Biology subjects

Kollmannsberger, P.

Publications and source records attributed to Kollmannsberger, P..

At least 19 recordsLinked to original sources

bioRxiv2026

Label-Free Quantification of Microtissue Growth Dynamics Using Optical Flow and Mitosis Detection

While geometric constraints shape tissue development, quantifying the resulting growth dynamics remains a central challenge in tissue engineering. Conventional methods often struggle to capture multi-scale kinetics without complex labeling or difficult single-cell tracking. Here, we analyze geometrically controlled growth of microtissues derived from human dermal fibroblasts using time-resolved, label-free brightfield microscopy, combined with optical flow and semi-automated deep learning mitosis detection. By extracting multi-scale flow fields and integrating them with tissue segmentation, we quantify directional tissue dynamics, separating flow into parallel and normal components relative to the local tissue contour. Applying this framework, we contrast the quiescent tissue interior with the advancing growth front where localized dynamics and cell proliferation drive expansion. Our results demonstrate that, compared to the bulk, the growth front exhibits higher fluctuations parallel to the tissue contour, positive mean normal flow, and significantly increased mitotic activity. Furthermore, evaluating flow divergence around mitotic events reveals distinct spatial behaviors: with the onset of mitosis, a contraction and subsequent expansion occurs in the vicinity of the dividing cells. Beyond the immediate cellular neighborhood, the broader regional dynamics remain consistent before and after mitosis onset, with net tissue expansion in proximity to the growth front and contraction within the tissue interior. By extracting continuous kinetic data from easily accessible, label-free brightfield imaging, this approach serves as a non-invasive, complementary tool for evaluating in vitro tissue morphogenesis and growth dynamics. This analytical framework can be expanded to study locally resolved tissue morphogenesis and growth kinetics in other microsystems, ranging from embryos to organoids. Statement of SignificanceUnderstanding how localized cellular forces drive tissue growth is critical for mechanobiology. However, mapping these dynamics traditionally requires complex, invasive fluorescent labeling. We present an accessible, label-free computational framework combining optical flow and deep learning-based mitosis detection to quantify continuous tissue kinematics directly from standard brightfield microscopy. Applying this to 3D microtissues, we reveal a distinct spatial coupling between cell division, local mechanical fluctuations, and directed tissue expansion at the active growth front. This non-invasive approach bridges the gap between single-cell mechanics and macroscopic morphogenesis, offering a versatile tool to monitor complex in vitro model systems-like organoids and bioengineered tissues-without disrupting their native state.

biophysics↗

bioRxiv2026

Structural cell biology by mega-expansion microscopy

Structural characterization of macromolecular assemblies within intact cells remains one of the central challenges in cell biology. While cryo-electron tomography provides unparalleled structural information, its applicability is limited by sample thickness, imaging throughput, and accessibility. Fluorescence microscopy offers molecular specificity and compatibility with intact biological specimens but has so far lacked the spatial resolution required to visualize cellular ultrastructure. Here we introduce Mega-expansion microscopy (Mega-ExM), a fluorescence imaging approach that enables structural visualization of whole cells using conventional confocal microscopes. Mega-ExM combines iterative hydrogel expansion with whole-proteome NHS-dye labeling and post-expansion immunostaining to achieve tunable expansion factors of up to [~]1,500-fold while preserving ultrastructure. At expansion factors of 40-260x, Mega-ExM resolves centrioles, mitochondrial cristae, protein-dense domains within mitochondrial cristae consistent with respiratory-chain supercomplexes, the synaptonemal complex, and nuclear pore complexes (NPCs) with high fidelity. Particle averaging of [~]200x expanded NPCs yields reconstructions with a structural resolution of [~]35 [A], approaching what cryo-electron tomography has achieved for selected protein assemblies. By combining molecular specificity, large imaging volumes, and nanoscale structural resolution on conventional fluorescence microscopes, Mega-ExM establishes a broadly accessible platform for in situ structural biology.

biophysics↗

bioRxiv2026

Programmable protein reference standards for benchmarking sub-10-nm fluorescence microscopy

