Selected Publication


AI & single-cell genomics: New software predicts cell fate

scVelo reveals fine-grained insights into the developmental processes in the pancreas. ©Helmholtz Zentrum München

Traditional single-cell sequencing methods help to reveal insights about cellular differences and functions – but they do this with static snapshots only rather than time-lapse films. This limitation makes it difficult to draw conclusions about the dynamics of cell development and gene activity. The recently introduced method “RNA velocity” aims to reconstruct the developmental trajectory of a cell on a computational basis (leveraging ratios of unspliced and spliced transcripts). This method, however, is applicable to steady-state populations only. Researchers were therefore looking for ways to extend the concept of RNA velocity to dynamic populations which are of crucial importance to understand cell development and disease response.

For more information read the press release of Helmholtz Zentrum München or click here for the original publication.