Biology subjects

Stegmaier, J.

Publications and source records attributed to Stegmaier, J..

15 recordsLinked to original sources

bioRxiv2025

MoMo - Combining Neuron Morphology and Connectivity forInteractive Motif Analysis in Connectomes

Connectomics, a subfield of neuroscience, reconstructs structural and functional brain maps at synapse-level resolution. These complex spatial maps consist of tree-like neurons interconnected by synapses. Motif analysis is a widely used method for identifying recurring subgraph patterns in connectomes. These motifs, thus, potentially represent fundamental units of information processing. However, existing computational tools often oversimplify neurons as mere nodes in a graph, disregarding their intricate morphologies. In this paper, we introduce MoMo, a novel interactive visualization framework for analyzing neuron morphology-aware motifs in large connectome graphs. First, we propose an advanced graph data structure that integrates both neuronal morphology and synaptic connectivity. This enables highly efficient, parallel subgraph isomorphism searches, allowing for interactive morphological motif queries. Second, we develop a sketch-based interface that facilitates the intuitive exploration of morphology-based motifs within our new data structure. Users can conduct interactive motif searches on state-of-the-art connectomes and visualize results as interactive 3D renderings. We present a detailed goal and task analysis for motif exploration in connectomes, incorporating neuron morphology. Finally, we evaluate MoMo through case studies with four domain experts, who asses the tools usefulness and effectiveness in motif exploration, and relevance to real-world neuroscience research. The source code for MoMo is available here.

neuroscience↗

bioRxiv2024

DEEP LEARNING APPROACH FOR RENAL CELL CARCINOMA DETECTION,SUBTYPING, AND GRADING

We propose a comprehensive end-to-end pipeline designed for the detection, subtyping, and grading of tumors. Our proposed method-ology initiates the generation of a heat map, indicating the severity of the tumor. Subsequently, the identification of the most critical patches is conducted based on the probability scores. These identified patches are then directed to a grade prediction network. A distinctive aspect of our research lies in being the first to explore an end-to-end pipeline for both heat map generation and grading prediction. Our experiments were conducted leveraging the public, The Cancer Genome Atlas (TCGA) repository, focusing specifically on renal cancer. We introduced additional patch-level labels to improve the model performance. The generation of tumor heat maps targeted three primary cancer subtypes: clear cell, papillary, and chromophobe. To enhance our approach, we implemented center-loss and introduced a method aimed at refining the quality of patches. The experimental outcomes highlight superior performance compared to state-of-the-art method. This research contributes to the advancement of tumor detection and grading, emphasizing the significance of an integrated approach for heat map generation and grading pre-diction.

bioinformatics↗

bioRxiv2024

Identification of Mitosis Stages Using Artificial Neural Networks for 3D Time Lapse Cell Sequences

Cells, the fundamental units of life, are central to medical research, particularly in cancer studies due to their rapid, uncontrolled division. Understanding cell behavior is crucial, with a focus on mitosis, which has distinct cell division stages. However, precise detection of these phases, especially mitosis initiation in 3D, remains an underexplored research area. Our work explores 3D cell behavior, leveraging the increasing computational capabilities and prevalence of 3D imaging techniques. We introduce diverse 3D Convolutional Neural Network (CNN) architectures such as a base 3D CNN model, 3D CNN binary model, and 3D CNN pairwise model. An ensemble model based on the 3D CNN architectures shows higher classification accuracy on two time-series datasets. This research gives better insights into understanding cell behaviour in a multidimensional manner, contributing to medical research. To the best of our understanding, we are the first to delve into the utilization of Convolutional Neural Network architectures for the 3D classification of mitosis stages.

cell biology↗

bioRxiv2023

Automatic Detection of Cell-cycle Stages using Recurrent Neural Networks

Mitosis is the process by which eukaryotic cells divide to produce two similar daughter cells with identical genetic material. Research into the process of mitosis is therefore of critical importance both for the basic understanding of cell biology and for the clinical approach to manifold pathologies resulting from its malfunctioning, including cancer. In this paper, we propose an approach to study mitotic progression automatically using deep learning. We used neural networks to predict different mitosis stages. We extracted video sequences of cells undergoing division and trained a Recurrent Neural Network (RNN) to extract image features. The use of RNN enabled better extraction of features. The RNN-based approach gave better performance compared to classifier based feature extraction methods which do not use time information. Evaluation of precision, recall, and F-score indicates the superiority of the proposed model compared to the baseline. To study the loss in performance due to confusion between adjacent classes, we plotted the confusion matrix as well, to quantify the amount of misclassification. In addition, we visualized the feature space to understand why RNNs are better at classifying the mitosis stages than other classifier models, which indicated the formation of strong clusters for the different classes, clearly confirming the advantage of the proposed RNN-based approach.

