Stein Aerts

The paper identifies ST18 as a regulator of deep CA3 pyramidal neuron identity, showing its role in restricting a superficial-like program and shaping circuit integration through molecular and electrophysiological changes.

DeepSCENIC uses transfer learning from sequence-to-function models to infer causal gene regulatory networks from single-cell multiome data.

The paper develops STEAM, an evolution-augmented deep learning model, to predict mammalian enhancer landscapes across species using comparative genomics and single-cell data.

This study identifies a lipid-mediated signaling pathway driven by microglia that promotes glioblastoma invasion and shows that targeting this pathway can suppress tumor invasion.

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 compares the gene regulatory network of compound eye development between Drosophila and Episyrphus balteatus, finding conserved and species-specific regulatory interactions.

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 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 extracts cis-regulatory rules from sequence-to-function models in human neural development using new single-cell multiome data and a computational tool called TF-MINDI.

The paper defines major axes of human basal ganglia astrocyte specialization using a multimodal atlas and identifies enhancer code for targeted viral tools.

The paper identifies regulatory elements and reconstructs gene regulatory networks in cnidarian cell types using chromatin accessibility data.

The paper identifies a dynamic gene regulatory code in hippocampal granule cells that orchestrates synapse development through sequential regulation of transcription factors and target genes.

This study shows that species-specific fatty acid oxidation rates influence temporal patterning of corticogenesis through protein acetylation dynamics in human and mouse cortical progenitors.

GAME provides a standardized framework for benchmarking genomics models across tasks, enabling consistent evaluation and community contributions.

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.

Nova-ST is a cost-effective, high-resolution spatial transcriptomics platform that outperforms existing methods in sensitivity for mouse brain sections.

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 identifies TZAP as a developmental regulator of mitochondrial homeostasis critical for activity-dependent synaptic connectivity in Drosophila.

The paper identifies molecular signatures of neuronal vulnerability to α-synuclein and tau toxicity, showing differential susceptibility independent of expression levels.

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.

The paper creates a single-cell transcriptomic atlas of the adult fruit fly, identifying over 250 cell types and analyzing gene signatures and sexual dimorphism across tissues.

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.