The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Decoding the regulatory network of early blood development from single-cell gene expression measurements
Open accessAbstract
Reconstruction of the molecular pathways controlling organ development has been hampered by a lack of methods to resolve embryonic progenitor cells. Here we describe a strategy to address this problem that combines gene expression profiling of large numbers of single cells with data analysis based on diffusion maps for dimensionality reduction and network synthesis from state transition graphs. Applying the approach to hematopoietic development in the mouse embryo, we map the progression of mesoderm toward blood using single-cell gene expression analysis of 3,934 cells with blood-forming potential captured at four time points between E7.0 and E8.5. Transitions between individual cellular states are then used as input to develop a single-cell network synthesis toolkit to generate a computationally executable transcriptional regulatory network model of blood development. Several model predictions concerning the roles of Sox and Hox factors are validated experimentally. Our results demonstrate that single-cell analysis of a developing organ coupled with computational approaches can reveal the transcriptional programs that underpin organogenesis.
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“From the ordering of cells, it is possible to identify the transcriptional changes that accompany developmental processes, which can also permit the reconstruction of gene regulatory networks (Moignard et al, 2015).”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Moignard et al, 2015
In the citing paper
“They have successfully been used to infer regulatory associations in multiple studies, and even to study regulatory mechanisms [9].”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [9]
In the citing paper
“Several studies inferred the cell cycle phase of individual cells from single-cell RNA-seq data30,35,97,99, relying on transcriptional signatures of discrete phases from earlier bulk profiling experiments146.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [146]
In the citing paper
“The cell cycle often has an enormous impact on cellular gene expression146,149,150 (although some87 have challenged this view), but this impact can extend to other processes, and in turn be affected by them.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [146]
In the citing paper
“This facilitated a mechanistic interpretation of the data, where predicted gene regulators were supported by motif searches and, for Erg1, validated in reporter systems.”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Moignard et al, 2015
In the citing paper
“Mesodermal cells from 7‐ to 8‐day‐old mouse embryos were analysed using single‐cell qPCR to understand the early development of blood lineages (Moignard et al, 2015). Here, diffusion maps were used to identify developmental pseudotime trajectories (Haghverdi et al, 2015), recovering correctly the ordering of known markers.”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Moignard et al, 2015
In the citing paper
“While this variable is related to transcriptional distances from a root cell, it is often interpreted as a proxy for developmental time (Moignard et al, 2015; Haghverdi et al, 2016; Fischer et al, 2018; Griffiths et al, 2018).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Moignard et al, 2015
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STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
(2008) · Journal of Machine Learning Research · Cited by 33,051
Epithelial-Mesenchymal Transitions in Development and Disease
Thiery, J. P., Acloque, H., Huang, R. Y. J., & Nieto, M. A. (2009) · Cell · doi:10.1016/j.cell.2009.11.007 · Cited by 9,931
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
Full-length RNA-seq from single cells using Smart-seq2
Picelli, S., Faridani, O. R., Björklund, Å. K., Winberg, G., Sagasser, S., & Sandberg, R. (2014) · Nature Protocols · doi:10.1038/nprot.2014.006 · Cited by 4,137
Failure of blood-island formation and vasculogenesis in Flk-1-deficient mice
Shalaby, F., Rossant, J., Yamaguchi, T. P., Gertsenstein, M., Wu, X.-F., Breitman, M. L., & Schuh, A. C. (1995) · Nature · doi:10.1038/376062a0 · Cited by 3,931
Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data
Sachs, K., Perez, O. D., Pe’er, D., Lauffenburger, D. A., & Nolan, G. P. (2005) · Science · doi:10.1126/science.1105809 · Cited by 1,785
Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells
Yan, L., Yang, M., Guo, H., Yang, L., Wu, J., Li, R., Liu, P., Lian, Y., Zheng, X., Yan, J., et al. (2013) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.2660 · Cited by 1,740
Coifman, R. R., Lafon, S., Lee, A. B., Maggioni, M., Nadler, B., Warner, F., & Zucker, S. W. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0500334102 · Cited by 1,675
Amir, E.-A. D., Davis, K. L., Tadmor, M. D., Simonds, E. F., Levine, J. H., Bendall, S. C., Shenfeld, D. K., Krishnaswamy, S., Nolan, G. P., & Pe'er, D. (2013) · Nature Biotechnology · doi:10.1038/nbt.2594 · Cited by 1,550
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Scenic: single-cell regulatory network inference and clustering.
Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4463 · Cited by 6,520
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232
Self-citationCurrent best practices in single-cell RNA-seq analysis: a tutorial
Luecken, M. D., & Theis, F. J. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188746 · Cited by 2,231
Self-citationSingle-cell mRNA quantification and differential analysis with Census
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Metadata
- Authors
- Moignard, V., Woodhouse, S., Haghverdi, L., Lilly, A. J., Tanaka, Y., Wilkinson, A. C., Buettner, F., Macaulay, I. C., Jawaid, W., Diamanti, E., Nishikawa, S.-I., Piterman, N., Kouskoff, V., Theis, F. J., Fisher, J., & Göttgens, B.
- Affiliations
- Wellcome/MRC Cambridge Stem Cell Institute (GB)
- Helmholtz Munich (DE)
- Cancer Research UK (GB)
- Wellcome Sanger Institute (GB)
- University of Cambridge (GB)
- RIKEN (JP)
- University of Leicester (GB)
- Corresponding author
- Theis, Fabian J
- Fisher, Jasmin
- Göttgens, Berthold
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2015-02-09
- Volume
- 33
- Issue
- 3
- Pages
- 269-276
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC4374163
- OpenAlex
- W2136453810
- Cited by
- 380
- References
- 58
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 24 · 2023: 26 · 2022: 29 · 2021: 30 · 2020: 45 · 2019: 38 · 2018: 42 · 2017: 50 · 2016: 51 · 2015: 25
- FWCI
- 24.30
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion)
- PubMed Central (acceptedVersion)
- Apollo (University of Cambridge) (acceptedVersion)
- Leicester Research Archive (University of Leicester) (submittedVersion)
- MDC Repository (Max-Delbrueck-Center for Molecular Medicine) (submittedVersion)
- Site cant be reached (submittedVersion)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
- Funders
- Leukemia and Lymphoma Society
- Microsoft Research
- Wellcome Trust (100140, 97922, 097922)
- Cancer Research UK (12486, 12765)
- Leukaemia and Lymphoma Research
- Medical Research Council (MR/M008975/1, MC_PC_12009)
- Biotechnology and Biological Sciences Research Council (BB/I00050X/1)
- Cambridge Institute for Medical Research, University of Cambridge
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- 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
- Keywords
- hematopoietic development, transcriptional regulatory networks, single-cell gene expression, diffusion maps, state diagrams, network synthesis, organogenesis, mouse embryo
- MeSH terms
- Animals, Base Sequence, Blood Cells, Computer Simulation, Diffusion, Female, Gene Expression Regulation, Male, Mice, Inbred ICR, Models, Genetic, Molecular Sequence Data, Transcription, Genetic, Gene Expression Profiling, Gene Regulatory Networks, Gastrulation, Single-Cell Analysis
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