bioinformatics↗

bioRxiv2023

Weakly-supervised temporal segmentation of cell-cycle stages with center-cell focus using Recurrent Neural Networks

Training deep-learning models for biomedical images has always been a problem due to the lack of annotated data. Here we propose using a model and a training approach for the weakly-supervised temporal classification of cell-cycle stages during mitosis. Instead of using annotated data, by using an ordered set of classes called transcript, our proposed approach classifies the cell-cycle stages of cell video sequences. The network design helps to propagate information in time using Recurrent Neural Network and helps to focus the features on the center-cell. The algorithm is evaluated on four datasets from LiveCellMiner and has a performance close to the supervised approaches, which is impressive, considering that annotated data is not used in training.

bioinformatics↗

bioRxiv2022

END-TO-END CLASSIFICATION OF CELL-CYCLE STAGES WITH CENTER-CELL FOCUS TRACKER USING RECURRENT NEURAL NETWORKS

Cell division, or mitosis, guarantees the accurate inheritance of the genomic information kept in the cell nucleus. Malfunctions in this process cause a threat to the health and life of the organism, including cancer and other manifold diseases. It is therefore crucial to study in detail the cell-cycle in general and mitosis in particular. Consequently, a large number of manual and semi-automated time-lapse microscopy image analyses of mitosis have been carried out in recent years. In this paper, we propose a method for automatic detection of cell-cycle stages using a recurrent neural network (RNN). An end-to-end model with center-cell focus tracker loss, and classification loss is trained. The evaluation was conducted on two time-series datasets, with 6-stages and 3-stages of cell splitting labeled. The frame-to-frame accuracy was calculated and precision, recall, and F1-Score were measured for each cell-cycle stage. We also visualized the learned feature space. Image reconstruction from the center-cell focus module was performed which shows that the network was able to focus on the center-cell and classify it simultaneously. Our experiments validate the superior performance of the proposed network compared to a classifier baseline.

bioinformatics↗

bioRxiv2021

Quantitative Mapping of Keratin Networks in 3D

Mechanobiology requires precise quantitative information on processes taking place in specific 3D microenvironments. Connecting the abundance of microscopical, molecular, biochemical and cell mechanical data with defined topologies has turned out to be extremely difficult. Establishing such structural and functional 3D maps needed for biophysical modeling is a particular challenge for the cytoskeleton, which consists of long and interwoven filamentous polymers coordinating subcellular processes and interactions of cells with their environment. To date, useful tools are available for the segmentation and modeling of actin filaments and microtubules but comprehensive tools for the mapping of intermediate filament organization are still lacking. In this work, we describe a workflow to model and examine the complete 3D arrangement of the keratin intermediate filament cytoskeleton in epithelial cells both in vitro and in vivo. Numerical models are derived from super resolution 3D imaging of fluorescence-tagged keratin filaments. They are interrogated and annotated at different length scales using different modes of visualization including immersive virtual reality. In this way, information is provided on network organization at the subcellular level including mesh arrangement, density and isotropic configuration as well as details on filament morphology such as bundling, curvature and orientation. We show that the comparison of these parameters helps to identify, in quantitative terms, similarities and differences of keratin network organization in epithelial cell types defining subcellular domains, notably basal, apical, lateral and perinuclear systems. The described approach and the presented data are pivotal for generating mechanobiological models that can be experimentally tested.

cell biology↗

bioRxiv2021

LiveCellMiner: A New Tool to Analyze Mitotic Progression

Live-cell imaging has become state of the art to accurately identify the nature of mitotic and cell cycle defects. Low- and high-throughput microscopy setups have yield huge data amounts of cells recorded in different experimental and pathological conditions. Tailored semi-automated and automated image analysis approaches allow the analysis of high-content screening data sets, saving time and avoiding bias. However, they were mostly designed for very specific experimental setups, which restricts their flexibility and usability. The general need for dedicated experiment-specific user-annotated training sets and experiment-specific user-defined segmentation parameters remains a major bottleneck for fully automating the analysis process. In this work we present LiveCellMiner, a highly flexible open-source software tool to automatically extract, analyze and visualize both aggregated and time-resolved image features with potential biological relevance. The software tool allows analysis across high-content data sets obtained in different platforms, in a quantitative and unbiased manner. As proof of principle application, we analyze here the dynamic chromatin and tubulin cytoskeleton features in human cells passing through mitosis highlighting the versatile and flexible potential of this tool set.

cell biology↗

bioRxiv2021

MorphoGraphX 2.0: Providing context for biological image analysis with positional information

