A single embryo, single cell time-resolved model for mouse gastrulation
This is an online tool to interact with the data presented in the publication A single embryo, single cell time-resolved model for mouse gastrulation (Cell).
The data currently represents 153 embryos sequenced individually using the sc-RNA-seq method MARS-seq1. This strategy allowed placing embryos on a transcriptional continuum, generating a de-facto time series experiment of gastrulation, spanning E6.5-8.25 (dpc). Employing a mass conservation strategy, we developed a flow model to track cellular differentiation connecting the manifold distributions of 13 groups of timed embryos.
This manifold is constructed using 461 metacells2, each representing a transcriptional state shared by cells from different embryos spanning a time range. In the manifold, metacells showing higher degree of transcriptional similarity are connected with edges (not to be confused with lineage relationship).
Features of Embflow
Click on the help button for step by step explanation of interface features!
Two genes can be compared over the manifold, or over time.
Vein plots describe the continuous transition of cell types to their direct descendants (represented by diagonal flows spanning time points), and the dynamic relative frequencies of these cell types in the embryo over time (represented by vein width on the Y-axis, as percentage of all cells making up each time point). Select a cell type to view its differentiation dynamics, or display the expression of either selected gene.
Clicking on any metacell will select it for further examination in the Metacells tab.
- Two metacells can be specifically examined, and compared.
Choose a metacell by entering its number, or by selecting directly from the 2D-projection.
- Histograms describe the time distribution of the single cells comprising each metacell.
- Selecting a gene by text or directly from the scatterplot will provide the temporal dynamic over the metacell’s trajectory.
- The predicted flow leading up to the selected metacell is also provided. In this representation, metacells are depicted as nodes distributed horizontally over time, and edges denote their predicted transition to the next time point.
Check in soon for updates and additional features!
Jaitin, D. A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776-779, doi:10.1126/science.1247651 (2014).
Baran, Y. et al. MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions. Genome Biol 20, 206, doi:10.1186/s13059-019-1812-2 (2019).