We apply high-throughput technologies to decipher enhancer logic and map gene regulatory networks, such as RNA-seq for transcriptomics and ATAC-seq and ChIP-seq for epigenomic profiling. To test the activities of promoters and enhancers we use massively parallel enhancer-reporter assays. Finally, to map high-resolution landscapes of possible cellular states we use single-cell transcriptomics and single-cell epigenomics. Our favorite model systems include Drosophila (the brain and the eye-antennal imaginal disc) as well as human cancer cells (short-term cultures, cell lines, primary cells, xenografts, and organ-on-chip).
We use bioinformatics methods for network inference and computational modeling of enhancers, such as machine learning and advanced motif discovery. Some of the bioinformatics methods we have developed and made available to the community include TOUCAN, ENDEAVOUR, iRegulon, i-cisTarget, mu-cisTarget, and SCENIC.
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.
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).
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.
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.
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.
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.
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:
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.
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.
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 (currently > 20.000).
We exploit single-cell RNA-seq and single-cell ATAC-seq data to improve the identification of GRNs and enhancers, with our tools SCENIC and cisTopic.
VSN-Pipelines Is a repository of pipelines for single-cell data analysis in Nextflow DSL2. It contains multiple workflows for analyzing single cell transcriptomics data, and depends on a number of tools, which are organized into submodules within the VIB-Singlecell-NF organization.
SCope is a fast visualization tool for large-scale and high dimensional scRNA-seq datasets. Visit https://scope.aertslab.org to test out SCope on several published datasets!
cisTopic is an R package to simultaneously identify cell states and cis-regulatory topics from single cell epigenomics data.
pySCENIC is a lightning-fast python implementation of the SCENIC pipeline (Single-CEll regulatory Network Inference and Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from single-cell RNA-seq data.
Github PyPi Read the Docs
Arboreto is a computational framework that offers scalable implementations of Gene Regulatory Network inference algorithms. It currently supports GRNBoost2 and GENIE3 (Huynh-Thu et al., 2010).
SCENIC is an R package to infer Gene Regulatory Networks and cell types from single-cell RNA-seq data.
In this Primer, we discuss these biochemical methods, as well as bioinformatics tools for analysing and interpreting the generated data, and insights into the key regulators underlying developmental, evolutionary and disease processes. We outline standards for data quality, reproducibility and deposition used by the genomics community.
Melanoma cells can switch between a melanocytic and a mesenchymal-like state. Scattered evidence indicates that additional intermediate state(s) may exist. Here, to search for such states and decipher their underlying gene regulatory network (GRN), we studied 10 melanoma cultures using single-cell RNA sequencing (RNA-seq) as well as 26 additional cultures using bulk RNA-seq.
Genomic enhancers form the central nodes of gene regulatory networks by harbouring combinations of transcription factor binding sites. In order to unravel the enhancer logic of the two most common melanoma cell states, namely the melanocytic and mesenchymal-like state, we combined comparative epigenomics with machine learning. By profiling chromatin accessibility using ATAC-seq on a cohort of 27 melanoma cell lines across six different species, we demonstrate the conservation of the two main melanoma states and their underlying master regulators.
Single‐cell technologies allow measuring chromatin accessibility and gene expression in each cell, but jointly utilizing both layers to map bona fide gene regulatory networks and enhancers remains challenging. Here, we generate independent single‐cell RNA ‐seq and single‐cell ATAC ‐seq atlases of the Drosophila eye‐antennal disc and spatially integrate the data into a virtual latent space that mimics the organization of the 2D tissue using ScoMAP (Single‐Cell Omics Mapping into spatial Axes using Pseudotime ordering).
Prioritization of non-coding genome variation benefits from explainable AI to predict and interpret the impact of a mutation on gene regulation. Here we apply a specialized deep learning model to phased melanoma genomes and identify functional enhancer mutations with allelic imbalance of chromatin accessibility and gene expression.
This protocol explains how to perform a fast SCENIC analysis alongside standard best practices steps on single-cell RNA-sequencing data using software containers and Nextflow pipelines.
“It’s still me,” says Stein Aerts about his diverse science chapters to date. […] Click on link below to read more.
Single-cell epigenomics provides new opportunities to decipher genomic regulatory programs from heterogeneous samples and dynamic processes. We present a probabilistic framework called cisTopic, to simultaneously discover “cis-regulatory topics” and stable cell states from sparse single-cell epigenomics data.
A single-cell atlas of the adult fly brain during aging:
Using ATAC-seq across a panel of Drosophila inbred strains, we found that SNPs affecting binding sites of the TF Grainy head (Grh) causally determine the accessibility of epithelial enhancers.
The Fly Cell Atlas will bring together Drosophila researchers interested in single-cell genomics, transcriptomics, and epigenomics, to build comprehensive cell atlases during different developmental stages and disease models.
Here we show that the Hippo pathway is critical for this decision. Loss of Hippo switches Ras activation from promoting cellular differentiation to aggressive cellular proliferation.
Using two complementary techniques of multiplex enhancer-reporter assays, we discovered that functional enhancers could be discriminated from nonfunctional binding events by the occurrence of a single TP53 canonical motif.
Using regulatory landscapes and in silico analysis, we show that transcriptional reprogramming underlies the distinct cellular states present in melanoma. Furthermore, it reveals an essential role for the TEADs, linking it to clinically relevant mechanisms such as invasion and resistance.
Together with B. Deplancke and R. Zinzen we founded the Fly Cell Atlas.
The Fly Cell Atlas will bring together Drosophila researchers interested in single-cell genomics, transcriptomics, and epigenomics, to build comprehensive cell atlases during different developmental stages and disease models.
Go to flycellatlas.org
MendelCraft is a MineCraft mod developed in the lab to teach children about DNA, genetics, and the laws of Mendel.
You can visit the website at http://mendelcraft.aertslab.org/
Stein Aerts |
Herestraat 49, PO Box 602, 3000 LEUVEN, Belgium |
+32-16-33 07 10 |
ScoMAP (Single-Cell Omics Mapping into spatial Axes using Pseudotemporall ordering) is an R package to spatially integrate single-cell omics data into virtual cells.
i-cisTarget is an integrative genomics method for the prediction of regulatory features and cis-regulatory modules.
iRegulon is a Cytoscape plugin that detects the TF, the targets and the motifs from a set of genes.
