Illumina HiSeq 2500 paired end sequencing; GSM1627975: zf1_p5_S1; Danio rerio; RNA-Seq

zf1_p5_S1

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. Overall design: We generated single-cell RNA-seq profiles from dissociated cells from developing zebrafish embryos (late blastula stage  - 50% epiboly)

Spatial reconstruction of single-cell gene expression

Submitted by Gene Expression Omnibus on 14-APR-2015

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 Seuratâ€™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. Overall design: We generated single-cell RNA-seq profiles from dissociated cells from developing zebrafish embryos (late blastula stage  - 50% epiboly)

Two pairs of watchmaker forceps were used to dissect the blastula cap of the embryo away from the yolk. First, one pair of forceps was used to hold and rotate the cap, while the other was used to cut and pinch away the yolk that extended below the blastula cap. Then, the blastula cap was cut slightly up the side, which exposed the yolk that was inside of the blastula cap, which could then be gently peeled away. The blastula cap was transferred by pipette into a microfuge tube that contained 60 μl of DMEM/F12 media. The cells were dissociated by vigorously flicking the tube 10 times, and then pipetting the entire volume twice while visually confirming that dissociation had occurred. A timer was started at this time to track the amount of time the embryo had been dissociated. If cell clumps were still visible, the tube was flicked again. 180 μl of DMEM/F12 was added to dilute the cell mixture, then 120 μl of the diluted cell mixture was pipetted across the surface of a new agarose-coated dish filled with DMEM/F12 media. To collect cells, a P2 pipettor was used, while observing the cells under the dissecting scope, to collect 0.5 μl of media that contained a single cell. This was pipetted into 3 μl of TCL lysis buffer (Qiagen) in the lid of a PCR strip and mixed 3 times to ensure that lysis occurred. After collection of 8 cells, the entire strip of lids was snapped into a 96-well plate and kept on dry ice. We used a modified strand-specific single-cell RNA-seq protocol based on the SMART template switching method, where the template- switch oligonucleotide included a stretch of 5 randomized nucleotides, thereby tagging each mRNA molecule with a random molecular tag (RMT) prior to PCR to mitigate amplification bias. Furthermore, we used a modified library preparation protocol that shares similarities with Soumillon et al. and is based on the Nextera Sample Preparation Kit. It selectively amplifies the 5’ transcript end, retains strand information, and is compatible with standard Illumina sequencing primers.

Spatial reconstruction of single-cell gene expression

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