Fluorescence microscopy is increasingly used to measure molecular organization at length scales where labeling, photophysics and sample preparation can dominate quantitative accuracy. Reference standards are therefore needed that combine defined nanoscale geometry with a protein-like environment and compatibility with biological imaging. Here we introduce circular tandem repeat protein as programmable protein standards for benchmarking sub-10-nm fluorescence microscopy. Using genetic code expansion and bioorthogonal labeling, we generated compact protein rings carrying up to six labeling sites. Photoswitching fingerprint analysis revealed geometry-dependent localization accumulation and blinking kinetics, demonstrating that short-range fluorophore interactions can be assessed as measurable benchmark parameters. DNA-PAINT confirmed accessible docking sites and programmed valency at the single-particle level. We further established recombinant tethering and genetically encoded membrane display, extending the standards to cellular environments. cTRP PicoRulers were also compatible with expansion microscopy. Together, cTRPs provide a modular protein-based platform for evaluating molecular-scale imaging performance in purified and cellular environments.

biophysics↗

bioRxiv2026

Two-dye-imager DNA-PAINT enables volumetric nanoscopy of expanded cells

Refined single-molecule localization microscopy methods demonstrated superior localization precisions on isolated sample but remain limited by labeling density and imaging speed in cells. Here we combine expansion microscopy (ExM) with two-dye-imager (TDI)-DNA-PAINT to resolve fine molecular details of protein assemblies in [~]8-fold expanded cells with nanometer resolution. Using lattice light-sheet (LLS) microscopy, Ex-TDI-DNA-PAINT provides a robust platform for three-dimensional (3D) volumetric nanoscopy of the molecular organization of cells.

biophysics↗

bioRxiv2026

Automated landmark and semilandmark annotation for wing geometric morphometrics in Diptera using deep learning

1. Diptera represent a diverse insect order, including vectors of human and animal pathogens. Their accurate species identification remains a major bottleneck in ecological and epidemiological studies. Morphological identification requires taxonomic expertise, while molecular methods are costly and not universally reliable. Wing geometric morphometrics offers an alternative, but manual landmark annotation is time-consuming and introduces observer bias. 2. We developed ITHILDIN, an automated pipeline for landmark and semilandmark annotation of Diptera wings, combining UNet++ segmentation and an Hourglass landmark prediction model. Using mosquitoes as the primary model system, we extended an existing repository with 5,793 additional images. Models were trained on 5991 annotations of landmarks and segmentations and then evaluated on 12,522 images across 34 taxa. We assessed landmark prediction accuracy against human observers and ML-morph, evaluated species identification using Linear Discriminant Analysis on 17 homologous landmarks and 52 semilandmarks, and tested out-of-distribution generalisation by reproducing an independent study. Transferability was demonstrated by adapting the pipeline to the Dipteran families Drosophilidae and Glossinidae. 3. The Hourglass model achieved a mean landmark error of 4.5 pixels (95% CI: 4.3-4.6), within human observer variability (4.7 pixels, 95% CI: 4.4-5.0) and substantially outperforming ML-Morph (12.7 pixels, 95% CI: 11.1-14.2). The semilandmark-based approach for species identification achieved 91% balanced accuracy across 34 taxa, comparable to CNN performance (94%). On out-of-distribution data, the landmark pipeline generalised substantially better than the CNN and a soft-voting ensemble of the landmark and CNN classifiers achieved 88% balanced accuracy on a replicated study. 4. Combining geometric morphometrics with deep learning provides a reproducible, interpretable, and generalisable alternative to black-box CNN classifiers for Diptera wing analysis. By acting as a consistent single observer comparable to human annotation, the system eliminates inter-observer bias, enabling large-scale and cross-study morphometric analyses of Dipteran wings. The system is publicly available at www.ithildin.bnitm.de and transferable to other Diptera families with moderate retraining effort. Data availabilityImages used in this study are accessible under CC BY 4.0 license at https://doi.org/10.6019/S-BIAD1478. Downloadable and installable docker application can be accessed on the applications git page: https://anonymous.4open.science/r/ITHILDIN-4313/

bioinformatics↗

bioRxiv2026

Nanoscale imaging of expanded cells and proteins with spontaneously blinking dyes

