Biology subjects

Wouters, D.

Publications and source records attributed to Wouters, D..

13 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↗

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

Spatial epigenomic niches underlie glioblastoma cell state plasticity

IDH-wildtype glioblastoma (GBM) is an aggressive brain tumor with poor survival and few therapeutic options. Transcriptionally-defined cell states coexist in GBM and occupy defined regions of the tumor. Evidence indicates that GBM cell states are plastic, but it remains unclear if they are determined by the underlying epigenetic state and/or by microenvironmental factors. Here, we present spatially-resolved epigenomic profiling of human GBM tissues that implicate chromatin structure as a key enabler of cell plasticity. We report two epigenetically-defined and spatially-nested tumor niches. Each niche activates short-range molecular signals to maintain its own state and, surprisingly, long-range signals to reinforce the state of the neighboring niche. The position of a cell along this gradient-like system of opposing signals determines its likelihood to be in one state or the other. Our results reveal an intrinsic system for cell plasticity that is encoded in the chromatin profiles of two adjacent niches that dot the topological architecture of GBM in cartesian space.

genomics↗

bioRxiv2025

Alzheimer's disease and its co-pathologies: implications for hippocampal degeneration, cognitive decline, and the role of APOE ϵ4

INTRODUCTIONIn neurodegenerative dementias, the co-occurrence and interaction of A{beta}, tau, and other pathological lesions confound their individual contributions to neurodegeneration and their modulation by risk factors. METHODSWe analyzed 480 post-mortem human brains (ages 50-99) using regression and structural equation models to assess the relationships among A{beta}, tau, LATE-NC, -synuclein, other age-related lesions, and APOE {varepsilon}4, as well as their effects on CA1 neuronal density, brain weight, and cognitive status. RESULTSA{beta}, tau, LATE-NC, and amygdala-predominant -synuclein pathology were highly interconnected. Tau was the strongest predictor of global neurodegeneration, while LATE-NC primarily, but not exclusively, affected hippocampal neuron loss. Small vessel disease correlated with both LATE-NC and -synuclein, while APOE {varepsilon}4 was mainly associated with extracellular and capillary A{beta}. DISCUSSIONAlthough Alzheimers pathology plays a central role in brain degeneration, coexisting pathologies can both exacerbate and independently contribute to it. These factors should be considered in patient stratification.

neuroscience↗

bioRxiv2023

Stacking of PRRs in potato to achieve enhanced resistance against Phytophthora infestans

Plants employ pattern recognition receptors (PRRs) to sense pathogen-associated molecular patterns (PAMPs) or apoplastic effectors at the plant cell surface, as well as nucleotide-binding domain leucine-rich-repeat-containing receptors (NLRs) to sense effectors inside the plant cell. Breeding for potato resistance to P. infestans has focused on the use of NLRs, however, these genes are typically quickly overcome since the matching avirulence genes evolve exceptionally quickly. Here, we stacked two PRRs, PERU and RLP23, that recognize the rather conserved Phytophthora PAMPs Pep-13/25 and nlp20, respectively, in the potato cultivar Atlantic, and evaluated their effect on P. infestans resistance. We found that PERU and RLP23 cooperate for the early immune responses like the accumulation of reactive oxygen species (ROS) and production of ethylene by recognizing their corresponding PAMPs. Furthermore, we show that potato plants overexpressing these two PRRs are slightly less affected by P. infestans compared to the single transformants. Together, our data suggest that pyramiding of surface receptors can provide additional enhanced resistance against pathogens, however, more effective or synergistic combinations that may include intracellular NLR receptors should be explored.

plant biology↗

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↗

bioRxiv2022

Tetraose glycoalkaloids from potato can provide complete protection against fungi and insects

Plants with innate disease and pest resistance can contribute to more sustainable agriculture. Natural defence compounds produced by plants have the potential to provide a general protective effect against pathogens and pests, but they are not a primary target in resistance breeding. Here, we identified a wild relative of potato, Solanum commersonii, that provides us with unique insight in the role of glycoalkaloids in plant immunity. We cloned two atypical resistance genes that provide resistance to Alternaria solani and Colorado potato beetle through the production of tetraose steroidal glycoalkaloids. Moreover, we provide in vitro evidence to show that these compounds have potential against a range of different (potato pathogenic) fungi. This research links structural variation in steroidal glycoalkaloids to resistance against potato diseases and pests. Further research on the biosynthesis of plant defence compounds in different tissues, their toxicity, and the mechanisms for detoxification, can aid the effective use of such compounds to improve sustainability of our food production.

plant biology↗

bioRxiv2020

The Rpi-mcq1 resistance gene family recognizes Avr2 of Phytophthora infestans but is distinct from R2

