Biology subjects

Weigert, M.

Publications and source records attributed to Weigert, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Automated Optimization of Bacterial Tracking Pipelines with TrackMate 8

Quantitative analysis of bacterial dynamics in time-lapse microscopy requires robust tracking pipelines, yet selecting and optimizing algorithms for specific experiments remains challenging. Indeed, Microbiologists are confronted with numerous algorithms that must be carefully chosen and parameterized to achieve optimal tracking for their experiments. We present an automated methodology to determine optimal tracking configurations for microbiological applications. It is based on TrackMate 8, a novel version of the TrackMate Fiji plugin extended with microbiology-specific tools. Our approach systematically evaluates algorithm-parameter combinations optimizing biologically relevant metrics (e.g., cell-cycle accuracy, bacteria morphology) and includes: (1) integration of deep-learning algorithms (Omnipose, YOLO, Trackastra) adequate for bacteria images in TrackMate, (2) a TrackMate-Helper extension for parameter optimization, and (3) a tracking and segmentation editor for tracking ground-truth generation. We demonstrate the effectiveness of the methodology on two use cases showing its adaptability to diverse experimental conditions. This methodology enables microbiologists with a widely applicable, automated framework to optimize tracking pipelines, facilitating quantitative analysis in bacterial imaging.

microbiology↗

bioRxiv2025

Smart hybrid microscopy for cell-friendlydetection of rare events

Fluorescence microscopy offers unparalleled access to the spatial organization and dynamics of biological events in living samples, yet capturing rare processes over extended durations remains challenging due to tradeoffs between exposure to excitation light and sample health. Here, we introduce hybrid-EDA, an event-driven acquisition (EDA) framework that combines the gentleness and contextual richness of phase contrast with the functional specificity of fluorescence. We developed surveillance for events of interest in label-free microscopy by novel dynamics-informed neural networks that trigger smart acquisitions in fluorescence upon detection. This allows us to dramatically reduce phototoxic damage while obtaining specific and functional information from fluorescence when beneficial. We demonstrate how hybrid-EDA enables improved imaging acquisitions of organelle contacts and mitochondrial divisions. We envision that hybrid-EDA will enable new insights into a range of dynamic and rare biological processes, providing a powerful and general strategy for cell-friendly imaging.

cell biology↗

bioRxiv2024

Morpho-molecular features of Epithelial Mesenchymal Transition associate with clinical outcome in patients with rectal cancer

In rectal cancer, where part of the patients undergoes chemoradiotherapy, there is a need for improved pretreatment biomarkers applicable to biopsies. Tumor budding (TB) is a biomarker used in colon cancer, and due to its link to epithelial-mesenchymal transition (EMT), is hypothesized to be a potential marker for therapy resistance. Assessment of the utility of tumor buds in rectal biopsies is challenging due to their rarity. As EMT-related processes are also seen in other morphological features beyond tumor buds, we investigated EMT in tumor tissue including morphological features such as tumor cluster size and fibril-like structures. To do so, we leveraged a cohort of colon cancer whole-slide images and another cohort consisting of rectal cancer biopsies, visualized using hyperplex immunofluorescence to identify tumor and EMT-associated proteins. We built a custom image analysis pipeline to detect and segment tumor buds and other morphological features and correlated them with molecular expression intensities. We found strong correlations of EMT up-regulation and morphological transition states, both at the invasive margin and the tumor center. We furthermore observed a link between morpho-molecular transitions and histological growth patterns, which in turn can inform novel biomarkers. Finally, quantification of these morpho-molecular transition states in rectal biopsies showed their impact on survival after neoadjuvant chemoradiotherapy.

cancer biology↗

bioRxiv2024

Specialized signaling centers direct cell fate and spatial organization in a limb organoid model

