Biology subjects

Kreshuk, A.

Publications and source records attributed to Kreshuk, A..

At least 19 recordsLinked to original sources

bioRxiv2026

AI4Life Open Calls and Public Challenges: why, how, and what we have learned.

Within AI4Life, we ran three Open Calls and three Public Challenges (2023-2025), supporting 22 bioimage analysis projects from 151 applications and engaging 225 challenge participants, with the aim of applying FAIR deep learning in the life sciences. Our experience offers a view of the current state of bioimage analysis, the landscape of available tools, as well as the existing gaps between method developers, tool producers and potential users. It highlights that even after careful selection for AI-ready projects, most still require substantial effort to apply deep learning, and that the field still relies heavily on established, well-rounded methods to solve common problems. We come to the conclusion that for scientific AI in biology, the rate-limiting step is not methods and models but data, annotations, and shared infrastructure underneath them.

bioinformatics↗

bioRxiv2026

Automated cryo-volume EM for high-resolution 3D imaging and in situ structural analysis of cells and tissues

Cryo-volume electron microscopy (CVEM) enables three-dimensional imaging of biological ultrastructure in a near-native state but has been limited by low image contrast and charging artifacts that hinder data interpretation and complicate automation of data acquisition. Here we present an experimental and computational workflow that combines orthogonal cryo-SEM imaging, spot-geometry optimized O+ plasma-FIB milling, dedicated acquisition-control routines, and dedicated image alignment procedure. The workflow enables autonomous acquisition of volumetric datasets from vitrified cells and tissues at [~]15-20 nm isotropic resolution. In addition, sub-volume averaging of 113 nuclear pore complexes extracted from CVEM dataset of Cos-7 cell yielded its reconstruction at 9.4 nm resolution. Together, these results establish CVEM as a robust platform for autonomous high-resolution volumetric imaging and structural analysis of vitrified biological specimens.

Cell Biology↗

bioRxiv2026

A stable cryogenic fluorescence microscope for correlative super-resolution light and electron microscopy

Cryogenic correlative light and electron microscopy (cryo-CLEM) enables visualization of biological specimens with molecular specificity while preserving near-native macromolecular structure. However, the severely limited resolution of conventional cryo-fluorescence microscopes restricts the accuracy of correlation with cryo-electron microscopy. Super-resolution cryogenic CLEM (SR-cryo-CLEM) offers a potential solution, but presents substantial technical challenges, including mechanical instability and ice contamination. Here, we introduce a modular cryogenic light microscope optimized for single-molecule localization microscopy (cryo-SMLM) that mitigates such limitations. The system is constructed primarily from off-the-shelf components, enabling straightforward and cost-effective assembly, and is operated using fully open-source Python software for flexible and customizable control. The mechanically and thermally stabilized architecture, combined with an axial focus-lock system, maintains sample positioning within a standard deviation of 40 nm. Ice contamination is minimized by imaging inside a purged enclosure, enabling prolonged acquisitions. Together, the platform provides robust localization precision, reproducible imaging performance, and an accessible solution for SR-cryo-CLEM.

molecular biology↗

bioRxiv2026

Enabling high-plex spectral imaging via DNA-barcoded signal tuning and panel optimization

High-plex spectral imaging has the potential to transform the analysis of spatial organization in cells and tissues, yet its practical implementation remains limited by challenges in panel design, sample preparation, signal balancing, and experimental validation. While cyclic imaging approaches are widely used in spatial omics, spectral imaging across the full fluorescence spectrum and computational unmixing remain underutilized due to these challenges. Here, we present a generalizable framework for high-plex spectral imaging that leverages DNA-barcoded labeling and programmable signal amplification to provide precise control over fluorescence signal composition. Orthogonal DNA barcodes decouple target labeling from fluorophore detection, enabling reversible fluorophore application and systematic panel optimization directly on the same sample. Programmable DNA-based amplification further enables independent and quantitative tuning of fluorescence intensities across targets, overcoming a key limitation of spectral unmixing, namely imbalanced signal contributions in overlapping channels, and thereby improving accuracy and robustness. The framework also supports the generation of experiment-specific ground truth datasets and systematic evaluation of unmixing algorithms, providing a quantitative basis for panel validation and performance assessment. We demonstrate the practical implementation of this framework by developing a panel for simultaneous imaging of 15 subcellular structures without fluidic cycling and using the optimized panel to profile the effects of chemical perturbations on subcellular organization. We quantitatively evaluate panel compilation and provide a rigorous assessment unmixing performance using both linear and reference-free unmixing methods. Importantly, we leverage foundation models trained on standard fluorescence data, for segmentation-free, high-dimensional analysis of spectrally unmixed images without needing large datasets or model retraining. Together, we establish a practical and tunable framework for high-plex spectral imaging that lowers experimental barriers and enables broader adoption of spectral unmixing for biological and biomedical applications.

