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Spatial reconstruction of single-cell gene expression data.

Satija R, Farrell JA, Gennert D, Schier AF, Regev A - Nat. Biotechnol. (2015)

Bottom Line: We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos and generated a transcriptome-wide map of spatial patterning.We confirmed Seurat's accuracy using several experimental approaches, then used the strategy to identify a set of archetypal expression patterns and spatial markers.Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups.

View Article: PubMed Central - PubMed

Affiliation: Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

ABSTRACT
Spatial localization is a key determinant of cellular fate and behavior, but methods for spatially resolved, transcriptome-wide gene expression profiling across complex tissues are lacking. RNA staining methods assay only a small number of transcripts, whereas single-cell RNA-seq, which measures global gene expression, separates 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 and generated a transcriptome-wide map of spatial patterning. We confirmed Seurat's accuracy using several experimental approaches, then used the strategy to identify a set of archetypal expression patterns and spatial markers. 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.

No MeSH data available.


Related in: MedlinePlus

Single-cell RNA-seq from zebrafish embryos(a) Cartoon schematic of the zebrafish embryo at 50% epiboly, depicting cell layers (enveloping layer, EVL; deep cell layer, DEL; yolk syncytial layer, YSL), important structures (the embryonic margin), and the two major spatial axes (animal–vegetal and dorsal–ventral). (b) To create the spatial reference map, we used 47 colorogenic in situ hybridization patterns (i.e., ‘landmark’ genes), which were previously published in the scientific literature. We subdivided the embryo into 64 bins and visually scored each landmark as ‘on’ or ‘off’ within each bin using in situs oriented in both lateral and animal views. Shown here is an in situ for ta/no tail and its resultant binary representation. (c) After dissection of the embryo, single cells were dissociated, plated and picked into microtiter plates, and profiled using a single-cell RNA-seq protocol that was modified to include unique molecule indices (Methods).
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Figure 2: Single-cell RNA-seq from zebrafish embryos(a) Cartoon schematic of the zebrafish embryo at 50% epiboly, depicting cell layers (enveloping layer, EVL; deep cell layer, DEL; yolk syncytial layer, YSL), important structures (the embryonic margin), and the two major spatial axes (animal–vegetal and dorsal–ventral). (b) To create the spatial reference map, we used 47 colorogenic in situ hybridization patterns (i.e., ‘landmark’ genes), which were previously published in the scientific literature. We subdivided the embryo into 64 bins and visually scored each landmark as ‘on’ or ‘off’ within each bin using in situs oriented in both lateral and animal views. Shown here is an in situ for ta/no tail and its resultant binary representation. (c) After dissection of the embryo, single cells were dissociated, plated and picked into microtiter plates, and profiled using a single-cell RNA-seq protocol that was modified to include unique molecule indices (Methods).

Mentions: We tested Seurat and demonstrated its utility using late blastula stage (50% epiboly) zebrafish embryos (Fig. 2a). We generated a reference spatial map by discretizing expression patterns for 47 genes obtained from published bright-field images of in situs generated by standard colorimetric deposition, primarily from ZFIN’s collection24 or high-throughput datasets25,26 (Supplementary Table 1). We divided the embryo into 128 bins (each ~40–120 cells), equally sized along the dorsal–ventral axis, based on the most restricted expression domain in our in situ set, and unevenly sized along the animal–vegetal axis, broadening as they approached the animal pole where patterns were less complex and less sharply defined (Fig. 2b). Since the embryo still exhibits left–right symmetry at this stage, we collapsed the equivalent left and right bins in our analysis, treating the embryo as 64 bins. We ignored the depth axis (from surface to interior), because there are no major examples of gene expression differences along this axis at this stage of development, with the exception of the specialized enveloping layer and yolk syncytial layers17. We manually scored the in situs in each bin once, prior to any data analysis (Fig. 2b, Methods). Binary discretization, while oversimplified, avoids over-interpretation, especially given that published images differ markedly in their resolution, lighting, and extent of staining (Supplementary Fig. 1). As we show below, Seurat robustly maps cells with high quality even based on an initial binary scoring.


