Claire Dunican, Clare Wilson, Dominic Habgood-Coote, Suzanna Paterson, Mahdad Noursadeghi, Raymond Moseki, Cari Stek, Robert J. Wilkinson, Philipp Agyeman, Coco R. Beudeker, Giske Biesbroek, U von Both, Karen Brengel‐Pesce, Enitan D. Carrol, Lachlan Coin, Giselle D’Souza, Tisham De, Marieke Emonts, Katy Fidler, Colin G. Fink, Michiel van der Flier, Ioanna Georgaki, L Kolberg, Mojca Kolnik, Taco Kuijpers, Federico Martinón-Torres, Marine Mommert-Tripon, Samuel Nichols, Stéphane Paulus, Marko Pokorn, Andrew J. Pollard, Irene Rivero-Calle, Aleksandra Rudzate, Luregn J. Schlapbach, Nina A. Schweintzger, Ching‐Fen Shen, Shrijana Shrestha, Chantal Tan, Maria Tsolia, Effua Usuf, Fabian van der Velden, Clementien L. Vermont, Marie Voice, Shunmay Yeung, Dace Zavadska, W Zenz, Victoria Wright, Michael Levin, Jethro Herberg, Lucas Baumard, Evangelos Bellos, Rachel Galassini, Shea Hamilton, Clive Hoggart, Sara Hourmat, Heather Jackson, Naomi Lin, Ian Maconochie, Stephanie Menikou, Ruud Nijman, Ivonne Pena Paz, Oliver Powell, Priyen Shah, Ortensia Vito, Molly Stevens, Eunjung Kim, Nayoung Kim, Amina Abdulla, Ladan Ali, Sarah Darnell, Rikke Jorgensen, Sobia Mustafa, Salina Persand, Julia Dudley, Vivien Richmond, Emma Tagliavini, Elizabeth Cocklin, Rebecca Jennings, Joanne Johnston, Aakash Khanijau, Simon Leigh, Nadia Lewis-Burke, Karen Newall, Sam Romaine, Rama Kandasamy, Michael J. Carter, Daniel O’Connor, Sagida Bibi, Dominic F. Kelly, Meeru Gurung, Stephen Thorson, Imran Ansari, David R. Murdoch, Z Oliver, Emma Lim, Lucille Valentine, Karen Allen, Kathryn Bell, Adora Chan, Stephen Crulley
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.