Back

Spatially and temporally distinct patterns of expression for VPS10P domain receptors in human cerebral organoids

Febbraro, F.; Andersen, H. H. B.; Willnow, T. E.

2023-06-07 developmental biology
10.1101/2023.06.07.543827 bioRxiv
Show abstract

Vacuolar protein sorting 10 protein (VPS10P) domain receptors are a unique class of intracellular sorting receptors that emerge as major risk factors associated with psychiatric and neurodegenerative diseases, including bipolar disorders, autism, schizophrenia, as well as Alzheimers disease and frontotemporal dementia. Yet, the lack of suitable experimental models to study receptor functions in the human brain has hampered elucidation of receptor actions in brain disease. Here, we have adapted protocols in human cerebral organoids to the detailed characterization of VPS10P domain receptor expression during development and in mature brain tissue, including single-cell RNA sequencing. Our studies uncovered spatial and temporal patterns of expression unique to individual receptor species in the human brain. While SORL1 expression is abundant in stem cells and SORCS1 peaks at onset of neurogenesis, SORT1 and SORCS2 show increasing expression with maturation of neuronal and non-neuronal cell types, arguing for distinct functions in development versus the adult brain. In neurons, subcellular localization also distinguishes between two types of receptors, either mainly localized to the cell soma (SORL1 and SORT1) or also to neuronal projections (SORCS1 and SORCS2), suggesting divergent functions in protein sorting between Golgi and the endo-lysosomal system or along axonal and dendritic tracks. Taken together, our findings provide an important resource concerning temporal, spatial, and subcellular patterns of VPS10P domain receptor expression in cerebral organoids for further elucidation of receptor (dys)functions causative of behavioral and cognitive defects of the human brain.

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.