Biology subjects

Jug, F.

Publications and source records attributed to Jug, F..

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↗

bioRxiv2025

MicroSplit: Semantic Unmixing of Fluorescent Microscopy Data

Fluorescence microscopy, a key driver for progress in the life sciences, faces limitations due to the microscopes optics, fluorophore chemistry, and photon exposure limits, necessitating trade-offs in imaging speed, resolution, and depth. Here, we introduce Micro[S]plit, a computational multiplexing technique based on deep learning that allows multiple cellular structures to be imaged in a single fluorescent channel and then unmix them by computational means, allowing faster imaging and reduced photon exposure. We show that Micro[S]plit efficiently separates up to four superimposed noisy structures into distinct denoised fluorescent image channels. Furthermore, using Variational Splitting Encoder-Decoder (VSE) networks, our approach can sample diverse predictions from a trained posterior of solutions. The diversity of these samples scales with the uncertainty in a given input, allowing us to estimate the true prediction errors by computing the variability between posterior samples. We demonstrate the robustness of Micro[S]plit networks, which are trained for each splitting task at hand, across various datasets and noise levels and show its utility to image more, to image faster, and to improve downstream analysis. We provide Micro[S]plit along with all associated training and evaluation datasets as open resources, enabling life scientists to immediately benefit from the potential of computational multiplexing and thus help accelerate the rate of their scientific discovery process.

bioinformatics↗

bioRxiv2024

CALIPERS: Cell cycle-aware live imaging for phenotyping experiments and regeneration studies

Cell cycle progression, migration, and proliferation shape development and regeneration, but simultaneous live-cell imaging remains challenging as conventional fluorescent cell cycle indicators (FUCCI) monopolize the green and red channels used by most structural and functional biosensors. To overcome this, we integrated a spectrally re-engineered FUCCI variant, open-source analysis software, and four-color human stem cell reporter lines into CALIPERS: a method for Cell-cycle-Aware Live-cell Imaging in Phenotyping and Regeneration Studies.

bioengineering↗

bioRxiv2024

FeatureForest: the power of foundation models, theusability of random forests

Once the work at the microscope is done, biological discoveries rely heavily on proper downstream analysis. This often amounts to first segmenting the biological objects of interest in the image before performing a quantitative analysis. Deep-learning (DL) is nowadays ubiquitous in such segmentation tasks. However, DL can be cumbersome to apply, as it often requires large amount of manual labeling to produce ground-truth data, and expert knowledge to train the models from scratch. Nonetheless, the performance of large foundation models, although trained on natural images, are improving on scientific images with every new model released. They, however, require either manual prompting or tedious post-processing to selectively segment the biological objects of interest. Classical machine learning algorithms, such as random forest classifiers, on the other hand, are well-established, easy to train, and often yield results of sufficient quality for downstream processing tasks, hence their continued popularity. Unfortunately, they are limited to objects with distinct, well-defined textures compared to their environment. This generally limits their usefulness to structures easy to recognize. Here, we present FeatureForest, an open-source tool that leverages the feature embeddings of large foundation models to train a random forest classifier, thereby providing users with a rapid way of semantically segmenting complex images using only a few labeling strokes. We demonstrate the improvement in performance over a variety of datasets, including large and complex volumetric electron microscopy stacks. Our implementation is available in napari, currently integrates four foundation models, and can easily be extended to any new model once they become available.

bioinformatics↗

bioRxiv2024

Hijacking and Integration of Algal Plastids andMitochondria in a Polar Planktonic Host

