[Seminar] Professor Justus Kebschull: Brain region evolution by duplication-and-divergence -- lessons from the cerebellar nuclei


Thursday, April 7, 2022 - 09:00


ZOOM Event


We are excited to have an online seminar by Prof. Justus Kebschull from Johns Hopkins University. He is a world-leading researcher studying brain circuit evolution by using cutting-edge circuit tracing and viral tools. Everyone is welcome to join the seminar!

Brain region evolution by duplication-and-divergence -- lessons from the cerebellar nuclei


How have complex brain regions, circuits, and cell types evolved from simple origins? Here we investigate brain region evolution at cell-type resolution in the cerebellar nuclei, the output structures of the cerebellum. In recent work, we applied single-nucleus RNA sequencing in chickens, mice, and humans, STARmap spatial transcriptomic analysis in chicken and mice, and whole-CNS projection mapping in mice. Our work revealed a conserved cell type set containing three classes of region-invariant inhibitory neurons and two classes of region-specific excitatory neurons. This cell typeset forms an archetypal cerebellar nucleus that was repeatedly duplicated to create new regions, and thus cerebellar output channels. In excitatory neurons, duplication was accompanied by divergence in gene expression and shifts in projection patterns. By contrast, inhibitory neurons maintained their gene expression signatures. Interestingly, the excitatory cell class that preferentially funnels information to lateral frontal cortices in mice becomes predominant in the massively expanded human Lateral CN. Our data provide the first characterization of CN transcriptomic cell types in three species and suggest a model of brain region evolution by duplication and divergence of entire cell type sets. We are now working to extend these findings to tetrapods and to query connectomic cell types using cellular barcoding techniques MAPseq and BARseq in different species.

You can join the seminar via ZOOM (meeting ID: 782 721 4941, Password: 436475).

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