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Single-cell sequencing techniques from individual to multiomics analyses. ​

Literature Information ​

DOI10.1038/s12276-020-00499-2
PMID32929221
JournalExperimental & molecular medicine
Impact Factor12.9
JCR QuartileQ1
Publication Year2020
Times Cited137
Keywordssingle-cell sequencing, multiomics analysis, genomics, epigenomics, transcriptomics
Literature TypeJournal Article, Research Support, Non-U.S. Gov't, Review
ISSN1226-3613
Pages1419-1427
Issue52(9)
AuthorsYukie Kashima, Yoshitaka Sakamoto, Keiya Kaneko, Masahide Seki, Yutaka Suzuki, Ayako Suzuki

TL;DR ​

This review explores single-cell sequencing techniques for genomic, epigenomic, and transcriptomic profiling, highlighting their applications and the computational methods for integrating multiomics data. The findings underscore the potential of these techniques to enhance our understanding of molecular profiles at the single-cell level, particularly in relation to diseases.

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single-cell sequencing · multiomics analysis · genomics · epigenomics · transcriptomics

Abstract ​

Here, we review single-cell sequencing techniques for individual and multiomics profiling in single cells. We mainly describe single-cell genomic, epigenomic, and transcriptomic methods, and examples of their applications. For the integration of multilayered data sets, such as the transcriptome data derived from single-cell RNA sequencing and chromatin accessibility data derived from single-cell ATAC-seq, there are several computational integration methods. We also describe single-cell experimental methods for the simultaneous measurement of two or more omics layers. We can achieve a detailed understanding of the basic molecular profiles and those associated with disease in each cell by utilizing a large number of single-cell sequencing techniques and the accumulated data sets.

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Primary Questions Addressed ​

  1. What are the key differences between single-cell genomic, epigenomic, and transcriptomic methods in terms of their applications and limitations?
  2. How do computational integration methods enhance the understanding of multilayered data sets in single-cell sequencing?
  3. What are the potential challenges and solutions in simultaneously measuring multiple omics layers in single-cell analyses?
  4. How can single-cell sequencing techniques be applied to identify disease-associated molecular profiles at the individual cell level?
  5. What advancements in single-cell sequencing technologies are expected to improve multiomics analyses in the near future?

Key Findings ​

Research Background and Purpose ​

Single-cell sequencing technologies have advanced rapidly, allowing for detailed exploration of genomic, epigenomic, and transcriptomic heterogeneity within individual cells. This review focuses on the methodologies, applications, and computational approaches for single-cell sequencing, emphasizing the integration of multiomics data to better understand cellular diversity and disease mechanisms.

Main Methods/Materials/Experimental Design ​

The review details several single-cell sequencing techniques, categorized into genomic, epigenomic, and transcriptomic methods. The integration of these methods is critical for a comprehensive understanding of cellular states.

Workflow Overview ​

Mermaid diagram

  • Single-cell Transcriptome Sequencing: Techniques such as Smart-seq, CEL-seq, and Drop-seq are highlighted for their capabilities in analyzing RNA from individual cells. The importance of cell dissociation and reverse transcription methods for accurate transcriptomic profiling is emphasized.

  • Single-cell Genome Sequencing: Methods like multiple displacement amplification (MDA) and DOP-PCR are discussed for their role in revealing genetic heterogeneity, including somatic mutations in cancer cells.

  • Single-cell Epigenome Sequencing: Techniques such as scATAC-seq and Drop-ChIP are described for analyzing chromatin accessibility and histone modifications, providing insights into cell differentiation and lineage tracing.

Key Results and Findings ​

  • Single-cell RNA sequencing has illuminated the intratumoral heterogeneity in cancers, revealing distinct transcriptional responses among tumor and immune cells.
  • Genetic analysis through single-cell genome sequencing has shown the accumulation of mutations in B lymphocytes and the relationship between aging and mutation rates in neurons.
  • Epigenomic studies have uncovered variations in chromatin states that correlate with cellular responses to therapies, enhancing the understanding of drug resistance mechanisms.

