(13) At the end of the training process, clones with similar contexts will have similar weights in this neural network — and these weights will be used as embedding for clones themselves. (For those who’re familiar with word2vec: clones are words, and contexts are defined by kNN)

(10) But the amount of fates is quite big, and clones are small, and the resulting representations weren’t robust to the clone size and subsampling of the data. Also, even in the example above we see that clones occupy only a small part of, for example, cartilage.

(8) So, at E7/E8 we’re infecting different cells with different behavior (and possibly even the tree structure). How can we study it? At this moment we found some interesting examples of clones distributed in very similar domains of the gene expression space.

(5) A good illustration in this case is the origin of mesenchyme in the face and trunk. In the trunk, neural crest-derived cells are mostly related to CNS fates, but in the face, they’re closer to mesenchyme, because almost all of the facial mesenchyme is NC-derived itself.

(3) What can we do with this data? Firstly, we can try to do what almost all developmental biologists dream of — reconstruct cell type trees. We did it — and got a pretty reasonable tree. Also, we observed a significant amount of clones sharing neuronal and mesodermal fates.