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.
%0 Journal Article
%1 lamanno2018velocity
%A La Manno, Gioele
%A Soldatov, Ruslan
%A Zeisel, Amit
%A Braun, Emelie
%A Hochgerner, Hannah
%A Petukhov, Viktor
%A Lidschreiber, Katja
%A Kastriti, Maria E.
%A Lönnerberg, Peter
%A Furlan, Alessandro
%A Fan, Jean
%A Borm, Lars E.
%A Liu, Zehua
%A van Bruggen, David
%A Guo, Jimin
%A He, Xiaoling
%A Barker, Roger
%A Sundström, Erik
%A Castelo-Branco, Gonçalo
%A Cramer, Patrick
%A Adameyko, Igor
%A Linnarsson, Sten
%A Kharchenko, Peter V.
%D 2018
%J Nature
%K RNA_abundance cellular_machinery transcription
%R 10.1038/s41586-018-0414-6
%T RNA velocity of single cells
%U https://doi.org/10.1038/s41586-018-0414-6
%X 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.
@article{lamanno2018velocity,
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.},
added-at = {2018-08-11T16:49:03.000+0200},
author = {La Manno, Gioele and Soldatov, Ruslan and Zeisel, Amit and Braun, Emelie and Hochgerner, Hannah and Petukhov, Viktor and Lidschreiber, Katja and Kastriti, Maria E. and Lönnerberg, Peter and Furlan, Alessandro and Fan, Jean and Borm, Lars E. and Liu, Zehua and van Bruggen, David and Guo, Jimin and He, Xiaoling and Barker, Roger and Sundström, Erik and Castelo-Branco, Gonçalo and Cramer, Patrick and Adameyko, Igor and Linnarsson, Sten and Kharchenko, Peter V.},
biburl = {https://www.bibsonomy.org/bibtex/244c48e1742cc878db81d591358193289/peter.ralph},
doi = {10.1038/s41586-018-0414-6},
interhash = {9139d1fd43303666673c91dcab8b69a6},
intrahash = {44c48e1742cc878db81d591358193289},
issn = {14764687},
journal = {Nature},
keywords = {RNA_abundance cellular_machinery transcription},
refid = {La Manno2018},
timestamp = {2018-08-11T16:49:03.000+0200},
title = {{RNA} velocity of single cells},
url = {https://doi.org/10.1038/s41586-018-0414-6},
year = 2018
}