Single Molecule and Single Cell Sequencing

153 Pages·2019·6.583 MB·English

by  Yutaka Suzuki

Advances in Experimental Medicine and Biology 1129
Yutaka Suzuki E ditor
Single
Molecule and
Single Cell
Sequencing
Advances in Experimental Medicine
and Biology
Volume 1129
Editorial Board
IRUN R. COHEN, The Weizmann Institute of Science, Rehovot, Israel
ABEL LAJTHA, N.S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
JOHN D. LAMBRIS, University of Pennsylvania, Philadelphia, PA, USA
RODOLFO PAOLETTI, University of Milan, Milan, Italy
NIMA REZAEI, Children’s Medical Center Hospital, Tehran University of
Medical Sciences, Tehran, Iran
More information about this series at http://www.springer.com/series/5584
Yutaka Suzuki
Editor
Single Molecule and Single
Cell Sequencing
Editor
Yutaka Suzuki
Department of Computational Biology and Medical Sciences
University of Tokyo
Chiba, Chiba, Japan
ISSN 0065-2598 ISSN 2214-8019 (electronic)
Advances in Experimental Medicine and Biology
ISBN 978-981-13-6036-7 ISBN 978-981-13-6037-4 (eBook)
https://doi.org/10.1007/978-981-13-6037-4
Library of Congress Control Number: 2019934708
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Contents
Strategies for Converting RNA to Amplifiable cDNA
for Single-Cell RNA Sequencing Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Yohei Sasagawa, Tetsutaro Hayashi, and Itoshi Nikaido
Integrated Fluidic Circuits for Single-Cell Omics
and Multi-omics Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Mark Lynch and Naveen Ramalingam
Single-Cell DNA-Seq and RNA-Seq in Cancer
Using the C1 System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Masahide Seki, Ayako Suzuki, Sarun Sereewattanawoot,
and Yutaka Suzuki
Nx1-Seq (Well Based Single-Cell Analysis System) . . . . . . . . . . . . . . . . . . . 51
Shinichi Hashimoto
Quantitation of mRNA Transcripts and Proteins
Using the BD Rhapsody™ Single-Cell Analysis System . . . . . . . . . . . . . . . 63
Eleen Y. Shum, Elisabeth M. Walczak, Christina Chang,
and H. Christina Fan
An Informative Approach to Single-Cell Sequencing Analysis . . . . . . . . . . 81
Yukie Kashima, Ayako Suzuki, and Yutaka Suzuki
Bionano Genome Mapping: High-T hroughput, Ultra-Long
Molecule Genome Analysis System for Precision Genome
Assembly and Haploid-Resolved Structural Variation Discovery . . . . . . . 97
Sven Bocklandt, Alex Hastie, and Han Cao
Informatics for PacBio Long Reads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
Yuta Suzuki
v
vi Contents
Challenges of Single-Molecule DNA Sequencing
with Solid-State Nanopores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
Yusuke Goto, Rena Akahori, and Itaru Yanagi
On-Site MinION Sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
Lucky R. Runtuwene, Josef S. B. Tuda, Arthur E. Mongan,
and Yutaka Suzuki
Strategies for Converting RNA
to Amplifiable cDNA for Single-Cell RNA
Sequencing Methods
Yohei Sasagawa, Tetsutaro Hayashi, and Itoshi Nikaido
Abstract This review describes the features of molecular biology techniques for
single-cell RNA sequencing (scRNA-seq), including methods developed in our
laboratory. Existing scRNA-seq methods require the conversion of first-strand
cDNA to amplifiable cDNA followed by whole-transcript amplification. There are
three primary strategies for this conversion: poly-A tagging, template switching,
and RNase H-DNA polymerase I-mediated second-strand cDNA synthesis for
in vitro transcription. We discuss the merits and limitations of these strategies and
describe our Reverse Transcription with Random Displacement Amplification tech-
nology that allows for direct first-strand cDNA amplification from RNA without the
need for conversion to an amplifiable cDNA. We believe that this review provides
all users of single-cell transcriptome technologies with an understanding of the rela-
tionship between the quantitative performance of various methods and their molec-
ular features.
Background
Single-cell transcriptome analysis is a useful tool to identify novel cell types and
states in a population. In the early stages of single-cell transcriptome analysis, sev-
eral methods were developed on the microarray platform (Eberwine et al. 2001;
Iscove et al. 2002; Tietjen et al. 2003; Kamme et al. 2003; Kurimoto 2006;
Subkhankulova and Livesey 2006). Nearly all molecular biology techniques used in
these methods—several of which have been reported since 2009 (Tang et al. 2009;
Islam et al. 2011, 2013; Hashimshony et al. 2012, 2016; Ramsköld et al. 2012;
Sasagawa et al. 2013, 2018; Wu et al. 2013; Picelli et al. 2013; Streets et al. 2014;
Jaitin et al. 2014; Soumillon et al. 2014; Fan et al. 2015; Nakamura et al. 2015;
Y. Sasagawa · T. Hayashi · I. Nikaido (*)
Laboratory for Bioinformatics Research, RIKEN Center for Biosystems Dynamics Research,
Wako, Saitama, Japan
e-mail: [email protected]; [email protected]; [email protected]
© Springer Nature Singapore Pte Ltd. 2019 1
Y. Suzuki (ed.), Single Molecule and Single Cell Sequencing, Advances in