Main Conclusions/Significance/Innovation ​

The review concludes that the integration of single-cell multiomics data is essential for a comprehensive understanding of cellular diversity and function. The advancements in sequencing technologies and computational methods enable researchers to investigate complex biological questions, particularly in the context of diseases like cancer. The ability to analyze multiple omics layers from the same cell enhances the reliability of findings and provides deeper insights into cellular behavior.

Research Limitations and Future Directions ​

  • The review acknowledges the challenges in integrating multiomics data due to the inherent sparsity and dimensionality of single-cell datasets.
  • Future research should focus on improving computational methods for data integration and analysis, particularly in addressing the limitations of existing integration techniques.
  • There is a need for the development of platforms that allow simultaneous multiomics profiling from single cells to better understand the interplay between different biological layers.

Summary Table of Key Techniques ​

MethodTypeFeatures
scRNA-seqTranscriptomicFull-length mRNA profiling
Drop-seqTranscriptomicHigh-throughput RNA sequencing
MDAGenomicWGA method for single-cell analysis
scATAC-seqEpigenomicChromatin accessibility profiling
Drop-ChIPEpigenomicHigh-throughput histone modification analysis

This structured summary encapsulates the key aspects of the review, providing insights into the methodologies and implications of single-cell sequencing in biomedical research.

References ​

  1. The Human Cell Atlas. - Aviv Regev;Sarah A Teichmann;Eric S Lander;Ido Amit;Christophe Benoist;Ewan Birney;Bernd Bodenmiller;Peter Campbell;Piero Carninci;Menna Clatworthy;Hans Clevers;Bart Deplancke;Ian Dunham;James Eberwine;Roland Eils;Wolfgang Enard;Andrew Farmer;Lars Fugger;Berthold Göttgens;Nir Hacohen;Muzlifah Haniffa;Martin Hemberg;Seung Kim;Paul Klenerman;Arnold Kriegstein;Ed Lein;Sten Linnarsson;Emma Lundberg;Joakim Lundeberg;Partha Majumder;John C Marioni;Miriam Merad;Musa Mhlanga;Martijn Nawijn;Mihai Netea;Garry Nolan;Dana Pe'er;Anthony Phillipakis;Chris P Ponting;Stephen Quake;Wolf Reik;Orit Rozenblatt-Rosen;Joshua Sanes;Rahul Satija;Ton N Schumacher;Alex Shalek;Ehud Shapiro;Padmanee Sharma;Jay W Shin;Oliver Stegle;Michael Stratton;Michael J T Stubbington;Fabian J Theis;Matthias Uhlen;Alexander van Oudenaarden;Allon Wagner;Fiona Watt;Jonathan Weissman;Barbara Wold;Ramnik Xavier;Nir Yosef; - eLife (2017)
  2. An Informative Approach to Single-Cell Sequencing Analysis. - Yukie Kashima;Ayako Suzuki;Yutaka Suzuki - Advances in experimental medicine and biology (2019)
  3. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. - Daniel Ramsköld;Shujun Luo;Yu-Chieh Wang;Robin Li;Qiaolin Deng;Omid R Faridani;Gregory A Daniels;Irina Khrebtukova;Jeanne F Loring;Louise C Laurent;Gary P Schroth;Rickard Sandberg - Nature biotechnology (2012)
  4. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. - Simone Picelli;Åsa K Björklund;Omid R Faridani;Sven Sagasser;Gösta Winberg;Rickard Sandberg - Nature methods (2013)
  5. Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity. - Yohei Sasagawa;Itoshi Nikaido;Tetsutaro Hayashi;Hiroki Danno;Kenichiro D Uno;Takeshi Imai;Hiroki R Ueda - Genome biology (2013)
  6. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. - Tamar Hashimshony;Florian Wagner;Noa Sher;Itai Yanai - Cell reports (2012)
  7. Single-cell full-length total RNA sequencing uncovers dynamics of recursive splicing and enhancer RNAs. - Tetsutaro Hayashi;Haruka Ozaki;Yohei Sasagawa;Mana Umeda;Hiroki Danno;Itoshi Nikaido - Nature communications (2018)
  8. Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. - Evan Z Macosko;Anindita Basu;Rahul Satija;James Nemesh;Karthik Shekhar;Melissa Goldman;Itay Tirosh;Allison R Bialas;Nolan Kamitaki;Emily M Martersteck;John J Trombetta;David A Weitz;Joshua R Sanes;Alex K Shalek;Aviv Regev;Steven A McCarroll - Cell (2015)
  9. Massively parallel single-nucleus RNA-seq with DroNc-seq. - Naomi Habib;Inbal Avraham-Davidi;Anindita Basu;Tyler Burks;Karthik Shekhar;Matan Hofree;Sourav R Choudhury;François Aguet;Ellen Gelfand;Kristin Ardlie;David A Weitz;Orit Rozenblatt-Rosen;Feng Zhang;Aviv Regev - Nature methods (2017)
  10. Mapping the Mouse Cell Atlas by Microwell-Seq. - Xiaoping Han;Renying Wang;Yincong Zhou;Lijiang Fei;Huiyu Sun;Shujing Lai;Assieh Saadatpour;Ziming Zhou;Haide Chen;Fang Ye;Daosheng Huang;Yang Xu;Wentao Huang;Mengmeng Jiang;Xinyi Jiang;Jie Mao;Yao Chen;Chenyu Lu;Jin Xie;Qun Fang;Yibin Wang;Rui Yue;Tiefeng Li;He Huang;Stuart H Orkin;Guo-Cheng Yuan;Ming Chen;Guoji Guo - Cell (2018)

