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However, when we analyze a pool of cells, as opposed to single cells, we obtain average values of expression and lose information about variability.

Single-cell transcriptomics analysis allows us to obtain additional information when studying the variability in a cell population, such as which genes have uniform expression in the whole population, which have high variability across the population and how this variability is changing dynamically across time, disclosing different bifurcations and stable points.

The specification of these three cell types is achieved through two ‘cell fate decisions’ (Bedzhov 2014, Review).

The first cell fate decision involves differentiation of the TE that gives rise to the placenta, and formation of the pluripotent inner cell mass (ICM), which during the second cell fate decision in its own turn is segregated into the PE, that gives rise to the yolk sac, and the pluripotent EPI, which generates the cells of the future body.

In the mouse preimplantation embryo, cells with the same phenotype take different fates based on the orchestration between topological clues (cell polarity, positional history and division orientation) and gene regulatory rules (at transcriptomics and epigenomics level), prompting the proposal of positional, stochastic and combined models explaining the specification mechanism.

PGC specification starts at E6.0–6.5 post-implantation. In view of the important role of DNA methylation in developmental events, the cross-talk between some transcription factors and DNA methyltransferases is of particular relevance.

Then we filtered out all the probes whose absolute value of difference of mean values between the two subgroups was less than a selection threshold scale].

Collectively, our results suggest that the combining of the precision of single-cell omics data with dynamic analysis of time-series data can establish the timing of some developmental stages as earlier than previously thought.

One of the important applications of these tools is to obtain a detailed picture of the early stages of embryonic development based on transcriptional characterization of single cells.

‘Bulk’ studies of embryogenesis have provided invaluable insight into transcription-factor networks of development and differentiation (Maekawa , 2012).

The transcriptomics dynamics analyses support the regulation of We identified a collection of previously undescribed E3.5-specific PE and EPI markers, and new steady PE and EPI markers.

Identification of these genes, many of which encode cell membrane proteins, will facilitate the isolation and characterization of early PE and EPI populations.




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