Expansion microscopy (ExM) enables nanoscale imaging on standard microscopes, but combining ExM with single-molecule localization microscopy (SMLM) remains difficult, owing to the incompatibility of expanded hydrogels with photoswitching buffers. Here, we introduce a single-step expansion microscopy method that allows SMLM with spontaneously blinking dyes in 6-14x expanded samples, without re-embedding. We demonstrate nanometer-resolution imaging by resolving the organization of the nuclear pore complex (NPC) and the molecular structure of recombinant homotrimeric proliferating cell nuclear antigen (PCNA).

biophysics↗

bioRxiv2025

Towards improved particle averaging for single-molecule localization microscopy using geometric deep learning

Single-molecule localization microscopy (SMLM) can resolve intracellular structures down to the nanoscale, but often produces sparse and incomplete data. Particle averaging (PA) can aid with the reconstruction of complete structures, but traditional PA methods can suffer from template bias or the high computational costs of geometric alignment. To address these limitations, we developed a geometric deep learning (GDL) framework for enhanced, template-free 3D particle averaging. Our pipeline uses a GDL autoencoder, trained on realistic simulated data, to map incomplete point clouds into a robust latent space. By averaging feature vectors directly within this space, our method bypasses the need for explicit 3D alignment. We validated our approach on simulated DNA origami and experimental nuclear pore complex (NPC) data. The latent space averaging successfully reconstructed NPC structures with key metrics (ring radius {approx} 46 nm, ring distance {approx} 52 nm) that are comparable to state-of-the-art methods. This work establishes a viable GDL pipeline for SMLM analysis, offering an efficient alternative to traditional PA. While the current model requires structure-specific training, our results highlight the significant potential of GDL for quantitative structural biology.

bioinformatics↗

bioRxiv2025

Resolving protein organization in cells with nanometer resolution

Advances in super-resolution microscopy have outpaced molecular labeling methods, impeding nanometer-scale imaging of endogenous multiprotein complexes in cells. To overcome these limitations, we developed an expansion microscopy (ExM) approach based on double-homogenized hydrogels that enables direct stochastic optical reconstruction microscopy (dSTORM) of 7-8-fold expanded immunolabeled samples. The resulting [~]4-fold increase in effective immunolabeling density resolved the 8 nm distance between neighboring -tubulin molecules in microtubules and the polyhedral lattice in clathrin-coated pits with nanometer precision in cells. Two-color Ex-dSTORM further revealed the molecular organization of RIM scaffolding protein and Munc13-1, an essential synaptic vesicle priming protein, in ring-like structures with diameters of 40-45 nm at the presynapse in hippocampal neurons. Our results demonstrate that Ex-dSTORM resolves the molecular organization of endogenous multiprotein complexes with nanometer spatial resolution in genetically unmodified cells. Thus, it provides a versatile method for the investigation of molecular protein distributions in their physiologically relevant context. One-Sentence SummaryThe optimized combination of dSTORM and double TREx ExM resolves previously unresolvable protein assemblies in cells

biophysics↗

bioRxiv2025

Cortical Tension Links Curvature to Tissue Growth in the Cellular Potts Model

The growth of biological tissue is sensitive to the physical properties of the environment. For example, the growth rate of contractile tissue under geometric confinement is proportional to local curvature as a result of tissue surface tension. It is not known how local cell behavior is coordinated to give rise to such behavior. Here, we use computer simulations based on the Cellular Potts Model (CPM) to investigate the role of cell adhesion and cortical contractility for curvature-dependent tissue growth. Our results show that tension-dependent local proliferation leads to the observed macroscopic curvature-driven growth kinetics via tissue surface tension, independent of soluble growth factors.

biophysics↗

bioRxiv2025

Improving single molecule localisation microscopy reconstruction by extending the temporal context

Single-molecule localization microscopy methods such as dSTORM require specific buffer conditions to enable blinking and detection of individual emitters, making them incompatible with live cell imaging and expansion microscopy. An alternative approach to achieve super-resolution without blinking is to observe the fluctuations of the emitter intensity over time. Existing localization algorithms for high-emitter density make use of radial symmetry or use artificial neural networks trained on single high-density frames to predict emitter positions. Here, we aim to improve the resolution by using a larger temporal context. We combine the U-Net architecture used previously for image reconstruction with multi-head attention used in the Transformer architecture. We compare the results to DECODE and eSRRF as well as to traditional fitting algorithms on public benchmark data. A generic pre-trained model is provided together with a fast and robust simulator for training data and all scripts needed to train custom networks.

