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RNA velocity of single cells. ​

Literature Information ​

DOI10.1038/s41586-018-0414-6
PMID30089906
JournalNature
Impact Factor48.5
JCR QuartileQ1
Publication Year2018
Times Cited1991
KeywordsRNA velocity, single-cell RNA sequencing, gene expression, cellular dynamics
Literature TypeJournal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, Non-P.H.S., Validation Study
ISSN0028-0836
Pages494-498
Issue560(7719)
AuthorsGioele 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 Borm, Zehua Liu, David van Bruggen, Jimin Guo, Xiaoling He, Roger Barker, Erik Sundström, Gonçalo Castelo-Branco, Patrick Cramer, Igor Adameyko, Sten Linnarsson, Peter V Kharchenko

TL;DR ​

This study introduces RNA velocity as a method to estimate the time derivative of gene expression by distinguishing between unspliced and spliced mRNAs in single-cell RNA sequencing, allowing for predictions of individual cell states over hours. The findings enhance the understanding of developmental lineages and cellular dynamics, particularly in human contexts, and are validated through analyses of the neural crest lineage and the developing mouse hippocampus.

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RNA velocity · single-cell RNA sequencing · gene expression · cellular dynamics

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.

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Primary Questions Addressed ​

  1. How does RNA velocity contribute to our understanding of cellular dynamics during tissue regeneration?
  2. What are the limitations of using RNA velocity in studying embryogenesis compared to traditional methods?
  3. In what ways can RNA velocity analysis be integrated with other single-cell sequencing techniques for enhanced insights?
  4. How might RNA velocity impact the study of neurodevelopmental disorders in humans?
  5. What future applications do you foresee for RNA velocity in personalized medicine and therapeutic interventions?

Key Findings ​

Research Background and Objectives ​

The study explores the concept of RNA velocity, which is defined as the time derivative of gene expression states in individual cells. Traditional single-cell RNA sequencing provides a static snapshot of gene expression, which limits its ability to analyze dynamic processes such as embryogenesis and tissue regeneration. The authors aim to develop a method to estimate RNA velocity by distinguishing between unspliced and spliced mRNAs, thereby predicting the future state of cells over short time scales.

Main Methods/Materials/Experimental Design ​

The authors utilized various single-cell RNA sequencing protocols (SMART-seq2, STRT/C1, inDrop, and 10x Chromium) to analyze RNA velocity. They employed a computational framework called Velocyto to estimate RNA velocity based on the balance between unspliced and spliced mRNA.

The experimental design includes:

  1. RNA Sequencing: Analysis of datasets from mouse tissues and human embryonic brain.
  2. Velocity Estimation: Quantifying the relationship between precursor (unspliced) and mature (spliced) mRNA abundances using a simple model of transcriptional dynamics.
  3. Data Analysis: Application of principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) for visualization of RNA velocity.
Mermaid diagram

Key Results and Findings ​

  1. Validation of RNA Velocity: The method was validated in various datasets, demonstrating its ability to predict transcriptional dynamics accurately.
  2. Neural Crest Lineage: The authors showed that RNA velocity could reveal branching lineage trees, specifically in the developing mouse hippocampus.
  3. Human Embryonic Brain Analysis: RNA velocity was successfully applied to human embryonic brain datasets, indicating its potential for studying human developmental processes.

Main Conclusions/Significance/Innovativeness ​

The study presents RNA velocity as a robust tool for analyzing dynamic cellular processes in developmental biology. By integrating RNA velocity with single-cell RNA sequencing, researchers can gain insights into cellular differentiation and lineage tracing. This approach is particularly innovative as it allows for the prediction of future cellular states based on current gene expression dynamics, enhancing our understanding of developmental biology and disease mechanisms.

Research Limitations and Future Directions ​

  1. Limitations:

    • The model assumes a linear relationship between unspliced and spliced mRNA, which may not capture all biological complexities.
    • The accuracy of RNA velocity estimates can be influenced by factors such as gene expression noise and alternative splicing.

  2. Future Directions:

    • Further refinement of the RNA velocity model to account for non-linear dynamics and additional biological variables.
    • Application of RNA velocity in more complex tissues and conditions, including disease models, to explore its utility in clinical research.

This comprehensive approach highlights the potential of RNA velocity in advancing our understanding of cellular dynamics and lineage relationships in both normal and pathological contexts.

