@article{MarquardtHartrampfKollmannsbergeretal.2023, author = {Marquardt, Andr{\´e} and Hartrampf, Philipp and Kollmannsberger, Philip and Solimando, Antonio G. and Meierjohann, Svenja and K{\"u}bler, Hubert and Bargou, Ralf and Schilling, Bastian and Serfling, Sebastian E. and Buck, Andreas and Werner, Rudolf A. and Lapa, Constantin and Krebs, Markus}, title = {Predicting microenvironment in CXCR4- and FAP-positive solid tumors — a pan-cancer machine learning workflow for theranostic target structures}, series = {Cancers}, volume = {15}, journal = {Cancers}, number = {2}, issn = {2072-6694}, doi = {10.3390/cancers15020392}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-305036}, year = {2023}, abstract = {(1) Background: C-X-C Motif Chemokine Receptor 4 (CXCR4) and Fibroblast Activation Protein Alpha (FAP) are promising theranostic targets. However, it is unclear whether CXCR4 and FAP positivity mark distinct microenvironments, especially in solid tumors. (2) Methods: Using Random Forest (RF) analysis, we searched for entity-independent mRNA and microRNA signatures related to CXCR4 and FAP overexpression in our pan-cancer cohort from The Cancer Genome Atlas (TCGA) database — representing n = 9242 specimens from 29 tumor entities. CXCR4- and FAP-positive samples were assessed via StringDB cluster analysis, EnrichR, Metascape, and Gene Set Enrichment Analysis (GSEA). Findings were validated via correlation analyses in n = 1541 tumor samples. TIMER2.0 analyzed the association of CXCR4 / FAP expression and infiltration levels of immune-related cells. (3) Results: We identified entity-independent CXCR4 and FAP gene signatures representative for the majority of solid cancers. While CXCR4 positivity marked an immune-related microenvironment, FAP overexpression highlighted an angiogenesis-associated niche. TIMER2.0 analysis confirmed characteristic infiltration levels of CD8+ cells for CXCR4-positive tumors and endothelial cells for FAP-positive tumors. (4) Conclusions: CXCR4- and FAP-directed PET imaging could provide a non-invasive decision aid for entity-agnostic treatment of microenvironment in solid malignancies. Moreover, this machine learning workflow can easily be transferred towards other theranostic targets.}, language = {en} } @article{KaltdorfSchulzeHelmprobstetal.2017, author = {Kaltdorf, Kristin Verena and Schulze, Katja and Helmprobst, Frederik and Kollmannsberger, Philip and Dandekar, Thomas and Stigloher, Christian}, title = {Fiji macro 3D ART VeSElecT: 3D automated reconstruction tool for vesicle structures of electron tomograms}, series = {PLoS Computational Biology}, volume = {13}, journal = {PLoS Computational Biology}, number = {1}, doi = {10.1371/journal.pcbi.1005317}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-172112}, year = {2017}, abstract = {Automatic image reconstruction is critical to cope with steadily increasing data from advanced microscopy. We describe here the Fiji macro 3D ART VeSElecT which we developed to study synaptic vesicles in electron tomograms. We apply this tool to quantify vesicle properties (i) in embryonic Danio rerio 4 and 8 days past fertilization (dpf) and (ii) to compare Caenorhabditis elegans N2 neuromuscular junctions (NMJ) wild-type and its septin mutant (unc-59(e261)). We demonstrate development-specific and mutant-specific changes in synaptic vesicle pools in both models. We confirm the functionality of our macro by applying our 3D ART VeSElecT on zebrafish NMJ showing smaller vesicles in 8 dpf embryos then 4 dpf, which was validated by manual reconstruction of the vesicle pool. Furthermore, we analyze the impact of C. elegans septin mutant unc-59(e261) on vesicle pool formation and vesicle size. Automated vesicle registration and characterization was implemented in Fiji as two macros (registration and measurement). This flexible arrangement allows in particular reducing false positives by an optional manual revision step. Preprocessing and contrast enhancement work on image-stacks of 1nm/pixel in x and y direction. Semi-automated cell selection was integrated. 3D ART VeSElecT removes interfering components, detects vesicles by 3D segmentation and calculates vesicle volume and diameter (spherical approximation, inner/outer diameter). Results are collected in color using the RoiManager plugin including the possibility of manual removal of non-matching confounder vesicles. Detailed evaluation considered performance (detected vesicles) and specificity (true vesicles) as well as precision and recall. We furthermore show gain in segmentation and morphological filtering compared to learning based methods and a large time gain compared to manual segmentation. 