Specialized signaling centers orchestrate robust development and regeneration. Limb morphogenesis, for instance, requires interactions between the mesoderm and the signaling center apical-ectodermal ridge (AER), whose properties and role in cell fate decisions have remained challenging to dissect. To tackle this, we developed mouse embryonic stem cells (mESCs)-based heterogeneous cultures and a limb organoid model, termed budoids, comprising cells with AER, surface ectoderm, and mesoderm properties. mESCs were first induced into heterogeneous cultures that self-organized into domes in 2D. Aggregating these cultures resulted in formation of limb bud-like structures in 3D, exhibiting chondrogenesis-based symmetry breaking and elongation. Using our organoids and quantitative in situ expression profiling, we uncovered that AER-like cells support nearby limb mesoderm and fibroblast identities while enhancing tissue polarization that permits distant cartilage formation. Together, our findings provide a powerful model to study aspects of limb morphogenesis, and reveal the ability of signaling center AER cells to concurrently modulate cell fate and spatial organization.

developmental biology↗

bioRxiv2024

Spotiflow: accurate and efficient spot detection for imaging-based spatial transcriptomics with stereographic flow regression

Identifying spot-like structures in large and noisy microscopy images is a crucial step to produce high quality results in various life-science applications. Imaging-based spatial transcriptomics (iST) methods, in particular, critically depend on the precise detection of millions of transcripts in images with low signal-to-noise ratio. Despite advances in computer vision that have revolutionized many biological imaging tasks, currently adopted spot detection techniques are mostly still based on classical signal processing methods that often lack robustness to changing imaging conditions and thus require tedious manual tuning per dataset. In this work, we introduce Spotiflow, a deep learning method that achieves subpixel-accurate localizations by formulating the spot detection task as a multi-scale heatmap and stereographic flow regression problem. Spotiflow can be used for 2D images and 3D volumetric stacks and can be trained to generalize across different imaging conditions, tissue types and chemical preparations, while being substantially more time- and memory-efficient than existing methods. We show the efficacy of Spotiflow via extensive quantitative experiments on a variety of diverse datasets and demonstrate that the enhanced accuracy of SO_SCPLOWPOTIFLOWC_SCPLOW leads to meaningful improvements in the biological insights obtained from iST and live imaging experiments. Spotiflow is available as an easy-to-use Python library as well as a napari plugin at https://github.com/weigertlab/spotiflow.

bioinformatics↗

bioRxiv2022

A molecular atlas of the human postmenopausal fallopian tube and ovary from single-cell RNA and ATAC sequencing

As part of the Human Cell Atlas initiative, we generated transcriptomic (scRNA-seq; 86,708 cells) and regulatory (scATAC-seq; 59,118 cells) profiles of the normal postmenopausal ovary and fallopian tube (FT) at single-cell resolution. In the FT, 22 cell clusters integrated into 11 cell types, including ciliated and secretory epithelial cells, while the ovary had 17 distinct cell clusters defining 6 major cell types. The dominant cell type in both the postmenopausal ovary and FT was stromal cells, which expressed several genes associated with aging. The fimbrial end of the FT had a significant number and variety of immune cells and active communication with the ovary; the ovary contained mostly stromal cells but few immune cells. The epithelial cells of the normal FT expressed multiple ovarian cancer risk-associated genes (CCDC170, RND3, TACC2, STK33, and ADGB). By integrating paired single-cell transcriptomics and chromatin accessibility data we found that the regulatory landscape of the fimbriae was markedly different from the isthmus and ampulla. Intriguingly, several cell types in the FT had comparable gene expression but different transcriptional regulations. Our single-cell transcriptional and regulatory maps allowed us to disentangle the complex cellular makeup of the postmenopausal FT and ovary and will contribute to a better understanding of gynecologic diseases in menopause.

genomics↗

bioRxiv2022

Deep neural network automated segmentation of cellular structures in volume electron microscopy