Positional information is a central concept in developmental biology. In developing organs, positional information can be idealized as a local coordinate system that arises from morphogen gradients controlled by organizers at key locations. This offers a plausible mechanism for the integration of the molecular networks operating in individual cells into the spatially-coordinated multicellular responses necessary for the organization of emergent forms. Understanding how positional cues guide morphogenesis requires the quantification of gene expression and growth dynamics in the context of their underlying coordinate systems. Here we present recent advances in the MorphoGraphX software (Barbier de Reuille et al. eLife 2015;4:e05864) that implement a generalized framework to annotate developing organs with local coordinate systems. These coordinate systems introduce an organ-centric spatial context to microscopy data, allowing gene expression and growth to be quantified and compared in the context of the positional information thought to control them.

developmental biology↗

bioRxiv2020

Mechanical competition alters the cellular interpretation of an endogenous genetic programme

The intrinsic genetic programme of a cell is not sufficient to explain all of the cells activities. External mechanical stimuli are increasingly recognized as determinants of cell behaviour. In the epithelial folding event that constitutes the beginning of gastrulation in Drosophila, the genetic programme of the future mesoderm leads to the establishment of a contractile actomyosin network that triggers apical constriction of cells, and thereby, tissue folding. However, some cells do not constrict but instead stretch, even though they share the same genetic programme as their constricting neighbours. We show here that tissue-wide interactions force these cells to expand even when an otherwise sufficient amount of apical, active actomyosin is present. Models based on contractile forces and linear stress-strain responses do not reproduce experimental observations, but simulations in which cells behave as ductile materials with non-linear mechanical properties do. Our models show that this behaviour is a general emergent property of actomyosin networks [in a supracellular context, in accordance with our experimental observations of actin reorganisation within stretching cells.

developmental biology↗

bioRxiv2020

3D MAPs discovers the morphological sequence chondrocytes undergo in the growth plate and the regulatory role of GDF5 in this process

The activity of the epiphyseal growth plates, which drive longitudinal growth of long bones, is dependent on the ability of chondrocytes to change their shape and size extensively as they differentiate. However, organ size, extracellular matrix density and cell number have hindered the study of chondrocyte morphology. Here, we describe a new pipeline called 3D Morphometric Analysis for Phenotypic significance (3D MAPs), which overcomes these obstacles. By using 3D MAPs, we have created an image database of hundreds of thousands of cells from orthologous long bones. Analysis of this database revealed the growth strategies that chondrocytes use during differentiation. We found that chondrocytes employed both allometric and isometric growth, and that allometric growth is achieved by changes either in volume or surface area along a specific cell axis in a zone-specific manner. Additionally, we discovered a new organization of chondrocytes within the growth plate, where cells are orientated such that their longest axis always aligns with the dorsal-ventral axis of the bone. To demonstrate the ability of 3D MAPs to explore mechanisms of growth plate regulation, we studied the abnormally short tibiae of Gdf5-null mice. 3D MAPs identified aberrant cellular growth behaviors which resulted in a 3-fold reduction in volumetric cell growth, as well as affected cell morphology and orientation, highlighting GDF5 as a new regulator of growth plate activity. Overall, our findings provide new insight into the morphological sequence that chondrocytes undergo during differentiation and highlight the ability of 3D MAPs to uncover molecular and cellular mechanisms regulating this process. More broadly, this work provides a new framework for studying growth plate biology.

developmental biology↗

bioRxiv2019

Tissue Mechanics Determines Cell Fate in the Axial Stem Zones

Most embryos and regenerating tissues grow by the action of stem zones. Two epithelial stem zones drive axial elongation in amniotes: the mature organizer generates mesoderm, the neuralised ectoderm around it extends the neuraxis. Bipotential progenitors were also shown to exist. How are these stem cell populations organised and what controls the cell fate of bipotential progenitors? We use direct, in vivo imaging of these stem cells in the chick. We find that progenitors of single and dual fates are mingled in a small region between the specialised stem zones. Divergent tissue movements surround this region. When transplanted downstream of these flows, cells from the region of mixed fates adopt the molecular identity and behaviour of the target stem zone, irrespective of their normal fate. Thus, multipotent cells serve to separate the specialized stem zones, instead of a classical boundary. We propose their fate is determined extrinsically by morphogenetic shearing.

developmental biology↗

bioRxiv2019

Automated high-throughput heart rate measurement in medaka and zebrafish embryos under physiological conditions

