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      SpatialDE: identification of spatially variable genes

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      Author(s): 1 , 2 , 1 , 3 , 2 , 4
      Publication date (Electronic):
      Journal: Nature methods

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          Abstract

          Technological advances have made it possible to measure spatially resolved gene expression at high throughput. However, methods to analyze these data are not established. Here, we develop SpatialDE, a statistical test to identify genes with spatial patterns of expression variation from multiplexed imaging or spatial RNA sequencing data. SpatialDE also implements “automatic expression histology”, a spatial gene clustering approach that enables expression-based tissue histology.

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          Accounting for technical noise in single-cell RNA-seq experiments.

          Philip Brennecke, Simon Anders, Jong Kim … (2013)
          Single-cell RNA-seq can yield valuable insights about the variability within a population of seemingly homogeneous cells. We developed a quantitative statistical method to distinguish true biological variability from the high levels of technical noise in single-cell experiments. Our approach quantifies the statistical significance of observed cell-to-cell variability in expression strength on a gene-by-gene basis. We validate our approach using two independent data sets from Arabidopsis thaliana and Mus musculus.
          Article 0 comments Cited 452 times – based on 0 reviews     Review now
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            Control of Transcript Variability in Single Mammalian Cells.

            Nico Battich, Thomas Stoeger, Lucas Pelkmans (2015)
            A central question in biology is whether variability between genetically identical cells exposed to the same culture conditions is largely stochastic or deterministic. Using image-based transcriptomics in millions of single human cells, we find that while variability of cytoplasmic transcript abundance is large, it is for most genes minimally stochastic and can be predicted with multivariate models of the phenotypic state and population context of single cells. Computational multiplexing of these predictive signatures across hundreds of genes revealed a complex regulatory system that controls the observed variability of transcript abundance between individual cells. Mathematical modeling and experimental validation show that nuclear retention and transport of transcripts between the nucleus and the cytoplasm is central to buffering stochastic transcriptional fluctuations in mammalian gene expression. Our work indicates that cellular compartmentalization confines transcriptional noise to the nucleus, thereby preventing it from interfering with the control of single-cell transcript abundance in the cytoplasm.
            Article 0 comments Cited 141 times – based on 0 reviews     Review now
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              Genome-wide RNA Tomography in the zebrafish embryo.

              Jan Philipp Junker, Emily S Noël, Victor Guryev … (2014)
              Advancing our understanding of embryonic development is heavily dependent on identification of novel pathways or regulators. Although genome-wide techniques such as RNA sequencing are ideally suited for discovering novel candidate genes, they are unable to yield spatially resolved information in embryos or tissues. Microscopy-based approaches, using in situ hybridization, for example, can provide spatial information about gene expression, but are limited to analyzing one or a few genes at a time. Here, we present a method where we combine traditional histological techniques with low-input RNA sequencing and mathematical image reconstruction to generate a high-resolution genome-wide 3D atlas of gene expression in the zebrafish embryo at three developmental stages. Importantly, our technique enables searching for genes that are expressed in specific spatial patterns without manual image annotation. We envision broad applicability of RNA tomography as an accurate and sensitive approach for spatially resolved transcriptomics in whole embryos and dissected organs.
              Article 0 comments Cited 129 times – based on 0 reviews     Review now
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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: 23 January 2019
                Publication date (Electronic): 19 March 2018
                Publication date (Print): May 2018
                Publication date PMC-release: 29 January 2019
                Volume: 15
                Issue: 5
                Pages: 343-346
                Affiliations
                [1 ]Wellcome Trust Sanger Institute, Wellcome Genome Campus, CB10 1SD Hinxton, Cambridge, UK
                [2 ]European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, CB10 1SD, Hinxton, Cambridge, UK
                [3 ]Theory of Condensed Matter Group, Cavendish Laboratory, 19 JJ Thomson Avenue, CB3 0HE, Cambridge, U.K
                [4 ]European Molecular Biology Laboratory, Genome Biology Unit, 69117 Heidelberg, Germany
                Author notes
                Corresponding authors: Valentine Svensson ( vale@ 123456ebi.ac.uk ), Oliver Stegle ( oliver.stegle@ 123456ebi.ac.uk )
                Article
                Manuscript ID: EMS76395
                DOI: 10.1038/nmeth.4636
                PMC ID: 6350895
                PubMed ID: 29553579
                SO-VID: 0aa240d0-b901-47db-8a71-8b3fe5aa461f
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                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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