skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • NSF Public Access
  • Search Results
  • A multiscale model via single-cell transcriptomics reveals robust patterning mechanisms during early mammalian embryo development
Title: A multiscale model via single-cell transcriptomics reveals robust patterning mechanisms during early mammalian embryo development
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.  more » « less
Award ID(s):
1763272
PAR ID:
10222818
Author(s) / Creator(s):
; ; ; ; ;
Editor(s):
Umulis, David
Date Published:
Journal Name:
PLOS Computational Biology
Volume:
17
Issue:
3
ISSN:
1553-7358
Page Range / eLocation ID:
e1008571
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; (, Nature Cell Biology)
    Aggregates of stem cells can break symmetry and self-organize into embryo-like structures with complex morphologies and gene expression patterns. Mechanisms including reaction-diffusion Turing patterns and cell sorting have been proposed to explain symmetry breaking but distinguishing between these candidate mechanisms of self-organization requires identifying which early asymmetries evolve into subsequent tissue patterns and cell fates. Here we use synthetic ‘signal-recording’ gene circuits to trace the evolution of signalling patterns in gastruloids, three-dimensional stem cell aggregates that form an anterior–posterior axis and structures resembling the mammalian primitive streak and tailbud. We find that cell sorting rearranges patchy domains of Wnt activity into a single pole that defines the gastruloid anterior–posterior axis. We also trace the emergence of Wnt domains to earlier heterogeneity in Nodal activity even before Wnt activity is detectable. Our study defines a mechanism through which aggregates of stem cells can form a patterning axis even in the absence of external spatial cues. 
    more » « less
  2. ; ; (, Development)
    ABSTRACT Identifying cell states during development from their mRNA profiles provides insight into their gene regulatory network. Here, we leverage the sea urchin embryo for its well-established gene regulatory network to interrogate the embryo using single cell RNA sequencing. We tested eight developmental stages in Strongylocentrotus purpuratus, from the eight-cell stage to late in gastrulation. We used these datasets to parse out 22 major cell states of the embryo, focusing on key transition stages for cell type specification of each germ layer. Subclustering of these major embryonic domains revealed over 50 cell states with distinct transcript profiles. Furthermore, we identified the transcript profile of two cell states expressing germ cell factors, one we conclude represents the primordial germ cells and the other state is transiently present during gastrulation. We hypothesize that these cells of the Veg2 tier of the early embryo represent a lineage that converts to the germ line when the primordial germ cells are deleted. This broad resource will hopefully enable the community to identify other cell states and genes of interest to expose the underpinning of developmental mechanisms. 
    more » « less
  3. ; ; ; (, Nucleic Acids Research)
    null (Ed.)
    Abstract Rapid growth of single-cell transcriptomic data provides unprecedented opportunities for close scrutinizing of dynamical cellular processes. Through investigating epithelial-to-mesenchymal transition (EMT), we develop an integrative tool that combines unsupervised learning of single-cell transcriptomic data and multiscale mathematical modeling to analyze transitions during cell fate decision. Our approach allows identification of individual cells making transition between all cell states, and inference of genes that drive transitions. Multiscale extractions of single-cell scale outputs naturally reveal intermediate cell states (ICS) and ICS-regulated transition trajectories, producing emergent population-scale models to be explored for design principles. Testing on the newly designed single-cell gene regulatory network model and applying to twelve published single-cell EMT datasets in cancer and embryogenesis, we uncover the roles of ICS on adaptation, noise attenuation, and transition efficiency in EMT, and reveal their trade-off relations. Overall, our unsupervised learning method is applicable to general single-cell transcriptomic datasets, and our integrative approach at single-cell resolution may be adopted for other cell fate transition systems beyond EMT. 
    more » « less
  4. ; (, Development)
    ABSTRACT The lineage decision that generates the epiblast and primitive endoderm from the inner cell mass (ICM) is a paradigm for cell fate specification. Recent mathematics has formalized Waddington's landscape metaphor and proven that lineage decisions in detailed gene network models must conform to a small list of low-dimensional stereotypic changes called bifurcations. The most plausible bifurcation for the ICM is the so-called heteroclinic flip that we define and elaborate here. Our re-analysis of recent data suggests that there is sufficient cell movement in the ICM so the FGF signal, which drives the lineage decision, can be treated as spatially uniform. We thus extend the bifurcation model for a single cell to the entire ICM by means of a self-consistently defined time-dependent FGF signal. This model is consistent with available data and we propose additional dynamic experiments to test it further. This demonstrates that simplified, quantitative and intuitively transparent descriptions are possible when attention is shifted from specific genes to lineages. The flip bifurcation is a very plausible model for any situation where the embryo needs control over the relative proportions of two fates by a morphogen feedback. 
    more » « less
  5. (, bioRxiv)
    Seeds, which provide a major source of calories for humans, are a unique stage of a flowering plant’s lifecycle. During seed germination the embryo reactivates rapidly and goes through major developmental transitions to become a seedling. This requires extensive and complex spatiotemporal coordination of cell and tissue activity. Existing gene expression profiling methods, such as laser capture microdissection followed by RNA-seq and single-cell RNA7 seq, suffer from either low throughput or the loss of spatial information about the cells analysed. Spatial transcriptomics methods couple high throughput analysis of gene expression simultaneously with the ability to record the spatial location of each individual region analysed. We developed a spatial transcriptomics workflow for germinating barley grain to better understand the spatiotemporal control of gene expression within individual seed cell types. More than 14,000 genes were differentially regulated across 0, 1, 3, 6 and 24 hours after imbibition. This approach enabled us to observe that many functional categories displayed specific spatial expression patterns that could be resolved at a sub-tissue level. Individual aquaporin gene family members, important for water and ion transport, had specific spatial expression patterns over time, as well as genes related to cell wall modification, membrane transport and transcription factors. Using spatial autocorrelation algorithms, we were able to identify auxin transport genes that had increasingly focused expression within subdomains of the embryo over germination time, suggestive of a role in establishment of the embryo axis. Together, our data provides an unprecedented spatially resolved cellular map for barley grain germination and specific genes to target for functional genomics to define cellular restricted processes in tissues during germination. The data can be viewed at https://spatial.latrobe.edu.au/. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email