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


High-throughput mapping of cellular differentiation hierarchies from single-cell data promises to empower systematic interrogations of vertebrate development and disease. Here, we applied single-cell RNA sequencing to >92,000 cells from zebrafish embryos during the first day of development. Using a graph-based approach, we mapped a cell state landscape that describes axis patterning, germ layer formation, and organogenesis. We tested how clonally related cells traverse this landscape by developing a transposon-based barcoding approach (“TracerSeq”) for reconstructing single-cell lineage histories. Clonally related cells were often restricted by the state landscape, including a case in which two independent lineages converge on similar fates. Cell fates remained restricted to this landscape in chordin-deficient embryos. We provide web-based resources for further analysis of the single-cell data.

Science 26 Apr 2018. doi:10.1126/science.aar4362

Get the data

The full dataset is deposited in NCBI GEO under accession number GSE112294.
Matlab code for generating the single-cell graph is available on GitHub.
A Matlab version of the dataset can be downloaded here.
A ScanPy version of the dataset can be downloaded here.

Explore the data using SPRING

SPRING is a tool for interactive exploration of single-cell data. We provide two SPRING-based interfaces for the Zebrafish time course data. The "single-cell graph" shows all individual cells across all time points and the "coarse-grained graph" shows cell clusters and their connectivity.

Single-cell graph

Coarse-grained graph

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