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

By Daniel E. Wagner, Caleb Weinreb, Zach M. Collins, J. Briggs, Sean Gregory Megason, Allon M. Klein. 2018

748 citations recorded by the source corpus.

Mapping the vertebrate developmental landscape As embryos develop, numerous cell types with distinct functions and morphologies arise from pluripotent cells. Three research groups have used single-cell RNA sequencing to analyze the transcriptional changes accompanying development of vertebrate embryos (see the Perspective by Harland). Wagner et al. sequenced the transcriptomes of more than 90,000 cells throughout zebrafish development to reveal how cells differentiate during axis patterning, germ layer formation, and early organogenesis. Farrell et al. profiled the transcriptomes of tens of thousands of embryonic cells and applied a computational approach to construct a branching tree describing the transcriptional trajectories that lead to 25 distinct zebrafish cell types. The branching tree revealed how cells change their gene expression as they become more and more specialized. Briggs et al. examined whole frog embryos, spanning zygotic genome activation through early organogenesis, to map cell states and differentiation across all cell lineages over time. These data and approaches pave the way for the comprehensive reconstruction of transcriptional trajectories during development. Science, this issue p. 981, p. eaar3131, p. eaar5780; see also p. 967 Single-cell RNA sequencing reveals cell type trajectories and cell lineage in the developing zebrafish embryo. 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 embryos lacking the chordin gene. We provide web-based resources for further analysis of the single-cell data.

Subfields: Biophysics, Cell Biology, Molecular Biology

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Citations in the corpus

Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization (2020, methodology): “This is the most common case it is used in single-cell transcriptomic data (see, e.g. Wagner et al., 2018; Weinreb et al., 2020).”
Staged developmental mapping and X chromosome transcriptional dynamics during mouse spermatogenesis (2018, background): “developmental differentiation processes which require the profiling of cellular populations at several time points (Kernfeld et al., 2018; Scialdone et al., 2016; Wagner et al., 2018), spermatogenesis occurs in continuous waves throughout the reproductive life span, with all intermediate cell types ”
The single cell transcriptional landscape of mammalian organogenesis (2019, neutral): “The transcriptional profiling of single cells (scRNA-seq) represents a promising avenue for obtaining a global view of developmental processes 5–7 .”
Lineage tracing on transcriptional landscapes links state to fate during differentiation (2018, background): “However, this assumption may not always hold: Similar end states could also arise from nonoverlapping clones (26), and distant end states could share lineage through asymmetric division.”
A Unifying Perspective on Neighbor Embeddings along the Attraction-Repulsion Spectrum (2020, background): “…ForceAtlas2 (Jacomy et al., 2014) has recently gained popularity in the single-cell transcriptomic community to visualize datasets capturing cells at di ﬀ erent stages of development (Weinreb et al., 2018, 2020; Wagner et al., 2018a; Tusi et al., 2018; Kanton et al., 2019; Sharma et al., 2020).”
Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming (2019, positive): “: This method was developed by (Wagner et al., 2018), in an application to zebrafish embryonic development.”
Single cell transcriptomics of human and mouse lung cancers reveals conserved myeloid populations across individuals and species (2019, methodology): “—Spectral clustering was used to define cell subsets, specifically k-means clustering on k graph spectral coordinates after L2 normalization (Wagner et al., 2018).”
Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells (2017, methodology): “Next, we applied PAGA to 53,181 cells collected at different developmental time points (embryo days) from the zebrafish embryo [30].”