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      Single-Cell RNA Sequencing and Its Combination with Protein and DNA Analyses

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          Abstract

          Heterogeneity in cell populations poses a significant challenge for understanding complex cell biological processes. The analysis of cells at the single-cell level, especially single-cell RNA sequencing (scRNA-seq), has made it possible to comprehensively dissect cellular heterogeneity and access unobtainable biological information from bulk analysis. Recent efforts have combined scRNA-seq profiles with genomic or proteomic data, and show added value in describing complex cellular heterogeneity than transcriptome measurements alone. With the rising demand for scRNA-seq for biomedical and clinical applications, there is a strong need for a timely and comprehensive review on the scRNA-seq technologies and their potential biomedical applications. In this review, we first discuss the latest state of development by detailing each scRNA-seq technology, including both conventional and microfluidic technologies. We then summarize their advantages and limitations along with their biomedical applications. The efforts of integrating the transcriptome profile with highly multiplexed proteomic and genomic data are thoroughly reviewed with results showing the integrated data being more informative than transcriptome data alone. Lastly, the latest progress toward commercialization, the remaining challenges, and future perspectives on the development of scRNA-seq technologies are briefly discussed.

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          Comparative Analysis of Single-Cell RNA Sequencing Methods.

          Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq, and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq, and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq, and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods, and it provides a framework for benchmarking further improvements of scRNA-seq protocols.
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            The technology and biology of single-cell RNA sequencing.

            The differences between individual cells can have profound functional consequences, in both unicellular and multicellular organisms. Recently developed single-cell mRNA-sequencing methods enable unbiased, high-throughput, and high-resolution transcriptomic analysis of individual cells. This provides an additional dimension to transcriptomic information relative to traditional methods that profile bulk populations of cells. Already, single-cell RNA-sequencing methods have revealed new biology in terms of the composition of tissues, the dynamics of transcription, and the regulatory relationships between genes. Rapid technological developments at the level of cell capture, phenotyping, molecular biology, and bioinformatics promise an exciting future with numerous biological and medical applications.
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              Eleven grand challenges in single-cell data science

              The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands—or even millions—of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.
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                Author and article information

                Journal
                Cells
                Cells
                cells
                Cells
                MDPI
                2073-4409
                04 May 2020
                May 2020
                : 9
                : 5
                : 1130
                Affiliations
                [1 ]Centre for Blood Research, Life Sciences Centre, University of British Columbia, 2350 Health Sciences Mall, Vancouver, BV V6T 1Z3, Canada
                [2 ]Department of Mechanical Engineering, University of British Columbia, 2054-6250 Applied Science Lane, Vancouver, BC V6T 1Z4, Canada
                [3 ]Department of Surgery, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB T6G 2R3, Canada
                [4 ]Ninewells Hospital & Medical School, Faculty of Medicine, University of Dundee, Dow Street, Dundee DD1 5EH, UK; j.y.choi@ 123456dundee.ac.uk (J.Y.C.); Acowie001@ 123456dundee.ac.uk (A.C.C.)
                Author notes
                Author information
                https://orcid.org/0000-0002-1537-6989
                https://orcid.org/0000-0002-9761-9402
                Article
                cells-09-01130
                10.3390/cells9051130
                7291268
                32375335
                b39390a1-c900-4381-9e73-810727079fb5
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 02 April 2020
                : 01 May 2020
                Categories
                Review

                single-cell rna sequencing,protein,genome,biomedical applications,commercialization

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