Biology subjects

Pelkmans, L.

Publications and source records attributed to Pelkmans, L..

16 recordsLinked to original sources

bioRxiv2026

Shield-4i: A Whole-mount Multiplexed Imaging Platform for Studying Multiscale Information Flow in 3D Multicellular Systems

Self-organization in multicellular systems emerges from reciprocal interactions across spatiotemporal scales. Understanding how subcellular organization, tissue remodeling and developmental outcome are coordinated, thus requires simultaneous profiling of biological processes spanning orders of magnitudes in space and time. Yet, a unified experimental and computational framework for capturing these multiscale properties across in vivo and stem cell-derived systems has been lacking. Here, we introduce Shield-4i, a high-throughput, versatile, and accessible method for automated in toto iterative immunofluorescence imaging of whole-mount structures at subcellular resolution. Through polyepoxide-mediated inter- and intramolecular crosslinking, Shield-4i preserves sample integrity during repeated SDS-based elution cycles. We benchmark this method in gastrulating zebrafish and post-implantation mouse embryos and demonstrate its applicability to stem cell-derived 3D gastruloids, achieving up to 30-plex measurements of proteins and their post-translational modifications across hundreds of samples. To enable scalable analysis, we developed a dedicated 3D workflow supporting OME-Zarr-based and FAIR-compliant data storage, standardized processing, and multiscale feature extraction. Applying this framework to investigate gastruloid self-organization, we quantify how cellular physicochemical state and signaling properties encode cell position along embryonic axes and connect molecular patterning and fate decisions to morphological symmetry breaking at the multicellular scale. Together, Shield-4i provides a high-content in toto spatial proteomics platform for dissecting multiscale information flow and self-organization in multicellular systems.

systems biology↗

bioRxiv2026

Fractal: Towards FAIR bioimage analysis at scale with OME-Zarr-native workflows

The rapid growth in microscopy data volume, dimensionality, and diversity urgently calls for scalable and reproducible analysis frameworks. While efforts on the open OME-Zarr format have helped standardize the storage of large microscopy datasets, solutions for standardized processing are still lacking. Here, we introduce two complementary contributions to address this gap: 1) the Fractal task specification, defining OME-Zarr processing units that can interoperate across computational environments and workflow engines, and 2) the Fractal platform, using this specification to enable scalable and modular OME-Zarr-native analysis workflows. We demonstrate their use across diverse biological research data, including terabyte-scale multiplexed, volumetric, and time-lapse imaging. In a clinical setting, we show that Fractal workflows achieve near-identical quantification of millions of cells across independent deployments, demonstrating the reproducibility required for translational applications. With its growing community of contributors, the Fractal ecosystem provides a foundation for FAIR microscopy image analysis relying on open file formats.

bioinformatics↗

bioRxiv2025

Multiplexed embryo profiling links cellular state to zygotic genome activation in single cells

Multicellular self-organization relies on complex interactions across multiple length scales, shaping the transcriptional activity and molecular phenotype of individual cells. Despite advances in spatial omics technologies, mapping protein state composition at high spatial resolution to capture the molecular phenotype across whole-mount structures has remained challenging. Here, we introduce high-throughput 3D iterative indirect immunofluorescence imaging (3D-4i) for the simultaneous detection of multiple proteins and protein states across the entire embryo, and a 3D-dedicated computer vision pipeline for quantifying morphological and molecular properties across subcellular, single-cell, and whole-embryo scales in hundreds of samples. Applying this pipeline to early zebrafish embryos undergoing mid-blastula transition, we determine the cell cycle phase for each cell across the embryo, allowing us to uncover the spatiotemporal dynamics by which global meta-synchronous mitotic waves transition to cell cycle desynchronization. Using statistical analysis, we find that cell cycle phase is the major source of variability in transcription within a division cycle, and combining this with analysis of key transcription factors and chromatin modifier state enables accurate prediction of transcriptional output during zygotic genome activation in individual cells. Collectively, our results demonstrate the strength of 3D-4i in quantifying multimodal effects across spatiotemporal scales and show how it can be utilized to unravel the complex contribution of causal factors that collectively drive multicellular self-organization.

