We study the diversity of the genetic mechanisms guiding interneuron development.
Interneuron development and diversity

CNS comprises remarkable cellular diversity. The most numerous cell types in the CNS are excitatory glutamatergic projection neurons and inhibitory GABAergic projection neurons and interneurons.

The interneuron subclass can be further divided into a wide range of subtypes, depending on their molecular profiles (gene expression), function, location and connectivity.

Single-cell genomics approaches have several advantages in studying complex biological systems such as the CNS. We are using single-cell RNA-sequencing, ATAC-sequencing and computational tools in order to study gene regulatory interactions in developing neurons.

Many ways to make a GABAergic neuron

Phenotypic convergence

Terminal differentiation of neurons is thought to be initiated and regulated by terminal selector transcription factors at the time of the cell cycle exit. In the interneurons, transcription factors Dlx1/2, Gata2/3 and Tal1/2 and Ptf1a act as selector genes, depending on the spatial context. Gad1 and Gad2 gene expression is initiated by DLX transcription factors in the forebrain, by GATA and TAL transcription factors in the thalamus and midbrain, and by PTF1a in the cerebellum.

The variation of the regulatory events leading to the expression of the same target genes is analogous to phenotypic convergence in species. We are interested in the molecular mechanisms of this process in the level of cell type diversification.

Gene regulatory events are studied using single-cell RNA-seq and single-cell ATAC-seq. In addition, we apply footprinting analysis and topic modelling for predicting TF binding events in accessible DNA. Hierarchical clustering of E14.5 mouse neuronal cells by chromatin accessibility shows segregation of clusters by spatial origin as well as by neurotransmitter type. Clustering of interneurons reveals additional tendency of grouping by differentiation stage.

Modelling a complex regulated process


The high resolution single-cell -omics is a tool to map genetic regulatory cascades and to study the variation of regulatory landscapes across cell types.

Variation of regulatory landscape can explain disease susceptibility, population variance, resistance to environmental factors and much more.