bioengineering↗

bioRxiv2025

CryoSiam: self-supervised representation learning for automated analysis of cryo-electron tomograms

Cryo-electron tomography (cryo-ET) enables visualization of macromolecular complexes in their native cellular context, but interpretation remains challenging due to high noise levels, missing information, and lack of ground-truth data. Here, we present CryoSiam (CRYO-electron tomography SIAMese networks), an open-source framework for self-supervised representation learning in cryo-ET. CryoSiam learns hierarchical representations of tomographic data spanning both voxel-level and subtomogram-level information. To train CryoSiam, we generated CryoETSim (CRYO-Electron Tomography SIMulated), a synthetic dataset that systematically models defocus variation, sample thickness, and molecular crowding. CryoSiam trained models transfer directly to experimental data without fine-tuning and support key aspects of cryo-ET data analysis, including tomogram denoising, segmentation of subcellular structures, and macromolecular detection and identification across both prokaryotic and eukaryotic systems. Publicly available pretrained models and the CryoETSim dataset provide a foundation for scalable and automated cryo-ET analysis.

bioinformatics↗

bioRxiv2025

A whole-body cell type atlas mapped into an electron microscopy volume of an annelid worm

Differential gene expression establishes the distinct physiology and morphology of cell types in an animal body. Single-cell sequencing and volume EM represent milestones toward the characterization of cell types, yet are difficult to combine for a comprehensive view on the cellular genotype-phenotype link. Here, we map a whole-body single-cell transcriptome into the PlatyBrowser, a multimodal cellular atlas for the marine annelid Platynereis dumerilii, and establish this combination uniquely for an entire animal. We learn that, in the 6-days-old worm, the majority of genes are tightly co-regulated to jointly implement one of eight major cellular morphotypes representing epidermis, gut, vasculature, myofibres, glia, motile cilia, glands, or neurons. Focusing on neurons, we uncover 14 families that by transcription factor identity, axonal projection, or sensory-secretory apparatus resemble conserved neuron types found in vertebrates, insects, or nematodes. We hypothesize that these existed in urbilaterian ancestors and represent the ancient core of nervous system centralization.

evolutionary biology↗

bioRxiv2025

Mesoglea biogenesis reveals a cryptic aboral valve for pressure regulation in cnidarian morphogenesis

Cnidarians are classically defined by a single oral opening, a hallmark of the "blind gut" model in early animal evolution. Here, we identify a pressure-sensitive aboral valve in Nematostella vectensis that operates independently of digestion. This valve dissipates elevated hydraulic pressure during morphogenesis, by expelling fluid through transient epidermal ruptures triggered by muscular ring opening. This unexpected function emerged from a comprehensive analysis of mesogleal basement membrane biogenesis. We show that the global dynamics of this extracellular matrix transduce muscular hydraulics to drive tissue rearrangement and stabilize shape, while localized FGFRb-dependent matrix remodeling establishes the aboral valve. By positioning the mesoglea as an integrator of biomechanics, tissue remodeling, and aboral valve function, these findings expand non-bilaterian openings beyond the digestive paradigm as a hydraulic regulator.

developmental biology↗

bioRxiv2024

A Spatiotemporal Atlas of Mouse Gastrulation and Early Organogenesis to Explore Axial Patterning and Project In Vitro Models onto In Vivo Space

At the onset of murine gastrulation, pluripotent epiblast cells migrate through the primitive streak, generating mesodermal and endodermal precursors, while the ectoderm arises from the remaining epiblast. Together, these germ layers establish the body plan, defining major body axes and initiating organogenesis. Although comprehensive single cell transcriptional atlases of dissociated mouse embryos across embryonic stages have provided valuable insights during gastrulation, the spatial context for cell differentiation and tissue patterning remain underexplored. In this study, we employed spatial transcriptomics to measure gene expression in mouse embryos at E6.5 and E7.5 and integrated these datasets with previously published E8.5 spatial transcriptomics1 and a scRNA-seq2 atlas spanning E6.5 to E9.5. This approach resulted in a comprehensive spatiotemporal atlas, comprising over 150,000 cells with 88 refined cell type annotations as well as genome-wide transcriptional imputation during mouse gastrulation and early organogenesis. The atlas facilitates exploration of gene expression dynamics along anterior-posterior and dorsal-ventral axes at cell type, tissue, and organismal scales, revealing insights into mesodermal fate decisions within the primitive streak. Moreover, we developed a bioinformatics pipeline to project additional scRNA-seq datasets into a spatiotemporal framework and demonstrate its utility by analysing cardiovascular models of gastrulation3. To maximise impact, the atlas is publicly accessible via a user-friendly web portal empowering the wider developmental and stem cell biology communities to explore mechanisms of early mouse development in a spatiotemporal context.