systems biology↗

bioRxiv2023

Multiscale chromatin dynamics and high entropy in plant iPSC cultures

Plant protoplasts constitute the starting material to induce pluripotent cell masses in vitro competent for tissue regeneration. Dedifferentiation is associated with large-scale chromatin reorganisation and massive transcriptome reprogramming, characterized by stochastic gene expression. How this cellular variability reflects on chromatin organisation in individual cells and what are the factors influencing chromatin transitions during culturing is largely unknown. High-throughput imaging and a custom, supervised image analysis protocol extracting over 100 chromatin features unravelled a rapid, multiscale dynamics of chromatin patterns which trajectory strongly depends on nutrients availability. Decreased abundance in H1 (linker histones) is hallmark of chromatin transitions. We measured a high heterogeneity of chromatin patterns indicating an intrinsic entropy as hallmark of the initial cultures. We further measured an entropy decline over time, and an antagonistic influence by external and intrinsic factors, such as phytohormones and epigenetic modifiers, respectively. Collectively, our study benchmarks an approach to understand the variability and evolution of chromatin patterns underlying plant cell reprogramming in vitro.

molecular biology↗

bioRxiv2023

Phosphorylation-controlled cohesion of a nuclear condensate regulates mRNA retention

Nuclear speckles are membraneless organelles that associate with active transcription sites and participate in post-transcriptional mRNA processing. During the cell cycle, nuclear speckles dissolve following phosphorylation of their protein components. Here, we identify the PP1 family as the phosphatases that counteract kinase-mediated dissolution. PP1 overexpression increases speckle cohesion and leads to retention of polyadenylated RNA within speckles and the nucleus. Using APEX2 proximity labeling combined with RNA-sequencing, we characterized the relationship between the cohesion of nuclear speckles and the recruitment of specific RNAs. We find that many transcripts are preferentially enriched within nuclear speckles compared to the nucleoplasm, particularly chromatin- and nucleus-associated transcripts. While total polyadenylated RNA retention increased with nuclear speckle cohesion, the ratios of most mRNA species to each other were constant, indicating non-selective, or proportional, retention. We then explored whether nuclear speckle cohesion changes in response to environmental perturbations associated with changes in kinase or phosphatase activity. We found that cellular responses to heat shock, oxidative stress, and hypoxia include changes to the cohesion of nuclear speckles and mRNA retention. Our results demonstrate that tuning the material properties of nuclear speckles provides a mechanism for the acute control of mRNA localization.

cell biology↗

bioRxiv2023

OME-Zarr: a cloud-optimized bioimaging file format with international community support

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself - OME-Zarr - along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain -- the file format that underlies so many personal, institutional, and global data management and analysis tasks.

bioinformatics↗

bioRxiv2022

Robust and Versatile Arrayed Libraries for Human Genome-Wide CRISPR Activation, Deletion and Silencing

Arrayed CRISPR libraries extend the scope of gene-perturbation screens but require large numbers of efficacious sgRNA-expressing vectors. Using a newly invented liquid-phase plasmid cloning methodology, we constructed genome-wide arrayed libraries for human gene ablation (19,936 plasmids), activation, and epigenetic silencing (22,442 plasmids). At least 76% of each plasmid preparation encoded an intact array of 4 non-overlapping sgRNAs designed to tolerate most human DNA polymorphisms. We achieved perturbation efficacies of 75-99%, 76-92% and up to 10,000x in deletion, silencing and activation experiments, respectively. Upon conversion into massively parallel lentiviral vectors, an arrayed activation screen of 1,634 human transcription factors yielded 11 novel regulators of the cellular prion protein PrPC. Furthermore, a screen using a pooled version of the ablation library identified 5 novel modifiers of autophagy that went undetected with either of two 1sgRNA libraries. The CRISPR libraries described here represent a powerful resource for the targeted perturbation of human protein-coding genes.

genomics↗

bioRxiv2022

Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps

Highly multiplexed quantitative subcellular imaging holds enormous promise for understanding how spatial context shapes the activity of our genome and its products at multiple scales. Yet unbiased analysis of subcellular organisation across experimental conditions remains challenging, because differences in molecular profiles between conditions confound differences in molecular profiles across space. Here, we introduce a deep-learning framework called CAMPA (Conditional Autoencoder for Multiplexed Pixel Analysis), which uses a variational autoencoder conditioned on cellular states and perturbations to learn consistent molecular signatures. Clustering the learned representations into subcellular landmarks allows quantitative comparisons of landmark sizes, shapes, molecular compositions and relative spatial organisation between conditions. By performing high-resolution multiplexed immunofluorescence on human cells, we use CAMPA to reveal how subnuclear organisation changes upon different perturbations of RNA production or processing, and how different membraneless organelles scale with cell size. Furthermore, by integrating information across the cellular and subcellular scales, we uncover new links between the molecular composition of membraneless organelles and bulk RNA synthesis rates of single cells. We anticipate that CAMPA will greatly accelerate the systematic mapping of multiscale atlases of biological organisation to identify the rules by which context shapes physiology and disease.

