Literature DB >> 35837267

Comparative Analysis of Single-Cell RNA Sequencing Platforms and Methods.

John M Ashton1, Hubert Rehrauer2, Jason Myers1, Jacqueline Myers1, Michelle Zanche1, Malene Balys1, Jonathan Foox3, Chistopher E Mason3, Robert Steen4, Marcy Kuentzel5, Catharine Aquino2, Natàlia Garcia-Reyero5, Sridar V Chittur6.   

Abstract

Single-cell RNA sequencing (scRNA-seq) offers great new opportunities for increasing our understanding of complex biological processes. In particular, development of an accurate Human Cell Atlas is largely dependent on the rapidly advancing technologies and molecular chemistries employed in scRNA-seq. These advances have already allowed an increase in throughput for scRNA-seq from 96 to 80,000 cells on a single instrument run by capturing cells within nanoliter droplets. Although this increase in throughput is critical for many experimental questions, a thorough comparison between microfluidic-based, plate-based, and droplet-based technologies or between multiple available platforms utilizing these technologies is largely lacking. Here, we report scRNA-seq data from SUM149PT cells treated with the histone deacetylase inhibitor trichostatin A versus untreated controls across several scRNA-seq platforms (Fluidigm C1, WaferGen iCell8, 10x Genomics Chromium Controller, and Illumina/BioRad ddSEQ). The primary goal of this project was to demonstrate RNA sequencing methods for profiling the ultra-low amounts of RNA present in individual cells, and this report discusses the results of the study, as well as technical challenges and lessons learned and present general guidelines for best practices in sample preparation and analysis.
Copyright ©️ 2021 Association of Biomolecular ResourceFacilities. All rights reserved.

Entities:  

Keywords:  RNA-seq; platforms; single cell

Mesh:

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Year:  2021        PMID: 35837267      PMCID: PMC9258609          DOI: 10.7171/3fc1f5fe.3eccea01

Source DB:  PubMed          Journal:  J Biomol Tech        ISSN: 1524-0215


  16 in total

1.  Comparative Analysis of Single-Cell RNA Sequencing Methods.

Authors:  Christoph Ziegenhain; Beate Vieth; Swati Parekh; Björn Reinius; Amy Guillaumet-Adkins; Martha Smets; Heinrich Leonhardt; Holger Heyn; Ines Hellmann; Wolfgang Enard
Journal:  Mol Cell       Date:  2017-02-16       Impact factor: 17.970

2.  Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain.

Authors:  Arpiar Saunders; Evan Z Macosko; Alec Wysoker; Melissa Goldman; Fenna M Krienen; Heather de Rivera; Elizabeth Bien; Matthew Baum; Laura Bortolin; Shuyu Wang; Aleksandrina Goeva; James Nemesh; Nolan Kamitaki; Sara Brumbaugh; David Kulp; Steven A McCarroll
Journal:  Cell       Date:  2018-08-09       Impact factor: 41.582

3.  Missing data and technical variability in single-cell RNA-sequencing experiments.

Authors:  Stephanie C Hicks; F William Townes; Mingxiang Teng; Rafael A Irizarry
Journal:  Biostatistics       Date:  2018-10-01       Impact factor: 5.899

4.  Tumour evolution inferred by single-cell sequencing.

Authors:  Nicholas Navin; Jude Kendall; Jennifer Troge; Peter Andrews; Linda Rodgers; Jeanne McIndoo; Kerry Cook; Asya Stepansky; Dan Levy; Diane Esposito; Lakshmi Muthuswamy; Alex Krasnitz; W Richard McCombie; James Hicks; Michael Wigler
Journal:  Nature       Date:  2011-03-13       Impact factor: 49.962

5.  Gene ontology analysis for RNA-seq: accounting for selection bias.

Authors:  Matthew D Young; Matthew J Wakefield; Gordon K Smyth; Alicia Oshlack
Journal:  Genome Biol       Date:  2010-02-04       Impact factor: 13.583

6.  Transcript length bias in RNA-seq data confounds systems biology.

Authors:  Alicia Oshlack; Matthew J Wakefield
Journal:  Biol Direct       Date:  2009-04-16       Impact factor: 4.540

7.  Histone deacetylase inhibitors modulate miRNA and mRNA expression, block metaphase, and induce apoptosis in inflammatory breast cancer cells.

Authors:  Namita Chatterjee; Wei-Lin Winnie Wang; Tucker Conklin; Sridar Chittur; Martin Tenniswood
Journal:  Cancer Biol Ther       Date:  2013-06-24       Impact factor: 4.742

8.  Massively parallel nanowell-based single-cell gene expression profiling.

Authors:  Leonard D Goldstein; Ying-Jiun Jasmine Chen; Jude Dunne; Alain Mir; Hermann Hubschle; Joseph Guillory; Wenlin Yuan; Jingli Zhang; Jeremy Stinson; Bijay Jaiswal; Kanika Bajaj Pahuja; Ishminder Mann; Thomas Schaal; Leo Chan; Sangeetha Anandakrishnan; Chun-Wah Lin; Patricio Espinoza; Syed Husain; Harris Shapiro; Karthikeyan Swaminathan; Sherry Wei; Maithreyan Srinivasan; Somasekar Seshagiri; Zora Modrusan
Journal:  BMC Genomics       Date:  2017-07-07       Impact factor: 3.969

9.  Mixture models reveal multiple positional bias types in RNA-Seq data and lead to accurate transcript concentration estimates.

Authors:  Andreas Tuerk; Gregor Wiktorin; Serhat Güler
Journal:  PLoS Comput Biol       Date:  2017-05-15       Impact factor: 4.475

Review 10.  Experimental design for single-cell RNA sequencing.

Authors:  Jeanette Baran-Gale; Tamir Chandra; Kristina Kirschner
Journal:  Brief Funct Genomics       Date:  2018-07-01       Impact factor: 4.241

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