biophysics↗

bioRxiv2025

Potentials and limitations in the application of Convolutional Neural Networks for mosquito species identification using wing images

1. This study addresses the pressing global health burden of mosquito-borne diseases by investigating the application of Convolutional Neural Networks (CNNs) for mosquito species identification using wing images. Conventional identification methods are hampered by the need for significant expertise and resources, while CNNs offer a promising alternative. Our research aimed to develop a reliable and applicable classification system that can be used under real-world conditions, with a focus on improving model adaptability to unencountered devices, mitigating dataset biases, and ensuring usability across different users without standardized protocols. 2. We utilized a large, diverse dataset of mosquito wing images of 21 taxa and three imagecapturing devices and an optimized preprocessing pipeline to standardize images and remove undesirable image features. 3. The developed CNN models demonstrated high performance, with an average balanced accuracy of 98.3% and a macro F1-score of 97.6%, effectively distinguishing between the 21 mosquito taxa, including morphologically similar pairs. The preprocessing pipeline improved the models robustness, reducing performance drops on unfamiliar devices effectively. However, the study also highlights the persistence of inherent dataset biases, which the preprocessing steps could only partially mitigate. The classification systems practical usability was demonstrated through a feasibility study, showing high inter-rater reliability. 4. The results underscore the potential of the proposed workflow to enhance vector surveillance, especially in resource-constrained settings, and suggest its applicability to other winged insect species. The classification system developed in this study is available for public use, providing a valuable tool for vector surveillance and research, supporting efforts to mitigate the spread of mosquito-borne diseases.

bioinformatics↗

bioRxiv2023

Multicellular dynamics on 3D scaffolds: Stress concentration is a key to controlling complex microtissue morphology on engineered surfaces

Tissue engineers have utilized a variety of three-dimensional (3D) scaffolds for controlling multicellular dynamics and the resulting tissue microstructures. In particular, cutting-edge microfabrication technologies, such as 3D bioprinting, provide increasingly complex structures. However, unpredictable microtissue detachment from scaffolds, which ruins desired tissue structures, is becoming an evident problem. To overcome this issue, we elucidated the mechanism underlying collective cellular detachment by combining a new computational simulation method with quantitative tissue-culture experiments. We first quantified the stochastic processes of cellular detachment shown by vascular smooth muscle cells on model curved scaffolds and found that microtissue morphologies vary drastically depending on cell contractility, substrate curvature, and cell-substrate adhesion strength. To explore this mechanism, we developed a new particle-based model that explicitly describes stochastic processes of multicellular dynamics, such as adhesion, rupture, and large deformation of microtissues on structured surfaces. Computational simulations using the developed model successfully reproduced characteristic detachment processes observed in experiments. Crucially, simulations revealed that cellular contractility-induced stress is locally concentrated at the cell-substrate interface, subsequently inducing a catastrophic process of collective cellular detachment, which can be suppressed by modulating cell contractility, substrate curvature, and cell-substrate adhesion. These results show that the developed computational method is useful for predicting engineered tissue dynamics as a platform for prediction-guided scaffold design.

biophysics↗

bioRxiv2022

Endogenous tagging of Unc-13 reveals nanocluster reorganization at active zones during presynaptic homeostatic potentiation

