Predicting trajectories of illness using RNA velocity of whole blood.

Abstract

Transcriptomic analyses reveal the status of cells, tissues, or organisms, across states of health and disease. RNA velocity adds a temporal dimension to single cell analyses, predicting future transcriptomic and phenotypic states, based on the current spliced and unspliced mRNA of each cell. We hypothesized that RNA velocity could be adapted to predict future clinical state of individuals with acute and chronic illnesses, using their whole-blood transcriptomes. We developed VeloCD, a method for quantitative prediction of transitions in clinical state from a single time-point RNA sample. This predicts transcriptomic trajectories and future infection status in influenza A and SARS-CoV-2 controlled human infection studies, which are consistent with trajectories in naturally acquired infections. In HIV-TB coinfected individuals, VeloCD predicts the onset of immune reconstitution inflammatory syndrome. In individuals receiving biological therapy for inflammatory bowel disease, whole blood RNA velocity after the first dose of treatment indicates whether remission will be achieved by the end of the treatment course. In a multinational observational study of acutely unwell febrile children, VeloCD predicts those with greatest medical care requirements. Our results demonstrate proof-of-concept for the use of RNA velocity to predict trajectories of human diseases.