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

Abstract

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.

The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution

“…2, E and F). The inferred cell state tree structure was consistent with ancestor assignments generated using an alternative state graph coarse-graining algorithm reported in our companion paper (7) (fig. S9).…”

Single-cell time-series mapping of cell fate trajectories reveals an expanded developmental potential for human PSC-derived distal lung progenitors

“…Genetic tagging of individual cells allowed tracing of their progeny during directed differentiation. Such an approach can match lineage relationships in both a supervised and unsupervised manner, as has been recently reported (Biddy et al, 2018;Wagner et al, 2018). Finally, we also found that cells that are assigned to the AEC2 path in our model appear to stabilize their phenotypes, consistent with in vivo patterns of time dependent restrictions of developing fates including endodermal lineages (Grapin-Botton, 2005).…”

Current best practices in single‐cell RNA‐seq analysis: a tutorial

“…In recent years, single‐cell RNA sequencing (scRNA‐seq) has significantly advanced our knowledge of biological systems. We have been able to both study the cellular heterogeneity of zebrafish, frogs and planaria (Briggs et al , ; Plass et al , ; Wagner et al , ) and discover previously obscured cellular populations (Montoro et al , ; Plasschaert et al , ). The great potential of this technology has motivated computational biologists to develop a range of analysis tools (Rostom et al , ).…”