Abstract
The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method, GESTALT, used CRISPR–Cas9 barcode editing for large-scale lineage tracing, but was restricted to early development and did not identify cell types. Here we present scGESTALT, which combines the lineage recording capabilities of GESTALT with cell-type identification by single-cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points, capturing lineage information from later stages of development. Sequencing of ∼60,000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data, we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types, brain regions, and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease.
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Acknowledgements
We thank G. Findlay and members of the Schier laboratory, particularly J. Farrell, for discussion and advice, the Bauer Core Facility (Harvard) and the Molecular Biology Core Facility (Dana Farber Cancer Institute) for sequencing services, and the Harvard zebrafish facility staff for technical support. This work was supported by a postdoctoral fellowship from the Canadian Institutes of Health Research to B.R., an HHMI Fellowship from the Life Sciences Research Foundation and 1K99GM121852 to D.E.W., a fellowship from the NIH/NHLBI (T32HL007312) to A.M., a Burroughs-Wellcome Fund CASI award and an Edward Mallinckrodt, Jr. Foundation grant to A.M.K., a Paul G. Allen Family Foundation grant and an NIH Director's Pioneer Award (DP1HG007811) to J.S., a postdoctoral fellowship from the American Cancer Society to J.A.G., NIH grants U01MH109560, R01HD85905 and DP1 HD094764-01 to A.F.S., and an Allen Discovery Center grant to A.F.S. and J.S. J.S. is an investigator of the Howard Hughes Medical Institute.
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Contributions
B.R., J.A.G., and A.F.S. designed the study, interpreted the data and wrote the manuscript. B.R. and J.A.G. generated transgenic lines and GESTALT genomic DNA libraries. B.R. performed barcode editing experiments for inDrops and performed data analysis with assistance from J.A.G. D.E.W. performed inDrops encapsulation, inDrops library preparations, and upstream bioinformatic processing of transcriptome and scGESTALT libraries. B.R. and D.E.W. developed the targeted scGESTALT amplification protocol. A.M. developed the scGESTALT processing pipeline and generated lineage trees. B.R. performed downstream processing of scGESTALT data. S.P. established the zebrafish neuron dissociation protocol. A.M.K. and J.S. provided resources and critical insights.
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Competing interests
A.M.K. is a co-inventor on a patent application (PCT/US2015/026443) that includes some of the ideas described in this article. A.M.K. is a cofounder and science advisory board member of 1CellBio. The rest of the authors declare no competing interests.
Integrated supplementary information
Supplementary Figure 1 Cell types, spatial context and gene expression patterns within the juvenile zebrafish brain.
a. Number of cells per cluster from each of the six biological replicates of whole brains (f1 to f6, color coded).
b. t-SNE plot of 58,492 cells highlighting the location of clusters within the brain (n=6 independent animals for whole brain analysis, n=6 independent animals for forebrain samples, n=4 independent animals for midbrain samples and n=6 independent animals for hindbrain samples; also refer to Supplementary Data 1). Brains from multiple fish were manually dissected into 3 regions: forebrain (green), midbrain (blue), and hindbrain (yellow). Cells from each section sequenced separately and clustered together with the unbiased whole brain samples.
c. Forebrain-specific expression of eomesa, dlx5a, foxg1a, and pitx2. Each t-SNE plot consists of n=58,492 cells. Cells in each plot are colored by their expression of each marker gene (grey is low, red is high).
d. Midbrain-specific expression of tal1, barhl2, onecut1, and atf5b, plotted as described in c. Each t-SNE plot consists of n=58,492 cells.
e. Hindbrain-specific expression of phox2a, phox2bb, hoxa3a, hoxb3a, plotted as described in c. Each t-SNE plot consists of n=58,492 cells.
Supplementary Figure 2 Select marker gene expression
a. Non-neural cell expression of flj13639, ggctb, mpeg1.1, and gpx1a. Each t-SNE plot consists of n=58,492 cells. Cells in each plot are colored by their expression of each marker gene (grey is low, red is high). Boxes highlight clusters where markers are enriched.
b. GABAergic neuron (gad1b, gad2) and glutamatergic neuron (slc17a6b) marker gene expression, plotted as described in a. Each t-SNE plot consists of n=58,492 cells.
c. Neuron-specific expression of kiss1, gng8, pax7b, nrgnb, oprd1b, fat2, gsg1l, penkb, pvalb7, lhx6, sst1.1, prdx1, fezf1, spry2, neurod1, and neurod6a marker gene expression, plotted as described in a. Each t-SNE plot consists of n=58,492 cells.
Supplementary Figure 3 Hierarchical clustering of identified cell types using average gene expression signatures
For each cluster, the average gene expression across all cells within the cluster was calculated.
This was used to cluster cell types in gene expression space and visualized as a dendrogram where the length of the branches represent distance between cell types.
Supplementary Figure 4 Iterative clustering of brain regions.
a. Iterative clustering of cells from the forebrain. Inset highlights four clusters within the initial t-SNE plot. Main panel, t-SNE plot of the resulting subclusters (n=6,357 cells).
b. Dotplot of gene expression patterns of select marker genes (columns) for each subcluster (rows) within the forebrain (n=6,357 cells). Dot size represents the percentage of cells expressing the marker; color represents the average scaled expression level. Initial cluster numbers are indicated to the left of subcluster numbers.
c. Iterative clustering of cells from the midbrain. Inset highlights seven clusters within initial t-SNE plot. Main panel, t-SNE plot of the resulting subclusters (n=10,909 cells).
d. Dotplot of gene expression patterns of select marker genes (columns) for each subcluster (rows) within the midbrain (n=10,909 cells). Dot size represents the percentage of cells expressing the marker; color represents the average scaled expression level. Initial cluster numbers are indicated to the left of subcluster numbers.
