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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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Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
(2008) · Journal of Machine Learning Research · Cited by 33,051
Takahashi, K., & Yamanaka, S. (2006) · Cell · doi:10.1016/j.cell.2006.07.024 · Cited by 26,673
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179
A Simple Sequentially Rejective Multiple Test Procedure
(1979) · Scandinavian Journal of Statistics · doi:10.2307/4615733 · Cited by 21,526
Davies, D. L., & Bouldin, D. W. (1979) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1979.4766909 · Cited by 9,145
Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
Self-citationSpatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055
Self-citationscikit-image: image processing in Python
(2014) · doi:10.7717/peerj.453 · Cited by 6,794
Graph drawing by force‐directed placement
Fruchterman, T. M. J., & Reingold, E. M. (1991) · Software: Practice and Experience · doi:10.1002/spe.4380211102 · Cited by 6,352
Maps of random walks on complex networks reveal community structure
Rosvall, M., & Bergstrom, C. T. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0706851105 · Cited by 4,583
Expression of a single transfected cDNA converts fibroblasts to myoblasts
Davis, R. L., Weintraub, H. M., & Lassar, A. B. (1987) · Cell · doi:10.1016/0092-8674(87)90585-x · Cited by 3,388
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Haghverdi, L., Lun, A. T. L., Morgan, M. D., & Marioni, J. C. (2018) · Nature Biotechnology · doi:10.1038/nbt.4091 · Cited by 2,416
Smart-seq2 for sensitive full-length transcriptome profiling in single cells
Picelli, S., Björklund, Å. K., Faridani, O. R., Sagasser, S., Winberg, G., & Sandberg, R. (2013) · Nature Methods · doi:10.1038/nmeth.2639 · Cited by 2,358
Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis
Levine, J. H., Simonds, E. F., Bendall, S. C., Davis, K. L., Amir, E.-A. D., Tadmor, M. D., Litvin, O., Fienberg, H. G., Jager, A., Zunder, E. R., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.047 · Cited by 2,303
Accelerating t-SNE using tree-based algorithms
van der Maaten, L. J. P. (2014) · Journal of machine learning research · doi:10.5555/2627435.2697068 · Cited by 2,211
Metagenes and molecular pattern discovery using matrix factorization
Brunet, J.-P., Tamayo, P., Golub, T. R., & Mesirov, J. P. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308531101 · Cited by 2,183
GARCÍA-ALONSO, C. R., Pérez, L. M., & Fernández, J. C. F. (2011) · Annals of Operations Research · doi:10.1007/s10479-011-0841-3 · Cited by 1,435
Diffusion pseudotime robustly reconstructs lineage branching
Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F., & Theis, F. J. (2016) · Nature Methods · doi:10.1038/nmeth.3971 · Cited by 1,385
Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics
Shekhar, K., Lapan, S. W., Whitney, I. E., Tran, N. M., Macosko, E. Z., Kowalczyk, M., Adiconis, X., Levin, J. Z., Nemesh, J., Goldman, M., et al. (2016) · Cell · doi:10.1016/j.cell.2016.07.054 · Cited by 1,176
Self-citationBendall, S. C., Davis, K. L., Amir, E.-A. D., Tadmor, M. D., Simonds, E. F., Chen, T. J., Shenfeld, D. K., Nolan, G. P., & Pe’er, D. (2014) · Cell · doi:10.1016/j.cell.2014.04.005 · Cited by 906
MyoD1: A Nuclear Phosphoprotein Requiring a Myc Homology Region to Convert Fibroblasts to Myoblasts
Tapscott, S. J., Davis, R. L., Thayer, M. J., Cheng, P.-F., Weintraub, H. M., & Lassar, A. B. (1988) · Science · doi:10.1126/science.3175662 · Cited by 845
Whole-organism lineage tracing by combinatorial and cumulative genome editing
McKenna, A., Findlay, G. M., Gagnon, J. A., Horwitz, M. S., Schier, A. F., & Shendure, J. (2016) · Science · doi:10.1126/science.aaf7907 · Cited by 766
Self-citationThe EGF-CFC Protein One-Eyed Pinhead Is Essential for Nodal Signaling
Gritsman, K., Zhang, J., Cheng, S. K., Heckscher, E. S., Talbot, W. S., & Schier, A. F. (1999) · Cell · doi:10.1016/s0092-8674(00)80720-5 · Cited by 713
Self-citationOrigin and organization of the zebrafish fate map
Kimmel, C. B., Warga, R. M., & Schilling, T. F. (1990) · Development · doi:10.1242/dev.108.4.581 · Cited by 698
Diffusion maps for high-dimensional single-cell analysis of differentiation data
Haghverdi, L., Buettner, F., & Theis, F. J. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv325 · Cited by 649
Revealing the vectors of cellular identity with single-cell genomics
Wagner, A., Regev, A., & Yosef, N. (2016) · Nature Biotechnology · doi:10.1038/nbt.3711 · Cited by 632
