Literature DB >> 35760914

High-throughput total RNA sequencing in single cells using VASA-seq.

Fredrik Salmen1,2, Joachim De Jonghe3,4, Tomasz S Kaminski3,5, Anna Alemany1,2, Guillermo E Parada6, Joe Verity-Legg1,2, Ayaka Yanagida7, Timo N Kohler3,8, Nicholas Battich1,2, Floris van den Brekel1,2, Anna L Ellermann3, Alfonso Martinez Arias9, Jennifer Nichols8,10, Martin Hemberg6,11, Florian Hollfelder12, Alexander van Oudenaarden13,14.   

Abstract

Most methods for single-cell transcriptome sequencing amplify the termini of polyadenylated transcripts, capturing only a small fraction of the total cellular transcriptome. This precludes the detection of many long non-coding, short non-coding and non-polyadenylated protein-coding transcripts and hinders alternative splicing analysis. We, therefore, developed VASA-seq to detect the total transcriptome in single cells, which is enabled by fragmenting and tailing all RNA molecules subsequent to cell lysis. The method is compatible with both plate-based formats and droplet microfluidics. We applied VASA-seq to more than 30,000 single cells in the developing mouse embryo during gastrulation and early organogenesis. Analyzing the dynamics of the total single-cell transcriptome, we discovered cell type markers, many based on non-coding RNA, and performed in vivo cell cycle analysis via detection of non-polyadenylated histone genes. RNA velocity characterization was improved, accurately retracing blood maturation trajectories. Moreover, our VASA-seq data provide a comprehensive analysis of alternative splicing during mammalian development, which highlighted substantial rearrangements during blood development and heart morphogenesis.
© 2022. The Author(s).

Entities:  

Year:  2022        PMID: 35760914     DOI: 10.1038/s41587-022-01361-8

Source DB:  PubMed          Journal:  Nat Biotechnol        ISSN: 1087-0156            Impact factor:   68.164


  69 in total

1.  Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.

Authors:  Allon M Klein; Linas Mazutis; Ilke Akartuna; Naren Tallapragada; Adrian Veres; Victor Li; Leonid Peshkin; David A Weitz; Marc W Kirschner
Journal:  Cell       Date:  2015-05-21       Impact factor: 41.582

2.  Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.

Authors:  Evan Z Macosko; Anindita Basu; Rahul Satija; James Nemesh; Karthik Shekhar; Melissa Goldman; Itay Tirosh; Allison R Bialas; Nolan Kamitaki; Emily M Martersteck; John J Trombetta; David A Weitz; Joshua R Sanes; Alex K Shalek; Aviv Regev; Steven A McCarroll
Journal:  Cell       Date:  2015-05-21       Impact factor: 41.582

3.  Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq.

Authors:  Saiful Islam; Una Kjällquist; Annalena Moliner; Pawel Zajac; Jian-Bing Fan; Peter Lönnerberg; Sten Linnarsson
Journal:  Genome Res       Date:  2011-05-04       Impact factor: 9.043

4.  CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification.

Authors:  Tamar Hashimshony; Florian Wagner; Noa Sher; Itai Yanai
Journal:  Cell Rep       Date:  2012-08-30       Impact factor: 9.423

5.  Single-cell messenger RNA sequencing reveals rare intestinal cell types.

Authors:  Dominic Grün; Anna Lyubimova; Lennart Kester; Kay Wiebrands; Onur Basak; Nobuo Sasaki; Hans Clevers; Alexander van Oudenaarden
Journal:  Nature       Date:  2015-08-19       Impact factor: 49.962

6.  mRNA-Seq whole-transcriptome analysis of a single cell.

Authors:  Fuchou Tang; Catalin Barbacioru; Yangzhou Wang; Ellen Nordman; Clarence Lee; Nanlan Xu; Xiaohui Wang; John Bodeau; Brian B Tuch; Asim Siddiqui; Kaiqin Lao; M Azim Surani
Journal:  Nat Methods       Date:  2009-04-06       Impact factor: 28.547

7.  Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types.

Authors:  Diego Adhemar Jaitin; Ephraim Kenigsberg; Hadas Keren-Shaul; Naama Elefant; Franziska Paul; Irina Zaretsky; Alexander Mildner; Nadav Cohen; Steffen Jung; Amos Tanay; Ido Amit
Journal:  Science       Date:  2014-02-14       Impact factor: 47.728

8.  Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells.

Authors:  Daniel Ramsköld; Shujun Luo; Yu-Chieh Wang; Robin Li; Qiaolin Deng; Omid R Faridani; Gregory A Daniels; Irina Khrebtukova; Jeanne F Loring; Louise C Laurent; Gary P Schroth; Rickard Sandberg
Journal:  Nat Biotechnol       Date:  2012-08       Impact factor: 54.908

9.  Massively parallel digital transcriptional profiling of single cells.

Authors:  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
Journal:  Nat Commun       Date:  2017-01-16       Impact factor: 14.919

10.  Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells.

Authors:  Alex K Shalek; Rahul Satija; Xian Adiconis; Rona S Gertner; Jellert T Gaublomme; Raktima Raychowdhury; Schraga Schwartz; Nir Yosef; Christine Malboeuf; Diana Lu; John J Trombetta; Dave Gennert; Andreas Gnirke; Alon Goren; Nir Hacohen; Joshua Z Levin; Hongkun Park; Aviv Regev
Journal:  Nature       Date:  2013-05-19       Impact factor: 49.962

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  3 in total

1.  Towards a full picture of the total transcriptome.

Authors:  Lei Tang
Journal:  Nat Methods       Date:  2022-08       Impact factor: 47.990

2.  Identification of the stress granule transcriptome via RNA-editing in single cells and in vivo.

Authors:  Wessel van Leeuwen; Michael VanInsberghe; Nico Battich; Fredrik Salmén; Alexander van Oudenaarden; Catherine Rabouille
Journal:  Cell Rep Methods       Date:  2022-06-20

3.  Single-cell isogrowth profiling: Uniform inhibition uncovers non-uniform drug responses.

Authors:  Martin Lukačišin; Adriana Espinosa-Cantú; Tobias Bollenbach
Journal:  Clin Transl Med       Date:  2022-08
  3 in total

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