring in heart transplantation.

## Key findings

- Multi-omics technologies offer noninvasive biomarkers for cardiac allograft rejection and vasculopathy.
- Integration of single-cell omics and machine learning improves predictive modeling and clinical translatability.
- Personalized risk stratification is achievable through comprehensive molecular analysis post-transplant.

## Abstract

Heart transplantation (HTx) remains the definitive treatment for patients with end-stage heart disease. Despite the number of HTx performed annually in worldwide continues to increase, complications of HTx still impact the quality of life and long-term prognosis, including rejection, infection, and allograft dysfunction. Endomyocardial biopsy remains the gold standard for monitoring cardiac allograft rejection post-heart transplantation, yet its invasiveness and interobserver error in histologic grading necessitate the development of novel noninvasive biomarkers to elucidate rejection mechanisms and progression. Cardiac allograft vasculopathy, a critical determinant of long-term outcomes, is challenging to detect early via intravascular ultrasound, underscoring the potential of plasma biomarkers for disease surveillance. Omic technologies usually refers to the application of multiple high-throughput screening technologies enabling comprehensive analysis of biological systems at a molecular level. Multi-omics technologies, including genomics(donor-derived cell-free DNA), transcriptomics(microRNAs panels, gene expression profiling), proteomics(cell signaling molecule), and metabolomics(ex situ heart perfusion), have demonstrated significant promise in post-transplant monitoring. These approaches provide personalized risk stratification and mechanical insights into cardiac allograft rejection, primary graft dysfunction, and cardiac allograft vasculopathy. Single–cell omics technologies and machine learning algorithms further resolve cellular heterogeneity and improve predictive modeling, thereby enhancing the clinical translatability of multi-omics data. This comprehensive review synthesizes these advances and highlights the transformative potential of integrating multi-omics with advanced analytics to achieve precision monitoring and therapy in HTx, ultimately improving long-term patient outcomes.