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
Citations
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Journal ArticleDOI
Comprehensive Integration of Single-Cell Data.
Tim Stuart,Andrew Butler,Paul J. Hoffman,Christoph Hafemeister,Efthymia Papalexi,William M. Mauck,Yuhan Hao,Marlon Stoeckius,Peter Smibert,Rahul Satija +9 more
TL;DR: A strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities.
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Fast, sensitive and accurate integration of single-cell data with Harmony.
Ilya Korsunsky,Nghia Millard,Jean Fan,Kamil Slowikowski,Fan Zhang,Kevin Wei,Yuriy Baglaenko,Michael B. Brenner,Po-Ru Loh,Po-Ru Loh,Po-Ru Loh,Soumya Raychaudhuri +11 more
TL;DR: Harmony, for the integration of single-cell transcriptomic data, identifies broad and fine-grained populations, scales to large datasets, and can integrate sequencing- and imaging-based data.
Posted ContentDOI
Comprehensive integration of single cell data
Tim Stuart,Andrew Butler,Paul J. Hoffman,Christoph Hafemeister,Efthymia Papalexi,William M. Mauck,Marlon Stoeckius,Peter Smibert,Rahul Satija +8 more
TL;DR: This work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets, and demonstrates how anchoring can harmonize in-situ gene expression and scRNA-seq datasets.
Journal ArticleDOI
The single-cell transcriptional landscape of mammalian organogenesis
Junyue Cao,Malte Spielmann,Xiaojie Qiu,Xingfan Huang,Daniel M. Ibrahim,Daniel M. Ibrahim,Andrew J. Hill,Fan Zhang,Stefan Mundlos,Stefan Mundlos,Lena Christiansen,Frank J. Steemers,Cole Trapnell,Jay Shendure +13 more
TL;DR: A cell atlas of mouse organogenesis provides a global view of developmental processes occurring during this critical period, including focused analyses of the apical ectodermal ridge, limb mesenchyme and skeletal muscle.
Journal ArticleDOI
Current best practices in single-cell RNA-seq analysis: a tutorial.
Malte D Luecken,Fabian J. Theis +1 more
TL;DR: The steps of a typical single‐cell RNA‐seq analysis, including pre‐processing (quality control, normalization, data correction, feature selection, and dimensionality reduction) and cell‐ and gene‐level downstream analysis, are detailed.
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Journal ArticleDOI
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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
TL;DR: This work has developed a high-throughput droplet-microfluidic approach for barcoding the RNA from thousands of individual cells for subsequent analysis by next-generation sequencing, which shows a surprisingly low noise profile and is readily adaptable to other sequencing-based assays.
Journal ArticleDOI
The glial nature of embryonic and adult neural stem cells
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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