Biology subjects

Vandereyken, K.

Publications and source records attributed to Vandereyken, K..

11 recordsLinked to original sources

bioRxiv2026

Modular click-chemistry-based engineering minimally perturbs CRISPR ribonucleoprotein formation for genome imaging applications

CRISPR-Cas9 has become a key genome imaging tool, because of its sequence-specific binding to genomic DNA. To achieve signal amplification or multiplexing, various complex CRISPR-Cas9 modifications were introduced, primarily relying on expression-based delivery. However, these engineering strategies often invoke structural perturbations and their performance remained dictated by vector design and in vivo CRISPR ribonucleoprotein (RNP) assembly. To date, research mainly focused on optimizing the expression-based delivery mechanisms of CRISPR components rather than addressing the engineering itself. Here, we developed a modular SPAAC (bio-orthogonal strain-promoted azide-alkyne cycloaddition) click-chemistry-based strategy for engineering the CRISPR RNP that is completely dissociated from its assembly. We demonstrated that CRISPR can be modified (with fluorophores and DNA) at all stages of RNP in vitro assembly and subsequently validated the technology for genome imaging. Importantly, we also revealed that engineering native CRISPR RNP only after it is being formed, minimizes structural perturbations to the complex and yields a two-fold enhanced accessibility of 'clicked' DNA. Finally, we utilized SPAAC to conjugate non-native RNA-DNA hybrids, regardless of 5'-3' directionality, to develop a covalently-attached rolling-circle signal amplification approach. The modularity and universal nature of our approach holds the potential to deliver significant advances to both CRISPR imaging and prime-editing fields.

bioengineering↗

bioRxiv2026

LipoGrid: A High-Throughput Multi-omics Perturbation Screen Dissects the Genetic Architecture of Lipid Metabolism

Lipids constitute one of the largest and most diverse classes of cellular molecules, sustaining membrane architecture, energy storage, and signaling. Consequently, their dysregulation underlies a broad spectrum of human disease. However, the genetic mechanisms governing lipid homeostasis have remained largely inaccessible, owing to the lack of approaches capable of systematically linking defined genetic perturbations to large-scale changes in cellular lipidome composition. Here we introduce LipoGrid, a spatial mass spectrometry platform that resolves the genetic architecture of lipid metabolism at single-cell resolution. LipoGrid arrays CRISPR/Cas9-perturbed cells on a micropatterned grid and sequentially captures lipidomic and gRNA identity from the same cells, complemented by single-cell RNA sequencing of matched cell populations subjected to the same perturbations. Using this approach, we quantified the relative abundance of 158 distinct lipid species across 143 target genes in a rigorously controlled experimental framework. We find that most gene knockouts produced measurable alterations in lipid composition, often affecting specific lipid classes and molecular subspecies. The screen accurately recapitulated established gene-lipid relationships, including enzyme-substrate specificities, lipid pathway regulators, and disease-associated loss-of-function phenotypes, thereby demonstrating the sensitivity and accuracy of LipoGrid. By jointly profiling transcriptomic and lipidomic responses, we further uncover compensatory feedback mechanisms that buffer the impact of genetic perturbations on the cellular lipidome. Collectively, these findings establish LipoGrid as a scalable multimodal platform for systematically mapping gene-lipid interactions and reveal the regulatory networks linking gene perturbation, transcriptional adaptation, and lipidome remodeling. HighlightsO_LIMicropatterned single-cell growth enables spatial lipidomic perturbation screens C_LIO_LILipoGrid maps 143 gene knockouts to 158 lipid species and transcriptomic states C_LIO_LIPerturbed lipidomes reveal compensatory feedback and lipid-class-specific uptake C_LIO_LIRecovers enzyme substrate specificities and disease-linked lipid signatures C_LI

systems biology↗

bioRxiv2026

SubCellSpace: Automated characterization of subcellular mRNA localization patterns in spatial transcriptomics