Experimental Medicine and Biology 1129,
https://doi.org/10.1007/978-981-13-6037-4_1
2 Y. Sasagawa et al.
Matsunaga et al. 2015; Klein et al. 2015; Bose et al. 2015; Macosko et al. 2015;
Muraro et al. 2016; Sheng et al. 2017; Yang et al. 2017; Avital et al. 2017; Hochgerner
et al. 2017; Zheng et al. 2017; Gierahn et al. 2017; Hashimoto et al. 2017; Herman
et al. 2018; Rosenberg et al. 2018; Han et al. 2018; Hayashi et al. 2018)—are applied
for single-cell RNA-sequencing (scRNA-seq) (Fig. 1). Cell barcoding is a key tech-
nology for improving the throughput of scRNA-seq (Islam et al. 2011), which
allows for obtainment of transcriptome data from thousands of single cells in a
single experiment (Klein et al. 2015; Macosko et al. 2015; Zheng et al. 2017). Full-
length read coverage across all transcripts from single cells has also improved
(Ramsköld et al. 2012; Picelli et al. 2013; Sheng et al. 2017), enabling the detection
of fusion genes, copy number variations, single nucleotide variations, allele-specific
expression, isoforms, and splice variants. These features make scRNA-seq methods
invaluable in medical and basic research (Regev et al. 2017). However, these meth-
ods have a limited capability to assess the expression of many genes and elucidate
their cellular function by these methods.
We recently improved the reaction efficiency of molecular biology techniques
for scRNA-seq methods (Sasagawa et al. 2018) and devised a novel reaction
(Hayashi et al. 2016) (manuscript in preparation), which increase the sensitivity and
quantitative capability of our Quartz-Seq2 and RamDA-seq methods (Sasagawa
et al. 2018; Hayashi et al. 2018). Other studies have focused on improving the reac-
tion efficiency of molecular biology techniques for quantitative scRNA-seq (Picelli
et al. 2013; Hashimshony et al. 2016; Zajac et al. 2013). Herein, we describe the
features of molecular biology techniques for scRNA-seq, including our methods.
This review will provide end-users and developers of single-cell transcriptome tech-
nologies with an understanding of the relationship between the quantitative perfor-
mance of various methods and their molecular features.
scRNA-Seq Methods Require cDNA Amplification
Single mammalian cells have a limited amount of total RNA (between 1 and 30 pg).
scRNA-seq methods typically target poly-adenylated (polyA) RNA, which accounts
for 1–5% of total RNA. However, sequencing platforms require approximately 5 ng
of DNA from a library. This requires the conversion of polyA RNA to amplifiable,
cDNA followed by whole-transcript amplification. Reverse transcription is per-
formed to convert mRNA to first-strand cDNA. A universal adaptor is then added to
the 5′ end. However, the resulting first-strand cDNA cannot be amplified. An addi-
tional step is required to convert first-strand cDNA to amplifiable cDNA. Unconverted
cDNA cannot be amplified and detected in the sequencer. Low conversion efficiency
can prevent the detection of the target gene; improving this process is therefore criti-
cal for quantitative scRNA-seq. There are three common strategies for this conver-
sion: poly-A tagging, template-switching, and RNase H-DNA polymerase
I-mediated second-strand cDNA synthesis for in vitro transcription (IVT). We
describe each of these in strategies in the following paragraphs.
PT IVT TS RamDA
[4 1 ] 3’ end labeling method
1976
using terminal deoxynucleotidyl transferase
1982 [4 2 ] Okayama-Berg method for cDNA cloning
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Dm 1995 [4 9 , 5 0 ] Single-cell
transcript analysis
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Template-switching (TS)
ell ay 2001 [1] Eberwine et al.
ngle-ccroarr 2-2000023[[23]] ITsiceotjveen eett aall.. [4] Kamme et al.
Simi 2006 [5] Kurimoto et al. [ 6 ] Subkhankulova
and Livesey
2009 [7] Tang et al.
†
2011 [8] STRT
†
2012 [9] CEL-seq [10]Smart-Seq
q
A-se 2013 [11]Quartz-Seq [[1123]]SCT1R-STm2a†r,t#-Seq§
N
R [14]Smart-Seq2
ell 2014 [15]Streets et al.§ [16]MARS-seq†,# [17]SCRB-seq†,#
c
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Singl 2015 [[1189]]SSUCP3-esRe-qseq [[2212]]iBnoDsreo pe†t, #a,l§†.,#,§ [23]Drop-seq †,#,§
[20]Beads-seq
2016 [[2245]]CSOELR-Tse-sqe2q††,,##,§ [ o3 f9 ] RETs-tRRabbaalmmisDhmAent
[26]MATQ-seq# [29]STRT-seq-2i†,#,§
2017 [27]MR-seq [30]Chromium†,#,§
†,# [31]Seq-Well†,#,§
[28]scDual-seq [32]Nx1-seq†,#,§
2018 [33]Quartz-Seq2†,# [34]mCEL-seq2†,# [[3356]]SMPicLroiTw-seellq-S†e,#q,†‡,#,§[37]RamDA-seq
†: using cell barcoding technology §: using microfluidics/microwell device
#: using molecular barcoding technology ‡: using combinatorial cell barcoding technology
Fig. 1 Chronology of single-cell transcriptome analysis from the standpoint of molecular biology
methods. The methods were classified in accordance with four general strategies (IVT, IVT with
RNase H-DNA polymerase I-mediated second-strand synthesis; PT, poly-A tagging; RT-RamDA,
reverse transcription with random displacement amplification; TS, template-switching)