Back

Dynamics of primitive streak regression controls the fate of neuro-mesodermal progenitors in the chicken embryo

guillot, c.; michaut, a.; Rabe, B. A.; Pourquie, O.

2020-05-05 developmental biology
10.1101/2020.05.04.077586 bioRxiv
Show abstract

In classical descriptions of vertebrate development, the segregation of the three embryonic germ layers is completed by the end of gastrulation. Body formation then proceeds in a head to tail fashion by progressive deposition of lineage committed progenitors during regression of the Primitive Streak (PS) and tail bud (Pasteels, 1937b; Stern, 2004). Identification of Neuro-Mesodermal Progenitors (NMPs) contributing to both musculo-skeletal precursors (paraxial mesoderm) and spinal cord during axis formation by retrospective clonal analysis challenged these notions (Henrique et al., 2015; Tzouanacou et al., 2009). However, in amniotes such as mouse and chicken, the precise identity and localization of these cells has remained unclear despite a wealth of fate mapping analyses of the PS region. Here, we use lineage tracing in the chicken embryo to show that single cells located in the SOX2/T positive anterior PS region contribute to both neural and mesodermal lineages in the trunk and tail, but only express this bipotential fate with some delay. We demonstrate that posterior to anterior gradients of convergence speed and ingression along the PS gradually lead to exhaustion of all mesodermal precursor territories except for NMPs where limited ingression and increased proliferation maintain and amplify this pool of axial progenitors. As a result, most of the remaining mesodermal precursors from the PS in the tail bud are bipotential NMPs. Together, our results provide a novel understanding of the contribution of the PS and tail bud to the formation of the body of amniote embryos.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.