aertslab.orgStein Aerts Lab - VIB - KULeuven

Toggle navigation Laboratory of computational biology ABOUT RESEARCH SOFTWARE MODELS TECH-DEV PUBLICATIONS OUTREACH DATA JOIN TEAM CONTACT LAB 360 VIEW ChIP-seq, epigenome profiling, integration of regulatory data "tracks" Enhancer training and prediction using Hidden Markov Models and Random Forest machine learning. Enhancer logic using pioneer factors, solitary factors, and TF Cooperativity. Enhancer testing using massively parallel enhancer-reporter assays, such as CHEQ-seq cis-regulatory variation Whole-genome sequencing TF binding site alterations Natural variation and chromatin accessibility QTLs Regulatory comparative genomics Single-cell RNA-seq using Drop-seq, 10X Chromium, and SMART-seq2 Transcription factors and their target genes Gene regulatory network inference Master regulators Single-cell networks with SCENIC Position Weight Matrices and Motif discovery Chromatin accessibility using ATAC-seq and single-cell ATAC-seq Transcriptional states underly cellular phenotypes. We study regulatory heterogeneity in melanoma and development and ageing in the fruit fly. DECIPHERING GENE CONTROL Welcome to the Laboratory of Computational Biology . Our lab is part of VIB.AI (the VIB Center for Computational Biology & AI), the KU Leuven Center for Human Genetics and the VIB Center for Brain and Disease Research . We are interested in decoding the genomic regulatory code and understanding how genomic regulatory programs drive dynamic changes in cellular states, both in normal and disease processes. Transcriptional states emerge from complex gene regulatory networks. The nodes in these networks are cis-regulatory regions such as enhancers and promoters, where usually multiple transcription factors bind to regulate the expression of their target genes. Wet lab We apply high-throughput, high-resolution technologies to decipher enhancer logic and map gene regulatory networks, such as single-cell RNA-seq for transcriptomics and single-cell ATAC-seq for chromatin accessibility. To test the activities of promoters and enhancers we use massively parallel enhancer-reporter assays. Our favorite model systems include Drosophila as well as human organoids and cancer cells. Dry lab We use and develop bioinformatics methods for regulatory network inference and computational modeling of enhancers, such as machine learning and advanced motif discovery. Using these, we have deciphered the enhancer code of melanoma, the fly brain, mammalian liver, mammalian and avian pallia, and others. Some of the bioinformatics methods we have developed and made available to the community include SCENIC+, i-cisTarget, and SCOPE. Tech lab We develop microfluidics chips, including droplet microfluidics for single-cell assays. We also develop microfluidic devices to analyse 3D tumoroids (organ-on-chip) and single-cell migration, in combination with lens-free imaging. RESEARCH Research in our lab is focusing on gene and genome regulation, with applications in neuroscience ( Drosophila melanogaster ) and cancer. Enhancer modeling We combine machine learning with epigenome profiling to decode enhancer logic. To test enhancers we developed a massively parallel enhancer-reporter assay, called CHEQ-seq. Our enhancer modeling focuses on mammalian TFs , such as TP53, SOX10/SOX9, GRHL1/2/3, AP-1, and TEADs; as well as on Drosophila TFs involved in eye development (e.g. Glass, Optix, sine oculis), epithelial development (Grainyhead), and tumour development (AP-1, STAT92E, and Scalloped). cis-Regulatory variation cis-regulatory variation is a major driver of phenotypic diversity and is associated to many diseases. By comparing chromatin accessibility across Drosophila inbred lines we aim to further our understanding of CRM divergence and plasticity, and the consequential divergence of gene expression and regulatory networks Similar techniques are applied to cancer genomes, where we sift through non-coding mutations to identify cis-regulatory driver mutations that have an impact on enhancer function and/or perturb the normal gene regulatory network in a cell. Evolution of cis-regulation By comparing transcriptomes, chromatin state and cis-regulatory modules across species, we learn about enhancer logic and the evolution of gene regulatory networks. We use RNA-seq, FAIRE-seq, and ATAC-seq across Drosophila species, alongside Ornstein-Uhlenbeck models to connect CRM evolution with variation in chromatin accessibility. We have also studied the evolution of epidermal and metabolic GRNs between Drosophila and Daphnia. Melanoma phenotype switching We are interested in deciphering regulatory programs of transcriptional state switches in mammalian systems, including human and mouse. To study the cis-regulatory code in mammalian genomes we mainly use cancer cells as model system. During cancer progression, gene expression profiles can change, causing regulatory heterogeneity in tumors. This heterogeneity has an important impact on therapy response, since some cell states may be more or less vulnerable to a particular drug therapy. Fly brain & ageing We study neuronal and glial cell types in the ageing Drosophila brain using single-cell RNA-seq, and compare normal cell states with disease mutations involved in Parkinson’s and Alzheimer’s disease. Fly eye & cancer The eye-antennal disc is a classical model system to study cellular differentiation. We use this system to unravel new genomic regulatory recipes” that control cell fate decisions, such as photoreceptor specification and differentiation. We also perturb this system using irradiation, transcription factor perturbations, and RasV12-driven malignant transformation, to study cancer-related transcriptional changes, controlled by JNK, EGFR, and Hippo signaling pathways. AI & MACHINE LEARNING Data-driven research in our lab is powered by machine learning and artificial intelligence (AI) to help us guide and understand more about biological systems and processes. Here is a non-exhaustive list what the lab has been and is currently working on: Deep learning for genomics; hybrid convolutional and recursive neural networks trained on epigenomes and whole genomes to predict non-coding variation in disease (including cancer). Machine-learning applied to single-cell genomics to predict therapy choice and patient outcome. Support vector machines , random forest, and deep learning to classify enhancers ( Verfaillie et al. Genome Research 2016 ; Svetlichnyy et al. PLoS Comp Biol 2015 ). Gradient boosting machines and random forest regression to predict gene networks from single-cell transcriptomics data ( Aibar et al., Nat Methods 2017 ; Moerman et al. Bioinformatics 2019 ). Latent Dirchlet Allocation , collapsed Gibbs Sampling, and topic modeling for the analysis of single-cell epigenomics data ( Bravo & Minnoye et al., Nat Methods 2019 ). Single-cell gene regulation Single-cell transcriptomics (scRNA-seq) and single-cell epigenomics (scATAC-seq) data revolutionize the field of regulatory genomics. We combine new computational strategies (e.g., SCENIC, cisTopic) with state-of-the-art single-cell measurements (Drop-seq, 10X, InDrops, SeqWell) to decipher cis-regulatory programs”, to reverse engineer gene regulatory networks, and to better define cell types and cell state transitions. Single-cell systems biology We develop new computational approaches that exploit single-cell technologies to link genome variation with changes in epigenome, transcriptome, proteome, and phenome. We apply this to human melanoma (e.g., phenotype switching), to the mouse liver, to the developing Drosophila eye and to ageing/neurodegeneration in the Drosophila brain. See also our collaborations . Gene regulation bioinformatics We develop new bioinformatics tools for motif and CRM detection, and for gene regulatory network inference, such as i-cisTarget, iRegulon, and TOUCAN. We also maintain a large collection of curated position weight matrices (currently20.000). We exploit single-cell...