Spatial reconstruction of single-cell gene expression data

Spatial localization is a key determinant of cellular fate and behavior, but spatial RNA assays traditionally rely on staining for a limited number of RNA species. In contrast, single-cell RNAseq allows for deep profiling of cellular gene expression, but established methods separate cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos, inferring a transcriptome-wide map of spatial patterning. We confirmed Seurat’s accuracy using several experimental approaches, and used it to identify a set of archetypal expression patterns and spatial markers. Additionally, Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.

[1]  C. Kimmel,et al.  Origin and organization of the zebrafish fate map. , 1990, Development.

[2]  J. Postlethwait,et al.  Structure of the zebrafish snail1 gene and its expression in wild-type, spadetail and no tail mutant embryos. , 1993, Development.

[3]  R. Ho,et al.  Commitment of cell fate in the early zebrafish embryo. , 1993, Science.

[4]  D. Grunwald,et al.  Lithium perturbation and goosecoid expression identify a dorsal specification pathway in the pregastrula zebrafish. , 1993, Development.

[5]  C. Kimmel,et al.  Stages of embryonic development of the zebrafish , 1995, Developmental dynamics : an official publication of the American Association of Anatomists.

[6]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[7]  N. Hopkins,et al.  Zebrafish vasa homologue RNA is localized to the cleavage planes of 2- and 4-cell-stage embryos and is expressed in the primordial germ cells. , 1997, Development.

[8]  Øyvind Drivenes,et al.  Expression of two zebrafish homologues of the murine Six3 gene demarcates the initial eye primordia , 1998, Mechanisms of Development.

[9]  M. Fürthauer,et al.  Three different noggin genes antagonize the activity of bone morphogenetic proteins in the zebrafish embryo. , 1999, Developmental biology.

[10]  I. Dawid,et al.  Antagonistic role of vega1 and bozozok/dharma homeobox genes in organizer formation. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[11]  C. Clarke,et al.  A gene expression screen in zebrafish embryogenesis. , 2001, Genome research.

[12]  D. Stainier,et al.  casanova encodes a novel Sox-related protein necessary and sufficient for early endoderm formation in zebrafish. , 2001, Genes & development.

[13]  I. Seiliez,et al.  Spatial and temporal expression of the zebrafish genome by large-scale in situ hybridization screening. , 2004, Methods in cell biology.

[14]  G. Morvan-Dubois,et al.  Skin development in bony fish with particular emphasis on collagen deposition in the dermis of the zebrafish (Danio rerio). , 2004, The International journal of developmental biology.

[15]  L. Ramakrishnan,et al.  Multiplex fluorescent in situ hybridization in zebrafish embryos using tyramide signal amplification. , 2005, Zebrafish.

[16]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Alexander F Schier,et al.  Molecular genetics of axis formation in zebrafish. , 2005, Annual review of genetics.

[18]  A. Schier,et al.  Nodal signaling activates differentiation genes during zebrafish gastrulation. , 2007, Developmental biology.

[19]  D. Hunter,et al.  mixtools: An R Package for Analyzing Mixture Models , 2009 .

[20]  Raymond K. Auerbach,et al.  Integrative Analysis of the Caenorhabditis elegans Genome by the modENCODE Project , 2010, Science.

[21]  S. Linnarsson,et al.  Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. , 2011, Genome research.

[22]  B. Graveley.  The developmental transcriptome of Drosophila melanogaster , 2010, Nature.

[23]  R. Sandberg,et al.  Full-Length mRNA-Seq from single cell levels of RNA and individual circulating tumor cells , 2012, Nature Biotechnology.

[24]  Michael F. Lin,et al.  Systematic identification of long noncoding RNAs expressed during zebrafish embryogenesis. , 2012, Genome research.

[25]  T. Hashimshony,et al.  CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. , 2012, Cell reports.

[26]  D. Sepich,et al.  Gastrulation: making and shaping germ layers. , 2012, Annual review of cell and developmental biology.

[27]  Stephen W. Wilson,et al.  Breaking symmetry: The zebrafish as a model for understanding left‐right asymmetry in the developing brain , 2012, Developmental neurobiology.

[28]  Peter A. Combs,et al.  Sequencing mRNA from Cryo-Sliced Drosophila Embryos to Determine Genome-Wide Spatial Patterns of Gene Expression , 2013, PloS one.

[29]  Aleksandra A. Kolodziejczyk,et al.  Accounting for technical noise in single-cell RNA-seq experiments , 2013, Nature Methods.

[30]  Stephen W. Wilson,et al.  Full Transcriptome Analysis of Early Dorsoventral Patterning in Zebrafish , 2013, PloS one.

[31]  Monte Westerfield,et al.  ZFIN, the Zebrafish Model Organism Database: increased support for mutants and transgenics , 2012, Nucleic Acids Res..

[32]  A. Schier.  Genomics: Zebrafish earns its stripes , 2013, Nature.

[33]  Andrew McDavid,et al.  Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments , 2012, Bioinform..

[34]  Rona S. Gertner,et al.  Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells , 2013, Nature.

[35]  R. Sandberg,et al.  Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells , 2014, Science.

[36]  P. Kharchenko,et al.  Bayesian approach to single-cell differential expression analysis , 2014, Nature Methods.

[37]  George M. Church,et al.  Highly Multiplexed Subcellular RNA Sequencing in Situ , 2014, Science.

[38]  Cole Trapnell,et al.  The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells , 2014, Nature Biotechnology.

[39]  I. Amit,et al.  Massively Parallel Single-Cell RNA-Seq for Marker-Free Decomposition of Tissues into Cell Types , 2014, Science.

[40]  A. Oudenaarden,et al.  Validation of noise models for single-cell transcriptomics , 2014, Nature Methods.

[41]  Jiao Ma,et al.  Toddler: An Embryonic Signal That Promotes Cell Movement via Apelin Receptors , 2014, Science.

[42]  D. Cacchiarelli,et al.  Characterization of directed differentiation by high-throughput single-cell RNA-Seq , 2014, bioRxiv.

[43]  N. Neff,et al.  Reconstructing lineage hierarchies of the distal lung epithelium using single cell RNA-seq , 2014, Nature.

[44]  Alex A. Pollen,et al.  Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex , 2014, Nature Biotechnology.

[45]  Rona S. Gertner,et al.  Single cell RNA Seq reveals dynamic paracrine control of cellular variation , 2014, Nature.

[46]  A. Oudenaarden,et al.  Genome-wide RNA Tomography in the Zebrafish Embryo , 2014, Cell.

[47]  Assaf Gottlieb,et al.  Reconstruction of the Mouse Otocyst and Early Neuroblast Lineage at Single-Cell Resolution , 2014, Cell.

[48]  John D. Storey,et al.  Statistical significance of variables driving systematic variation in high-dimensional data , 2013, Bioinform..

[49]  J. Marioni,et al.  High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin , 2015, Nature Biotechnology.