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      Single-Cell Multiomics: Multiple Measurements from Single Cells

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          Abstract

          Single-cell sequencing provides information that is not confounded by genotypic or phenotypic heterogeneity of bulk samples. Sequencing of one molecular type (RNA, methylated DNA or open chromatin) in a single cell, furthermore, provides insights into the cell's phenotype and links to its genotype. Nevertheless, only by taking measurements of these phenotypes and genotypes from the same single cells can such inferences be made unambiguously. In this review, we survey the first experimental approaches that assay, in parallel, multiple molecular types from the same single cell, before considering the challenges and opportunities afforded by these and future technologies.

          Trends

          Unambiguous inference that a cellular phenotype is caused by a genotype can only be achieved by their measurement from the same single cell.

          Estimating RNA and DNA copy number abundance in single cells is now possible using a variety of experimental approaches.

          Parallel measurement of single-cell epigenomes and transcriptomes provides further insight into the regulation of cellular identity and phenotypes.

          Parallel measurement of single-cell transcriptomes and protein abundance (by FACS, proximity ligation assays/PEA or mass cytometry) allows insight into expression dynamics.

          Our understanding of cancer progression and diversity is likely to be advanced greatly by the multiomics investigation of single cells, as is our understanding of normal developmental and other disease processes.

          Ongoing technological advances will see improvements in the coverage, sensitivity of multiomics approaches, as well the number of analytes that can be surveyed in parallel.

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          Most cited references31

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          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.
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            A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation.

            Maintenance of the blood system requires balanced cell fate decisions by hematopoietic stem and progenitor cells (HSPCs). Because cell fate choices are executed at the individual cell level, new single-cell profiling technologies offer exciting possibilities for mapping the dynamic molecular changes underlying HSPC differentiation. Here, we have used single-cell RNA sequencing to profile more than 1600 single HSPCs, and deep sequencing has enabled detection of an average of 6558 protein-coding genes per cell. Index sorting, in combination with broad sorting gates, allowed us to retrospectively assign cells to 12 commonly sorted HSPC phenotypes while also capturing intermediate cells typically excluded by conventional gating. We further show that independently generated single-cell data sets can be projected onto the single-cell resolution expression map to directly compare data from multiple groups and to build and refine new hypotheses. Reconstruction of differentiation trajectories reveals dynamic expression changes associated with early lymphoid, erythroid, and granulocyte-macrophage differentiation. The latter two trajectories were characterized by common upregulation of cell cycle and oxidative phosphorylation transcriptional programs. By using external spike-in controls, we estimate absolute messenger RNA (mRNA) levels per cell, showing for the first time that despite a general reduction in total mRNA, a subset of genes shows higher expression levels in immature stem cells consistent with active maintenance of the stem-cell state. Finally, we report the development of an intuitive Web interface as a new community resource to permit visualization of gene expression in HSPCs at single-cell resolution for any gene of choice.
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              Somatic mutation, genomic variation, and neurological disease.

              Genetic mutations causing human disease are conventionally thought to be inherited through the germ line from one's parents and present in all somatic (body) cells, except for most cancer mutations, which arise somatically. Increasingly, somatic mutations are being identified in diseases other than cancer, including neurodevelopmental diseases. Somatic mutations can arise during the course of prenatal brain development and cause neurological disease-even when present at low levels of mosaicism, for example-resulting in brain malformations associated with epilepsy and intellectual disability. Novel, highly sensitive technologies will allow more accurate evaluation of somatic mutations in neurodevelopmental disorders and during normal brain development.
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                Author and article information

                Contributors
                Journal
                Trends Genet
                Trends Genet
                Trends in Genetics
                Elsevier Trends Journals
                0168-9525
                1 February 2017
                February 2017
                : 33
                : 2
                : 155-168
                Affiliations
                [1 ]Earlham Institute, Norwich Research Park, Norwich NR4 7UH, UK
                [2 ]Sanger Institute – EBI Single-Cell Genomics Centre, Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK
                [3 ]MRC Human Genetics Unit, MRC IGMM, University of Edinburgh, Crewe Road, Edinburgh EH4 2XU, UK
                [4 ]Department of Human Genetics, University of Leuven, KU Leuven, Leuven, 3000, Belgium
                Author notes
                Article
                S0168-9525(16)30169-X
                10.1016/j.tig.2016.12.003
                5303816
                28089370
                d17d6e9e-8d93-4e1b-b60e-e01b693cc032
                © 2016 The Authors. Published by Elsevier Ltd.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Categories
                Review

                Genetics
                epigenomics,genomics,multiomics,proteomics,single cell,transcriptomics
                Genetics
                epigenomics, genomics, multiomics, proteomics, single cell, transcriptomics

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