RationaleAccurate and efficient quantification of heartbeats in small fish models is an important readout to study cardiovascular biology, disease states and pharmacology at large scale. However, dependence on anesthesia, laborious sample orientation or requirement for fluorescent reporters have hampered the establishment of high-throughput heartbeat analysis. ObjectiveTo overcome these limitations, we aimed to develop a high-throughput assay with automated heart rate scoring in medaka (Oryzias latipes) and zebrafish (Danio rerio) embryos under physiological conditions designed for genetic screens and drug discovery and validation. Methods and ResultsWe established an efficient screening assay employing automated label-free heart rate determination of randomly oriented, non-anesthetized specimen in microtiter plates. Automatically acquired bright-field data feeds into an easy-to-use HeartBeat software, a MATLAB algorithm with graphical user interface developed for automated quantification of heart rate and rhythm. Sensitivity of the assay and algorithm was demonstrated by profiling heart rates during entire embryonic development. Our analysis pipeline revealed acute temperature changes triggering rapid adaption of heart rates, which has implications for standardization of experimental layout. The approach is scalable and allows scoring of multiple embryos per well resulting in a throughput of >500 embryos per 96-well plate. In a proof of principle screen for compound testing, our assay captured concentration-dependent effects of nifedipine and terfenadine over time. ConclusionA novel workflow and HeartBeat software provide efficient means for reliable and direct quantification of heart rate and rhythm of small fish in a physiological environment. Importantly, confounding factors such as anesthetics or laborious mounting are eliminated. We provide detailed profiles of embryonic heart rate dynamics in medaka and zebrafish as reference for future assay development. Ease of sample handling, automated imaging, physiological conditions and software-assisted analysis now facilitate various large-scale applications ranging from phenotypic screening, interrogation of gene functions to cardiovascular drug development pipelines.

developmental biology↗

bioRxiv2018

Multiscale analysis of 3D nuclear morphology reveals new insights into growth plate organization in mice

The shape of the nucleus is tightly associated with cell morphology, the mechanical environment, and differentiation and transcriptional states. Yet, imaging of nuclei in three dimensions while preserving the spatial context of the tissue has been highly challenging. Here, using the embryonic tibial growth plate as a model for cell differentiation, we study nuclear morphology by imaging cleared samples by light-sheet fluorescence microscopy. Next, we quickly segmented tens of thousands of nuclei using several open-source tools including machine learning. Finally, segmented nuclei underwent morphometric analysis and 3D spatial reconstruction using newly designed algorithms. Our method revealed differences in nuclear morphology between chondrocytes at different differentiation stages. Additionally, we identified different morphological patterns in opposing growth plates, such as gradients of volume and surface area, as well as features characteristic of specific growth plate zones, such as sphericity and orientation. Altogether, this work supports a link between nuclear morphology and cell differentiation. Moreover, it demonstrates the suitability of our approach for studying the relationships between nuclear morphology and organ development.\n\nAuthor summaryThere has been a growing interest in the relationship between nuclear morphology and its regulation of gene expression. However, to study global patterns of nuclear morphology within a tissue we must address the problem of acquiring and analyzing multiscale data, ranging from the tissue level through to subcellular resolution. We have established a new pipeline that enables acquisition and segmentation of hundreds of thousands of nuclei at a resolution that allows quantitative analysis. Moreover we have developed new algorithms that allow superimposing morphological aspects of hundreds of thousands of nuclei onto a visual representation of the entire tissue, allowing us to study nuclear morphology at an organ level. Using mouse growth plates as a model for the relationship between nuclear morphology and tissue differentiation, we show that nuclei change different aspects of their morphology during chondrocyte differentiation. Growth plates are usually described generically in the literature, suggesting they lack unique characteristics. We challenge this dogma by showing that morphological features such as volume distribute differently in opposing growth plates. Altogether, this work highlights the possible role of nuclear shape in the regulation of cell differentiation and demonstrates that our approach enables the study of nuclear morphology patterns within a tissue.

developmental biology↗

bioRxiv2018

Third-generation in situ hybridization chain reaction: multiplexed, quantitative, sensitive, versatile, robust

In situ hybridization based on the mechanism of hybridization chain reaction (HCR) has addressed multi-decade challenges to imaging mRNA expression in diverse organisms, offering a unique combination of multiplexing, quantitation, sensitivity, resolution, and versatility. Here, with third-generation in situ HCR, we augment these capabilities using probes and amplifiers that combine to provide automatic background suppression throughout the protocol, ensuring that even if reagents bind non-specifically within the sample they will not generate amplified background. Automatic background suppression dramatically enhances performance and robustness, combining the benefits of higher signal-to-background with the convenience of using unoptimized probe sets for new targets and organisms. In situ HCR v3.0 enables multiplexed quantitative mRNA imaging with subcellular resolution in the anatomical context of whole-mount vertebrate embryos, multiplexed quantitative mRNA flow cytometry for high-throughput single-cell expression profiling, and multiplexed quantitative single-molecule mRNA imaging in thick autofluorescent samples.\n\nSUMMARYIn situ hybridization chain reaction (HCR) v3.0 exploits automatic background suppression to enable multiplexed quantitative mRNA imaging and flow cytometry with dramatically enhanced ease-of-use and performance.

developmental biology↗