RNA velocity of single cells

Nature

Volumn 560, Issue 7719, 2018, Pages 494-498

  La Manno, Gioele ^a,b   Soldatov, Ruslan ^c   Zeisel, Amit ^a,b   Braun, Emelie ^a,b   Hochgerner, Hannah ^a,b   Petukhov, Viktor ^c,d   Lidschreiber, Katja ^a   Kastriti, Maria E ^a   Lönnerberg, Peter ^a,b   Furlan, Alessandro ^a   Fan, Jean ^c   Borm, Lars E ^a,b   Liu, Zehua ^c   van Bruggen, David ^a   Guo, Jimin ^c   He, Xiaoling ^e   Barker, Roger ^e   Sundström, Erik ^a   Castelo Branco, Gonçalo ^a   Cramer, Patrick ^a,f   Adameyko, Igor ^a   Linnarsson, Sten ^a,b   Kharchenko, Peter V ^c,g   more..

^a KAROLINSKA INSTITUTE   (Sweden)

^b SCIENCE FOR LIFE LABORATORY   (Sweden)

^c HARVARD MEDICAL SCHOOL   (United States)

^d PETER THE GREAT ST PETERSBURG POLYTECHNIC UNIVERSITY   (Russian Federation)

^e UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^f MAX PLANCK INSTITUTE FOR BIOPHYSICAL CHEMISTRY   (Germany)

^g HARVARD STEM CELL INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMAL EXPERIMENT; EMBRYO; GENE EXPRESSION; GENETIC TRANSCRIPTION; HIPPOCAMPUS; KINETICS; LETTER; MOUSE; NEURAL CREST; NONHUMAN; RNA SEQUENCE; ANIMAL; BRAIN; CELL LINEAGE; CHROMAFFIN CELL; CYTOLOGY; EMBRYOLOGY; FEMALE; GENETICS; INFORMATION PROCESSING; MALE; METABOLISM; NERVE CELL; REPRODUCIBILITY; RNA SPLICING; SEQUENCE ANALYSIS; SINGLE CELL ANALYSIS; TIME FACTOR; VALIDATION STUDY;

GLUTAMIC ACID; RNA;

ANIMALS; BRAIN; CELL LINEAGE; CHROMAFFIN CELLS; DATASETS AS TOPIC; FEMALE; GLUTAMIC ACID; HIPPOCAMPUS; KINETICS; MALE; MICE; NEURAL CREST; NEURONS; REPRODUCIBILITY OF RESULTS; RNA; RNA SPLICING; SEQUENCE ANALYSIS, RNA; SINGLE-CELL ANALYSIS; TIME FACTORS; TRANSCRIPTION, GENETIC;

EID: 85052109231     PISSN: 00280836     EISSN: 14764687     Source Type: Journal    
DOI: 10.1038/s41586-018-0414-6     Document Type: Article

References (24)

1. Linnarsson, S. & Teichmann, S. A. Single-cell genomics: coming of age. Genome Biol. 17, 97 (2016)
2. Zeisel, A. et al. Coupled pre-mRNA and mRNA dynamics unveil operational strategies underlying transcriptional responses to stimuli. Mol. Syst. Biol. 7, 529 (2011)
3. Gray, J. M. et al. SnapShot-Seq: a method for extracting genome-wide, in vivo mRNA dynamics from a single total RNA sample. PLoS ONE 9, e89673 (2014)
4. Gaidatzis, D., Burger, L., Florescu, M. & Stadler, M. B. Analysis of intronic and exonic reads in RNA-seq data characterizes transcriptional and post-transcriptional regulation. Nat. Biotechnol. 33, 722–729 (2015)
5. Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013)
6. Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2013)
7. Klein, A. M. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015)
8. Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017)
9. Schwalb, B. et al. TT-seq maps the human transient transcriptome. Science 352, 1225–1228 (2017)
10. Islam, S. et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 21, 1160–1167 (2011)
11. The Tabula Muris Consortium, Quake, S. R., Wyss-Coray, T., & Darmanis, S. Single-cell transcriptomic characterization of 20 organs and tissues from individual mice creates a Tabula Muris. Preprint at https://biorxiv.org/content/early/2018/03/29/237446 (2018)
12. Vollmers, C. et al. Circadian oscillations of protein-coding and regulatory RNAs in a highly dynamic mammalian liver epigenome. Cell Metab. 16, 833–845 (2012)
13. Furlan, A. et al. Multipotent peripheral glial cells generate neuroendocrine cells of the adrenal medulla. Science 357, eaal3753 (2017)
14. Kriegstein, A. & Alvarez-Buylla, A. The glial nature of embryonic and adult neural stem cells. Annu. Rev. Neurosci. 32, 149–184 (2009)
15. Malatesta, P. et al. Neuronal or glial progeny: regional differences in radial glia fate. Neuron 37, 751–764 (2003)
16. Johnston, R. J. & Desplan, C. Stochastic mechanisms of cell fate specification that yield random or robust outcomes. Annu. Rev. Cell Dev. Biol. 26, 689–719 (2010)
17. Iwano, T., Masuda, A., Kiyonari, H., Enomoto, H. & Matsuzaki, F. Prox1 postmitotically defines dentate gyrus cells by specifying granule cell identity over CA3 pyramidal cell fate in the hippocampus. Development 139, 3051–3062 (2012)
18. Plass, M. et al. Prox1 postmitotically defines dentate gyrus cells by specifying granule cell identity over CA3 pyramidal cell fate in the hippocampus. Science 360, eaaq1723 (2018)
19. Petukhov, V. et al. dropEst: pipeline for accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments. Genome Biol. 19, 78 (2018)
20. Zeisel, A. et al. Molecular architecture of the mouse nervous system. Preprint at https://biorxiv.org/content/early/2018/04/06/294918 (2018)
21. Hrvatin, S. et al. Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex. Nat. Neurosci. 21, 120–129 (2018)
22. Haber, A. L. et al. A single-cell survey of the small intestinal epithelium. Nature 551, 333–339 (2017)
23. Hochgerner, H., Zeisel, A., Lönnerberg, P. & Linnarsson, S. Conserved properties of dentate gyrus neurogenesis across postnatal development revealed by single-cell RNA sequencing. Nat. Neurosci. 21, 290–299 (2018)
24. Lein, E. S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007)