systems biology↗

bioRxiv2022

Multimodal spatiotemporal phenotyping of human organoid development

Organoids generated from human pluripotent stem cells (PSCs) provide experimental systems to study development and disease. However, we lack quantitative spatiotemporal descriptions of organoid development that incorporate measurements across different molecular modalities. Here we focus on the retina and use a single-cell multimodal approach to reconstruct human retinal organoid development. We establish an experimental and computational pipeline to generate multiplexed spatial protein maps over a retinal organoid time course and primary adult human retina, registering protein expression features at the population, cellular, and subcellular levels. We develop an analytical toolkit to segment nuclei, identify local and global tissue units, infer morphology trajectories, and analyze cell neighborhoods from multiplexed imaging data. We use this toolkit to visualize progenitor and neuron location, the spatial arrangements of extracellular and subcellular components, and global patterning in each organoid and primary tissue. In addition, we generate a single-cell transcriptome and chromatin accessibility time course dataset and infer a gene regulatory network underlying organoid development. We then integrate genomic data with spatially segmented nuclei into a multi-modal atlas enabling virtual exploration of retinal organoid development. We visualize molecular, cellular, and regulatory dynamics during organoid lamination, and identify regulons associated with neuronal differentiation and maintenance. We use the integrated atlas to explore retinal ganglion cell (RGC) spatial neighborhoods, highlighting pathways involved in RGC cell death. Finally, we show that mosaic CRISPR/Cas genetic perturbations in retinal organoids provide insight into cell fate regulation. Altogether, our work is a major advance toward a virtual human retinal organoid, and provides new directions for how to approach disorders of the visual system. More broadly, our approaches can be adapted to many organoid systems.

systems biology↗

bioRxiv2021

Learning Single-Cell Perturbation Responses using Neural Optimal Transport

The ability to understand and predict molecular responses towards external perturbations is a core question in molecular biology. Technological advancements in the recent past have enabled the generation of high-resolution single-cell data, making it possible to profile individual cells under different experimentally controlled perturbations. However, cells are typically destroyed during measurement, resulting in unpaired distributions over either perturbed or non-perturbed cells. Leveraging the theory of optimal transport and the recent advents of convex neural architectures, we learn a coupling describing the response of cell populations upon perturbation, enabling us to predict state trajectories on a single-cell level. We apply our approach, CO_SCPLOWELLC_SCPLOWOT, to predict treatment responses of 21,650 cells subject to four different drug perturbations. CO_SCPLOWELLC_SCPLOWOT outperforms current state-of-the-art methods both qualitatively and quantitatively, accurately capturing cellular behavior shifts across all different drugs.

bioinformatics↗

bioRxiv2021

Non-specific adhesive forces between filaments and membraneless organelles

Membraneless organelles are liquid-like domains that form inside living cells by phase-separation. While standard physical models of their formation assume their surroundings to be a simple liquid, the cytoplasm is an active viscoelastic environment. To investigate potential coupling of phase separation with the cytoskeleton, we quantify structural correlations of stress granules and microtubules in a human-derived epithelial cell line. We find that microtubule networks are significantly perturbed in the vicinity of stress granules, and that large stress granules conform to the local pore-structure of the microtubule network. When microtubules are depolymerized by nocodazole, tubulin enrichment is localized near the surface of stress granules. We interpret these data using a thermodynamic model of partitioning of particles to the surface and bulk of droplets. This analysis shows that proteins generically have a non-specific affinity for droplet interfaces, which becomes most apparent when they weakly partition to the bulk of droplets and have a large molecular weight. In this framework, our data is consistent with a weak ([lsim] kbT) affinity of tubulin sub-units for stress granule interfaces. As microtubules polymerize their affinity for interfaces increases, providing sufficient adhesion to deform droplets and/or the network. We validate this basic physical phenomena in vitro through the interaction of a simple protein-RNA condensate with tubulin and microtubules.

biophysics↗

bioRxiv2021

Nuclear RNA concentration coordinates RNA production with cell size in human cells