developmental biology↗

bioRxiv2024

Spherical harmonics texture extraction for versatile analysis of biological objects

The characterization of phenotypes in cells or organisms from microscopy data largely depends on differences in the spatial distribution of image intensity. Multiple methods exist for quantifying the intensity distribution - or image texture - across objects in natural images. However, many of these texture extraction methods do not directly adapt to 3D microscopy data. Here, we present Spherical Texture extraction, which measures the variance in intensity per angular wavelength by calculating the Spherical Harmonics or Fourier power spectrum of a spherical or circular projection of the angular mean intensity of the object. This method provides a 20-value characterization that quantifies the scale of features in the spherical projection of the intensity distribution, giving a different signal if the intensity is, for example, clustered in parts of the volume or spread across the entire volume. We apply this method to different systems and demonstrate its ability to describe various biological problems through feature extraction. The Spherical Texture extraction characterizes biologically defined gene expression patterns in Drosophila melanogaster embryos, giving a quantitative read-out for pattern formation. Our method can also quantify morphological differences in Caenorhabditis elegans germline nuclei, which lack a predefined pattern. We show that the classification of germline nuclei using their Spherical Texture outperforms a convolutional neural net when training data is limited. Additionally, we use a similar pipeline on 2D cell migration data to extract polarization direction, quantifying the alignment of fluorescent markers to the migration direction. We implemented the Spherical Texture method as a plugin in ilastik, making it easy to install and apply to any segmented 3D or 2D dataset. Additionally, this technique can also easily be applied through a Python package to provide extra feature extraction for any object classification pipeline or downstream analysis. Author summaryWe introduce a novel method to extract quantitative data from microscopy images by precisely measuring the distribution of intensities within objects in both 3D or 2D. This method is easily accessible through the object classification workflow of ilastik, provided the original image is segmented into separate objects. The method is specifically designed to analyze mostly convex objects, focusing on the variation in fluorescence intensity caused by differences in their shapes or patterns. We demonstrate the versatility of our method by applying it to very different biological samples. Specifically, we showcase its effectiveness in quantifying the patterning in D. melanogaster embryos, in classifying the nuclei in C. elegans germlines, and in extracting polarization information from individual migratory cells. Through these examples, we illustrate that our technique can be employed across different biological scales. Furthermore, we highlight the multiple ways in which the data generated by our method can be used, including quantifying the strength of a specific pattern, employing machine learning to classify diverse morphologies, or extracting directionality or polarization from fluorescence intensity.

bioinformatics↗

bioRxiv2024

A deep learning-based toolkit for 3D nuclei segmentation and quantitative analysis in cellular and tissue context

We present a new set of computational tools that enable accurate and widely applicable 3D segmentation of nuclei in various 3D digital organs. We developed a novel approach for ground truth generation and iterative training of 3D nuclear segmentation models, which we applied to popular CellPose, PlantSeg, and StarDist algorithms. We provide two high-quality models trained on plant nuclei that enable 3D segmentation of nuclei in datasets obtained from fixed or live samples, acquired from different plant and animal tissues, and stained with various nuclear stains or fluorescent protein-based nuclear reporters. We also share a diverse high-quality training dataset of about 10,000 nuclei. Furthermore, we advanced the MorphoGraphX analysis and visualization software by, among other things, providing a method for linking 3D segmented nuclei to their surrounding cells in 3D digital organs. We found that the nuclear-to-cell volume ratio varies between different ovule tissues and during the development of a tissue. Finally, we extended the PlantSeg 3D segmentation pipeline with a proofreading script that uses 3D segmented nuclei as seeds to correct cell segmentation errors in difficult-to-segment tissues. Summary StatementWe present computational tools that allow versatile and accurate 3D nuclear segmentation in plant organs, enable the analysis of cell-nucleus geometric relationships, and improve the accuracy of 3D cell segmentation.