Literatures Citing This Work ​

  1. Opportunities for Single-Cell Sequencing Technologies and Data Science. - Lisa Maria Mustachio;Jason Roszik - Cancers (2020)
  2. Potentiality of multiple modalities for single-cell analyses to evaluate the tumor microenvironment in clinical specimens. - Yukie Kashima;Yosuke Togashi;Shota Fukuoka;Takahiro Kamada;Takuma Irie;Ayako Suzuki;Yoshiaki Nakamura;Kohei Shitara;Tatsunori Minamide;Taku Yoshida;Naofumi Taoka;Tatsuya Kawase;Teiji Wada;Koichiro Inaki;Masataka Chihara;Yukihiko Ebisuno;Sakiyo Tsukamoto;Ryo Fujii;Akihiro Ohashi;Yutaka Suzuki;Katsuya Tsuchihara;Hiroyoshi Nishikawa;Toshihiko Doi - Scientific reports (2021)
  3. From Transcriptomics to Treatment in Inherited Optic Neuropathies. - Michael James Gilhooley;Nicholas Owen;Mariya Moosajee;Patrick Yu Wai Man - Genes (2021)
  4. Bioinformatic Approaches to Validation and Functional Analysis of 3D Lung Cancer Models. - P Jonathan Li;Jeroen P Roose;David M Jablons;Johannes R Kratz - Cancers (2021)
  5. The role of epigenetic modifications for the pathogenesis of Crohn's disease. - M Hornschuh;E Wirthgen;M Wolfien;K P Singh;O Wolkenhauer;J Däbritz - Clinical epigenetics (2021)
  6. Novel Tools and Investigative Approaches for the Study of Oligodendrocyte Precursor Cells (NG2-Glia) in CNS Development and Disease. - Christophe Galichet;Richard W Clayton;Robin Lovell-Badge - Frontiers in cellular neuroscience (2021)
  7. Temporal single-cell regeneration studies: the greatest thing since sliced pancreas? - Juan Domínguez-Bendala;Mirza Muhammad Fahd Qadir;Ricardo Luis Pastori - Trends in endocrinology and metabolism: TEM (2021)
  8. Single cell RNA sequencing approaches to cardiac development and congenital heart disease. - Tahmina Samad;Sean M Wu - Seminars in cell & developmental biology (2021)
  9. Perspectives and Benefits of High-Throughput Long-Read Sequencing in Microbial Ecology. - Leho Tedersoo;Mads Albertsen;Sten Anslan;Benjamin Callahan - Applied and environmental microbiology (2021)
  10. Single-Cell Mapping of GLP-1 and GIP Receptor Expression in the Dorsal Vagal Complex. - Mette Q Ludwig;Petar V Todorov;Kristoffer L Egerod;David P Olson;Tune H Pers - Diabetes (2021)

... (127 more literatures)

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