Author
Listed:
- Gioele La Manno
(Karolinska Institutet
Science for Life Laboratory)
- Ruslan Soldatov
(Harvard Medical School)
- Amit Zeisel
(Karolinska Institutet
Science for Life Laboratory)
- Emelie Braun
(Karolinska Institutet
Science for Life Laboratory)
- Hannah Hochgerner
(Karolinska Institutet
Science for Life Laboratory)
- Viktor Petukhov
(Harvard Medical School
Peter The Great St. Petersburg Polytechnic University, St)
- Katja Lidschreiber
(Karolinska Institutet)
- Maria E. Kastriti
(Karolinska Institutet)
- Peter Lönnerberg
(Karolinska Institutet
Science for Life Laboratory)
- Alessandro Furlan
(Karolinska Institutet)
- Jean Fan
(Harvard Medical School)
- Lars E. Borm
(Karolinska Institutet
Science for Life Laboratory)
- Zehua Liu
(Harvard Medical School)
- David Bruggen
(Karolinska Institutet)
- Jimin Guo
(Harvard Medical School)
- Xiaoling He
(University of Cambridge)
- Roger Barker
(University of Cambridge)
- Erik Sundström
(Care Sciences and Society, Karolinska Institutet)
- Gonçalo Castelo-Branco
(Karolinska Institutet)
- Patrick Cramer
(Karolinska Institutet
Max Planck Institute for Biophysical Chemistry, Department of Molecular Biology)
- Igor Adameyko
(Karolinska Institutet)
- Sten Linnarsson
(Karolinska Institutet
Science for Life Laboratory)
- Peter V. Kharchenko
(Harvard Medical School
Harvard Stem Cell Institute)
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.
Suggested Citation
Gioele La Manno & Ruslan Soldatov & Amit Zeisel & Emelie Braun & Hannah Hochgerner & Viktor Petukhov & Katja Lidschreiber & Maria E. Kastriti & Peter Lönnerberg & Alessandro Furlan & Jean Fan & Lars E, 2018.
"RNA velocity of single cells,"
Nature, Nature, vol. 560(7719), pages 494-498, August.
Handle:
RePEc:nat:nature:v:560:y:2018:i:7719:d:10.1038_s41586-018-0414-6
DOI: 10.1038/s41586-018-0414-6
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