90
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Single-cell RNA sequencing technologies and bioinformatics pipelines

      review-article
      1 , 2 , 3 , , 1 ,
      Experimental & Molecular Medicine
      Nature Publishing Group UK

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Rapid progress in the development of next-generation sequencing (NGS) technologies in recent years has provided many valuable insights into complex biological systems, ranging from cancer genomics to diverse microbial communities. NGS-based technologies for genomics, transcriptomics, and epigenomics are now increasingly focused on the characterization of individual cells. These single-cell analyses will allow researchers to uncover new and potentially unexpected biological discoveries relative to traditional profiling methods that assess bulk populations. Single-cell RNA sequencing (scRNA-seq), for example, can reveal complex and rare cell populations, uncover regulatory relationships between genes, and track the trajectories of distinct cell lineages in development. In this review, we will focus on technical challenges in single-cell isolation and library preparation and on computational analysis pipelines available for analyzing scRNA-seq data. Further technical improvements at the level of molecular and cell biology and in available bioinformatics tools will greatly facilitate both the basic science and medical applications of these sequencing technologies.

          Genetic data: Zooming in on single cells

          Showing which genes are expressed, or switched on, in individual cells may help to reveal the first signs of disease. Each cell in an organism contains the same genetic information, but cell type and behavior depend on which genes are expressed. Previously, researchers could only sequence cells in batches, averaging the results, but technological improvements now allow sequencing of the genes expressed in an individual cell, known as single-cell RNA sequencing (scRNA-seq). Ji Hyun Lee (Kyung Hee University, Seoul) and Duhee Bang and Byungjin Hwang (Yonsei University, Seoul) have reviewed the available scRNA-seq technologies and the strategies available to analyze the large quantities of data produced. They conclude that scRNA-seq will impact both basic and medical science, from illuminating drug resistance in cancer to revealing the complex pathways of cell differentiation during development.

          Related collections

          Most cited references55

          • Record: found
          • Abstract: found
          • Article: not found

          Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells

          Recent molecular studies have revealed that, even when derived from a seemingly homogenous population, individual cells can exhibit substantial differences in gene expression, protein levels, and phenotypic output 1–5 , with important functional consequences 4,5 . Existing studies of cellular heterogeneity, however, have typically measured only a few pre-selected RNAs 1,2 or proteins 5,6 simultaneously because genomic profiling methods 3 could not be applied to single cells until very recently 7–10 . Here, we use single-cell RNA-Seq to investigate heterogeneity in the response of bone marrow derived dendritic cells (BMDCs) to lipopolysaccharide (LPS). We find extensive, and previously unobserved, bimodal variation in mRNA abundance and splicing patterns, which we validate by RNA-fluorescence in situ hybridization (RNA-FISH) for select transcripts. In particular, hundreds of key immune genes are bimodally expressed across cells, surprisingly even for genes that are very highly expressed at the population average. Moreover, splicing patterns demonstrate previously unobserved levels of heterogeneity between cells. Some of the observed bimodality can be attributed to closely related, yet distinct, known maturity states of BMDCs; other portions reflect differences in the usage of key regulatory circuits. For example, we identify a module of 137 highly variable, yet co-regulated, antiviral response genes. Using cells from knockout mice, we show that variability in this module may be propagated through an interferon feedback circuit involving the transcriptional regulators Stat2 and Irf7. Our study demonstrates the power and promise of single-cell genomics in uncovering functional diversity between cells and in deciphering cell states and circuits.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Quantitative single-cell RNA-seq with unique molecular identifiers.

            Single-cell RNA sequencing (RNA-seq) is a powerful tool to reveal cellular heterogeneity, discover new cell types and characterize tumor microevolution. However, losses in cDNA synthesis and bias in cDNA amplification lead to severe quantitative errors. We show that molecular labels--random sequences that label individual molecules--can nearly eliminate amplification noise, and that microfluidic sample preparation and optimized reagents produce a fivefold improvement in mRNA capture efficiency.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Microfluidic large-scale integration.

              We developed high-density microfluidic chips that contain plumbing networks with thousands of micromechanical valves and hundreds of individually addressable chambers. These fluidic devices are analogous to electronic integrated circuits fabricated using large-scale integration. A key component of these networks is the fluidic multiplexor, which is a combinatorial array of binary valve patterns that exponentially increases the processing power of a network by allowing complex fluid manipulations with a minimal number of inputs. We used these integrated microfluidic networks to construct the microfluidic analog of a comparator array and a microfluidic memory storage device whose behavior resembles random-access memory.
                Bookmark

                Author and article information

                Contributors
                hyunihyuni@khu.ac.kr
                duheebang@yonsei.ac.kr
                Journal
                Exp Mol Med
                Exp. Mol. Med
                Experimental & Molecular Medicine
                Nature Publishing Group UK (London )
                1226-3613
                2092-6413
                7 August 2018
                7 August 2018
                August 2018
                : 50
                : 8
                : 96
                Affiliations
                [1 ]ISNI 0000 0004 0470 5454, GRID grid.15444.30, Department of Chemistry, , Yonsei University, ; Seoul, Korea
                [2 ]ISNI 0000 0001 2171 7818, GRID grid.289247.2, Department of Clinical Pharmacology and Therapeutics, College of Medicine, , Kyung Hee University, ; Seoul, Korea
                [3 ]ISNI 0000 0001 2171 7818, GRID grid.289247.2, Kyung Hee Medical Science Research Institute, , Kyung Hee University, ; Seoul, Korea
                Article
                71
                10.1038/s12276-018-0071-8
                6082860
                30089861
                1ca8acec-47aa-460f-b71e-0349c494b01d
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, and provide a link to the Creative Commons license. You do not have permission under this license to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, http://creativecommons.org/licenses/by-nc-nd/4.0/.

                History
                : 13 November 2017
                : 13 December 2017
                Categories
                Review Article
                Custom metadata
                © The Author(s) 2018

                Molecular medicine
                Molecular medicine

                Comments

                Comment on this article