Spatial reconstruction of single-cell gene expression data.

Satija R, Farrell JA, Gennert D, Schier AF, Regev A - Nat. Biotechnol. (2015)

Single-cell RNA-seq from zebrafish embryos(a) Cartoon schematic of the zebrafish embryo at 50% epiboly, depicting cell layers (enveloping layer, EVL; deep cell layer, DEL; yolk syncytial layer, YSL), important structures (the embryonic margin), and the two major spatial axes (animal–vegetal and dorsal–ventral). (b) To create the spatial reference map, we used 47 colorogenic in situ hybridization patterns (i.e., ‘landmark’ genes), which were previously published in the scientific literature. We subdivided the embryo into 64 bins and visually scored each landmark as ‘on’ or ‘off’ within each bin using in situs oriented in both lateral and animal views. Shown here is an in situ for ta/no tail and its resultant binary representation. (c) After dissection of the embryo, single cells were dissociated, plated and picked into microtiter plates, and profiled using a single-cell RNA-seq protocol that was modified to include unique molecule indices (Methods).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4430369&req=5

Figure 2: Single-cell RNA-seq from zebrafish embryos(a) Cartoon schematic of the zebrafish embryo at 50% epiboly, depicting cell layers (enveloping layer, EVL; deep cell layer, DEL; yolk syncytial layer, YSL), important structures (the embryonic margin), and the two major spatial axes (animal–vegetal and dorsal–ventral). (b) To create the spatial reference map, we used 47 colorogenic in situ hybridization patterns (i.e., ‘landmark’ genes), which were previously published in the scientific literature. We subdivided the embryo into 64 bins and visually scored each landmark as ‘on’ or ‘off’ within each bin using in situs oriented in both lateral and animal views. Shown here is an in situ for ta/no tail and its resultant binary representation. (c) After dissection of the embryo, single cells were dissociated, plated and picked into microtiter plates, and profiled using a single-cell RNA-seq protocol that was modified to include unique molecule indices (Methods).
Mentions: We tested Seurat and demonstrated its utility using late blastula stage (50% epiboly) zebrafish embryos (Fig. 2a). We generated a reference spatial map by discretizing expression patterns for 47 genes obtained from published bright-field images of in situs generated by standard colorimetric deposition, primarily from ZFIN’s collection24 or high-throughput datasets25,26 (Supplementary Table 1). We divided the embryo into 128 bins (each ~40–120 cells), equally sized along the dorsal–ventral axis, based on the most restricted expression domain in our in situ set, and unevenly sized along the animal–vegetal axis, broadening as they approached the animal pole where patterns were less complex and less sharply defined (Fig. 2b). Since the embryo still exhibits left–right symmetry at this stage, we collapsed the equivalent left and right bins in our analysis, treating the embryo as 64 bins. We ignored the depth axis (from surface to interior), because there are no major examples of gene expression differences along this axis at this stage of development, with the exception of the specialized enveloping layer and yolk syncytial layers17. We manually scored the in situs in each bin once, prior to any data analysis (Fig. 2b, Methods). Binary discretization, while oversimplified, avoids over-interpretation, especially given that published images differ markedly in their resolution, lighting, and extent of staining (Supplementary Fig. 1). As we show below, Seurat robustly maps cells with high quality even based on an initial binary scoring.

Bottom Line: We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos and generated a transcriptome-wide map of spatial patterning.We confirmed Seurat's accuracy using several experimental approaches, then used the strategy to identify a set of archetypal expression patterns and spatial markers.Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups.

View Article: PubMed Central - PubMed

Affiliation: Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

ABSTRACT
Spatial localization is a key determinant of cellular fate and behavior, but methods for spatially resolved, transcriptome-wide gene expression profiling across complex tissues are lacking. RNA staining methods assay only a small number of transcripts, whereas single-cell RNA-seq, which measures global gene expression, separates 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 and generated a transcriptome-wide map of spatial patterning. We confirmed Seurat's accuracy using several experimental approaches, then used the strategy to identify a set of archetypal expression patterns and spatial markers. 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.

No MeSH data available.


Related in: MedlinePlus