A5001053440- Single-cell and spatial transcriptomics
- Genomics and Chromatin Dynamics
- Cell Image Analysis Techniques
- CRISPR and Genetic Engineering
- Cancer Genomics and Diagnostics
- Acute Lymphoblastic Leukemia research
- Neurobiology and Insect Physiology Research
- RNA Research and Splicing
- Gene Regulatory Network Analysis
- Genetics, Aging, and Longevity in Model Organisms
- Olfactory and Sensory Function Studies
- Epigenetics and DNA Methylation
- Retinal Development and Disorders
- Melanoma and MAPK Pathways
- Chronic Myeloid Leukemia Treatments
- Bioinformatics and Genomic Networks
- Invertebrate Immune Response Mechanisms
- Gene expression and cancer classification
- vaccines and immunoinformatics approaches
- RNA and protein synthesis mechanisms
- Hippo pathway signaling and YAP/TAZ
- RNA modifications and cancer
- Advanced Fluorescence Microscopy Techniques
- Computational Drug Discovery Methods
- interferon and immune responses
VIB-KU Leuven Center for Brain & Disease Research
2017-2025
Allen Institute for Brain Science
2024-2025
KU Leuven
2016-2025
VIB-KU Leuven Center for Cancer Biology
2017-2025
Identifying master regulators of biological processes and mapping their downstream gene networks are key challenges in systems biology. We developed a computational method, called iRegulon, to reverse-engineer the transcriptional regulatory network underlying co-expressed set using cis-regulatory sequence analysis. iRegulon implements genome-wide ranking-and-recovery approach detect enriched transcription factor motifs optimal sets direct targets. increase accuracy inference by very large...
The diversity of cell types and regulatory states in the brain, how these change during aging, remains largely unknown. We present a single-cell transcriptome atlas entire adult Drosophila melanogaster brain sampled across its lifespan. Cell clustering identified 87 initial clusters that are further subclustered validated by targeted cell-sorting. Our data show high granularity identify wide range types. Gene network analyses using SCENIC revealed heterogeneity linked to energy consumption....
Abstract Transcriptional reprogramming of proliferative melanoma cells into a phenotypically distinct invasive cell subpopulation is critical event at the origin metastatic spreading. Here we generate transcriptome, open chromatin and histone modification maps cultures; integrate this data with existing transcriptome DNA methylation profiles from tumour biopsies to gain insight mechanisms underlying key event. This shows thousands genomic regulatory regions states, identifying SOX10/MITF...
Abstract Joint profiling of chromatin accessibility and gene expression in individual cells provides an opportunity to decipher enhancer-driven regulatory networks (GRNs). Here we present a method for the inference GRNs, called SCENIC+. SCENIC+ predicts genomic enhancers along with candidate upstream transcription factors (TFs) links these target genes. To improve both recall precision TF identification, curated clustered motif collection more than 30,000 motifs. We benchmarked on diverse...
i-cisTarget is a web tool to predict regulators of set genomic regions, such as ChIP-seq peaks or co-regulated/similar enhancers. can also be used identify upstream and their target enhancers starting from co-expressed genes. Whereas the original version was focused on Drosophila data, 2015 update provides support for human mouse data. detects transcription factor motifs (position weight matrices) experimental data tracks (e.g. ENCODE, Roadmap Epigenomics) that are enriched in input regions....
Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation their target genes
Abstract In the mammalian liver, hepatocytes exhibit diverse metabolic and functional profiles based on their location within liver lobule. However, it is unclear whether this spatial variation, called zonation, governed by a well-defined gene regulatory code. Here, using combination of single-cell multiomics, omics, massively parallel reporter assays deep learning, we mapped enhancer-gene networks across mouse cell types. We found that zonation affects expression chromatin accessibility in...
Combinations of transcription factors govern the identity cell types, which is reflected by genomic enhancer codes. We used deep learning to characterize these codes and devised three metrics compare types in telencephalon across amniotes. To this end, we generated single-cell multiome spatially resolved transcriptomics data chicken telencephalon. Enhancer orthologous nonneuronal γ-aminobutyric acid–mediated (GABAergic) show a high degree similarity amniotes, whereas excitatory neurons...
RNA-seq is a promising technology to re-sequence protein coding genes for the identification of single nucleotide variants (SNV), while simultaneously obtaining information on structural variations and gene expression perturbations. We asked whether suitable detection driver mutations in T-cell acute lymphoblastic leukemia (T-ALL). These leukemias are caused by combination fusions, over-expression transcription factors cooperative point oncogenes tumor suppressor genes. analyzed 31 T-ALL...
Deciphering the genomic regulatory code of enhancers is a key challenge in biology because this underlies cellular identity. A better understanding how work will improve interpretation noncoding genome variation and empower generation cell type–specific drivers for gene therapy. Here, we explore combination deep learning cross-species chromatin accessibility profiling to build explainable enhancer models. We apply strategy decipher melanoma, relevant case study owing presence distinct...
Single-cell technologies allow measuring chromatin accessibility and gene expression in each cell, but jointly utilizing both layers to map bona fide regulatory networks enhancers remains challenging. Here, we generate independent single-cell RNA-seq ATAC-seq atlases of the Drosophila eye-antennal disc spatially integrate data into a virtual latent space that mimics organization 2D tissue using ScoMAP (Single-Cell Omics Mapping spatial Axes Pseudotime ordering). To validate predicted...
Joint profiling of chromatin accessibility and gene expression individual cells provides an opportunity to decipher enhancer-driven regulatory networks (eGRN). Here we present a new method for the inference eGRNs, called SCENIC+. SCENIC+ predicts genomic enhancers along with candidate upstream transcription factors (TF) links these target genes. Specific TFs each cell type or state are predicted based on concordance TF binding site accessibility, expression, expression. To improve both...
Single-cell RNA-seq and single-cell assay for transposase-accessible chromatin (ATAC-seq) technologies are used extensively to create cell type atlases a wide range of organisms, tissues, disease processes. To increase the scale these atlases, lower cost pave way more specialized multiome assays, custom droplet microfluidics may provide solutions complementary commercial setups. We developed HyDrop, flexible open-source microfluidic platform encompassing three protocols. The first protocol...
Abstract Octopuses are mollusks that have evolved intricate neural systems comparable with vertebrates in terms of cell number, complexity and size. The brain types control their sophisticated behavioral repertoire still unknown. Here, we profile the diversity paralarval Octopus vulgaris to build a type atlas comprises mostly cells, but also multiple glial subtypes, endothelial cells fibroblasts. We spatially map vertical, subesophageal optic lobes. Investigation conservation reveals shared...
Transcription factors regulate their target genes by binding to regulatory regions in the genome. Although preferences of TP53 are known, it remains unclear what distinguishes functional enhancers from nonfunctional binding. In addition, genome is scattered with recognition sequences that remain unoccupied. Using two complementary techniques multiplex enhancer-reporter assays, we discovered could be discriminated events occurrence a single canonical motif. By combining machine learning...
are validated by ChIP-seq and correlate with chromatin opening
Genomic sequence variation within enhancers and promoters can have a significant impact on the cellular state phenotype. However, sifting through millions of candidate variants in personal genome or cancer genome, to identify those that cis -regulatory function, remains major challenge. Interpretation noncoding benefits from explainable artificial intelligence predict interpret mutation gene regulation. Here we generate phased whole genomes with matched chromatin accessibility, histone...