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Decoding the regulatory network of early blood development from single-cell gene expression measurements.

ABSTRACT: 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.

ORGANISM(S): Mus musculus

PROVIDER: GSE61470 | GEO | 2015/02/09

SECONDARY ACCESSION(S): PRJNA261245

REPOSITORIES: GEO

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Project description:In cattle, harnessing information from the maternal blood to predict fetal health and development is attractive yet scarcely explored. We hypothesised that variations in fetal growth at the end of the organogenesis period impact on the molecular profile of fetal organs and is reflected in the maternal blood transcriptome. The objectives were to determine the transcriptomic modifications in maternal blood and in fetal liver, gonads, and heart according to fetal weight and to model a molecular signature based on the fetal organs allowing the prediction of fetal weight from the maternal blood transcriptome. In addition to a contemporaneous maternal blood sample, organ samples were collected from 10 male fetuses at day 42 day of gestation for RNA-sequencing. Fetal weight ranged from 1.25 to 1.69 gr (mean 1.44 ± 0.15 gr). Data were analysed through co-expression cluster analysis to identify biologically relevant genes dynamically changing according to fetal weight in each fetal organ and in maternal blood. Results revealed clusters of genes, the expression of which was positively correlated with fetal weight, and which enriched ontological terms involved in the organ functionality. For the heart, the 1346 co-expressed genes were involved in energy generation and protein synthesis. For the gonads, the 1042 co-expressed genes enriched seminiferous tubule development. The 459 co-expressed genes identified in the liver were associated with lipid synthesis and metabolism. Finally, the cluster of 571 co-expressed genes determined in maternal blood enriched oxidative phosphorylation and thermogenesis. The expression patterns of the common genes between the clusters in each fetal organ and the cluster in maternal blood were employed to construct a predictive regression model of fetal weight. The data from the fetal organs were used to train the model, and the data from the maternal blood were used to test it. The best prediction was achieved when the model was trained with the 35 common genes between heart and maternal blood (root mean square error=0.04, R-square=0.93). In conclusion, linking transcriptomic information from maternal blood with that from the fetal heart unveils maternal blood as a sensor of fetal development.

Project description:Mammalian embryogenesis is characterized by rapid cellular proliferation and diversification. Within a few weeks, a single cell zygote gives rise to millions of cells expressing a panoply of molecular programs, including much of the diversity that will subsequently be present in adult tissues. Although intensively studied, a comprehensive delineation of the major cellular trajectories that comprise mammalian development in vivo remains elusive. For mouse embryogenesis in particular, we and others have performed single cell or single nucleus RNA-seq data (scRNA-seq) during implantation, gastrulation and organogenesis. Here we set out to integrate several single cell RNA-seq datasets (scRNA-seq) that collectively span mouse gastrulation and organogenesis. However, a technical challenge that we faced is that the datasets that we sought to integrate were generated by different groups at different times using different scRNA-seq technologies. In particular, probably because there was no overlapping timepoint, the integration of scRNA-seq data generated at E8.5 (cells, 10X Genomics) and E9.5 (nuclei, sci-RNA-seq3) was challenging (Cao et al. 2019; Pijuan-Sala et al. 2019). To address this, we set out to generate new data at E8.5 that might serve to “bridge” these two datasets. Because of how quickly changes are occurring during this window of development, we focused on individual, somite-resolved E8.5 embryos using a simplified, optimized version of sci-RNA-seq3. We selected 12 embryos from 2 separate litters harvested at E8.5, including a single primitive streak stage embryo (prior to somitogenesis) and 11 embryos staged in 1-somite increments from 2 to 12 somites. The optimized sci-RNA-seq3 method markedly improved data quality, with 9-fold higher UMIs and 6-fold higher gene detection per nucleus, relative to (Cao et al. 2019). Overall, we collected published data (Cheng et al. 2019; Mohammed et al. 2017; Pijuan-Sala et al. 2019), the new E8.5 data, and published data from one study spanning E9.5 to E13.5 but with deeper sequencing of those libraries (Cao et al. 2019). Altogether, we define cell states at each of 19 successive stages spanning E3.5 to E13.5, heuristically connect them to their pseudo-ancestors and pseudo-descendants. Despite being constructed through automated procedures, the resulting trajectories of mammalian embryogenesis (TOME) are largely consistent with our contemporary understanding of mammalian development. In addition, the new E8.5 data itself comprises a foundational resource for mammalian developmental biology (especially for the early somitogenesis), and are made available in a way that will facilitate their ongoing annotation by the research community.

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Decoding the regulatory network of early blood development from single-cell gene expression measurements.

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