RNA velocity of single cells
Gioele La Manno,Gioele La Manno,Ruslan A. Soldatov,Amit Zeisel,Amit Zeisel,Emelie Braun,Emelie Braun,Hannah Hochgerner,Hannah Hochgerner,Viktor Petukhov,Viktor Petukhov,Katja Lidschreiber,Maria Eleni Kastriti,Peter Lönnerberg,Peter Lönnerberg,Alessandro Furlan,Jean Fan,Lars E. Borm,Lars E. Borm,Zehua Liu,David van Bruggen,Jimin Guo,Xiaoling He,Roger A. Barker,Erik Sundström,Gonçalo Castelo-Branco,Patrick Cramer,Patrick Cramer,Igor Adameyko,Sten Linnarsson,Sten Linnarsson,Peter V. Kharchenko +31 more
TLDR
It is shown 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, and expected to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.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.read more
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TL;DR: A droplet-based system that enables 3′ mRNA counting of tens of thousands of single cells per sample is described and sequence variation in the transcriptome data is used to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients.
Journal ArticleDOI
Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells
Allon M. Klein,Linas Mazutis,Linas Mazutis,Ilke Akartuna,Naren Tallapragada,Adrian Veres,Victor C. Li,Leonid Peshkin,David A. Weitz,Marc W. Kirschner +9 more
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TL;DR: The timing in development and location of NSCs, a property tightly linked to their neuroepithelial origin, appear to be the key determinants of the types of neurons generated.
Journal ArticleDOI
Smart-seq2 for sensitive full-length transcriptome profiling in single cells
Simone Picelli,Åsa K. Björklund,Åsa K. Björklund,Omid R. Faridani,Sven Sagasser,Sven Sagasser,Gösta Winberg,Gösta Winberg,Rickard Sandberg,Rickard Sandberg +9 more
TL;DR: Smart-seq2 with improved reverse transcription, template switching and preamplification to increase both yield and length of cDNA libraries generated from individual cells to improve detection, coverage, bias and accuracy.
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