Recent advances in automated segmentation using deep neural network models allow identification of intracellular structures. This study describes a new pipeline to train a convolutional neural network for rapid and efficient detection of structures of wide range in size and complexity. AbstractThree-dimensional electron microscopy is an important imaging modality in contemporary cell biology. Identification of intracellular structures is laborious and time-consuming, however, and impairs effective use of a potentially powerful tool. Resolving this bottleneck is therefore a critical next step in frontier biomedical imaging. We describe Automated Segmentation of intracellular substructures in Electron Microscopy (ASEM), a new pipeline to train a convolutional neural network to detect structures of wide range in size and complexity. We obtain for each structure a dedicated model based on a small number of sparsely annotated ground truth annotations from only one or two cells. To improve model generalization to different imaging conditions, we developed a rapid, computationally effective strategy to refine an already trained model by including a few additional annotations. We show the successful automated identification of mitochondria, Golgi apparatus, endoplasmic reticulum, nuclear pore complexes, caveolae, clathrin coated pits and coated vesicles in cells imaged by focused ion beam scanning electron microscopy with quasi-isotropic resolution. ASEM enabled us to uncover a wide range of membrane-nuclear pore diameters within a single cell and to derive morphological metrics from clathrin coated pits and vesicles at all stages of maturation consistent with the classical constant-growth assembly model.

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↗

bioRxiv2021

Event-driven acquisition for content-enriched microscopy

A common goal of fluorescence microscopy is to collect data on specific biological events. Yet, the event-specific content that can be collected from a sample is limited, especially for rare or stochastic processes. This is due in part to photobleaching and phototoxicity, which constrain imaging speed and duration. We developed an event-driven acquisition (EDA) framework, in which neural network-based recognition of specific biological events triggers real-time control in an instant structured illumination microscope (iSIM). Our setup adapts acquisitions on-the-fly by switching between a slow imaging rate while detecting the onset of events, and a fast imaging rate during their progression. Thus, we capture mitochondrial and bacterial divisions at imaging rates that match their dynamic timescales, while extending overall imaging durations. Because EDA allows the microscope to respond specifically to complex biologi-cal events, it acquires data enriched in relevant content.

cell biology↗

bioRxiv2021

Expansion-Assisted Iterative-FISH defines lateral hypothalamus spatio-molecular organization

Determining the spatial organization and morphological characteristics of molecularly defined cell types is a major bottleneck for characterizing the architecture underpinning brain function. We developed Expansion-Assisted Iterative Fluorescence In Situ Hybridization (EASI-FISH) to survey gene expression in brain tissue, as well as a turnkey computational pipeline to rapidly process large EASI-FISH image datasets. EASI-FISH was optimized for thick brain sections (300 {micro}m) to facilitate reconstruction of spatio-molecular domains that generalize across brains. Using the EASI-FISH pipeline, we investigated the spatial distribution of dozens of molecularly defined cell types in the lateral hypothalamic area (LHA), a brain region with poorly defined anatomical organization. Mapping cell types in the LHA revealed nine novel spatially and molecularly defined subregions. EASI-FISH also facilitates iterative re-analysis of scRNA-Seq datasets to determine marker-genes that further dissociated spatial and morphological heterogeneity. The EASI-FISH pipeline democratizes mapping molecularly defined cell types, enabling discoveries about brain organization. Highlights- EASI-FISH enables robust gene expression profiling in thick brain slices - A turnkey analysis pipeline for facile analysis of large EASI-FISH image datasets - EASI-FISH reveals novel subregions of the lateral hypothalamus - Identification of rare cell types based on morphological and spatial heterogeneity

neuroscience↗

bioRxiv2021

Bidirectional neuronal migration coordinates retinal morphogenesis bypreventing spatial competition

While the design of industrial products is often optimized for the sequential assembly of single components, organismal development is hallmarked by the concomitant occurrence of tissue growth and organization. Often this means that proliferating and differentiating cells occur at the same time in a shared tissue environment that continuously changes. How cells adapt to architectural changes in order to prevent spatial interference remains unclear. To understand how cell movements important for growth and organization are orchestrated, we here study the emergence of photoreceptor neurons that occur during the peak of retinal growth using zebrafish, human tissue and human organoids. Quantitative imaging reveals that successful retinal morphogenesis depends on active bidirectional photoreceptor translocation. This leads to a transient transfer of the entire cell population away from the apical proliferative zone. This migration pattern is driven by distinct cytoskeletal machineries, depending on direction: microtubules are required for basal translocation, while actomyosin drives apical movement. Blocking photoreceptor translocation leads to apical overcrowding that hampers progenitor movements. Thus, photoreceptor migration is crucial to prevent competition for space and thereby allows concurrent tissue growth and lamination. This shows that neuronal migration, in addition to its canonical role in cell positioning, is involved in coordinating morphogenesis.