Supplementary Figure 5 Intra-site edit diversity at each GESTALT barcode target site
a. GESTALT barcode zebrafish were crossed to zebrafish that express heat shock-inducible Cas9 along with gRNAs 5-9. Resulting embryos were either not injected (lanes 1-2) or injected with Cas9 protein at the one-cell stage (lanes 3-16). Embryos were then either not heat shocked (lanes 1-8) or heat shocked at 30 hpf to induce transgenic Cas9 for a late round of editing (lanes 9-16). Top gel, PCR results of amplifying the GESTALT barcode from embryos (unedited = ~300 bp). Barcode is substantially edited only after heat shock (observed as smear). Bottom gel, presence of Cas9 transgene was confirmed by PCR.
b. Heat map of the size and frequency (log10 scale) of intra-site deletions (rows) at each target site (columns) across all eight edited embryos. The matrix is organized from base position 1 in target 1 (left) to base position 363 at the end of target 9 (right). Early edits correspond to sites 1 to 4 (blue bar). Late edits correspond to sites 5 to 9 (yellow bar).
Supplementary Figure 6 Editing patterns of scGESTALT barcodes
a. Mutations within the nine CRISPR target sites of the scGESTALT barcode obtained with inDrops from three animals ZF1, ZF2 and ZF3. Red lines represent deletions, blue lines represent insertions. Blue bar, early sites targeted by Cas9 + gRNAs 1-4 injection; Yellow bar, late sites targeted by heat shock-induced Cas9 expression and zygotic gRNAs 5-9. Overall edit patterns observed with scRNA-seq are similar to those obtained with genomic DNA extracted from embryos (Fig. 3).
b. Heat map of the frequency (log10 scale) of intra-site and inter-site deletions within and across the barcode target sites obtained using scRNA-seq.
Supplementary Figure 7 A reconstructed lineage tree of a single juvenile zebrafish brain generated using scGESTALT
750 barcodes recovered from a single juvenile zebrafish brain (ZF1) using scRNAseq were assembled into a cell lineage tree based on shared edits using a maximum parsimony approach. Black nodes indicate early barcode edits; red nodes indicate late edits. Dashed lines connect individual cells to nodes on the tree. Cell types (identified from simultaneous transcriptome capture) are color coded as indicated in the legend. The barcode for each cell is displayed as a white bar with deletions (red) and insertions (blue). Green, blue and orange colored boxes represent clades ‘a’, ‘b’ and ‘c’, respectively and their subclades, which are shown as mini lineage trees in Fig. 5. For reasons of space, the tree is split into left and right halves. Interactive trees and the very large lineage tree for ZF2 can be found at: http://krishna.gs.washington.edu/content/members/aaron/fate_map/harvard_temp_trees/
Supplementary Figure 8 Lineage relationships identified by scGESTALT
a. Heat map of the distribution of ZF2 (n=150 barcodes, 2,309 cells) and ZF3 (n=49 barcodes, 266 cells) barcodes (rows, clone size >= 4 cells) for each region of the brain (columns). Cell types were classified as belonging to the forebrain, midbrain or hindbrain, and the proportions of cells within each region were calculated for each barcode. Region proportions were scaled by row and colored as shown in the legend.
b. Left, mini tree showing lineage segregation of endothelial cells and microglia (non-neuronal cell types, purple shades) from neurons in ZF1. Right, t-SNE plot highlighting indicated cell types.
c. Left, Bar plots showing the distribution of descendant cells from two ZF2 barcodes into cell types of the hypothalamus/preoptic area. Right, mini tree of these barcodes.
Supplementary Figure 9 Barcodes shared between progenitor and differentiated cell types
a. t-SNE plots highlighting cells of the largest barcode clones in ZF1, ZF2 and ZF3, distributed across many cell types including progenitors.
b. Trajectory of oligodendrocyte differentiation generated with Monocle 2. Cells are colored by pseudotime. Inset highlights these clusters within the initial t-SNE plot from Figure 2.
c. Cells along the trajectory are colored by cluster: 36 (precursors); 47 (immature oligodendrocytes) and 45 (mature oligodendrocytes). Cells (red circles) containing a single scGESTALT barcode from ZF1 are shown along the trajectory.
d. Heat map of gene expression changes of selected markers during oligodendrocyte differentiation. Rows are marker genes, columns are single cells arranged in pseudotime.
Supplementary information
Supplementary Text and Figures
Supplementary Figures 1–9 (PDF 1882 kb)
Supplementary Table 1
Sequences of oligonucleotides used in this study. (PDF 70 kb)
Supplementary Note 1
Glossary of Terms (PDF 94 kb)
Supplementary Dataset 1
Description of samples for transcriptome and scGESTALT analyses. (XLSX 33 kb)
Supplementary Dataset 2
Differential gene expression analysis of broadly defined cell type clusters and their proportions as a percentage of the total dataset. (XLSX 1865 kb)
Supplementary Dataset 3
Regional and marker description of broadly defined cell type clusters. (XLSX 14 kb)
Supplementary Dataset 4
Differential gene expression analysis of subclusters resulting from iterative clustering. (XLSX 1213 kb)
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Raj, B., Wagner, D., McKenna, A. et al. Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain. Nat Biotechnol 36, 442–450 (2018). https://doi.org/10.1038/nbt.4103
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DOI: https://doi.org/10.1038/nbt.4103
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