Back

Ultrastructure of stemness and differentiated state in Hydra epithelial cells

Seybold, A.; Salvenmoser, W.; Pfaller, K.; Redl, S.; Hess, M. W.; Hobmayer, B.

2026-07-23 evolutionary biology
10.64898/2026.07.20.739505 bioRxiv
Show abstract

Epithelial cells in Hydra perform an unusual combination of functions: they divide continuously like adult stem cells while simultaneously executing the complex physiological tasks of differentiated epithelia. This challenges the traditional distinction between proliferative stem cells and terminally differentiated tissue, raising the question of how a single cell type integrates these opposing roles. Using electron microscopy, we examined morphological characteristics that define the stem-like and differentiated states of Hydras ectodermal and endodermal epithelial cells. Stemness is reflected by nuclear characteristics of active proliferation, including extensive euchromatin, large nucleoli, and the presence of nuage. However, differentiated epithelial cells exhibit strong apical-basal polarity, various endomembrane compartments for endocytosis and transport, specialized secretion mechanisms, and basal muscle processes with dense-core vesicles implicated in hormonal communication. Cryofixation improved ultrastructure preservation, elucidating the pleiomorphic configurations of complex intracellular channel systems traditionally presenting as singular vacuoles. This may shed new light on possible functions of this compartment. Taken together, Hydra epithelial cells combine ancient stem cell traits with highly specialized differentiated functions. This multifunctionality provides insight into the cellular organization of early-branching animals and suggests that multifunctional epithelia may represent an ancestral condition preceding the strict segregation of stem and differentiated cell lineages in bilaterians.

Matching journals

The top 8 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.