Research Article

Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo

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Science  01 Jun 2018:
Vol. 360, Issue 6392, pp. 981-987
DOI: 10.1126/science.aar4362
  • Fig. 1 A single-cell transcriptional atlas of the zebrafish embryo.

    (A) Experimental workflow. Single-cell suspensions were dissociated from staged zebrafish embryos and introduced into the inDrops microfluidic device. Single-cell transcriptome libraries were prepared and sequenced by RNA-seq. (B) tSNE maps for each time point, constructed in dimensionality-reduced principal component analysis subspace defined by highly covariable genes (see methods). Cells are colored by germ layer identities inferred from expressed marker genes (see also fig. S2A and table S2).

  • Fig. 2 Single-cell graph reveals a continuous developmental landscape of cell states.

    (A) Overview of graph construction strategy and a force-directed layout of the resulting single-cell graph (nodes colored by collection time point). For each cell, up to 20 within– or between–time point mutual nearest neighbor edges are retained. sc-kNN, single cell–k-nearest neighbor; EVL, enveloping layer epidermis. (B) Single-cell graph colored by germ layer identities inferred from differentially expressed marker genes (see table S2). (C) Single-cell graphs, colored by log10 expression counts for indicated cell type–specific marker genes. UMI, unique molecular identifier.

  • Fig. 3 Single-cell and coarse-grained graphs encode progenitor-fate relationships.

    (A) tSNE map of 6-hpf epiblast and hypoblast states, colored by normalized transcript counts for select positional marker genes. Overlapping color gradients demonstrate continuous expression domains defined by position. Diagram relates positions of cells in the tSNE map to theoretical positions in the embryo. 2D, two dimensions. (B) In silico fate predictions for 6-hpf embryo cells. The top 100 cells with predicted 24-hpf fate outcomes are indicated for shortest graph diffusion distances (red) or direct single-cell gene expression correlation distances (blue) between 6-hpf cells and 24-hpf cluster centroids. ρ, Pearson correlation. (C) Construction and overview of the coarse-grained graph (see also fig. S5). Nodes indicate states (groups of transcriptionally similar cells), colored by time point. Weighted edges connect similar states within or between time points. Spanning tree edges connecting each node to the 4-hpf root state through the top weighted edges are highlighted in dark gray. (D) Coarse-grained graph nodes are colored by a canalization score, defined as the ratio of diffusion distances between each node (DPTall) and the 4-hpf root node through state tree edges only (DPTscaff) versus through all graph edges. Highly canalized regions of the graph correspond to branches with the fewest off-tree edges.

  • Fig. 4 Single-cell transcriptomic barcoding of cell lineages using TracerSeq.

    (A) Method overview. Tol2 transposase system integrates barcode-containing GFP reporter cassettes into zebrafish genome. Asterisks denote integration events. Colors (red, blue, black, and green) indicate unique barcode sequences. (B) Clustered heatmap for one of five TracerSeq embryos (see also fig. S9, A to D), displaying lineage and transcriptome information for each cell. Heatmap rows are single cells for which both transcriptome and >1 TracerSeq barcodes were recovered. Columns denote unique TracerSeq barcodes (left: black squares, ≥1 UMI) and tissue identities (right: red squares) inferred from cluster annotations (table S2). Heatmaps were clustered using Jaccard similarity and average linkage. (C) Examples of TracerSeq founder clones with positions of constituent cells (colored nodes) overlaid on the single-cell graph. Graph edges are shown in dark gray. Colors indicate the first lineage bifurcation within each founder clone. In the three cases shown, the founder clone included cells that differentiated into both ectodermal (E) and mesodermal (M) states, whereas one of the two first subclones was restricted to ectoderm.

  • Fig. 5 TracerSeq reveals systematic relationships between cell lineage and cell state.

    (A) Heatmap of TracerSeq lineage coupling scores (see methods) between pairs of 24-hpf states, clustered by correlation distance and average linkage. Groups of states with similar lineage coupling signatures are annotated. (B) Quantitative relationships between lineage coupling correlation distances and scaled state tree diffusion distances for (i) endothelial, (ii) optic cup, and (iii) myl+ muscle states (see also fig. S10, A to F).

  • Fig. 6 Regulatory features of the developmental landscape identified by genetic perturbation.

    (A) Overview of the CRISPR experiment. Three pairs of chordin- and tyrosinase (control)–targeted samples were prepared and processed by inDrops at ~14 to 16 hpf. (B) Histogram depicting numbers of differentially expressed genes (DEGs) identified in chordin versus control (tyrosinase) cells for each state (blue bars), compared to DEG numbers when comparing between all state pairs (red bars). DEGs were identified by Wilcoxon rank sum test (adjusted P value < 0.01, absolute log2 fold change > 1, average expression > 25 transcripts per million). (C) Histogram of Pearson correlation similarities (after PCA projection) between each chordin or tyrosinase cell and its nearest neighbor from 10-, 14-, and 18-hpf wild-type datasets (see methods). (D) Log2 ratios of cell states with significant differential abundance (false discovery rate < 0.25) in the chordin versus tyrosinase samples. Purple and green regions correspond to wild-type cell states that are over- or underrepresented in the chordin mutant, respectively. Adjacent graph domains with opposing chordin sensitivity are highlighted by brackets. TB, tailbud region (see cdx4 expression in fig. S3).

Supplementary Materials

  • Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo

    Daniel E. Wagner, Caleb Weinreb, Zach M. Collins, James A. Briggs, Sean G. Megason, Allon M. Klein

    Materials/Methods, Supplementary Text, Tables, Figures, and/or References

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    • Materials and Methods
    • Figs. S1 to S17
    • Captions for Tables S1 to S3
    • References
    Table S1
    Summary of sequencing statistics for all inDrops RNA-seq libraries.
    Table S2
    Table of significantly enriched marker genes and corresponding annotations for all 195 cell state clusters identified in the study. The top 20 positive differentially expressed genes (ranked by fold enrichment) determined by MAST (46) and Wilcoxon Rank Sum Test were determined by comparing cells of each cluster to all other cells from the same collection timepoint. Differentially expressed genes were identified using the FindAllMarkers routine in Seurat 2.2.0 according to the following criteria: (1) a log2-fold change >0.5, (2) Adjusted p-value <0.05. (3) >10% of cells in either test group must express at least one UMI.
    Table S3
    Sequences of primers used in this study.

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