Literature DB >> 33654857

Yeast Single-cell RNA-seq, Cell by Cell and Step by Step.

Mariona Nadal-Ribelles1,2,3,4, Saiful Islam1,2, Wu Wei1,2,5, Pablo Latorre3,4, Michelle Nguyen1,2, Eulàlia de Nadal3,4, Francesc Posas3,4, Lars M Steinmetz1,2,6.   

Abstract

Single-cell RNA-seq (scRNA-seq) has become an established method for uncovering the intrinsic complexity within populations. Even within seemingly homogenous populations of isogenic yeast cells, there is a high degree of heterogeneity that originates from a compact and pervasively transcribed genome. Research with microorganisms such as yeast represents a major challenge for single-cell transcriptomics, due to their small size, rigid cell wall, and low RNA content per cell. Because of these technical challenges, yeast-specific scRNA-seq methodologies have recently started to appear, each one of them relying on different cell-isolation and library-preparation methods. Consequently, each approach harbors unique strengths and weaknesses that need to be considered. We have recently developed a yeast single-cell RNA-seq protocol (yscRNA-seq), which is inexpensive, high-throughput and easy-to-implement, tailored to the unique needs of yeast. yscRNA-seq provides a unique platform that combines single-cell phenotyping via index sorting with the incorporation of unique molecule identifiers on transcripts that allows to digitally count the number of molecules in a strand- and isoform-specific manner. Here, we provide a detailed, step-by-step description of the experimental and computational steps of yscRNA-seq protocol. This protocol will ease the implementation of yscRNA-seq in other laboratories and provide guidelines for the development of novel technologies.
Copyright © 2019 The Authors; exclusive licensee Bio-protocol LLC.

Entities:  

Keywords:  Noncoding RNA; Single-cell RNA-seq; Transcript isoforms; Transcriptomics; Yeast

Year:  2019        PMID: 33654857      PMCID: PMC7854150          DOI: 10.21769/BioProtoc.3359

Source DB:  PubMed          Journal:  Bio Protoc        ISSN: 2331-8325


  20 in total

1.  Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments.

Authors:  Christopher A Jackson; Dayanne M Castro; Richard Bonneau; David Gresham; Giuseppe-Antonio Saldi
Journal:  Elife       Date:  2020-01-27       Impact factor: 8.140

2.  Comparative Analysis of Droplet-Based Ultra-High-Throughput Single-Cell RNA-Seq Systems.

Authors:  Xiannian Zhang; Tianqi Li; Feng Liu; Yaqi Chen; Jiacheng Yao; Zeyao Li; Yanyi Huang; Jianbin Wang
Journal:  Mol Cell       Date:  2018-11-21       Impact factor: 17.970

3.  STAR: ultrafast universal RNA-seq aligner.

Authors:  Alexander Dobin; Carrie A Davis; Felix Schlesinger; Jorg Drenkow; Chris Zaleski; Sonali Jha; Philippe Batut; Mark Chaisson; Thomas R Gingeras
Journal:  Bioinformatics       Date:  2012-10-25       Impact factor: 6.937

4.  Bidirectional promoters generate pervasive transcription in yeast.

Authors:  Zhenyu Xu; Wu Wei; Julien Gagneur; Fabiana Perocchi; Sandra Clauder-Münster; Jurgi Camblong; Elisa Guffanti; Françoise Stutz; Wolfgang Huber; Lars M Steinmetz
Journal:  Nature       Date:  2009-01-25       Impact factor: 49.962

5.  Single-cell imaging and RNA sequencing reveal patterns of gene expression heterogeneity during fission yeast growth and adaptation.

Authors:  Malika Saint; François Bertaux; Wenhao Tang; Xi-Ming Sun; Laurence Game; Anna Köferle; Jürg Bähler; Vahid Shahrezaei; Samuel Marguerat
Journal:  Nat Microbiol       Date:  2019-02-04       Impact factor: 17.745

6.  Software for computing and annotating genomic ranges.

Authors:  Michael Lawrence; Wolfgang Huber; Hervé Pagès; Patrick Aboyoun; Marc Carlson; Robert Gentleman; Martin T Morgan; Vincent J Carey
Journal:  PLoS Comput Biol       Date:  2013-08-08       Impact factor: 4.475

7.  Extensive transcriptional heterogeneity revealed by isoform profiling.

Authors:  Vicent Pelechano; Wu Wei; Lars M Steinmetz
Journal:  Nature       Date:  2013-04-24       Impact factor: 49.962

8.  Classification of low quality cells from single-cell RNA-seq data.

Authors:  Tomislav Ilicic; Jong Kyoung Kim; Aleksandra A Kolodziejczyk; Frederik Otzen Bagger; Davis James McCarthy; John C Marioni; Sarah A Teichmann
Journal:  Genome Biol       Date:  2016-02-17       Impact factor: 13.583

9.  Large-Scale Low-Cost NGS Library Preparation Using a Robust Tn5 Purification and Tagmentation Protocol.

Authors:  Bianca P Hennig; Lars Velten; Ines Racke; Chelsea Szu Tu; Matthias Thoms; Vladimir Rybin; Hüseyin Besir; Kim Remans; Lars M Steinmetz
Journal:  G3 (Bethesda)       Date:  2018-01-04       Impact factor: 3.154

10.  Single-cell RNA sequencing reveals intrinsic and extrinsic regulatory heterogeneity in yeast responding to stress.

Authors:  Audrey P Gasch; Feiqiao Brian Yu; James Hose; Leah E Escalante; Mike Place; Rhonda Bacher; Jad Kanbar; Doina Ciobanu; Laura Sandor; Igor V Grigoriev; Christina Kendziorski; Stephen R Quake; Megan N McClean
Journal:  PLoS Biol       Date:  2017-12-14       Impact factor: 8.029

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  1 in total

1.  Data-driven identification of inherent features of eukaryotic stress-responsive genes.

Authors:  Pablo Latorre; René Böttcher; Mariona Nadal-Ribelles; Constance H Li; Carme Solé; Gerard Martínez-Cebrián; Paul C Boutros; Francesc Posas; Eulàlia de Nadal
Journal:  NAR Genom Bioinform       Date:  2022-03-07
  1 in total

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