Literature DB >> 30472192

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

Xiannian Zhang1, Tianqi Li2, Feng Liu3, Yaqi Chen4, Jiacheng Yao2, Zeyao Li5, Yanyi Huang6, Jianbin Wang7.   

Abstract

Since its establishment in 2009, single-cell RNA sequencing (RNA-seq) has been a major driver behind progress in biomedical research. In developmental biology and stem cell studies, the ability to profile single cells confers particular benefits. Although most studies still focus on individual tissues or organs, the recent development of ultra-high-throughput single-cell RNA-seq has demonstrated potential power in characterizing more complex systems or even the entire body. However, although multiple ultra-high-throughput single-cell RNA-seq systems have attracted attention, no systematic comparison of these systems has been performed. Here, with the same cell line and bioinformatics pipeline, we developed directly comparable datasets for each of three widely used droplet-based ultra-high-throughput single-cell RNA-seq systems, inDrop, Drop-seq, and 10X Genomics Chromium. Although each system is capable of profiling single-cell transcriptomes, their detailed comparison revealed the distinguishing features and suitable applications for each system.
Copyright © 2018 Elsevier Inc. All rights reserved.

Keywords:  10X genomics; Drop-seq; RNA-Seq analysis pipeline; barcode analysis; droplet-based microfluidics; high throughput; inDrop; method comparison; single-cell RNA-seq

Mesh:

Substances:

Year:  2018        PMID: 30472192     DOI: 10.1016/j.molcel.2018.10.020

Source DB:  PubMed          Journal:  Mol Cell        ISSN: 1097-2765            Impact factor:   17.970


  92 in total

Review 1.  Revolutionizing immunology with single-cell RNA sequencing.

Authors:  Haide Chen; Fang Ye; Guoji Guo
Journal:  Cell Mol Immunol       Date:  2019-02-22       Impact factor: 11.530

2.  Single cell analysis of adult mouse skeletal muscle stem cells in homeostatic and regenerative conditions.

Authors:  Stefania Dell'Orso; Aster H Juan; Kyung-Dae Ko; Faiza Naz; Jelena Perovanovic; Gustavo Gutierrez-Cruz; Xuesong Feng; Vittorio Sartorelli
Journal:  Development       Date:  2019-04-11       Impact factor: 6.868

3.  Micro-scale technologies propel biology and medicine.

Authors:  Iago Pereiro; Julien Aubert; Govind V Kaigala
Journal:  Biomicrofluidics       Date:  2021-04-27       Impact factor: 2.800

4.  A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples.

Authors:  Wanqiu Chen; Yongmei Zhao; Xin Chen; Zhaowei Yang; Xiaojiang Xu; Yingtao Bi; Vicky Chen; Jing Li; Hannah Choi; Ben Ernest; Bao Tran; Monika Mehta; Parimal Kumar; Andrew Farmer; Alain Mir; Urvashi Ann Mehra; Jian-Liang Li; Malcolm Moos; Wenming Xiao; Charles Wang
Journal:  Nat Biotechnol       Date:  2020-12-21       Impact factor: 54.908

5.  Advanced Materials to Enhance Central Nervous System Tissue Modeling and Cell Therapy.

Authors:  Riya J Muckom; Rocío G Sampayo; Hunter J Johnson; David V Schaffer
Journal:  Adv Funct Mater       Date:  2020-08-12       Impact factor: 18.808

6.  Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview.

Authors:  Shaked Slovin; Annamaria Carissimo; Francesco Panariello; Antonio Grimaldi; Valentina Bouché; Gennaro Gambardella; Davide Cacchiarelli
Journal:  Methods Mol Biol       Date:  2021

Review 7.  Beyond bulk: a review of single cell transcriptomics methodologies and applications.

Authors:  Ashwinikumar Kulkarni; Ashley G Anderson; Devin P Merullo; Genevieve Konopka
Journal:  Curr Opin Biotechnol       Date:  2019-04-10       Impact factor: 9.740

8.  Two Tales of Single-Cell RNA Sequencing: Gene Expression and Alternative Splicing in Mouse Kidney Development.

Authors:  Lihe Chen
Journal:  J Am Soc Nephrol       Date:  2020-09-03       Impact factor: 10.121

9.  Transcriptional and Spatial Resolution of Cell Types in the Mammalian Habenula.

Authors:  Yoshiko Hashikawa; Koichi Hashikawa; Mark A Rossi; Marcus L Basiri; Yuejia Liu; Nathan L Johnston; Omar R Ahmad; Garret D Stuber
Journal:  Neuron       Date:  2020-04-08       Impact factor: 17.173

10.  LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data.

Authors:  Changlin Wan; Wennan Chang; Yu Zhang; Fenil Shah; Xiaoyu Lu; Yong Zang; Anru Zhang; Sha Cao; Melissa L Fishel; Qin Ma; Chi Zhang
Journal:  Nucleic Acids Res       Date:  2019-10-10       Impact factor: 16.971

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