Potato late blight, which is caused by the destructive oomycete pathogen Phytophthora infestans, is a major threat to global food security. Several nucleotide binding, leucine-rich repeat (NLR) Resistance to P. infestans (Rpi) genes have been introgressed into potato cultivars from wild Solanum species that are native to Mexico, but these were quickly defeated. Positional cloning in Solanum mochiquense, combined with allele mining in Solanum huancabambense, were used to identify a new family of Rpi genes from Peruvian Solanum species. Rpi-mcq1, Rpi-hcb1.1 and Rpi-hcb1.2 confer race-specific resistance to a panel of P. infestans isolates. Effector assays showed that the Rpi-mcq1 family mediates a hypersensitive response upon recognition of the RXLR effector AVR2, which had previously been found to be exclusively recognized by the family of R2 resistance proteins. The Rpi-mcq1 and R2 genes are distinct and reside on chromosome IX and IV, respectively. This is the first report of two unrelated R protein families that recognize the same AVR protein. We anticipate that this likely is a consequence of a geographically separated dynamic co-evolution of R gene families of Solanum with an important effector gene of P. infestans. Author summaryPotato is the largest non-grain staple crop and essential for food security world-wide. However, potato plants are continuously threatened by the notorious oomycete pathogen Phytophthora infestans that causes late blight. This devastating disease has led to the Irish famine more then 150 years ago, and is still a major threat for potato. Resistance against P. infestans can be found in wild relatives of potato, which carry resistance genes that belong to the nucleotide binding site-leucine-rich repeat (NLR) class. Known NLR proteins typically recognize a matching effector from Phytophthora, which leads to a hypersensitive resistance response (HR). For example, R2 from Mexican Solanum species recognizes AVR2 from P. infestans. So far, these R genes exclusively match to one Avr gene. Here, we identified a new class of NLR proteins that are different from R2, but also recognize the same effector AVR2. This new family of NLR occurs in South American Solanum species, and we anticipate that it is likely a product of a geographically separated co-evolution with AVR2. This is the first report of two unrelated R protein families that recognize the same AVR protein.

plant biology↗

bioRxiv2020

ELR is a true pattern recognition receptor that associates with elicitins from diverse Phytophthora species

The first layer of plant immunity against pathogens is mediated by cell surface pattern recognition receptors (PRRs) that recognize pathogen molecules in the apoplast. Several pairs of PRRs and their matching extracellular ligands have been described but, in many cases, actual evidence for ligand binding by the PRR is lacking. The receptor-like protein ELR from Solanum microdontum, which triggers cell death upon co-expression with elicitins of various Phytophthora species and enhances resistance to late blight caused by Phytophthora infestans, was previously identified as the elicitin receptor by forward genetic screenings employing the INF1 elicitin of P. infestans. In this study, we investigated whether ELR associates with INF1 and other elicitins that are secreted by diverse Phytophthora spp. We performed in planta and in vitro co-immunoprecipitation of ELR with several affinity-tagged elicitins, as well as in planta transient co-expression assays. We found that ELR physically interacts with the class I elicitins INF1 and ParA1, from P. infestans and Phytophthora parasitica, respectively, which is in line with their ability to cause cell death when co-expressed with ELR in potato. Together, we demonstrate that ELR is a genuine PRR that binds elicitins of Phytophthora species.

plant biology↗

bioRxiv2020

RLP/K enrichment sequencing; a novel method to identify receptor-like protein (RLP) and receptor-like kinase (RLK) genes

O_LIThe identification of immune receptors in crop plants is time-consuming but important for disease control. Previously, resistance gene enrichment sequencing (RenSeq) was developed to accelerate mapping of nucleotide-binding domain and leucine-rich repeat containing (NLR) genes. However, resistances mediated by pattern recognition receptors (PRRs) remain less utilised. C_LIO_LIHere, our pipeline shows accelerated mapping of PRRs. Effectoromics leads to precise identification of plants with target PRRs, and subsequent RLP/K enrichment sequencing (RLP/KSeq) leads to detection of informative SNPs that are linked to the trait. C_LIO_LIUsing Phytophthora infestans as a model, we identified Solanum microdontum plants that recognize the apoplastic effectors INF1 or SCR74. RLP/KSeq in a segregating Solanum population confirmed the localization of the INF1 receptor on chromosome 12, and lead to the rapid mapping of the response to SCR74 to chromosome 9. By using markers obtained from RLP/KSeq in conjunction with additional markers, we fine-mapped the SCR74 receptor to a 43-kbp G-LecRK locus. C_LIO_LIOur findings show that RLP/KSeq enables rapid mapping of PRRs and is especially beneficial for crop plants with large and complex genomes. This work will enable the elucidation and characterisation of the non-NLR plant immune receptors and ultimately facilitate informed resistance breeding. C_LI

plant biology↗

bioRxiv2018

Estimating parasite load dynamics to reveal novel resistance mechanisms to human malaria

Improved methods are needed to identify host mechanisms which directly protect against human infectious diseases in order to develop better vaccines and therapeutics1,2. Pathogen load determines the outcome of many infections3, and is a consequence of pathogen multiplication rate, duration of the infection, and inhibition or killing of pathogen by the host (resistance). If these determinants of pathogen load could be quantified then their mechanistic correlates might be determined. In humans the timing of infection is rarely known and treatment cannot usually be withheld to monitor serial changes in pathogen load and host response. Here we present an approach to overcome this and identify potential mechanisms of resistance which control parasite load in Plasmodium falciparum malaria. Using a mathematical model of longitudinal infection dynamics for orientation, we made individualized estimates of parasite multiplication and growth inhibition in Gambian children at presentation with acute malaria and used whole blood RNA-sequencing to identify their correlates. We identified novel roles for secreted proteases cathepsin G and matrix metallopeptidase 9 (MMP9) as direct effector molecules which inhibit P. falciparum growth. Cathepsin G acts on the erythrocyte membrane, cleaving surface receptors required for parasite invasion, whilst MMP9 acts on the parasite. In contrast, the type 1 interferon response and expression of CXCL10 (IFN-{gamma}-inducible protein of 10 kDa, IP-10) were detrimental to control of parasite growth. Natural variation in iron status and plasma levels of complement factor H were determinants of parasite multiplication rate. Our findings demonstrate the importance of accounting for the dynamic interaction between host and pathogen when seeking to identify correlates of protection, and reveal novel mechanisms controlling parasite growth in humans. This approach could be extended to identify additional mechanistic correlates of natural- and vaccine-induced immunity to malaria and other infections.

systems biology↗