Biology subjects

Kollmannsberger, P.

Publications and source records attributed to Kollmannsberger, P..

24 records · Page 2Linked to original sources

bioRxiv2020

Biological network growth in complex environments - a computational framework

Spatial biological networks are abundant on all scales of life, from single cells to ecosystems, and perform various important functions including signal transmission and nutrient transport. These biological functions depend on the architecture of the network, which emerges as the result of a dynamic, feedback-driven developmental process. While cell behavior during growth can be genetically encoded, the resulting network structure depends on spatial constraints and tissue architecture. Since network growth is often difficult to observe experimentally, computer simulations can help to understand how local cell behavior determines the resulting network architecture. We present here a computational framework based on directional statistics to model network formation in space and time under arbitrary spatial constraints. Growth is described as a biased correlated random walk where direction and branching depend on the local environmental conditions and constraints, which are presented as 3D multilayer grid. To demonstrate the application of our tool, we perform growth simulations of a dense network between cells and compare the results to experimental data from osteocyte networks in bone. Our generic framework might help to better understand how network patterns depend on spatial constraints, or to identify the biological cause of deviations from healthy network function. Author summaryWe present a novel modeling approach and computational implementation to better understand the development of spatial biological networks under the influence of external signals. Our tool allows us to study the relationship between local biological growth parameters and the emerging macroscopic network function using simulations. This computational approach can generate plausible network graphs that take local feedback into account and provide a basis for comparative studies using graph-based methods.

biophysics↗

bioRxiv2019

Active zone compaction for presynaptic strength

Brain function relies on neurotransmission which is stabilized by presynaptic homeostatic potentiation (PHP). PHP operates on time scales ranging from minute- to life-long adaptations and likely involves reorganization of presynaptic active zones (AZs). At Drosophila melanogaster neuromuscular junctions, earlier work ascribed AZ enlargement by incorporating more Bruchpilot (Brp) scaffold protein a central mechanistic role in PHP. We used localization microscopy (direct stochastic optical reconstruction microscopy, dSTORM) and hierarchical density-based spatial clustering of applications with noise (HDBSCAN) to study AZ plasticity during PHP. We found that both acute, philanthotoxin (PhTx)-induced and chronic, genetically-induced PHP lead to compaction of individual AZs without altering Brp copy numbers per AZ. This compaction even occurs within Brp subclusters of the AZ scaffold which also move towards AZ centers. Furthermore, lowering imaging resolution revealed how AZ compaction in PHP translates into apparent increases in AZ area and Brp protein content as implied earlier. Our results suggest AZ compaction in PHP as an effective mechanism to raise presynaptic protein density and transmitter release. SIGNIFICANCE STATEMENTHomeostatic plasticity stabilizes chemical synaptic transmission in multiple organisms ranging from insects to humans. Changes in active zones (AZs), membrane specializations of the presynapse where synaptic vesicles are discharged, are thought to be crucial in homeostatic adaptations. AZ growth by protein incorporation was proposed as a core mechanism in presynaptic homeostatic potentiation (PHP). Localization microscopy of an abundant AZ scaffold protein uncovered that instead of growing, AZs are compacted in acute and chronic PHP. At lower imaging resolution, however, AZs appear larger and brighter although protein numbers are not increased. In summary, our findings suggest AZ compaction as new and effective mechanism to raise presynaptic protein density and transmitter release in PHP.

neuroscience↗

bioRxiv2019

Automated interpretation of Cryo-EM density maps with convolutional neural networks

In recent years, three-dimensional density maps reconstructed from single particle images obtained by electron cryo-microscopy (Cryo-EM) have reached unprecedented resolution. However, map interpretation can be challenging, in particular if the constituting structures require de-novo model building or are very mobile. Here, we demonstrate the potential of convolutional neural networks for the annotation of Cryo-EM maps: our network Haruspex has been trained on a carefully curated set of 293 experimentally derived reconstruction maps to automatically annotate protein secondary structure elements as well as RNA/DNA. It can be straightforwardly applied to annotate newly reconstructed maps to support domain placement or to supply a starting point for main-chain placement. Due to its high recall and precision rates of 95.1% and 80.3%, respectively, on an independent test set of 122 maps, it can also be used for validation during model building. The trained network will be available as part of the CCP-EM suite.