3D ART VeSElecT shows small error rates and its speed gain can be up to 68 times faster in comparison to manual annotation. Both automatic and semi-automatic modes are explained including a tutorial.}, language = {en} } @article{LichterPaulPaulietal.2022, author = {Lichter, Katharina and Paul, Mila Marie and Pauli, Martin and Schoch, Susanne and Kollmannsberger, Philip and Stigloher, Christian and Heckmann, Manfred and Sir{\´e}n, Anna-Leena}, title = {Ultrastructural analysis of wild-type and RIM1α knockout active zones in a large cortical synapse}, series = {Cell Reports}, volume = {40}, journal = {Cell Reports}, number = {12}, doi = {10.1016/j.celrep.2022.111382}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-300913}, year = {2022}, abstract = {Rab3A-interacting molecule (RIM) is crucial for fast Ca\(^{2+}\)-triggered synaptic vesicle (SV) release in presynaptic active zones (AZs). We investigated hippocampal giant mossy fiber bouton (MFB) AZ architecture in 3D using electron tomography of rapid cryo-immobilized acute brain slices in RIM1α\(^{-/-}\) and wild-type mice. In RIM1α\(^{-/-}\), AZs are larger with increased synaptic cleft widths and a 3-fold reduced number of tightly docked SVs (0-2 nm). 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 RIM1α\(^{-/-}\) is more heterogeneous. Thus, RIM1α, besides its role in tight SV docking, is crucial for synaptic architecture and vesicle pool organization in MFBs.}, language = {en} } @article{DannhaeuserMrestaniGundelachetal.2022, author = {Dannh{\"a}user, Sven and Mrestani, Achmed and Gundelach, Florian and Pauli, Martin and Komma, Fabian and Kollmannsberger, Philip and Sauer, Markus and Heckmann, Manfred and Paul, Mila M.}, title = {Endogenous tagging of Unc-13 reveals nanoscale reorganization at active zones during presynaptic homeostatic potentiation}, series = {Frontiers in Cellular Neuroscience}, volume = {16}, journal = {Frontiers in Cellular Neuroscience}, issn = {1662-5102}, doi = {10.3389/fncel.2022.1074304}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-299440}, year = {2022}, abstract = {Introduction Neurotransmitter release at presynaptic active zones (AZs) requires concerted protein interactions within a dense 3D nano-hemisphere. Among the complex protein meshwork the (M)unc-13 family member Unc-13 of Drosophila melanogaster is essential for docking of synaptic vesicles and transmitter release. Methods We employ minos-mediated integration cassette (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. Results and discussion Electrophysiological characterization using two-electrode voltage clamp (TEVC) reveals that evoked and spontaneous synaptic transmission remain unaffected in unc-13\(^{GFSTF}\) 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-13\(^{GFSTF}\), Bruchpilot, and GluRIIA-receptor subunits within the synaptic mesoscale. Localization microscopy in combination with HDBSCAN algorithms detect Unc-13\(^{GFSTF}\) subclusters that move toward the AZ center during PHP with unaltered Unc-13\(^{GFSTF}\) protein levels.}, language = {en} } @article{MarquardtKollmannsbergerKrebsetal.2022, author = {Marquardt, Andr{\´e} and Kollmannsberger, Philip and Krebs, Markus and Argentiero, Antonella and Knott, Markus and Solimando, Antonio Giovanni and Kerscher, Alexander Georg}, title = {Visual clustering of transcriptomic data from primary and metastatic tumors — dependencies and novel pitfalls}, series = {Genes}, volume = {13}, journal = {Genes}, number = {8}, issn = {2073-4425}, doi = {10.3390/genes13081335}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-281872}, year = {2022}, abstract = {Personalized oncology is a rapidly evolving area and offers cancer patients therapy options that are more specific than ever. However, 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 and 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 the site of resection and cluster formation outcome could be demonstrated, or for datasets consisting of solely metastasis or 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 the interpretation of visual clustering approaches along with initialization and different parameters. Furthermore, the results highlight the need for a more thorough examination of parameters used in the analysis of clusters.