A multiscale model via single-cell transcriptomics reveals robust patterning mechanisms during early mammalian embryo development

Investigators
Qing Nie
Contact info (email)
qnie@uci.edu
1. Define context(s)
reveal new biological insights
Current Conformance Level / Target Conformance Level
Extensive
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.)
ex vivo (excised tissues)
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)
Human subjects/clinical
Other: ________________________
Data for validating the model Published? Private? How is credibility checked? Current Conformance Level / Target Conformance Level
in vitro (primary cells cell, lines, etc.)
ex vivo (excised tissues)
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)
Human subjects/clinical
Other: ________________________
3. Validate within context(s)
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
Sensitivity analysis
Other:__________
Additional Comments
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
5. Version control
Current Conformance Level / Target Conformance Level
Extensive
Naming Conventions? Repository? Code Review?
individual modeler Yes Yes Peer
within the lab Yes Yes Peer
collaborators
6. Documentation
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
Results
Implications of results
8. Independent reviews
Current Conformance Level / Target Conformance Level
Insufficient
Reviewer(s) name & affiliation:
When was review performed?
How was review performed and outcomes of the review?
9. Test competing implementations
Current Conformance Level / Target Conformance Level
N/A
Yes or No (briefly summarize)
Were competing implementations tested?
Did this lead to model refinement or improvement?
10. Conform to standards
Current Conformance Level / Target Conformance Level
Adequate
Yes or No (briefly summarize)
Are there operating procedures, guidelines, or standards for this type of multiscale modeling? The gene regulatory network and spatial arrangement are often modeled with ODE systems and subcellular element models for this type of multiscale modeling
How do your modeling efforts conform? We used a similar modeling framework as described above.