plant biology↗

bioRxiv2023

Deep intravital brain tumor imaging enabled by tailored three-photon microscopy and analysis

Intravital two-photon microscopy has emerged as a powerful technology to study brain tumor biology and its temporal dynamics, including invasion, proliferation and therapeutic resistance in the superficial layers of the mouse cortex. However, intravital microscopy of deeper cortical layers and especially the subcortical white matter, an important route of glioblastoma invasion and recurrence, has not yet been feasible due to low signal-to-noise ratios, missing spatiotemporal resolution and the inability to delineate myelinated axonal tracts. Here, we present a tailored intravital microscopy and artificial intelligence-based analysis methodology and workflow that enables routine deep imaging of glioblastoma over extended time periods, named Deep3P. We show that three-photon microscopy, adaptive optics, as well as customized deep learning-based denoising and machine learning segmentation together allow for deep brain intravital investigation of tumor biology up to 1.2 mm depth. Leveraging this approach, we find that perivascular invasion is a preferred invasion route into the corpus callosum as compared to intracortical glioblastoma invasion and uncover two vascular mechanisms of glioblastoma migration in the white matter. Furthermore, we can define an imaging biomarker of white matter disruption during early glioblastoma colonization. Taken together, Deep3P allows for an efficient and non-invasive investigation of brain tumor biology and its tumor microenvironment in unprecedented deep white and gray matter of the living mouse, opening up novel opportunities for studying the neuroscience of brain tumors and other model systems.

cancer biology↗

bioRxiv2023

CebraEM: A practical workflow to segmentcellular organelles in volume SEM datasetsusing a transferable CNN-based membraneprediction

Segmentation of large-volume datasets obtained by volume SEM techniques is a challenging task that generally requires a considerable amount of human effort. Despite recent advances in deep learning leading to the successful segmentation of cellular organelles in a variety of datasets, it is still challenging and time-consuming to produce the necessary data for training a convolutional neural network as well as to set up targeted post-processing pipelines to obtain a good quality full-volume semantic instance segmentation. We present CebraEM, a software package that uses a novel workflow for the segmentation of organelles in volume EM datasets, which helps to minimize the annotation time for the generation of training data. It relies on a generic CNN-based membrane prediction, followed by a well-established machine-learning pipeline that includes over-segmentation before random forest classification and graph multi-cut grouping. The workflow was tested for the segmentation of organelles on different datasets originating from various sample preparations and imaging modalities in volume SEM, in each case resulting in state-of-the-art semantic instance segmentations without additional post-processing. Importantly, by considerably simplifying the segmentation problem, CebraEM empowers single users with the ability to efficiently segment hundreds of gigabytes of data.

cell biology↗

bioRxiv2023

Temporal variability and cell mechanics control robustness in mammalian embryogenesis

How living systems achieve precision in form and function despite their intrinsic stochasticity is a fundamental yet open question in biology. Here, we establish a quantitative morphomap of pre-implantation embryogenesis in mouse, rabbit and monkey embryos, which reveals that although blastomere divisions desynchronise passively without compensation, 8-cell embryos still display robust 3D structure. Using topological analysis and genetic perturbations in mouse, we show that embryos progressively change their cellular connectivity to a preferred topology, which can be predicted by a simple physical model where noise and actomyosin-driven compaction facilitate topological transitions lowering surface energy. This favours the most compact embryo packing at the 8- and 16-cell stage, thus promoting higher number of inner cells. Impairing mitotic desynchronisation reduces embryo packing compactness and generates significantly more cell mis-allocation and a lower proportion of inner-cell-mass-fated cells, suggesting that stochasticity in division timing contributes to achieving robust patterning and morphogenesis.

developmental biology↗

bioRxiv2022

COORDINATION BETWEEN EMBRYO GROWTH AND TROPHOBLAST MIGRATION UPON IMPLANTATION DELINEATES MOUSE EMBRYOGENESIS

Implantation marks a key transition in mammalian development. The role of embryo-uterus interaction in periimplantation development is however poorly understood due to inaccessibility in utero. Here, we develop an engineered uterus-like microenvironment to recapitulate mouse development ex vivo up to E5.25 and discover an essential role of integrin-mediated trophoblast adhesion to the uterine matrix. Light-sheet microscopy shows that trophoblast cells undergo Rac1-dependent collective migration upon implantation, displacing Reicherts membrane and generating space for egg cylinder growth. The key role of coordination between trophoblast migration and embryo growth is verified by experimentally manipulating the migration velocity and geometry of the engineered uterus. Modeling the implanting embryo as a wetting droplet links the tissue shape dynamics to underlying changes in trophoblast adhesion and suggests that the corresponding tension release facilitates egg cylinder formation. Together, this study provides mechanisms by which dynamic embryo-uterus interactions play an essential role in peri-implantation development.

