1. Define context(s)
reveal new biological insights
Primary goal of the model/tool/database
During early mammalian embryo development, a small number of cells make robust fate decisions at particular spatial locations in a tight time window to form inner cell mass (ICM), and later epiblast (Epi) and primitive endoderm (PE). While recent single-cell transcriptomics data allows scrutinization of heterogeneity of individual cells, consistent spatial and temporal mechanisms the early embryo utilize to robustly form the Epi/PE layers from ICM remain elusive. Here we build a multiscale three-dimensional model for mammalian embryo to recapitulate the observed patterning process from zygote to late blastocyst. By integrating the spatiotemporal information reconstructed from multiple single-cell transcriptomic datasets, the data-informed modeling analysis suggests two major processes critical to the formation of Epi/PE layers: a selective cell-cell adhesion mechanism (via EphA4/EphrinB2) for fate-location coordination and a temporal attenuation mechanism of cell signaling (via Fgf). Spatial imaging data and distinct subsets of single-cell gene expression data are then used to validate the predictions. Together, our study provides a multiscale framework that incorporates single-cell gene expression datasets to analyze gene regulations, cell-cell communications, and physical interactions among cells in complex geometries at single-cell resolution, with direct application to late-stage development of embryogenesis.
Biological domain of the model
Early mammalian embryo
Structure(s) of interest in the model
Cell fate aquisition, cell migration, embryo pattern formation
Spatial scales included in the model
The entire early mouse embryo in 3D
Time scales included in the model
From 1-cell stage to late 128-cell stage.
2. Data for building and validating the model
Data for building the model |
Published? |
Private? |
How is credibility checked? |
Current Conformance Level / Target Conformance Level |
in vitro (primary cells cell, lines, etc.) |
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ex vivo (excised tissues) |
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in vivo pre-clinical (lower-level organism or small animal) |
Yes |
No |
The model was built in an unsupervised way on unbiased single-cell RNA sequencing data and spatial data. |
Extensive |
in vivo pre-clinical (large animal) |
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Human subjects/clinical |
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Other: ________________________ |
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Data for validating the model |
Published? |
Private? |
How is credibility checked? |
Current Conformance Level / Target Conformance Level |
in vitro (primary cells cell, lines, etc.) |
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ex vivo (excised tissues) |
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in vivo pre-clinical (lower-level organism or small animal) |
Yes |
No |
By comparing the model results to 3D imaging data and existing knowledge. |
Adequate |
in vivo pre-clinical (large animal) |
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Human subjects/clinical |
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Other: ________________________ |
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3. Validate within context(s)
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Who does it? |
When does it happen? |
How is it done? |
Current Conformance Level / Target Conformance Level |
Verification |
Students/postdocs/investigators |
Throughout the project |
The simulations are repeated several times. The baseline model agrees with knowledge. |
Adequate |
Validation |
Students/postdocs/investigators |
After the baseline model is established. |
By comparing simulated cell type proportion and cell type spatial arrangement to experimental results and by confirming the obtained biological insights with scRNA-seq data |
Adequate |
Uncertainty quantification |
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Sensitivity analysis |
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Other:__________ |
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Additional Comments |
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4. Limitations
Disclaimer statement (explain key limitations) |
Who needs to know about this disclaimer? |
How is this disclaimer shared with that audience? |
Current Conformance Level / Target Conformance Level |
Only the known key genes are modeled. |
Scientific community who intends to extend this model for a different system. |
In discussion of the paper. |
Adequate |
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5. Version control
Current Conformance Level / Target Conformance Level |
Extensive |
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Naming Conventions? |
Repository? |
Code Review? |
individual modeler |
Yes |
Yes |
Peer |
within the lab |
Yes |
Yes |
Peer |
collaborators |
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6. Documentation
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Current Conformance Level / Target Conformance Level |
Code commented? |
Partial |
Scope and intended use described? |
Extensive |
User’s guide? |
Adequate |
Developer’s guide? |
Partial |
7. Dissemination
Current Conformance Level / Target Conformance Level |
Extensive |
Target Audience(s): |
“Inner circle” |
Scientific community |
Public |
Simulations |
Paper and Github repo: https://github.com/yangyaw1/embryo-rule-based |
Paper and Github repo: https://github.com/yangyaw1/embryo-rule-based |
Paper and Github repo: https://github.com/yangyaw1/embryo-rule-based |
Models |
Paper and Github repo: https://github.com/yangyaw1/embryo-rule-based |
Paper and Github repo: https://github.com/yangyaw1/embryo-rule-based |
Paper and Github repo: https://github.com/yangyaw1/embryo-rule-based |
Software |
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Results |
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Implications of results |
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8. Independent reviews
Current Conformance Level / Target Conformance Level |
Insufficient |
Reviewer(s) name & affiliation: |
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When was review performed? |
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How was review performed and outcomes of the review? |
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9. Test competing implementations
Current Conformance Level / Target Conformance Level |
N/A |
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Yes or No (briefly summarize) |
Were competing implementations tested? |
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Did this lead to model refinement or improvement? |
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