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Farrell, Jeffrey

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Farrell

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Jeffrey

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Farrell, Jeffrey
Author's Publications: search.filters.isAuthorOfPublication.27d67e75-29fc-4f1b-8e71-ae70f6e69d2c

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    Response to Nodal morphogen gradient is determined by the kinetics of target gene induction
    (eLife Sciences Publications, Ltd, 2015) Dubrulle, Julien; Jordan, Benjamin; Akhmetova, Laila; Farrell, Jeffrey; Kim, Seok-Hyung; Solnica-Krezel, Lilianna; Schier, Alexander
    Morphogen gradients expose cells to different signal concentrations and induce target genes with different ranges of expression. To determine how the Nodal morphogen gradient induces distinct gene expression patterns during zebrafish embryogenesis, we measured the activation dynamics of the signal transducer Smad2 and the expression kinetics of long- and short-range target genes. We found that threshold models based on ligand concentration are insufficient to predict the response of target genes. Instead, morphogen interpretation is shaped by the kinetics of target gene induction: the higher the rate of transcription and the earlier the onset of induction, the greater the spatial range of expression. Thus, the timing and magnitude of target gene expression can be used to modulate the range of expression and diversify the response to morphogen gradients. DOI: http://dx.doi.org/10.7554/eLife.05042.001
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    Spatial reconstruction of single-cell gene expression
    (2015) Satija, Rahul; Farrell, Jeffrey; Gennert, David; Schier, Alexander; Regev, Aviv
    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 RNA-seq 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.