Neurotransmitter release at presynaptic active zones (AZs) requires concerted protein interactions within a dense 3D nano-hemisphere. Among the complex protein mesh-work the (M)unc-13 family member Unc-13 of Drosophila melanogaster is essential for docking of synaptic vesicles and transmitter release. We employ MiMIC-based gene editing using GFSTF (EGFP-FlAsH-StrepII-TEV-3xFlag) to endogenously tag all annotated Drosophila Unc-13 isoforms enabling visualization of endogenous Unc-13 expression within the central and peripheral nervous system. Electrophysiological characterization using two-electrode voltage clamp (TEVC) reveals that evoked and spontaneous synaptic transmission remain unaffected in unc-13GFSTF 3rd instar larvae and acute presynaptic homeostatic potentiation (PHP) can be induced at control levels. Furthermore, multi-color structured-illumination shows precise co-localization of Unc-13GFSTF, Bruchpilot and GluRIIA-receptor subunits within the synaptic mesoscale. Localization microscopy in combination with HDBSCAN algorithms detect Unc-13GFSTF nanoclusters that move towards the AZ center during PHP with unaltered Unc-13GFSTF protein levels.

neuroscience↗

bioRxiv2022

Single-molecule fluorescence microscopy demonstrates fast dynamics of the variant surface glycoprotein coat on living trypanosomes

The fluidity of Trypanosoma bruceis dense coat of GPI-anchored variant surface glycoproteins (VSGs) is fundamental for the survival of the parasite. In order to maintain the integrity of the coat, it is recycled on the time scale of a few minutes. This is surprisingly fast as endo- and exocytosis take place in the same small membrane invagination called the flagellar pocket. Here, we present measurements of VSG dynamics on the single-molecule level in living trypanosomes. A large number of short protein trajectories sampling the parasites surface were analysed in two distinct scenarios: diffusion and directed motion. To this end, we employed a previously published algorithm and implemented two extensions to consider rim effects as well as localisations errors inherent to single-mole tracking. Neglect of the latter can have a significant distortive effect on the measured diffusion coefficient; in our case resulting in an underestimation by 20 %. We found large heterogeneity in the local diffusion coefficients and velocities with a surprisingly high average value of [Formula] and [Formula], respectively. To decide on the locally dominant motion mode, we present a guideline based on random walk simulations. We find that VSG dynamics is indeed dominated by diffusion. Complementary simulations on long time scales not accessible in the experiment showed that passive VSG randomisation is fast enough to prevent re-endocytosis newly exocytosed VSGs and to accomplish turnover of the full VSG coat within a few minutes. Author summarySingle-molecule tracking in biological systems often suffers from trajectories being too short to obtain statistically robust decisions on the present motion mode. We faced this issue when investigating the dynamics of the protein surface coat of African trypanosomes. To address the question whether diffusion or directed motion governs coat dynamics, we have adopted an algorithm based on temporal decomposition and spatial binning of an ensemble of single-molecule trajectories. We introduced several extensions to the original approach, including the consideration of localisation errors inherent to single-molecule tracking. This improvement alone already prevented the diffusion coefficient from being underestimated by 20 %. We analysed the coat dynamics in two scenarios, diffusion and directed motion, and offer a decision guideline to identify the locally dominating motion mode. Our extended algorithm is available to the scientific community via GitHub. For the trypanosome surface coat we found that the motion is indeed mainly characterised by diffusion with a surprisingly high diffusion coefficient. This finding solves a long-standing question how the parasite maintains its protein coat.

biophysics↗

bioRxiv2022

ReCSAI Recursive compressed sensing artificial intelligence for confocal lifetime localization microscopy

Localization-based super-resolution microscopy resolves macromolecular structures down to a few nanometers by computationally reconstructing fluorescent emitter coordinates from diffraction-limited spots. The most commonly used algorithms are based on fitting parametric models of the point spread function (PSF) to a measured photon distribution. These algorithms make assumptions about the symmetry of the PSF and thus, do not work well with irregular, non-linear PSFs that occur for example in confocal lifetime imaging, where a laser is scanned across the sample. An alternative method for reconstructing sparse emitter sets from noisy, diffraction-limited images is compressed sensing, but due to its high computational cost it has not yet been widely adopted. Deep neural network fitters have recently emerged as a new competitive method for localization microscopy. They can learn to fit arbitrary PSFs, but require extensive simulated training data and do not generalize well. A method to efficiently fit the irregular PSFs from confocal lifetime localization microscopy combining the advantages of deep learning and compressed sensing would greatly improve the acquisition speed and throughput of this method. Here we introduce ReCSAI, a compressed sensing neural network to reconstruct localizations for confocal dSTORM, together with a simulation tool to generate training data. We implemented and compared different artificial network architectures, aiming to combine the advantages of compressed sensing and deep learning. We found that a U-Net with a recursive structure inspired by iterative compressed sensing showed the best results on realistic simulated datasets with noise, as well as on real experimentally measured confocal lifetime scanning data. Adding a trainable wavelet denoising layer as prior step further improved the reconstruction quality. Our deep learning approach can reach a similar reconstruction accuracy for confocal dSTORM as frame binning with traditional fitting without requiring the acquisiton of multiple frames. In addition, our work offers generic insights on the reconstruction of sparse measurements from noisy experimental data by combining compressed sensing and deep learning. We provide the trained networks, the code for network training and inference as well as the simulation tool as python code and Jupyter notebooks for easy reproducibility.