The localized translation of transcripts is a universal phenomenon across biological domains. Many examples of subcellular RNA localization and their functional importance have been described. However, these examples remain anecdotal, and a more systematic genome and cell-type-wide analysis is needed. Current spatial transcriptomic techniques can characterize hundreds to thousands of transcript species at subcellular resolutions, enabling the large-scale investigation of subcellular mRNA localization. Here we describe SubCellSpace, a computational framework to learn general representations of mRNA localization patterns. By embedding observed single-cell subcellular localization patterns (SLPs) to an interpretable latent space, SubCellSpace can detect and statistically infer the presence of SLPs, uncover colocalizing gene-pairs and characterize cellular heterogeneity for pattern-presentation. We benchmark SubCellSpace in both synthetic and real data, showing it can correctly detect previously described apical/basal polarized genes in the enterocytes of mouse small-intestine, as well as encode the enterocytes orientation. Additionally, we provide a tailored spatial transcriptomics validation dataset for benchmarking SLP identification based on transcripts previously described to be enriched near subcellular structures in HEK293T cells. We propose a practical and computationally-efficient classification workflow that automatically detects localized transcript species and quantifies their degree of patterning, while controlling false positive rates. Finally, we showcase SubCellSpace in both supervised and unsupervised settings, to either classify pre-determined SLPs or to explore spatial patterning without specifying pattern types a priori. Automated AI models such as SubCellSpace and their integration in spatial transcriptomics analysis workflows will help characterize previously undiscovered subcellular RNA localization phenomena, providing novel insights into post-transcriptional regulation mechanisms.

bioinformatics↗

bioRxiv2026

Modeling cis-regulatory variation in human brain enhancers across a large Parkinson's Disease cohort

Genome-wide association studies (GWAS) have linked more than a hundred non-coding genomic loci to Parkinsons disease (PD) risk. Deciphering their functional impact on gene regulation requires cell type-aware modeling approaches to assess the effects of sequence variation on enhancer function and target gene expression. To address this challenge, we generated a comprehensive matched dataset from 190 human donors (115 controls and 75 PD), comprising long-read whole-genome sequencing alongside single nucleus multiome atlases (snATAC-seq and snRNA-seq for 3.1 and 1.1 million nuclei respectively) of the anterior cingulate cortex and substantia nigra. By integrating chromatin accessibility quantitative trait loci (caQTL), DNA methylation QTL (meQTL), and allele-specific chromatin accessibility (ASCA), we identified 53,841 high-confidence cis-acting genetic variants that modulate cell type-specific enhancer accessibility in one or both brain regions. We further demonstrate that sequence-to-function models can accurately predict the impact of these variants directly from the genomic sequence. Novel explainability approaches allowed stratifying these variants according to their regulatory function, with the majority disrupting specific transcription factor binding sites in a cell type specific manner. Integrating these "enhancer variants" (EV) with eQTL mapping and gene locus modeling linked a subset of EVs to their target genes. Finally, we applied these models to prioritize regulatory variants at known PD GWAS loci, bypassing statistical limitations in rare disease-relevant populations like dopaminergic neurons. All together, we establish a unique resource and new sequence modeling strategies to interpret functional non-coding variation in the human brain.

genomics↗

bioRxiv2025

Multi-omics integration and batch correction using a modality-agnostic deep learning framework

State-of-the-art biotechnologies allow the detection of different molecular species on the same biological sample, generating complex highly-dimensional multi-modal datasets. Gaining a holistic understanding of biological phenomena, such as oncogenesis or aging, requires integrating these diverse modalities into low-dimensional data representations while correcting for technical artifacts. Here we present MIMA, a modular, unsupervised AI framework for multi-omics data integration and batch correction. Applied to complex spatial and single-cell datasets, MIMA effectively removes batch effects, while preserving biologically relevant information, and learns representations predictive of expert pathologist annotations. Additionally, it enables cross-modal translation, uncovers molecular patterns not captured by manual annotations, and despite being modality-agnostic performs on par with specialized state-of-the-art tools. MIMAs flexibility and scalability make it a powerful tool for multimodal data analysis. MIMA provides a foundation for AI-based, multi-omics augmented digital pathology frameworks, offering new opportunities for improved patient stratification and precision medicine through the comprehensive integration of high-dimensional molecular data and histopathological imaging.

bioinformatics↗

bioRxiv2025

Systematic evaluation of single-cell multimodal data integration for comprehensive human reference atlas.

