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      SC3 - consensus clustering of single-cell RNA-Seq data

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          Abstract

          Single-cell RNA-seq (scRNA-seq) enables a quantitative cell-type characterisation based on global transcriptome profiles. We present Single-Cell Consensus Clustering (SC3), a user-friendly tool for unsupervised clustering which achieves high accuracy and robustness by combining multiple clustering solutions through a consensus approach. We demonstrate that SC3 is capable of identifying subclones based on the transcriptomes from neoplastic cells collected from patients.

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          FastQC: a quality-control tool for high-throughput sequence data.

          S Andrews, S. ANDREWS, A. ANDREWS … (2010)
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            Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst.

            Guoji Guo, Mikael Huss, Guo Qing Tong … (2010)
            Three distinct cell types are present within the 64-cell stage mouse blastocyst. We have investigated cellular development up to this stage using single-cell expression analysis of more than 500 cells. The 48 genes analyzed were selected in part based on a whole-embryo analysis of more than 800 transcription factors. We show that in the morula, blastomeres coexpress transcription factors specific to different lineages, but by the 64-cell stage three cell types can be clearly distinguished according to their quantitative expression profiles. We identify Id2 and Sox2 as the earliest markers of outer and inner cells, respectively. This is followed by an inverse correlation in expression for the receptor-ligand pair Fgfr2/Fgf4 in the early inner cell mass. Position and signaling events appear to precede the maturation of the transcriptional program. These results illustrate the power of single-cell expression analysis to provide insight into developmental mechanisms. The technique should be widely applicable to other biological systems. Copyright 2010 Elsevier Inc. All rights reserved.
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              Lineage-Specific Profiling Delineates the Emergence and Progression of Naive Pluripotency in Mammalian Embryogenesis

              Thorsten Boroviak, Remco Loos, Patrick Lombard … (2015)
              Summary Naive pluripotency is manifest in the preimplantation mammalian embryo. Here we determine transcriptome dynamics of mouse development from the eight-cell stage to postimplantation using lineage-specific RNA sequencing. This method combines high sensitivity and reporter-based fate assignment to acquire the full spectrum of gene expression from discrete embryonic cell types. We define expression modules indicative of developmental state and temporal regulatory patterns marking the establishment and dissolution of naive pluripotency in vivo. Analysis of embryonic stem cells and diapaused embryos reveals near-complete conservation of the core transcriptional circuitry operative in the preimplantation epiblast. Comparison to inner cell masses of marmoset primate blastocysts identifies a similar complement of pluripotency factors but use of alternative signaling pathways. Embryo culture experiments further indicate that marmoset embryos utilize WNT signaling during early lineage segregation, unlike rodents. These findings support a conserved transcription factor foundation for naive pluripotency while revealing species-specific regulatory features of lineage segregation.
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                Author and article information

                Journal
                Journal ID (nlm-journal-id): 101215604
                Journal ID (pubmed-jr-id): 32338
                Journal ID (nlm-ta): Nat Methods
                Journal ID (iso-abbrev): Nat. Methods
                Title: Nature methods
                ISSN (Print): 1548-7091
                ISSN (Electronic): 1548-7105
                Publication date Nihms-submitted: 13 March 2017
                Publication date (Electronic): 27 March 2017
                Publication date (Print): May 2017
                Publication date PMC-release: 27 September 2017
                Volume: 14
                Issue: 5
                Pages: 483-486
                Affiliations
                [1 ]Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
                [2 ]Cambridge Institute for Medical Research, Wellcome Trust/MRC Stem Cell Institute and Department of Haematology, University of Cambridge, Hills Road, Cambridge, UK
                [3 ]Department of Mathematics and naXys, University of Namur, Belgium
                [4 ]ICTEAM, Université catholique de Louvain, Belgium
                [5 ]Epigenetics Programme, The Babraham Institute, Babraham, Cambridge, UK
                [6 ]EMBL-European Bioinformatics Institute, Hinxton, Cambridge, UK
                [7 ]Centre for Trophoblast Research, University of Cambridge, Cambridge, UK
                [8 ]Department of Mathematics, Imperial College London, London, UK
                Author notes
                Corresponding author: Martin Hemberg ( mh26@ 123456sanger.ac.uk )
                Article
                Manuscript ID: EMS71741
                DOI: 10.1038/nmeth.4236
                PMC ID: 5410170
                PubMed ID: 28346451
                SO-VID: 54185a87-a3bf-47c1-b2f8-4553357e7dcc
                License:

                Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

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                ScienceOpen disciplines: Life sciences

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