Nikolai Hecker

This study identifies conserved and diverged gene regulatory networks in mammalian cerebellum development using single-nucleus gene expression and chromatin accessibility data from six species.

The paper evaluates computational methods for predicting cell type-specific enhancers in the mouse cortex using in vivo validation data and finds that open chromatin and sequence models improve prediction accuracy.

The paper applies spatial transcriptomics to the adult Drosophila brain and body to map mRNA locations and integrate with single-cell data.

The paper compares enhancer codes of cell types between mammalian neocortex and avian pallium using deep learning, finding higher divergence in excitatory neurons than other cell types.

SCENIC+ infers enhancer-driven gene regulatory networks from single-cell multi-omics data, improving TF identification and enabling analysis of regulatory dynamics and perturbations.

The paper identifies convergent loss of TLR5 in four mammalian lineages and shows TLR11 is also absent, suggesting reduced ability to detect extracellular flagellin.