Back

A spatial-temporal map of glutamatergic neurogenesis in embryonic cerebellar nuclei uncovers a high degree of cellular heterogeneity

Casoni, F.; Croci, L.; Marroni, F.; Demenego, G.; Cremona, O.; Codazzi, F.; Consalez, G.

2023-10-22 developmental biology
10.1101/2023.10.22.563467 bioRxiv
Show abstract

The nuclei are the main output structures of the cerebellum. Each and every cerebellar cortical computation reaches several areas of the brain by means of CN processing and integration. Nevertheless our knowledge of these structures is still limited compared to the cerebellar cortex. Here, we present a genetic inducible fate mapping study characterizing rhombic lip-derived glutamatergic neurons of the nuclei, the most conspicuous family of long-range cerebellar efferent neurons. Glutamatergic neurons mainly occupy dorsal and lateral territories of the lateral and interposed nuclei, as well as the entire medial nucleus. They are born starting from about embryonic day 9.5, with a peak between 10.5 and 12.5, and invade the nuclei with a lateral to medial progression. While some markers label a heterogeneous population of neurons sharing a common location (Brn2), others appear to be lineage specific (Tbr1, Lmx1a, Meis2). A comparative analysis of Tbr1 and Lmx1a distributions reveals an incomplete overlap in their expression domains, in keeping with the existence of separate efferent subpopulations. Finally, some tagged glutamatergic progenitors are not labeled by any of the markers used in this study, disclosing further complexity. Taken together, our results obtained in late embryonic nuclei shed light on the heterogeneity of the excitatory neuron pool, underlying the diversity in connectivity and functions of this largely unexplored cerebellar territory. Our findings lay the groundwork for focused functional analyses of individual subpopulations of nuclear neurons.

Matching journals

The top 7 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.