Gert J. Hulselmans

The paper identifies cis-acting genetic variants affecting enhancer function in Parkinson's Disease using multi-omics data and develops models to predict their regulatory impact.

CREsted is a software package for modeling and designing cell type-specific enhancers using deep learning, validated across tissues and species.

The paper introduces HyDrop v2, a microfluidic system for generating large scATAC-seq atlases, and shows its data is comparable to commercial platforms for training sequence-to-function models.

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 identifies synaptic dysfunction in cholinergic projection neurons as an early indicator of Parkinsonism across multiple fly models and links it to olfactory dysfunction and dopaminergic decline.

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.

This study uses deep learning to design synthetic enhancers that target specific cell types in fruit flies and humans, revealing the enhancer code as optimal TF activator motifs and absence of repressor motifs.

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 maps enhancer gene regulatory networks (GRNs) in liver cell types and identifies TFs governing hepatocyte zonation states using multiomics and deep learning.

The paper profiles cell diversity in the paralarval octopus brain to create a cell type atlas and identifies molecular similarities between octopus and fly glial cells.

HyDrop is a droplet microfluidic platform for scATAC-seq and scRNA-seq that improves throughput and sensitivity while reducing cost.

This study compares antibody- and lipid-based hashing methods for single-cell RNA-seq, finding antibody-based hashing more efficient and cost-effective for clinical samples.

The paper identifies regulatory regions and gene regulatory networks in Drosophila brain cell types using chromatin accessibility and transcriptome data.

The paper identifies and characterizes enhancer activity in melanoma cell states using MPRA and DeepMEL2, showing AP-1 drives MES enhancers and SOX/MITF influence MEL enhancers.

The paper uses a deep learning model on melanoma genomes to prioritize enhancer mutations by analyzing allelic imbalance in chromatin accessibility and gene expression.

The paper uses deep learning to analyze enhancer logic across species in melanoma, identifying conserved regulatory complexes and their roles.

The paper spatially integrates single-cell transcriptomics and epigenomics data to identify enhancers and gene regulatory relationships in Drosophila eye-antennal disc.

The paper identifies an intermediate cell state in melanoma through single-cell RNA-seq and ATAC-seq, revealing a gene regulatory network that governs phenotype switching and potential therapeutic targets.

The paper introduces cisTopic, a probabilistic framework for discovering cis-regulatory topics and cell states from single-cell epigenomics data, and validates its findings using RNA-seq co-expression networks and perturbation experiments.

This study creates a single-cell transcriptome catalogue of the Drosophila brain across its lifespan, revealing regulatory erosion and identifying novel neuronal states linked to ageing.

Building gene regulatory networks from scRNA-seq and clustering cells on them identifies stable states robustly to batch effects, applied to mouse and human brain and to melanoma.

The paper introduces a computational pipeline, cisTarget, to identify functional non-coding mutations in cancer genomes by analyzing their impact on personalized gene regulatory networks.