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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Single-cell sequencing technologies have successfully been used to infer regulatory associations in multiple studies, and even to study regulatory mechanisms.
“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]
Several studies inferred individual cells' cell cycle phase from scRNA-seq using transcriptional signatures of discrete phases from earlier bulk profiling.
“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]
The cell cycle often has an enormous impact on cellular gene expression, and this impact can extend to other processes and be affected by them.
“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]
From the ordering of cells, it is possible to identify the transcriptional changes that accompany developmental processes.
“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
Predicted gene regulators were supported by motif searches and, for Erg1, validated in reporter systems.
“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
Moignard et al. used single-cell qPCR on mesodermal cells from 7- to 8-day-old mouse embryos; diffusion maps identified developmental pseudotime trajectories, recovering the ordering of known markers.
“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
Pseudotime is often interpreted as a proxy for developmental time.
“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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- 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 · 2014: 1
- 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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