BACKGROUND: Heart transplantation provides a significant improvement in survival and quality of life for patients with end-stage heart disease, however many recipients experience different levels of graft rejection that can be associated with significant morbidities and mortality. Current clinical standard-of-care for the evaluation of heart transplant acute rejection (AR) consists of routine endomyocardial biopsy (EMB) followed by visual assessment by histopathology for immune infiltration and cardiomyocyte damage. We assessed whether the sensitivity and/or specificity of this process could be improved upon by adding RNA sequencing (RNA-seq) of EMBs coupled with histopathological interpretation. METHODS: Up to 6 standard-of-care, or for-cause EMBs, were collected from 26 heart transplant recipients from the prospective observational Clinical Trials of Transplantation (CTOT)-03 study, during the first 12-months post-transplant and subjected to RNA-seq (n = 125 EMBs total). Differential expression and random-forest-based machine learning were applied to develop signatures for classification and prognostication. RESULTS: Leveraging the unique longitudinal nature of this study, we show that transcriptional hallmarks for significant rejection events occur months before the actual event and are not visible using traditional histopathology. Using this information, we identified a prognostic signature for 0R/1R biopsies that with 90% accuracy can predict whether the next biopsy will be 2R/3R. CONCLUSIONS: RNA-seq-based molecular characterization of EMBs shows significant promise for the early detection of cardiac allograft rejection.