developmental biology↗

bioRxiv2022

BioImage Model Zoo: A Community-Driven Resource for Accessible Deep Learning in BioImage Analysis

Deep learning-based approaches are revolutionizing imaging-driven scientific research. However, the accessibility and reproducibility of deep learning-based workflows for imaging scientists remain far from sufficient. Several tools have recently risen to the challenge of democratizing deep learning by providing user-friendly interfaces to analyze new data with pre-trained or fine-tuned models. Still, few of the existing pre-trained models are interoperable between these tools, critically restricting a models overall utility and the possibility of validating and reproducing scientific analyses. Here, we present the BioImage Model Zoo (https://bioimage.io): a community-driven, fully open resource where standardized pre-trained models can be shared, explored, tested, and downloaded for further adaptation or direct deployment in multiple end user-facing tools (e.g., ilastik, deepImageJ, QuPath, StarDist, ImJoy, ZeroCostDL4Mic, CSBDeep). To enable everyone to contribute and consume the Zoo resources, we provide a model standard to enable cross-compatibility, a rich list of example models and practical use-cases, developer tools, documentation, and the accompanying infrastructure for model upload, download and testing. Our contribution aims to lay the groundwork to make deep learning methods for microscopy imaging findable, accessible, interoperable, and reusable (FAIR) across software tools and platforms.

bioinformatics↗

bioRxiv2022

MoBIE: A Fiji plugin for sharing and exploration of multi-modal cloud-hosted big image data

Facing the challenge of exploring and sharing multi-terabyte, multi-modal and multi-scale image data of heterogeneous dimensionality, we developed MoBIE, a Fiji plugin that provides rich visualization features to enable browsing data from numerous biomedical applications on a standard laptop computer. MoBIE also supports segmentations, associated measurements and annotations. Users can configure complex views of datasets, share them with collaborators, and use them for interactive figure panels. The MoBIE plugin also offers a convenient interface for converting data into compatible data formats; an additional Python library facilitates managing diverse MoBIE projects.

bioinformatics↗

bioRxiv2022

MorphoFeatures: unsupervised exploration of cell types, tissues and organs in volume electron microscopy

Electron microscopy (EM) provides a uniquely detailed view of cellular morphology, including organelles and fine subcellular ultrastructure. While the acquisition and (semi-)automatic segmentation of multicellular EM volumes is now becoming routine, large-scale analysis remains severely limited by the lack of generally applicable pipelines for automatic extraction of comprehensive morphological descriptors. Here, we present a novel unsupervised method for learning cellular morphology features directly from 3D EM data: a convolutional neural network delivers a representation of cells by shape and ultrastructure. Applied to the full volume of an entire three-segmented worm of the annelid Platynereis dumerilii, it yields a visually consistent grouping of cells supported by specific gene expression profiles. Integration of features across spatial neighbours can retrieve tissues and organs, revealing, for example, a detailed organization of the animal foregut. We envision that the unbiased nature of the proposed morphological descriptors will enable rapid exploration of very different biological questions in large EM volumes, greatly increasing the impact of these invaluable, but costly resources.

developmental biology↗

bioRxiv2022

Convolutional networks for supervised mining of molecular patterns within cellular context

Cryo-electron tomograms capture a wealth of structural information on the molecular constituents of cells and tissues. We present DeePiCt (Deep Picker in Context), an open-source deep-learning framework for supervised structure segmentation and macromolecular complex localization in cellular cryo-electron tomography. To train and benchmark DeePiCt on experimental data, we comprehensively annotated 20 tomograms of Schizosaccharomyces pombe for ribosomes, fatty acid synthases, membranes, nuclear pore complexes, organelles and cytosol. By comparing our method to state-of-the-art approaches on this dataset, we show its unique ability to identify low-abundance and low-density complexes. We use DeePiCt to study compositionally-distinct subpopulations of cellular ribosomes, with emphasis on their contextual association with mitochondria and the endoplasmic reticulum. Finally, by applying pre-trained networks to a HeLa cell dataset, we demonstrate that DeePiCt achieves high-quality predictions in unseen datasets from different biological species in a matter of minutes. The comprehensively annotated experimental data and pre-trained networks are provided for immediate exploitation by the community.

bioinformatics↗