References ​

  1. Analysis of intronic and exonic reads in RNA-seq data characterizes transcriptional and post-transcriptional regulation. - Dimos Gaidatzis;Lukas Burger;Maria Florescu;Michael B Stadler - Nature biotechnology (2015)
  2. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. - Saiful Islam;Una Kjällquist;Annalena Moliner;Pawel Zajac;Jian-Bing Fan;Peter Lönnerberg;Sten Linnarsson - Genome research (2011)
  3. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. - Simone Picelli;Åsa K Björklund;Omid R Faridani;Sven Sagasser;Gösta Winberg;Rickard Sandberg - Nature methods (2013)
  4. Stochastic mechanisms of cell fate specification that yield random or robust outcomes. - Robert J Johnston;Claude Desplan - Annual review of cell and developmental biology (2010)
  5. Conserved properties of dentate gyrus neurogenesis across postnatal development revealed by single-cell RNA sequencing. - Hannah Hochgerner;Amit Zeisel;Peter Lönnerberg;Sten Linnarsson - Nature neuroscience (2018)
  6. The glial nature of embryonic and adult neural stem cells. - Arnold Kriegstein;Arturo Alvarez-Buylla - Annual review of neuroscience (2009)
  7. TT-seq maps the human transient transcriptome. - Björn Schwalb;Margaux Michel;Benedikt Zacher;Katja Frühauf;Carina Demel;Achim Tresch;Julien Gagneur;Patrick Cramer - Science (New York, N.Y.) (2016)
  8. A single-cell survey of the small intestinal epithelium. - Adam L Haber;Moshe Biton;Noga Rogel;Rebecca H Herbst;Karthik Shekhar;Christopher Smillie;Grace Burgin;Toni M Delorey;Michael R Howitt;Yarden Katz;Itay Tirosh;Semir Beyaz;Danielle Dionne;Mei Zhang;Raktima Raychowdhury;Wendy S Garrett;Orit Rozenblatt-Rosen;Hai Ning Shi;Omer Yilmaz;Ramnik J Xavier;Aviv Regev - Nature (2017)
  9. Massively parallel digital transcriptional profiling of single cells. - Grace X Y Zheng;Jessica M Terry;Phillip Belgrader;Paul Ryvkin;Zachary W Bent;Ryan Wilson;Solongo B Ziraldo;Tobias D Wheeler;Geoff P McDermott;Junjie Zhu;Mark T Gregory;Joe Shuga;Luz Montesclaros;Jason G Underwood;Donald A Masquelier;Stefanie Y Nishimura;Michael Schnall-Levin;Paul W Wyatt;Christopher M Hindson;Rajiv Bharadwaj;Alexander Wong;Kevin D Ness;Lan W Beppu;H Joachim Deeg;Christopher McFarland;Keith R Loeb;William J Valente;Nolan G Ericson;Emily A Stevens;Jerald P Radich;Tarjei S Mikkelsen;Benjamin J Hindson;Jason H Bielas - Nature communications (2017)
  10. Neuronal or glial progeny: regional differences in radial glia fate. - Paolo Malatesta;Michael A Hack;Eva Hartfuss;Helmut Kettenmann;Wolfgang Klinkert;Frank Kirchhoff;Magdalena Götz - Neuron (2003)

Literatures Citing This Work ​

  1. Dynamics and Spatial Genomics of the Nascent Transcriptome by Intron seqFISH. - Sheel Shah;Yodai Takei;Wen Zhou;Eric Lubeck;Jina Yun;Chee-Huat Linus Eng;Noushin Koulena;Christopher Cronin;Christoph Karp;Eric J Liaw;Mina Amin;Long Cai - Cell (2018)
  2. Toward mapping the human body at a cellular resolution. - Ananda L Roy;Richard S Conroy - Molecular biology of the cell (2018)
  3. Power in Numbers: Single-Cell RNA-Seq Strategies to Dissect Complex Tissues. - Kenneth D Birnbaum - Annual review of genetics (2018)
  4. Transition state characteristics during cell differentiation. - Rowan D Brackston;Eszter Lakatos;Michael P H Stumpf - PLoS computational biology (2018)
  5. The adult human testis transcriptional cell atlas. - Jingtao Guo;Edward J Grow;Hana Mlcochova;Geoffrey J Maher;Cecilia Lindskog;Xichen Nie;Yixuan Guo;Yodai Takei;Jina Yun;Long Cai;Robin Kim;Douglas T Carrell;Anne Goriely;James M Hotaling;Bradley R Cairns - Cell research (2018)
  6. Perspectives on defining cell types in the brain. - Eran A Mukamel;John Ngai - Current opinion in neurobiology (2019)
  7. Single-Cell Transcriptome Profiling of Mouse and hESC-Derived Pancreatic Progenitors. - Nicole A J Krentz;Michelle Y Y Lee;Eric E Xu;Shannon L J Sproul;Alexandra Maslova;Shugo Sasaki;Francis C Lynn - Stem cell reports (2018)
  8. Opening the black box: Stem cell-based modeling of human post-implantation development. - Kenichiro Taniguchi;Idse Heemskerk;Deborah L Gumucio - The Journal of cell biology (2019)
  9. An integrative approach for building personalized gene regulatory networks for precision medicine. - Monique G P van der Wijst;Dylan H de Vries;Harm Brugge;Harm-Jan Westra;Lude Franke - Genome medicine (2018)
  10. The Human Cell Atlas: making 'cell space' for disease. - Chris P Ponting - Disease models & mechanisms (2019)

... (1981 more literatures)

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