Biology subjects

De Winter, S.

Publications and source records attributed to De Winter, S..

5 recordsLinked to original sources

bioRxiv2026

DeepSCENIC: transfer learning from sequence-to-function models enables causal gene regulatory network inference

Sequence-to-function (S2F) deep learning models have become an important aid to decipher the genomic cis-regulatory code. However, current S2F models do not take the cellular trans-environment of transcription factors (TF) into account. Conversely, methods for gene regulatory network (GRN) inference often rely on heuristics or simple position weight matrices (PWMs), without exploiting the combinatorial grammar of genomic enhancers. Here, we present DeepSCENIC, a deep learning framework that enables causal GRN inference by performing transfer learning from S2F models to single-cell multiome atlases. We first test and validate DeepSCENIC on ENCODE cell lines, demonstrating that the framework accurately predicts single-cell gene expression and chromatin accessibility by leveraging pretrained S2F models like Enformer and Borzoi. We show that DeepSCENIC recovers TF-region (TF-RE) interactions with high fidelity, and captures de novo TF binding motifs without prior PWM knowledge. The model improves enhancer-gene associations (RE-TG) over correlation-based baselines when benchmarked against large-scale CRISPRi screens. After training a DeepSCENIC model, it enables the prediction of perturbation effects during cell state changes by acting as a mechanistic simulator. In a melanoma cell line atlas, the model accurately recapitulates the transcriptional shift from melanocytic to mesenchymal states, and predicted knock-down effects show high concordance with experimental time series data. Finally, we use DeepSCENIC to identify mouse-human cortex conserved GRNs, finding high cross-species concordance in TF activity programs across matched neuronal subclasses and validating top-ranked enhancers against experimental reporter assays. By unifying S2F enhancer representations with single-cell multiomics, DeepSCENIC provides a new paradigm for jointly modeling and simulating cis-sequence and trans-cellular perturbations.

bioinformatics↗

bioRxiv2026

System-wide extraction of cis-regulatory rules from sequence-to-function models in human neural development

The genomic cis-regulatory code (CRC) underlies spatiotemporal specificity of gene expression. While sequence-to-function (S2F) models can accurately encode the CRC of transcriptional enhancers, decoding these models into human-interpretable rules remains a major challenge. Here we tackle this challenge in human neural development, for which we generate two new single-cell multiome atlases, one from a human embryo and one from neural tube organoids. We use this comparative framework to robustly extract combinations of transcription factor (TF) binding sites that are necessary and sufficient to design enhancers. As such we extract cis-regulatory rules for dorsal-ventral progenitors, neural crest, mesenchyme and neurons. To enable this, we develop a new strategy and computational package, called TF-MINDI, to embed, cluster, and annotate candidate TF binding sites, and to extract combinatorial rules for each cell type. We evaluate rule-based models in conjunction with blackbox S2F models through simulations, evolutionary comparisons with zebrafish, topic modeling, and enhancer reporter-assays. Our findings show robust and interpretable rule extraction and constitute a step forward in deciphering, explaining, and formalizing the CRC. TF-MINDI is available at: https://github.com/aertslab/TF-MINDI.

bioinformatics↗

bioRxiv2025

CREsted: modeling genomic and synthetic cell type-specific enhancers across tissues and species

Sequence-based deep learning models have become the state of the art for the analysis of the genomic regulatory code. Particularly for transcriptional enhancers, deep learning models excel at deciphering sequence features and grammar that underlie their spatiotemporal activity. To enable end-to-end enhancer modeling and design, we developed a software and modeling package, called CREsted. It combines preprocessing starting from single-cell ATAC-seq data; modeling with a choice of several architectures for training classification and regression models on either topics or pseudobulk peak heights; sequence design using multiple strategies; and downstream analysis through a collection of tools to locate transcription factor (TF) binding sites, infer the effect of a TF (activating or repressing) on enhancer accessibility, decipher enhancer grammar, and score gene loci. We demonstrate CREsted using a mouse cortex model that we validate using the BICCN collection of in vivo validated mouse brain enhancers. Classical enhancers in immune cells, including the IFNB1 enhanceosome are revisited using a PBMC model, and we assess the accuracy of TF binding site predictions with ChIP-seq. Additionally, we use CREsted to compare mesenchymal-like cancer cell states between tumor types; and we investigate different fine-tuning strategies of Borzoi within CREsted, comparing their performance and explainability with CREsted models trained from scratch. Finally, we train a CREsted model on a scATAC-seq atlas of zebrafish development and use this to design and in vivo validate cell type-specific synthetic enhancers in three tissues. For varying datasets, we demonstrate that CREsted facilitates efficient training and analyses, enabling scrutinization of the enhancer logic and design of synthetic enhancers across tissues and species. CREsted is available at https://crested.readthedocs.io.

genomics↗

bioRxiv2022

SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks

Joint profiling of chromatin accessibility and gene expression of individual cells provides an opportunity to decipher enhancer-driven gene regulatory networks (eGRN). Here we present a new method for the inference of eGRNs, called SCENIC+. SCENIC+ predicts genomic enhancers along with candidate upstream transcription factors (TF) and links these enhancers to candidate target genes. Specific TFs for each cell type or cell state are predicted based on the concordance of TF binding site accessibility, TF expression, and target gene expression. To improve both recall and precision of TF identification, we curated and clustered more than 40,000 position weight matrices that we could associate with 1,553 human TFs. We validated and benchmarked each of the SCENIC+ components on diverse data sets from different species, including human peripheral blood mononuclear cell types, ENCODE cell lines, human melanoma cell states, and Drosophila retinal development. Next, we exploit SCENIC+ predictions to study conserved TFs, enhancers, and GRNs between human and mouse cell types in the cerebral cortex. Finally, we provide new capabilities that exploit the inferred eGRNs to study the dynamics of gene regulation along differentiation trajectories; to map regulatory activities onto tissues using spatial omics data; and to predict the effect of TF perturbations on cell state. SCENIC+ provides critical insight into gene regulation, starting from multiome atlases of scATAC-seq and scRNA-seq. The SCENIC+ suite is available as a set of Python modules at https://scenicplus.readthedocs.io.

bioinformatics↗

bioRxiv2022

Shared enhancer gene regulatory networks between wound and oncogenic programs

Wound response programs are often activated during neoplastic growth in tumors. In both wound repair and tumor growth, cells respond to acute stress and balance the activation of multiple programs including apoptosis, proliferation, and cell migration. Central to those responses are the activation of the JNK/MAPK and JAK/STAT signaling pathways. Yet, to what extent these signaling cascades interact at the cis-regulatory level, and how they orchestrate different regulatory and phenotypic responses is still unclear. Here, we aim to characterize the regulatory states that emerge and cooperate in the wound response, using the Drosophila melanogaster wing disc as a model system, and compare these with cancer cell states induced by rasV12scrib-/- in the eye disc. We used single-cell multiome profiling to derive enhancer Gene Regulatory Networks (eGRNs) by integrating chromatin accessibility and gene expression signals. We identify a "proliferative" eGRN, active in the majority of wounded cells and controlled by AP-1 and STAT. In a smaller, but distinct population of wound cells, a "senescent" eGRN is activated and driven by C/EBP-like transcription factors (Irbp18, Xrp1, Slow border, and Vrille) and Scalloped. In tumor cells on the other hand, both eGRN signatures are simultaneously active within the same cell. Our single-cell multiome and eGRNs resource offers an in-depth characterisation of the senescence markers, together with a new perspective on the shared gene regulatory programs acting during wound response and oncogenesis.

cell biology↗