In oceanic plankton, various host organisms are capable of engulfing and temporarily integrating microalgae (photosymbiosis) or just their photosynthetic plastids (kleptoplastidy) as a solar-powered energy source. These cellular interactions can be considered to be representative of evolutionary steps in plastid acquisition in eukaryotes, but the underlying cellular mechanisms and dynamics are not fully understood. Here, we studied a kleptoplastidic dinoflagellate (RSD: Ross Sea Dinoflagellate), which is known to steal plastids of the microalga Phaeocystis antarctica. We tracked the morphology and activity of stolen plastids over several months by combining multimodal subcellular imaging and photophysiology. Upon integration inside a host vacuole, the volume of plastids and pyrenoids significantly increased and photosynthetic activity was boosted along with carbon fixation and transfer to the host. This may be supported by the retention of a 50-fold larger algal nucleus for [~]1 week. Once the algal nucleus was lost, there was a decrease in plastid volume and photosynthesis, but plastids were still beneficial for the host after > 2 months. Unlike other kleptoplastidic interactions, we showed that the algal mitochondrion was also stolen and retained for several months, transforming into an extensive network in close proximity with plastids. This highlights a new strategy in plankton along the continuum of plastid symbioses where both the energy-producing plastid and mitochondrion of a microalga are hijacked for several months by a host. This symbiosis that we found to be widely-distributed in polar regions suggests that plastid-mitochondrion interaction may have played a role in the evolution of plastid acquisition.

ecology↗

bioRxiv2024

Local DNA compaction creates TF-DNA clusters that enable transcription

Transcription factor (TF) clusters have been suggested to facilitate transcription. The mechanisms driving the formation of TF clusters and their impact on transcription, however, remain largely unclear. This is mostly due to the lack of a tractable system. Here, we exploit the transcriptional activation of mir430 in zebrafish embryos to simultaneously follow the dynamic formation of a large Nanog cluster, the underlying DNA, and transcription output by live imaging at high temporal and spatial resolution. We find that the formation of a Nanog cluster that can support transcription requires local DNA compaction. This brings more Nanog-binding sites into the cluster, and therefore more Nanog. Importantly, we find that Nanog stabilizes this TF-DNA cluster, which emphasizes the interdependent relationship between TFs and DNA dynamics in cluster formation. Once the Nanog-DNA cluster at the mir430 locus reaches a maximum amount of Nanog, transcription begins. This maximum is a locus-intrinsic feature, which shows that the locus self-regulates the recruitment of an optimal amount of Nanog. Our study supports a model in which endogenous TF clusters positively impact transcription and form through a combination of DNA binding and local DNA compaction.

cell biology↗

bioRxiv2023

Mechanical positive feedback and biochemical negative feedback combine to generate complex contractile oscillations in cytokinesis

Contractile force generation by the cortical actomyosin cytoskeleton is essential for a multitude of biological processes. The actomyosin cortex behaves as an active material that drives local and large-scale shape changes via cytoskeletal remodeling in response to biochemical cues and feedback loops. Cytokinesis is the essential cell division event during which a cortical actomyosin ring generates contractile force to change cell shape and separate two daughter cells. Our recent work with active gel theory predicts that actomyosin systems under the control of a biochemical oscillator and experiencing mechanical strain will exhibit complex spatiotemporal behavior, but cytokinetic contractility was thought to be kinetically simple. To test whether active materials in vivo exhibit spatiotemporally complex kinetics, we used 4-dimensional imaging with unprecedented temporal resolution and discovered sections of the cytokinetic cortex undergo periodic phases of acceleration and deceleration. Quantification of ingression speed oscillations revealed wide ranges of oscillation period and amplitude. In the cytokinetic ring, activity of the master regulator RhoA pulsed with a timescale of approximately 20 seconds, shorter than that reported for any other biological context. Contractility oscillated with 20-second periodicity and with much longer periods. A combination of in vivo and in silico approaches to modify mechanical feedback revealed that the period of contractile oscillation is prolonged as a function of the intensity of mechanical feedback. Effective local ring ingression is characterized by slower speed oscillations, likely due to increased local stresses and therefore mechanical feedback. Fast ingression also occurs where material turnover is high, in vivo and in silico. We propose that downstream of initiation by pulsed RhoA activity, mechanical positive feedback, including but not limited to material advection, extends the timescale of contractility beyond that of biochemical input and therefore makes it robust to fluctuations in activation. Circumferential propagation of contractility likely allows sustained contractility despite cytoskeletal remodeling necessary to recover from compaction. Our work demonstrates that while biochemical feedback loops afford systems responsiveness and robustness, mechanical feedback must also be considered to describe and understand the behaviors of active materials in vivo.

cell biology↗

bioRxiv2022

Afadin and zyxin contribute to coupling between cell junctions and contractile actomyosin networks during apical constriction