molecular biology↗

bioRxiv2019

Targeted volumetric single-molecule localization microscopy of defined presynaptic structures in brain sections

Revealing the molecular organization of anatomically precisely defined brain regions is necessary for the refined understanding of synaptic plasticity. Although, three-dimensional (3D) single-molecule localization microscopy can provide the required molecular resolution, single-molecule imaging more than a few micrometers deep into tissue remains challenging. To quantify presynaptic active zones (AZ) of entire, large, conditional detonator hippocampal mossy fiber (MF) boutons with diameters as large as 10 {micro}m, we developed a method for aberration-free volumetric direct stochastic optical reconstruction microscopy (dSTORM). An optimized protocol for fast repeated axial scanning and efficient sequential labeling of the AZ scaffold Bassoon and membrane bound GFP with Alexa Fluor 647 enables 3D-dSTORM imaging of 25 {micro}m thick mouse brain sections and assignment of AZs to specific neuronal substructures. Quantitative data analysis revealed large differences in Bassoon cluster size and density for distinct hippocampal regions with largest clusters in MF boutons.

neuroscience↗

bioRxiv2018

Surface tension determines tissue shape and growth kinetics

The growth of tissue is an essential process controlling morphogenesis and regeneration of organs [1]. In general tissue forming cells are interactive and motile [2], which can give rise to emergent physical properties such as viscous fluid behaviour as has been shown for epithelial monolayers during embryogenesis [3, 4], and for cell-agglomerates [5] with a measurable surface tension [6]. However, the mechanical integrity of tissues is provided by extracellular matrices (ECM) that turn tissues into solids with well-defined elastic properties [7]. Paradoxically, it has been shown by in-vitro experiments that even osteoid-like tissue with large amounts of ECM grows according to rules reminiscent of fluid behavior [8, 9]. Motivated by this conundrum, we show here quantitatively, by constraining growing tissues to surfaces of con ...

biophysics↗

bioRxiv2018

Automated classification of synaptic vesicles in electron tomograms of C. elegans using machine learning

1. AbstractSynaptic vesicles (SVs) are a key component of neuronal signaling and fulfil different roles depending on their composition. In electron micrograms of neurites, two types of vesicles can be distinguished by morphological criteria, the classical \"clear core\" vesicles (CCV) and the typically larger \"dense core\" vesicles (DCV), with differences in electron density due to their diverse cargos. Compared to CCVs, the precise function of DCVs is less defined. DCVs are known to store neuropeptides, which function as neuronal messengers and modulators [1]. In C. elegans, they play a role in locomotion, dauer formation, egg-laying, and mechano- and chemosensation [2]. Another type of DCVs, also referred to as granulated vesicles, are known to transport Bassoon, Piccolo and further constituents of the presynaptic density in the center of the active zone (AZ), and therefore are important for synaptogenesis [3].\n\nTo better understand the role of different types of SVs, we present here a new automated approach to classify vesicles. We combine machine learning with an extension of our previously developed vesicle segmentation workflow, the ImageJ macro 3D ART VeSElecT. With that we reliably distinguish CCVs and DCVs in electron tomograms of C. elegans NMJs using image-based features. Analysis of the underlying ground truth data shows an increased fraction of DCVs as well as a higher mean distance between DCVs and AZs in dauer larvae compared to young adult hermaphrodites. Our machine learning based tools are adaptable and can be applied to study properties of different synaptic vesicle pools in electron tomograms of diverse model organisms.\n\n2. Author summaryVesicles are important components of the cell, and synaptic vesicles are central for neuronal signaling. Two types of synaptic vesicles can be distinguished by electron microscopy: the classical \"clear core\" vesicles (CCVs) and the typically larger \"dense core\" vesicles (DCVs). The distinct appearance of vesicles is caused by their different cargos. To rapidly distinguish between both vesicle types, we present here a new automated approach to classify vesicles in electron tomograms. We combine machine learning with an extension of our previously developed vesicle segmentation workflow, an ImageJ macro, to reliably distinguish CCVs and DCVs using specific image-based features. The approach was trained and validated using data-sets that were hand curated by microscopy experts. Our technique can be transferred to more extensive comparisons in both stages as well as to other neurobiology questions regarding synaptic vesicles.

neuroscience↗