33
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      SAVER: Gene expression recovery for single-cell RNA sequencing

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      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

          In single-cell RNA sequencing (scRNA-seq) studies, only a small fraction of the transcripts present in each cell are sequenced. This leads to unreliable quantification of lowly and moderately expressed genes which hinders downstream analysis. To address this challenge, we introduce SAVER (Single-cell Analysis Via Expression Recovery), an expression recovery method for UMI-based scRNA-seq data that borrows information across genes and cells to obtain accurate expression estimates for all genes.

          Related collections

          Most cited references17

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

          A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure.

          Although the function of the mammalian pancreas hinges on complex interactions of distinct cell types, gene expression profiles have primarily been described with bulk mixtures. Here we implemented a droplet-based, single-cell RNA-seq method to determine the transcriptomes of over 12,000 individual pancreatic cells from four human donors and two mouse strains. Cells could be divided into 15 clusters that matched previously characterized cell types: all endocrine cell types, including rare epsilon-cells; exocrine cell types; vascular cells; Schwann cells; quiescent and activated stellate cells; and four types of immune cells. We detected subpopulations of ductal cells with distinct expression profiles and validated their existence with immuno-histochemistry stains. Moreover, among human beta- cells, we detected heterogeneity in the regulation of genes relating to functional maturation and levels of ER stress. Finally, we deconvolved bulk gene expression samples using the single-cell data to detect disease-associated differential expression. Our dataset provides a resource for the discovery of novel cell type-specific transcription factors, signaling receptors, and medically relevant genes.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found
            Is Open Access

            Molecular Diversity of Midbrain Development in Mouse, Human, and Stem Cells

            Summary Understanding human embryonic ventral midbrain is of major interest for Parkinson’s disease. However, the cell types, their gene expression dynamics, and their relationship to commonly used rodent models remain to be defined. We performed single-cell RNA sequencing to examine ventral midbrain development in human and mouse. We found 25 molecularly defined human cell types, including five subtypes of radial glia-like cells and four progenitors. In the mouse, two mature fetal dopaminergic neuron subtypes diversified into five adult classes during postnatal development. Cell types and gene expression were generally conserved across species, but with clear differences in cell proliferation, developmental timing, and dopaminergic neuron development. Additionally, we developed a method to quantitatively assess the fidelity of dopaminergic neurons derived from human pluripotent stem cells, at a single-cell level. Thus, our study provides insight into the molecular programs controlling human midbrain development and provides a foundation for the development of cell replacement therapies.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Single-Cell RNA-Seq Reveals Hypothalamic Cell Diversity

                Bookmark

                Author and article information

                Journal
                101215604
                32338
                Nat Methods
                Nat. Methods
                Nature methods
                1548-7091
                1548-7105
                5 May 2018
                25 June 2018
                July 2018
                25 December 2018
                : 15
                : 7
                : 539-542
                Affiliations
                [1 ]Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA
                [2 ]Department of Bioengineering, University of Pennsylvania, Philadelphia, PA
                [3 ]Department of Genetics, University of Pennsylvania, Philadelphia, PA
                [4 ]Department of Cell and Developmental Biology, University of Pennsylvania, Philadelphia, PA
                [5 ]Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA
                Author notes
                [* ]Correspondence: Nancy R. Zhang, nzh@ 123456wharton.upenn.edu , (215) 898-8007, Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA
                Article
                NIHMS964612
                10.1038/s41592-018-0033-z
                6030502
                29941873
                445b1c74-e7f6-4223-960d-133d801dc9c1

                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

                History
                Categories
                Article

                Life sciences
                Life sciences

                Comments

                Comment on this article