One of the most common cell shape changes driving morphogenesis in diverse animals is the constriction of the apical cell surface. Apical constriction depends on contraction of an actomyosin network in the apical cell cortex, but such actomyosin networks have been shown to undergo continual, conveyor belt-like contractions before the shrinking of an apical surface begins. This finding suggests that apical constriction is not necessarily triggered by the contraction of actomyosin networks, but rather can be triggered by unidentified, temporally-regulated mechanical links between actomyosin and junctions. Here, we used C. elegans gastrulation as a model to seek genes that contribute to such dynamic linkage. We found that -catenin and {beta}-catenin initially failed to move centripetally with contracting cortical actomyosin networks, suggesting that linkage is regulated between intact cadherin-catenin complexes and actomyosin. We used proteomic and transcriptomic approaches to identify new players, including the candidate linkers AFD-1/afadin and ZYX-1/zyxin, as contributing to C. elegans gastrulation. We found that ZYX-1/zyxin is among a family of LIM domain proteins that have transcripts that become enriched in multiple cells just before they undergo apical constriction. We developed a semi-automated image analysis tool to describe rates of myosin and membrane movement, which we then used to discover that ZYX-1/zyxin contributes to cell-cell junctions centripetal movement in concert with contracting actomyosin networks. These results identify several new genes that contribute to C. elegans gastrulation, and they identify zyxin as a key protein important for actomyosin networks to effectively pull cell-cell junctions inward during apical constriction. The transcriptional upregulation of ZYX-1/zyxin in specific cells in C. elegans points to one way that developmental patterning spatiotemporally regulates cell biological mechanisms in vivo. Because zyxin and related proteins contribute to membrane-cytoskeleton linkage in other systems, we anticipate that its roles in regulating apical constriction in this manner may be conserved.

genetics↗

bioRxiv2022

Nanog organizes transcription bodies

The localization of transcriptional activity in specialized transcription bodies is a hallmark of gene expression in eukaryotic cells. How proteins of the transcriptional machinery come together to form such bodies, however, is unclear. Here, we take advantage of two large, isolated, and long-lived transcription bodies that reproducibly form during early zebrafish embryogenesis, to characterize the dynamics of transcription body formation. Once formed, these transcription bodies are enriched for initiating and elongating RNA polymerase II, as well as the transcription factors Nanog and Sox19b. Analyzing the events leading up to transcription, we find that Nanog and Sox19b cluster prior to transcription, and independently of RNA accumulation. The clustering of transcription factors is sequential; Nanog clusters first, and this is required for the clustering of Sox19b and the initiation of transcription. Mutant analysis revealed that both the DNA-binding domain, as well as one of the two intrinsically disordered regions of Nanog are required to organize the two bodies of transcriptional activity. Taken together, our data suggests that the clustering of transcription factors dictates the formation of transcription bodies. HIGHLIGHTSO_LITranscription factors cluster prior to, and independently of transcription C_LIO_LINanog organizes transcription bodies: it is required for the clustering of Sox19b as well as RNA polymerase II C_LIO_LIThis organizing activity requires its DNA binding domain as well as one of its intrinsically disordered regions C_LIO_LITranscription elongation results in the disassembly of transcription factor clusters C_LI

cell 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

Prediction of designer-recombinases for DNA editing with generative deep learning

Site-specific tyrosine-type recombinases are effective tools for genome engineering, with the first engineered variants having demonstrated therapeutic potential. So far, adaptation to new DNA target site selectivity of designer-recombinases has been achieved mostly through iterative cycles of directed molecular evolution. While effective, directed molecular evolution methods are laborious and time consuming. Here we present RecGen (Recombinase Generator), an algorithm for the intelligent generation of designer-recombinases. We gathered the sequence information of over two million Cre-like recombinase sequences evolved for 89 different target sites with which we trained Conditional Variational Autoencoders for recombinase generation. Experimental validation demonstrated that the algorithm can predict recombinase sequences with activity on novel target-sites, indicating that RecGen is useful to accelerate the development of future designer-recombinases. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=137 SRC="FIGDIR/small/486669v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@c08326org.highwire.dtl.DTLVardef@e26984org.highwire.dtl.DTLVardef@7d7542org.highwire.dtl.DTLVardef@9d6642_HPS_FORMAT_FIGEXP M_FIG C_FIG Teaser Figure: Recombinase prediction generates active recombinases for a desired target site, while existing libraries with a similar target site need to go through directed evolution to achieve activity on the new site. While evolution takes weeks, prediction and synthesis of recombinases can be done in days.