Self-citationSimultaneous single-cell profiling of lineages and cell types in the vertebrate brain
Raj, B., Wagner, D. E., McKenna, A., Pandey, S., Klein, A. M., Shendure, J., Gagnon, J. A., & Schier, A. F. (2018) · Nature Biotechnology · doi:10.1038/nbt.4103 · Cited by 610
Self-citationWishbone identifies bifurcating developmental trajectories from single-cell data
Setty, M., Tadmor, M. D., Reich-Zeliger, S., Angel, O., Salame, T. M., Kathail, P., Choi, K., Bendall, S., Friedman, N., & Pe'er, D. (2016) · Nature Biotechnology · doi:10.1038/nbt.3569 · Cited by 558
Molecular Genetics of Axis Formation in Zebrafish
Schier, A. F., & Talbot, W. S. (2005) · Annual Review of Genetics · doi:10.1146/annurev.genet.37.110801.143752 · Cited by 499
Self-citationA homeobox gene essential for zebrafish notochord development
Talbot, W. S., Trevarrow, B., Halpern, M. E., Melby, A. E., Farr, G., Postlethwait, J. H., Jowett, T., Kimmel, C. B., & Kimelman, D. (1995) · Nature · doi:10.1038/378150a0 · Cited by 465
The cis-regulatory dynamics of embryonic development at single-cell resolution
Cusanovich, D. A., Reddington, J. P., Garfield, D. A., Daza, R. M., Aghamirzaie, D., Marco-Ferreres, R., Pliner, H. A., Christiansen, L., Qiu, X., Steemers, F. J., et al. (2018) · Nature · doi:10.1038/nature25981 · Cited by 363
Hammerschmidt, M., Pelegri, F. J., Mullins, M. C., Kane, D. A., Brand, M., van Eeden, F. J. M., Furutani‐Seiki, M., Granato, M., Haffter, P., Heisenberg, C., et al. (1996) · Development · doi:10.1242/dev.123.1.143 · Cited by 287
Kikuchi, Y., Agathon, A., Alexander, J. S., Thisse, C., Waldron, S., Yelon, D. L., Thisse, B., & Stainier, D. Y. R. (2001) · Genes & Development · doi:10.1101/gad.892301 · Cited by 286
Nodal signaling patterns the organizer
Gritsman, K., Talbot, W. S., & Schier, A. F. (2000) · Development · doi:10.1242/dev.127.5.921 · Cited by 261
Self-citationgoosecoid Expression in neurectoderm and mesendoderm is disrupted in zebrafish cyclops gastrulas
Thisse, C., Thisse, B., Halpern, M. E., & Postlethwait, J. H. (1994) · Developmental Biology · doi:10.1006/dbio.1994.1212 · Cited by 230
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Moens, C. B., & Prince, V. (2002) · Developmental Dynamics · doi:10.1002/dvdy.10086 · Cited by 225
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Cannoodt, R., Saelens, W., & Saeys, Y. (2016) · European Journal of Immunology · doi:10.1002/eji.201646347 · Cited by 210
Chechik, G., Oh, E., Rando, O. J., Weissman, J. S., Regev, A., & Koller, D. (2008) · Nature Biotechnology · doi:10.1038/nbt.1499 · Cited by 179
Self-citationHalpern, M. E., Thisse, C., Ho, R. K., Thisse, B., Riggleman, B., Trevarrow, B., Weinberg, E. S., Postlethwait, J. H., & Kimmel, C. B. (1995) · Development · doi:10.1242/dev.121.12.4257 · Cited by 178
Dickmeis, T., Mourrain, P., Saint-Etienne, L., Fischer, N., Aanstad, P., Clark, M. D., Strähle, U., & Rosa, F. (2001) · Genes & Development · doi:10.1101/gad.196901 · Cited by 177
Pandey, S., Shekhar, K., Regev, A., & Schier, A. F. (2018) · Current Biology · doi:10.1016/j.cub.2018.02.040 · Cited by 144
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Zhang, L. J., Radtke, K., Zheng, L., Cai, A. Q., Schilling, T. F., & Nie, Q. H. (2012) · Molecular Systems Biology · doi:10.1038/msb.2012.45 · Cited by 94
Single-cell internalization during zebrafish gastrulation
Carmany-Rampey, A., & Schier, A. F. (2001) · Current Biology · doi:10.1016/s0960-9822(01)00353-0 · Cited by 80
Self-citationFate maps of the zebrafish embryo
Woo, K., Shih, J., & Fraser, S. E. (1995) · Current Opinion in Genetics & Development · doi:10.1016/0959-437x(95)90046-j · Cited by 67
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Gardiner, M. R., Gongora, M., Grimmond, S. M., & Perkins, A. C. (2007) · Mechanisms of Development · doi:10.1016/j.mod.2007.06.005 · Cited by 57
Seiliez, I., Thisse, B., & Thisse, C. (2005) · Developmental Biology · doi:10.1016/j.ydbio.2005.11.021 · Cited by 57
Jang, S., Choubey, S., Furchtgott, L., Zou, L.-N., Doyle, A. M., Menon, V., Loew, E. B., Krostag, A.-R., Martinez, R. A., Madisen, L., et al. (2017) · eLife · doi:10.7554/elife.20487 · Cited by 52
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Haghverdi, L., Lun, A. T. L., Morgan, M. D., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/165118 · Cited by 26
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Swindell, E. C., Zilinski, C. A., Hashimoto, R., Shah, R., Lane, M. E., & Jamrich, M. (2008) · genesis · doi:10.1002/dvg.20380 · Cited by 19
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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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