Nature. 2018 Aug;560(7719):494-498. doi: 10.1038/s41586-018-0414-6. Epub 2018 Aug 8.

RNA velocity of single cells.

La Manno G^1,², Soldatov R³, Zeisel A^1,², Braun E^1,², Hochgerner H^1,², Petukhov V^3,⁴, Lidschreiber K⁵, Kastriti ME⁶, Lönnerberg P^1,², Furlan A¹, Fan J³, Borm LE^1,², Liu Z³, van Bruggen D¹, Guo J³, He X⁷, Barker R⁷, Sundström E⁸, Castelo-Branco G¹, Cramer P^5,⁹, Adameyko I⁶, Linnarsson S^10,¹¹, Kharchenko PV^12,¹³.

Author information

1: Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.
2: Science for Life Laboratory, Solna, Sweden.
3: Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
4: Department of Applied Mathematics, Peter The Great St. Petersburg Polytechnic University, St, Petersburg, Russia.
5: Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden.
6: Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.
7: John van Geest Centre for Brain Repair, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
8: Division of Neurodegeneration, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
9: Max Planck Institute for Biophysical Chemistry, Department of Molecular Biology, Göttingen, Germany.
10: Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden. sten.linnarsson@ki.se.
11: Science for Life Laboratory, Solna, Sweden. sten.linnarsson@ki.se.
12: Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. peter_kharchenko@hms.harvard.edu.
13: Harvard Stem Cell Institute, Cambridge, MA, USA. peter_kharchenko@hms.harvard.edu.

Abstract

RNA abundance is a powerful indicator of the state of individual cells. Single-cell RNA sequencing can reveal RNA abundance with high quantitative accuracy, sensitivity and throughput¹. However, this approach captures only a static snapshot at a point in time, posing a challenge for the analysis of time-resolved phenomena such as embryogenesis or tissue regeneration. Here we show that RNA velocity-the time derivative of the gene expression state-can be directly estimated by distinguishing between unspliced and spliced mRNAs in common single-cell RNA sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours. We validate its accuracy in the neural crest lineage, demonstrate its use on multiple published datasets and technical platforms, reveal the branching lineage tree of the developing mouse hippocampus, and examine the kinetics of transcription in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.