bioinformatics↗

bioRxiv2021

Labkit: Labeling and Segmentation Toolkit for Big Image Data

We present LO_SCPLOWABKITC_SCPLOW, a user-friendly Fiji plugin for the segmentation of microscopy image data. It offers easy to use manual and automated image segmentation routines that can be rapidly applied to single- and multi-channel images as well as to timelapse movies in 2D or 3D. LO_SCPLOWABKITC_SCPLOW is specifically designed to work efficiently on big image data and enables users of consumer laptops to conveniently work with multiple-terabyte images. This efficiency is achieved by using ImgLib2 and BigDataViewer as the foundation of our software. Furthermore, memory efficient and fast random forest based pixel classification inspired by the Waikato Environment for Knowledge Analysis (Weka) is implemented. Optionally we harness the power of graphics processing units (GPU) to gain additional runtime performance. LO_SCPLOWABKITC_SCPLOW is easy to install on virtually all laptops and workstations. Additionally, LO_SCPLOWABKITC_SCPLOW is compatible with high performance computing (HPC) clusters for distributed processing of big image data. The ability to use pixel classifiers trained in LO_SCPLOWABKITC_SCPLOW via the ImageJ macro language enables our users to integrate this functionality as a processing step in automated image processing workflows. Last but not least, LO_SCPLOWABKITC_SCPLOW comes with rich online resources such as tutorials and examples that will help users to familiarize themselves with available features and how to best use LO_SCPLOWABKITC_SCPLOW in a number of practical real-world use-cases.

bioinformatics↗

bioRxiv2021

Tissue scale properties of the extracellular matrix regulates nuclear shape, organisation and fate in the embryonic midline sutures

Balancing self-renewal and differentiation is a key feature of every stem cell niche and one that is tuned by mechanical interactions of cells with their neighbors and surrounding extracellular matrix. The fibrous stem cell niches that develop as sutures between skull bones must balance the complex extracellular environment that emerges to define them with self-renewal and bone production. Here, we address the role for physical stimuli in suture development by probing the relationship between nuclear shape, organization and gene expression in response to a developing collagen network in embryonic midline sutures. This work complements genetic approaches used to study sutures and provides the first quantitative analyses of physical structure in these sutures. By combining multiple imaging modalities with novel shape description, in addition to network analysis methods, we find the early emergence of a complex extracellular collagen network to have an important role in regulating morphogenesis and cell fate. We show that disrupted collagen crosslinking can alter ECM organization of midline sutures as well as stimulate expression of bone differentiation markers. Further, our findings suggest that in vivo, skeletal tissues can uncouple the response of the nuclear lamina from collagen mediated tissue stiffening seen in vitro. Our findings highlight a crucial relationship between the cellular microenvironment, tissue stiffness and geometry with gene expression in normal development and maintenance of progenitor fate in embryonic sutures.

developmental biology↗

bioRxiv2021

Extracellular mechanical forces drive endocardial cell volume decrease during cardiac valve morphogenesis

Organ morphogenesis involves dynamic changes of tissue properties at the cellular scale. In addition, cells need to adapt to their mechanical environment through mechanosensitive pathways. How mechanical cues influence cell behaviors during morphogenesis, however, remains poorly understood. Here we studied the influence of mechanical forces during the formation of the atrioventricular canal (AVC) where cardiac valves develop. We show that in zebrafish the AVC forms within a zone of tissue convergence between the atrium and the ventricle which is associated with increased activation of the actomyosin meshwork and endocardial cell orientation changes. We demonstrate that tissue convergence occurs with a major reduction of endocardial cell volume triggered by mechanical forces and the mechanosensitive channels TRPP2/TRPV4. In addition, we show that the extracellular matrix component hyaluronic acid controls cell volume changes. Together, our data suggest that cell volume change is a key cellular feature activated by mechanical forces during cardiovascular morphogenesis. This work further unravels how mechanical forces and extracellular matrix can influence tissue remodeling in developing organs.

