The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis
Open accessAbstract
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
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“Almost all previous work quantifying differences between single-cell datasets relies on discrete partitioning of the data before downstream analysis9–16.”
From Quantifying the effect of experimental perturbations at single-cell resolution (2021) · cites this paper as [16]
In the citing paper
“We made use of a 38,731-cell atlas of zebrafish embryogenesis published in a study by Farrell et al.32, comprising 25 developmental trajectories that span zygotic genome activation to early somitogenesis.”
From Dissecting cell identity via network inference and in silico gene perturbation (2023) · cites this paper as [32]
In the citing paper
“We first inferred GRN configurations for the 38 cell types and states identified in the Farrell et al. study32, splitting the main branching trajectory into four sub-branches: ectoderm; axial mesoderm; other mesendoderm; and germ layer branching point (Extended Data Fig. 10a,b).”
From Dissecting cell identity via network inference and in silico gene perturbation (2023) · cites this paper as [32]
In the citing paper
“First, we generated a 38,606-cell wild-type (WT) reference atlas from dissociated WT embryos at 6, 8 and 10 hpf (2 technical replicates per stage) and used Seurat’s label transfer function39 to cluster and label the WT reference cells according to the annotations in Farrell et al.32 (Extended Data Fig. 11).”
From Dissecting cell identity via network inference and in silico gene perturbation (2023) · cites this paper as [32]
In the citing paper
“Nevertheless, existing single-cell data collected after severe developmental disruption do not report the emergence of new transcriptional states in the context of loss of gene function, which suggests extensive canalization even during abnormal development32, supporting the use of CellOracle to accurately simulate TF perturbation effects.”
From Dissecting cell identity via network inference and in silico gene perturbation (2023) · cites this paper as [32]
In the citing paper
“The resulting cell-cell similarity network can then be analyzed to track cell developmental trajectories over time (Farrell et al., 2018; Wagner et al., 2018; Zhong et al., 2018) or to recognize groups of cells forming a distinct cell type or tissue (Jaitin et al., 2014; Zeisel et al., 2015).”
From Mapping the multiscale structure of biological systems (2021) · cites this paper as Farrell et al., 2018
In the citing paper
“These datasets represent diverse developmental and differentiation processes and consist of 141,267 single cells spanning 266 phenotypes, nine biological systems, five species [including two whole organisms (29, 30)], and nine scRNA-seq platforms”
From Single-cell transcriptional diversity is a hallmark of developmental potential (2020) · cites this paper as [30]
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Self-citationSpatial reconstruction of single-cell gene expression data
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Cao, J., Spielmann, M., Qiu, X., Huang, X., Ibrahim, D. M., Hill, A. J., Zhang, F., Mundlos, S., Christiansen, L., Steemers, F. J., et al. (2019) · Nature · doi:10.1038/s41586-019-0969-x · Cited by 4,490
Wolf, F. A., Hamey, F. K., Plass, M., Solana, J., Dahlin, J. S., Göttgens, B., Rajewsky, N., Simon, L., & Theis, F. J. (2019) · Genome Biology · doi:10.1186/s13059-019-1663-x · Cited by 1,698
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Single-cell transcriptional diversity is a hallmark of developmental potential
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Metadata
- Authors
- Farrell, J. A., Wang, Y., Riesenfeld, S. J., Shekhar, K., Regev, A., & Schier, A. F.
- Affiliations
- Harvard University (US)
- Broad Institute (US)
- Corresponding author
- Farrell, Jeffrey A.
- Wang, Yiqun
- Regev, Aviv
- Schier, Alexander F.
- ORCID
- Venue
- Science
- Published
- 2018-06
- Volume
- 360
- Issue
- 6392
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC6247916
- OpenAlex
- W2800912249
- Cited by
- 876
- References
- 51
- Citations by year
- 2026: 98 · 2025: 103 · 2024: 133 · 2023: 82 · 2022: 119 · 2021: 107 · 2020: 104 · 2019: 94 · 2018: 34
- FWCI
- 30.41
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion)
- edoc (University of Basel) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by-nc-nd)
- Funders
- Howard Hughes Medical Institute
- National Human Genome Research Institute
- Jane Coffin Childs Memorial Fund for Medical Research
- Charles A. King Trust
- Eunice Kennedy Shriver National Institute of Child Health and Human Development
- Klarman Cell Observatory
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Developmental Biology and Gene Regulation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Zebrafish Biomedical Research Applications
- Keywords
- zebrafish embryonic development, developmental trajectories, cell differentiation, single-cell transcriptomics, transcriptional trajectories, cell lineage, germ layer formation, organogenesis, zygotic genome activation, frog embryo, cell types
- MeSH terms
- Animals, Embryo, Nonmammalian, Transcription, Genetic, Zebrafish, Signal Transduction, Gene Expression Regulation, Developmental, Zebrafish Proteins, Blastula, Embryonic Development, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, Transcriptome
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