How can you describe pseudo time analysis?
This helps the understanding of the relationship between the oRG and vRG and the relationship that they have with their ancestor cells, which will help with pseudo time analyses, as explained below. Subpopulation analyses also provide new ways of classifying cell types and the ability to single out and study of rare cell populations (which can then be reaffirmed with biological information of structure and function). There are many studies that are advancing the field of developmental biology (along with other fields as well). One study looked at Zscan4 genes in mouse embryonic stem cells, finding a cluster of them that was previously unknown. The implication is that there is a ‘rare subpopulation that has a greater differentiation potential than commonly thought’. Another study documented and visualized the transcription profile of each cell of the C. elegans embryo, up until the 16-cell stage. This type of research is vital for developmental biology. The entire diversity of an organism will come from such cells, so it is incredibly important to understand each of those cells individually and not simply on a population level, to hopefully shine more light on the mechanisms to which these diversifications occur. These differentiated and refined cell types are the first steps to further analyses as well, including pseudo time.
Pseudo-time analyses may sound intimidating, but it is a relatively simple concept. It is known that development doesn’t happen in a straight line and that there are cells at many stages of development at most given points in an organism. So, to arrange the cells analytically in order of development isn’t exactly temporal, but based on the developmental timeline; thus, pseudo time. This type of analysis has allowed for new understandings of cell lineages. Cell lineage is how we understand development, and scRNAseq has allowed us to evaluate classical views of how one progenitor cell type differentiates into the many cell types we see in systems.