developmental biology↗

bioRxiv2021

Intraflagellar transport trains can turn around without the ciliary tip complex

Cilia and flagella are microtubule doublet based organelles found across the eukaryotic tree of life. Their very high aspect ratio and crowded interior are unfavourable to diffusive transport for their assembly and maintenance. Instead, a highly dynamic system of intraflagellar transport (IFT) trains moves rapidly up and down the cilium. However, the mechanism of how these trains turn around upon reaching the ciliary tip has remained elusive. It has been hypothesized that there exists a dedicated calcium-dependent protein-based machinery at the ciliary tip to mediate this conversion. In this work, we use physical and chemical methods to manipulate IFT in the cilia of the unicellular green alga Chlamydomonas reinhardtii to show that no such stationary tip-machinery is required for IFT turnaround. Instead, we demonstrate that the conversion from anterograde to retrograde IFT trains is a calcium independent intrinsic ability of the IFT system.

cell biology↗

bioRxiv2020

Three-dimensional FIB-SEM reconstruction ofmicrotubule-organelle interaction in wholeprimary mouse beta cells

Microtubules play a major role in intracellular trafficking of vesicles in endocrine cells. Detailed knowledge of microtubule organization and their relation to other cell constituents is crucial for understanding cell function. However, their role in insulin transport and secretion is currently under debate. Here, we use FO_SCPLOWIBC_SCPLOW-SO_SCPLOWEMC_SCPLOW to image islet beta cells in their entirety with unprecedented resolution. We reconstruct mitochondria, Golgi apparati, centrioles, insulin secretory granules and micro-tubules of seven beta cells, and generate a comprehensive spatial map of microtubule-organelle interactions. We find that micro-tubules form non-radial networks that are predominantly not connected to either centrioles or endomembranes. Microtubule number and length, but not microtubule polymer density, vary with glucose stimulation. Furthermore, insulin secretory granules are enriched near the plasma membrane where they associate with microtubules. In summary, we provide the first 3D reconstructions of complete microtubule networks in primary mammalian cells together with evidence regarding their importance for insulin secretory granule positioning and thus supportive role in insulin secretion.

cell biology↗

bioRxiv2020

ZeroCostDL4Mic: an open platform to simplify access and use of Deep-Learning in Microscopy

The resources and expertise needed to use Deep Learning (DL) in bioimaging remain significant barriers for most laboratories. We present https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki, a platform simplifying access to DL by exploiting the free, cloud-based computational resources of Google Colab. https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki allows researchers to train, evaluate, and apply key DL networks to perform tasks including segmentation, detection, denoising, restoration, resolution enhancement and image-to-image translation. We demonstrate the application of the platform to study multiple biological processes.

bioinformatics↗

bioRxiv2019

Regionalized tissue fluidization by an actomyosin cable is required for epithelial gap closure during insect gastrulation

Many animal embryos pull and close an epithelial sheet around the spherical or ellipsoidal egg surface during a gastrulation process known as epiboly. The ovoidal geometry dictates that the epithelial sheet first expands and subsequently compacts. Moreover, the epithelial sheet spreading over the sphere is mechanically stressed and this stress needs to be released. Here we show that during extraembryonic tissue (serosa) epiboly in the red flour beetle Tribolium castaneum, the non-proliferative serosa becomes regionalized into two distinct territories: a dorsal region under higher tension away from the leading edge with larger non-rearranging cells, and a more fluid ventral region under lower tension surrounding the leading edge with smaller cells undergoing cell intercalation. Our results suggest that fluidization of the leading edge is caused by a heterogeneous actomyosin cable that drives sequential eviction and intercalation of individual cells away from the serosa margin. Since this developmental solution utilized during epiboly resembles the mechanism of wound healing in other systems, we propose actomyosin cable-driven local tissue fluidization as a conserved morphogenetic module for closure of epithelial gaps.

developmental biology↗