developmental biology↗

bioRxiv2020

Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features

Patient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis generated with such models is often confounded by the presence of host cells and can give rise to data misinterpretation. For instance, it is important to discriminate between xenografted and host cells in histological sections prior to performing immunostainings. We developed Single Cell Classifier (SCC), a data-driven deep learning-based computational tool that provides an innovative approach for automated cell species discrimination based on a multi-step process entailing nuclei segmentation and single cell classification. We show that human and murine cells contextual features, more than cell-intrinsic ones, can be exploited to discriminate between cell species in both normal and malignant tissues, yielding up to 96% classification accuracy. SCC will facilitate the interpretation of H&E stained histological sections of xenografted human-in-mouse tissues and it is open to new in-house built models for further applications. SCC is released as an open-source plugin in ImageJ/Fiji available at the following link: https://github.com/Biomedical-Imaging-Group/SingleCellClassifier. Author summaryBreast cancer is the most commonly diagnosed tumor in women worldwide and its incidence in the population is increasing over time. Because our understanding of such disease has been hampered by the lack of adequate human preclinical model, efforts have been made in order to develop better approaches to model the human complexity. Recent advances in this regard were achieved with Patient-Derived Xenografts (PDXs), which entail the implantation of human-derived specimens to recipient immunosuppressed mice and are, thus far, the preclinical system best recapitulating the heterogeneity of both normal and malignant human tissues. However, histological analyses of the resulting tissues are usually confounded by the presence of cells of different species. To circumvent this hurdle and to facilitate the discrimination of human and murine cells in xenografted samples, we developed Single Cell Classifier (SCC), a deep learning-based open-source software, available as a plugin in ImageJ/Fiji, performing automated species classification of individual cells in H&E stained sections. We show that SCC can reach up to 96% classification accuracy to classify cells of different species mainly leveraging on their contextual features in both normal and tumor PDXs. SCC will improve and automate histological analyses of human-in-mouse xenografts and is open to new in-house built models for further classification tasks and applications in image analysis.

systems 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

Deep learning-enhanced light-field imaging with continuous validation

Light-field microscopy (LFM) has emerged as a powerful tool for fast volumetric image acquisition in biology, but its effective throughput and widespread use has been hampered by a computationally demanding and artefact-prone image reconstruction process. Here, we present a novel framework consisting of a hybrid light-field light-sheet microscope and deep learning-based volume reconstruction, where single light-sheet acquisitions continuously serve as training data and validation for the convolutional neural network reconstructing the LFM volume. Our network delivers high-quality reconstructions at video-rate throughput and we demonstrate the capabilities of our approach by imaging medaka heart dynamics and zebrafish neural activity.

bioengineering↗

bioRxiv2020

YeaZ: A convolutional neural network for highly accurate, label-free segmentation of yeast microscopy images

