Lucas Pelkmans

Multicellular self-organization depends on interactions across multiple length scales, yet mapping protein states at high spatial resolution in whole-mount embryos remains challenging. Here, we introduce high-throughput 3D in toto iterative immunofluorescence imaging (3D-4i) and a dedicated computer vision pipeline to quantify morphological and molecular features from subcellular to whole-embryo scales across 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, and uncover the spatiotemporal dynamics by which global meta-synchronous mitotic waves transition to cell cycle desynchronization. Using statistical analysis, we find that the cell cycle phase is the major source of variability in transcription within a division cycle, and combining this with the analysis of key transcription factors and chromatin modifier state, included in our multiplexed dataset, enables accurate prediction of transcriptional output during zygotic genome activation in individual cells. Together, these findings establish 3D-4i as a powerful approach for quantifying multimodal, multiscale biological processes underlying multicellular self-organization.

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.

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 mRNA within speckles and the nucleus. Using APEX2 proximity labeling combined with RNA-sequencing, we characterize 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 increases with nuclear speckle cohesion, the ratios of most mRNA species to each other are constant, indicating non-selective retention. We further find that cellular responses to heat shock, oxidative stress, and hypoxia include changes to the phosphorylation and cohesion of nuclear speckles and to mRNA retention. Our results demonstrate that tuning the material properties of nuclear speckles provides a mechanism for the acute control of mRNA localization.

The subcellular localization of RNA plays a critical role in various biological processes, including development and stress response. Proximity labeling eases the detection of localized transcripts and protein enrichment compared to previous techniques that rely on biochemical isolation of subcellular structures. The rapid reaction and small labeling radius of APEX2 make it an attractive alternative to other proximity labeling approaches, such as BioID. However, we found that standard protocols for APEX proximity labeling fail in human induced pluripotent stem cells. Moreover, standard protocols yield heterogeneous labeling of biomolecules across single cells in MCF10A breast epithelial cells. Our results indicate that low biotin permeability in these cell lines is the main cause for failed or inefficient labeling. This protocol outlines improved labeling by combining the rapid hydrogen peroxide-driven APEX2 reaction with the addition of a mild detergent during biotin incubation. This adaptation leads to efficient proximity labeling in hiPSCs and more homogeneous biotinylation across single cells in MCF10As. The adapted protocol extends the use of APEX2 proximity labeling to cell lines with poor biotin permeability.

Deep single-cell multi-omic profiling offers a promising approach to understand and overcome drug resistance in relapsed or refractory (rr) acute myeloid leukemia (AML). Here, we combine single-cell ex vivo drug profiling (pharmacoscopy) with single-cell and bulk DNA, RNA, and protein analyses, alongside clinical data from 21 rrAML patients. Unsupervised data integration reveals reduced ex vivo response to the Bcl-2 inhibitor venetoclax (VEN) in patients treated with both a hypomethylating agent (HMA) and VEN, compared to those pre-exposed to chemotherapy or HMA alone. Integrative analysis identifies both known and unreported mechanisms of innate and treatment-related VEN resistance and suggests alternative treatments, like targeting increased proliferation with the PLK inhibitor volasertib. Additionally, high CD36 expression in VEN-resistant blasts associates with sensitivity to CD36-targeted antibody treatment ex vivo. This study demonstrates how single-cell multi-omic profiling can uncover drug resistance mechanisms and treatment vulnerabilities, providing a valuable resource for future AML research.

Plant protoplasts provide starting material for of inducing pluripotent cell masses that are competent for tissue regeneration in vitro, analogous to animal induced pluripotent stem cells (iPSCs). Dedifferentiation is associated with large-scale chromatin reorganisation and massive transcriptome reprogramming, characterised by stochastic gene expression. How this cellular variability reflects on chromatin organisation in individual cells and what factors influence chromatin transitions during culturing are largely unknown. Here, we used high-throughput imaging and a custom supervised image analysis protocol extracting over 100 chromatin features of cultured protoplasts. The analysis revealed rapid, multiscale dynamics of chromatin patterns with a trajectory that strongly depended on nutrient availability. Decreased abundance in H1 (linker histones) is hallmark of chromatin transitions. We measured a high heterogeneity of chromatin patterns indicating intrinsic entropy as a 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.

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 .

Understanding and predicting molecular responses in single cells upon chemical, genetic or mechanical perturbations is a core question in biology. Obtaining single-cell measurements typically requires the cells to be destroyed. This makes learning heterogeneous perturbation responses challenging as we only observe unpaired distributions of perturbed or non-perturbed cells. Here we leverage the theory of optimal transport and the recent advent of input convex neural architectures to present CellOT, a framework for learning the response of individual cells to a given perturbation by mapping these unpaired distributions. CellOT outperforms current methods at predicting single-cell drug responses, as profiled by scRNA-seq and a multiplexed protein-imaging technology. Further, we illustrate that CellOT generalizes well on unseen settings by (1) predicting the scRNA-seq responses of holdout patients with lupus exposed to interferon-β and patients with glioblastoma to panobinostat; (2) inferring lipopolysaccharide responses across different species; and (3) modeling the hematopoietic developmental trajectories of different subpopulations.