bioinformatics↗

bioRxiv2022

Ultrastructural analysis of wildtype and RIM1α knock-out active zones in a large cortical synapse

Rab3A-interacting molecule (RIM) is crucial for fast Ca2+-triggered synaptic vesicle (SV) release in presynaptic active zones (AZ). While loss of RIM1 impairs long-term plasticity at hippocampal giant mossy fiber boutons (MFB), it remains unclear how AZ ultrastructure is altered. We investigated MFB AZ architecture in 3D using electron tomography of rapid cryo-immobilized acute brain slices in RIM1-/- and wild-type mice. In RIM1-/-, AZs are larger with increased synaptic cleft heights and with a three-fold reduced number of tightly docked SVs (0-2nm). The distance of tightly docked SVs to the AZ center is increased from 110 to 195 nm, and the width of their electron dense material between outer SV membrane and AZ membrane is reduced. Furthermore, the SV pool in RIM1-/- is more heterogeneous. Thus, RIM1, beside its role in tight SV docking, is crucial for synaptic architecture and vesicle pool organization in MFBs.

neuroscience↗

bioRxiv2021

Visual Clustering of Transcriptomic Data from Primary and Metastatic Tumors: Dependencies and Novel Pitfalls

Personalized Oncology is a rapidly evolving area and offers cancer patients therapy options more specific than ever. Yet, there is still a lack of understanding regarding transcriptomic similarities or differences of metastases and corresponding primary sites. Applying two unsupervised dimension reduction methods (t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation Projection (UMAP)) on three datasets of metastases (n=682 samples) with three different data transformations (unprocessed, log10 as well as log10+1 transformed values), we visualized potential underlying clusters. Additionally, we analyzed two datasets (n=616 samples) containing metastases and primary tumors of one entity, to point out potential familiarities. Using these methods, no tight link between site of resection and cluster formation outcome could be demonstrated, neither for datasets consisting of solely metastasis nor mixed datasets. Instead, dimension reduction methods and data transformation significantly impacted visual clustering results. Our findings strongly suggest data transformation to be considered as another key element in interpretation of visual clustering approaches along with initialization and different parameters. Furthermore, the results indicate only minor transcriptional differences for metastases and corresponding primary tumors.

cancer biology↗

bioRxiv2021

Structural analysis of the C. elegans dauer larval anterior sensilla by Focused Ion Beam-Scanning Electron Microscopy

At the end of the first larval stage, the nematode Caenorhabditis elegans developing in harsh environmental conditions is able to choose an alternative developmental path called the dauer diapause. Dauer larvae exhibit different physiology and behaviors from non-dauer larvae. Using focused ion beam scanning electron microscopy (FIB-SEM), we volumetrically reconstructed the anterior sensory apparatus of C. elegans dauer larvae with unprecedented precision. We provide a detailed description of some neurons, focusing on structural details that were unknown or unresolved by previously published studies. They include: 1) dauer-specific branches of the IL2 sensory neurons project into the periphery of anterior sensilla and motor or putative sensory neurons at the sub-lateral cords; 2) ciliated endings of URX sensory neurons are supported by both ILso and AMso socket cells near the amphid openings; 3) variability in amphid sensory dendrites among dauers; 4) somatic RIP interneurons maintain their projection into the pharyngeal nervous system. Our results support the notion that dauer larvae structurally expand their sensory system to facilitate searching for more favorable environments.

neuroscience↗