The processing of microscopy images constitutes a bottleneck for large-scale experiments. A critical step is the establishment of cell borders ( segmentation), which is required for a range of applications such as growth or fluorescent reporter measurements. For the model organism budding yeast (Saccharomyces cerevisiae), a number of methods for segmentation exist. However, in experiments involving multiple cell cycles, stress, or various mutants, cells crowd or exhibit irregular visible features, which necessitate frequent manual corrections. Furthermore, budding events are visually subtle but important to detect. Convolutional neural networks (CNNs) have been successfully employed for a range of image processing applications. They require large, diverse training sets. Here, we present i) the first set of publicly available, high-quality segmented yeast images (>10000 cells) including mutants, stressed cells, and time courses, ii) a corresponding U-Net-based CNN, iii) a Python-based graphical user interface (GUI) to efficiently use the system, and iv) a web application to test it (www.quantsysbio.com). A key feature is a cell-cell boundary test which avoids the need for additional input from fluorescent channels. A bipartite graph matching algorithm tracks cells in time with high reliability. Our network is highly accurate and outperforms existing methods on benchmark images recorded by others, suggesting it transfers well to other conditions. Furthermore, new buds are detected early with high reliability. We apply the system to detect differences in geometry between wild-type and cyclin mutant cells. Our results indicate that morphogenesis control occurs unexpectedly early in the cell cycle and is gradual, demonstrating how the efficient processing of large numbers of cells uncovers new biology. Our system can serve as a resource to the community, expanded continuously with new images. Furthermore, the techniques we develop here are likely to be useful for other organisms as well. The identification of cell borders ( segmentation) in microscopy images constitutes a bottleneck for large-scale experiments. For the model organism Saccharomyces cerevisiae, current segmentation methods face challenges when cells bud, crowd, or exhibit irregular features. Here, we present i) the first set of publicly available, high-quality segmented yeast images (>10000 cells) including mutants, stressed cells, and time courses, ii) a corresponding convolutional neural network (CNN), iii) a graphical user interface and a web application (www.quantsysbio.com) to efficiently employ, test, and expand the system. A key feature is a cell-cell boundary test which avoids the need for fluorescent markers. Our CNN is highly accurate, including for buds, and outperforms existing methods on benchmark images, indicating it transfers well to other conditions. To demonstrate how efficient, large-scale image processing uncovers new biology, we analyzed the geometries of {approx}2200 wild-type and cyclin mutant cells and found that morphogenesis control occurs unexpectedly early and gradually.

bioinformatics↗

bioRxiv2019

Rod nuclear architecture determines contrast transmission of the retina and behavioral sensitivity in mice

Rod photoreceptors of nocturnal mammals display a striking inversion of nuclear architecture, which has been proposed as an evolutionary adaptation to dark environments. However, the nature of visual benefits and underlying mechanisms remains unclear. It is widely assumed that improvements in nocturnal vision would depend on maximization of photon capture, at the expense of image detail. Here we show that retinal optical quality improves 2-fold during terminal development, which, confirmed by a mouse model, happens due to nuclear inversion. We further reveal that improved retinal contrast-transmission, rather than photon-budget or resolution, leads to enhanced contrast sensitivity under low light condition. Our findings therefore add functional significance to a prominent exception of nuclear organization and establish retinal contrast-transmission as a decisive determinant of mammalian visual perception. One Sentence SummaryOur study reveals that chromatin compaction in rod cells augments contrast sensitivity in mice.

biophysics↗

bioRxiv2019

CLIJ: Enabling GPU-accelerated image processing in Fiji

Graphics processing units (GPU) allow image processing at unprecedented speed. We present CLIJ, a Fiji plugin enabling end-users with entry level experience in programming to benefit from GPU-accelerated image processing. Freely programmable workflows can speed up image processing in Fiji by factor 10 and more using high-end GPU hardware and on affordable mobile computers with built-in GPUs.

bioinformatics↗

bioRxiv2018

Automated detection of the HER2 gene amplification status in Fluorescence in situ hybridization images for the diagnostics of cancer tissues

The human epidermal growth factor receptor 2 (HER2) gene amplification status is a crucial marker for evaluating clinical therapies of breast or gastric cancer. We propose a deep learning-based pipeline for the detection, localization and classification of interphase nuclei depending on their HER2 gene amplification state in Fluorescence in situ hybridization (FISH) images. Our pipeline combines two RetinaNet-based object localization networks which are trained (1) to detect and classify interphase nuclei into distinct classes normal, low-grade and high-grade and (2) to detect and classify FISH signals into distinct classes HER2 or centromere of chromosome 17 (CEN17). By independently classifying each nucleus twice, the two-step pipeline provides both robustness and interpretability for the automated detection of the HER2 amplification status. The accuracy of our deep learning-based pipeline is on par with that of three pathologists and FISH images on a set of 57 validation images containing several hundreds of nuclei are accurately classified. The automatic pipeline is a first step towards assisting pathologists in evaluating the HER2 status of tumors using FISH images, for analyzing FISH images in retrospective studies, and for optimizing the documentation of each tumor sample by automatically annotating and reporting of the HER2 gene amplification specificities.

pathology↗