The integration of multimodal single-cell data enables comprehensive organ reference atlases, yet its impact remains largely unexplored, particularly in complex tissues. We generated a benchmarking dataset for the renal cortex by integrating 3 and 5 scRNA-seq with joint snRNA-seq and snATAC-seq, profiling 119,744 high-quality nuclei/cells from 19 donors. To align cell identities and enable consistent comparisons, we developed the interpretable machine learning tool scOMM (single-cell Omics Multimodal Mapping) and systematically assessed integration strategies. "Horizontal" integration of scRNA and snRNA-seq improved cell-type identification, while "vertical" integration of snRNA-seq and snATAC-seq had an additive effect, enhancing resolution in homogeneous populations and difficult-to-identify states. Global integration was especially effective in identifying adaptive states and rare cell types, including WFDC2-expressing Thick Ascending Limb and Norn cells, previously undetected in kidney atlases. Our work establishes a robust framework for multimodal reference atlas generation, advancing single-cell analysis and extending its applicability to diverse tissues.

genomics↗

bioRxiv2024

Single cell transcriptomics reveal mast cell heterogeneity in the human gut

Gut mast cells are key players in infection and inflammation, as well as in homeostasis. Mast cells regulate gastrointestinal (GI) function by controlling vascular and epithelial permeability, and interacting with the immune system and the enteric nervous system. Mucosal (MCT) and connective tissue (MCTC) mast cells coexist in the gastrointestinal tract, but are located in distinct microanatomical niches. However, little is known about the transcriptional heterogeneity of human intestinal mast cells. Additionally, whether distinct microanatomical niches instruct mast cell phenotypes in the human gut is currently unknown. We therefore performed 10x single cell RNA sequencing on healthy rectal biopsies and on dissected layers of human sigmoid colon. We identified five transcriptionally distinct mast cell subsets (MC1-5) which exhibited a layer-specific distribution in the healthy human colon and distinct cytokine, chemokine, protease, and transcription factor profiles, and were associated with putative immune and neuro-immune functions. This study provides a framework for the integration of mast cell heterogeneity in the study of gastrointestinal disease mechanisms in humans.

immunology↗

bioRxiv2023

Integration of Multiple Spatial-Omics Modalities Reveals Unique Insights into Molecular Heterogeneity of Prostate Cancer

Recent advances in spatial omics methods are revolutionising biomedical research by enabling detailed molecular analyses of cells and their interactions in their native state. As most technologies capture only a specific type of molecules, there is an unmet need to enable integration of multiple spatial-omics datasets. This, however, presents several challenges as these analyses typically operate on separate tissue sections at disparate spatial resolutions. Here, we established a spatial multi-omics integration pipeline enabling co-registration and granularity matching, and applied it to integrate spatial transcriptomics, mass spectrometry-based lipidomics, single nucleus RNA-seq and histomorphological information from human prostate cancer patient samples. This approach revealed unique correlations between lipids and gene expression profiles that are linked to distinct cell populations and histopathological disease states and uncovered molecularly different subregions not discernible by morphology alone. By its ability to correlate datasets that span across the biomolecular and spatial scale, the application of this novel spatial multi-omics integration pipeline provides unprecedented insight into the intricate interplay between different classes of molecules in a tissue context. In addition, it has unique hypothesis-generating potential, and holds promise for applications in molecular pathology, biomarker and target discovery and other tissue-based research fields.

cancer biology↗

bioRxiv2023

A multi-omics genome-and-transcriptome single-cell atlas of human preimplantation embryogenesis reveals the cellular and molecular impact of chromosome instability