Unlike its DNA template, RNA abundance and synthesis rates increase with cell size, as part of a mechanism of cellular RNA concentration homeostasis. Here, we study this scaling phenomenon in human cells by combining genome-wide perturbations with quantitative single-cell measurements. Despite relative ease in perturbing RNA synthesis, we find that RNA concentrations remain highly constant. Systems-level analysis indicates that perturbations that would lead to increased nuclear mRNA abundance result in downregulation of mRNA synthesis. This is associated with reduced levels of several transcription-associated proteins and protein states that are normally coordinated with RNA production in single cells, including RNA polymerase II (Pol II) itself. Acute shut-down of nuclear RNA degradation, elevation of nuclear mRNA levels, and mathematical modelling indicate that mammalian cells achieve RNA concentration homeostasis by an mRNA-based negative feedback on transcriptional activity in the nucleus. Ultimately, this acts to robustly scale Pol II abundance with cell volume and coordinate mRNA synthesis and degradation.

molecular biology↗

bioRxiv2020

Co-translational targeting of transcripts to endosomes

Asymmetric localization and translation of mRNAs is used by single cells to sense their environment and integrate extrinsic cues with the appropriate cellular response. Here we investigate the extent to which endosomes impact subcellular patterning of transcripts and provide a platform for localized translation. Using image-based transcriptomics, indirect immunofluorescence, and RNAseq of isolated organelles, we discover mRNAs that associate with early endosomes in a translation-dependent and -independent manner. We explore this in more detail for the mRNA of a major endosomal tethering factor and fusogen, Early Endosomal Antigen 1, EEA1, which localizes to early endosomes in a puromycin-sensitive manner. By reconstituting EEA1 knock-out cells with either the coding sequence or 3UTR of EEA1, we show that the coding region is sufficient for endosomal localization of mRNA. Finally, we use quantitative proteomics to discover proteins associated with EEA1 mRNA and identify CSRP1 as a factor that controls EEA1 translational efficiency. Our findings reveal that multiple transcripts associate with early endosomes in a translation-dependent manner and identify mRNA-binding proteins that may participate in controlling endosome-localized translation.

cell biology↗

bioRxiv2020

DYRK3-Controlled Phase Separation Organizes the Early Secretory Pathway

The dual-specificity kinase DYRK3 controls formation and dissolution of several intracellular condensates thereby regulating various cell physiological processes. Here we report that DYRK3 establishes a dynamic equilibrium between condensation and dissolution of proteins associated with membranous structures of the early secretory pathway to organize membrane traffic between the ER and the Golgi complex in mammalian cells. This depends on the peripheral membrane protein Sec16A, whose N-terminal disordered region forms DYRK3-controlled liquid-like condensates on the surface of the ER and co-phase separates with multiple ER exit site components and a subset of matrix proteins specifically associated with ERGIC and cis-Golgi. Our findings support a mechanism whereby multiple interacting and differentially regulated intracellular condensates create favorable environments for directional membrane traffic in eukaryotic cells.

cell biology↗

bioRxiv2019

Cellular state determines the multimodal signaling response of single cells

Numerous fundamental biological processes require individual cells to correctly interpret and accurately respond to incoming cues. How intracellular signaling networks achieve the integration of complex information from various contexts remains unclear. Here we quantify epidermal growth factor-induced heterogeneous activation of multiple signaling proteins, as well as cellular state markers, in the same single cells across multiple spatial scales. We find that the acute response of each node in a signaling network is tightly coupled to the cellular state in a partially non-redundant manner. This generates a multimodal response that senses the diversity of cellular states better than any individual response alone and allows individual cells to accurately place growth factor concentration in the context of their cellular state. We propose that the non-redundant multimodal property of signaling networks in mammalian cells underlies specific and context-aware cellular decision making in a multicellular setting.

systems biology↗

bioRxiv2019

KDML: a machine-learning framework for inference of multi-scale gene functions from genetic perturbation screens

Characterising context-dependent gene functions is crucial for understanding the genetic bases of health and disease. To date, inference of gene functions from large-scale genetic perturbation screens is based on ad-hoc analysis pipelines involving unsupervised clustering and functional enrichment. We present Knowledge-Driven Machine Learning (KDML), a framework that systematically predicts multiple functions for a given gene based on the similarity of its perturbation phenotype to those with known function. As proof of concept, we test KDML on three datasets describing phenotypes at the molecular, cellular and population levels, and show that it outperforms traditional analysis pipelines. In particular, KDML identified an abnormal multicellular organisation phenotype associated with the depletion of olfactory receptors and TGF{beta} and WNT signalling genes in colorectal cancer cells. We validate these predictions in colorectal cancer patients and show that olfactory receptors expression is predictive of worse patient outcome. These results highlight KDML as a systematic framework for discovering novel scale-crossing and clinically relevant gene functions. KDML is highly generalizable and applicable to various large-scale genetic perturbation screens.

systems biology↗