High-throughput full-length single-cell RNA-seq automation

被引:0
|
作者
Lira Mamanova
Zhichao Miao
Ayesha Jinat
Peter Ellis
Lesley Shirley
Sarah A. Teichmann
机构
[1] Wellcome Sanger Institute,European Molecular Biology Laboratory
[2] European Bioinformatics Institute (EMBL-EBI),Department of Physics, Cavendish Laboratory
[3] University of Cambridge,undefined
来源
Nature Protocols | 2021年 / 16卷
关键词
D O I
暂无
中图分类号
学科分类号
摘要
Existing protocols for full-length single-cell RNA sequencing produce libraries of high complexity (thousands of distinct genes) with outstanding sensitivity and specificity of transcript quantification. These full-length libraries have the advantage of allowing probing of transcript isoforms, are informative regarding single-nucleotide polymorphisms and allow assembly of the VDJ region of the T- and B-cell-receptor sequences. Since full-length protocols are mostly plate-based at present, they are also suited to profiling cell types where cell numbers are limiting, such as rare cell types during development. A disadvantage of these methods has been the scalability and cost of the experiments, which has limited their popularity as compared with droplet-based and nanowell approaches. Here, we describe an automated protocol for full-length single-cell RNA sequencing, including both an in-house automated Smart-seq2 protocol and a commercial kit–based workflow. The protocols take 3–5 d to complete, depending on the number of plates processed in a batch. We discuss these two protocols in terms of ease of use, equipment requirements, running time, cost per sample and sequencing quality. By benchmarking the lysis buffers, reverse transcription enzymes and their combinations, we have optimized the in-house automated protocol to dramatically reduce its cost. An automated setup can be adopted easily by a competent researcher with basic laboratory skills and no prior automation experience. These pipelines have been employed successfully for several research projects allied with the Human Cell Atlas initiative (www.humancellatlas.org).
引用
收藏
页码:2886 / 2915
页数:29
相关论文
共 50 条
  • [1] High-throughput full-length single-cell RNA-seq automation
    Mamanova, Lira
    Miao, Zhichao
    Jinat, Ayesha
    Ellis, Peter
    Shirley, Lesley
    Teichmann, Sarah A.
    [J]. NATURE PROTOCOLS, 2021, 16 (06) : 2886 - +
  • [2] High-throughput and high-sensitivity full-length single-cell RNA-seq analysis on third-generation sequencing platform
    Liao, Yuhan
    Liu, Zhenyu
    Zhang, Yu
    Lu, Ping
    Wen, Lu
    Tang, Fuchou
    [J]. CELL DISCOVERY, 2023, 9 (01)
  • [3] High-throughput and high-sensitivity full-length single-cell RNA-seq analysis on third-generation sequencing platform
    Yuhan Liao
    Zhenyu Liu
    Yu Zhang
    Ping Lu
    Lu Wen
    Fuchou Tang
    [J]. Cell Discovery, 9
  • [4] High-throughput full-length single-cell mRNA-seq of rare cells
    Ooi, Chin Chun
    Mantalas, Gary L.
    Koh, Winston
    Neff, Norma F.
    Fuchigami, Teruaki
    Wong, Dawson J.
    Wilson, Robert J.
    Park, Seung-min
    Gambhir, Sanjiv S.
    Quake, Stephen R.
    Wang, Shan X.
    [J]. PLOS ONE, 2017, 12 (11):
  • [5] High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin
    Kaia Achim
    Jean-Baptiste Pettit
    Luis R Saraiva
    Daria Gavriouchkina
    Tomas Larsson
    Detlev Arendt
    John C Marioni
    [J]. Nature Biotechnology, 2015, 33 : 503 - 509
  • [6] An ultra high-throughput, massively multiplexable, single-cell RNA-seq platform in yeasts
    Brettner, Leandra
    Eder, Rachel
    Schmidlin, Kara
    Geiler-Samerotte, Kerry
    [J]. YEAST, 2024, 41 (04) : 242 - 255
  • [7] High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin
    Achim, Kaia
    Pettit, Jean-Baptiste
    Saraiva, Luis R.
    Gavriouchkina, Daria
    Larsson, Tomas
    Arendt, Detlev
    Marioni, John C.
    [J]. NATURE BIOTECHNOLOGY, 2015, 33 (05) : 503 - U215
  • [8] Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling
    Yamawaki, Tracy M.
    Lu, Daniel R.
    Ellwanger, Daniel C.
    Bhatt, Dev
    Manzanillo, Paolo
    Arias, Vanessa
    Zhou, Hong
    Yoon, Oh Kyu
    Homann, Oliver
    Wang, Songli
    Li, Chi-Ming
    [J]. BMC GENOMICS, 2021, 22 (01)
  • [9] Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling
    Tracy M. Yamawaki
    Daniel R. Lu
    Daniel C. Ellwanger
    Dev Bhatt
    Paolo Manzanillo
    Vanessa Arias
    Hong Zhou
    Oh Kyu Yoon
    Oliver Homann
    Songli Wang
    Chi-Ming Li
    [J]. BMC Genomics, 22
  • [10] Cloud accelerated alignment and assembly of full-length single-cell RNA-seq data using Falco
    Andrian Yang
    Abhinav Kishore
    Benjamin Phipps
    Joshua W. K. Ho
    [J]. BMC Genomics, 20