The frequent acquisition of genomic abnormalities in human preimplantation embryos is a leading cause of pregnancy loss, but does not necessarily prohibit healthy offspring. However, the impact of genomic abnormalities on cellular states and development of the early human embryo remains largely unclear. Here, we characterise aneuploidy and reconstruct gene regulatory networks in human preimplantation embryos, and investigate gene expression and developmental perturbations instigated by aneuploidy using single-cell genome-and-transcriptome sequencing (G&T-seq). At the genomic level, we show that acquired numerical and structural chromosomal aberrations are frequent across all stages of early embryogenesis and in all cell lineages. At the transcriptome level, we identify regulators of cell identity and uncover a network of 248 transcription factors from 10 major gene regulatory modules that characterise the distinct lineages of human preimplantation embryos. By integrating single-cell DNA-with RNA-information, we unveil how expression levels are affected by losses or gains of the corresponding genes in embryonic cells across human preimplantation development, as well as how copy-number aberrant transcription factor genes perturb the expression of their cognate target genes in euploid regions. Furthermore, we reveal a majority of aneuploid cells show a developmental delay and reduced fitness, indicating cell competition within the mosaic diploid-aneuploid embryo, which may contribute to selection against aneuploid cells and the birth of healthy offspring from mosaic diploid-aneuploid embryos. In summary, our multi-modal analyses provide unprecedented insights into early human embryo development.

developmental biology↗

bioRxiv2022

A TCF4/BRD4-dependent regulatory network confers cross-resistance to targeted and immune checkpoint therapy in melanoma

Primary resistance drastically limits the clinical success of immune checkpoint blockade (ICB) in melanoma. Resistance to ICB may also develop when tumours relapse after targeted therapy. To identify cancer cell-intrinsic mechanisms driving resistance to ICB, we generated single-cell RNA-sequencing (scRNA-seq) data from a prospective longitudinal cohort of patients on ICB therapy, including an early time point obtained after only one cycle of treatment. Comparing these data with murine scRNA-seq datasets, we established a comprehensive view of the cellular architecture of the treatment-naive melanoma ecosystem, and defined 6 evolutionarily conserved melanoma transcriptional metaprograms (Melanocytic or MEL, Mesenchymal-like or MES, Neural Crest-like, Antigen Presentation, Stress (hypoxia response) and Stress (p53 response)). Spatial multi-omics revealed a non-random geographic distribution of cell states that is, at least partly, driven by the tumour microenvironment. The single-cell data allowed unambiguous discrimination between melanoma MES cells and cancer-associated fibroblasts both in silico and in situ, a long-standing challenge in the field. Importantly, two of the melanoma transcriptional metaprograms were associated with divergent clinical responses to ICB. While the Antigen Presentation cell population was more abundant in tumours from patients who exhibited a clinical response to ICB, MES cells were significantly enriched in early on-treatment biopsies from non-responders, and their presence significantly predicted lack of response. Critically, we identified TCF4 (E2-2) as a master regulator of the MES program and suppressor of both MEL and Antigen Presentation programs. Targeting TCF4 expression in MES cells either genetically or pharmacologically using a bromodomain inhibitor increased immunogenicity and sensitivity to targeted therapy. This study describes an increasingly complex melanoma transcriptional landscape and its rapid evolution under ICB. It also identifies a putative biomarker of early response to ICB and an epigenetic therapeutic strategy that increases both immunogenicity of ICB-refractory melanoma and their sensitivity to targeted therapy.

cancer biology↗

bioRxiv2022

Neuro-immune Crosstalk in the Enteric Nervous System from Early Postnatal Development to Adulthood

Correct development and maturation of the enteric nervous system (ENS) is critical for survival. Early in life, the ENS requires significant refinement in order to adapt to the evolving needs of the tissue, changing from milk to solid food at the time of weaning. Here, we demonstrate that resident macrophages of the muscularis externa, MM{phi}, refine the ENS early in life by pruning synapses and phagocytosing abundant enteric neurons. After weaning, MM{phi} continue to closely interact with the ENS, acquire a microglia-like phenotype and are crucial for the survival of enteric neurons. Of note, this microglia-like phenotype is instructed by TGF{beta} produced by the ENS, introducing a novel reciprocal cell-cell communication responsible for the maintenance of the neuron-associated MM[FE] niche in the gut. These findings elucidate a novel role of intestinal macrophages in ENS refinement early in life, and open new opportunities to treat intestinal neurodegenerative disorders by manipulating the ENS-macrophage niche.

immunology↗