}, language = {en} } @article{ReinhardHelmerichBorasetal.2022, author = {Reinhard, Sebastian and Helmerich, Dominic A. and Boras, Dominik and Sauer, Markus and Kollmannsberger, Philip}, title = {ReCSAI: recursive compressed sensing artificial intelligence for confocal lifetime localization microscopy}, series = {BMC Bioinformatics}, volume = {23}, journal = {BMC Bioinformatics}, number = {1}, doi = {10.1186/s12859-022-05071-5}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-299768}, year = {2022}, abstract = {Background 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. Results 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. Conclusions Our deep learning approach can reach a similar reconstruction accuracy for confocal dSTORM as frame binning with traditional fitting without requiring the acquisition 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.}, language = {en} } @article{MrestaniPauliKollmannsbergeretal.2021, author = {Mrestani, Achmed and Pauli, Martin and Kollmannsberger, Philip and Repp, Felix and Kittel, Robert J. and Eilers, Jens and Doose, S{\"o}ren and Sauer, Markus and Sir{\´e}n, Anna-Leena and Heckmann, Manfred and Paul, Mila M.}, title = {Active zone compaction correlates with presynaptic homeostatic potentiation}, series = {Cell Reports}, volume = {37}, journal = {Cell Reports}, number = {1}, doi = {10.1016/j.celrep.2021.109770}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-265497}, pages = {109770}, year = {2021}, abstract = {Neurotransmitter release is stabilized by homeostatic plasticity. Presynaptic homeostatic potentiation (PHP) operates on timescales 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 role in PHP. We use 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 at the synaptic mesoscale. We find compaction of individual AZs in acute philanthotoxin-induced and chronic genetically induced PHP but unchanged copy numbers of AZ proteins. Compaction even occurs at the level of Brp subclusters, which move toward AZ centers, and in Rab3 interacting molecule (RIM)-binding protein (RBP) subclusters. Furthermore, correlative confocal and dSTORM imaging reveals how AZ compaction in PHP translates into apparent increases in AZ area and Brp protein content, as implied earlier.}, language = {en} } @article{BerberichKurzReinhardetal.2021, author = {Berberich, Andreas and Kurz, Andreas and Reinhard, Sebastian and Paul, Torsten Johann and Burd, Paul Ray and Sauer, Markus and Kollmannsberger, Philip}, title = {Fourier Ring Correlation and anisotropic kernel density estimation improve deep learning based SMLM reconstruction of microtubules}, series = {Frontiers in Bioinformatics}, volume = {1}, journal = {Frontiers in Bioinformatics}, doi = {10.3389/fbinf.2021.752788}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-261686}, year = {2021}, abstract = {Single-molecule super-resolution microscopy (SMLM) techniques like dSTORM can reveal biological structures down to the nanometer scale. The achievable resolution is not only defined by the localization precision of individual fluorescent molecules, but also by their density, which becomes a limiting factor e.g., in expansion microscopy. Artificial deep neural networks can learn to reconstruct dense super-resolved structures such as microtubules from a sparse, noisy set of data points. This approach requires a robust method to assess the quality of a predicted density image and to quantitatively compare it to a ground truth image. Such a quality measure needs to be differentiable to be applied as loss function in deep learning. We developed a new trainable quality measure based on Fourier Ring Correlation (FRC) and used it to train deep neural networks to map a small number of sampling points to an underlying density. Smooth ground truth images of microtubules were generated from localization coordinates using an anisotropic Gaussian kernel density estimator. We show that the FRC criterion ideally complements the existing state-of-the-art multiscale structural similarity index, since both are interpretable and there is no trade-off between them during optimization. The TensorFlow implementation of our FRC metric can easily be integrated into existing deep learning workflows.}, language = {en} } @article{PauliPaulProppertetal.2021, author = {Pauli, Martin and Paul, Mila M. and Proppert, Sven and Mrestani, Achmed and Sharifi, Marzieh and Repp, Felix and K{\"u}rzinger, Lydia and Kollmannsberger, Philip and Sauer, Markus and Heckmann, Manfred and Sir{\´e}n, Anna-Leena}, title = {Targeted volumetric single-molecule localization microscopy of defined presynaptic structures in brain sections}, series = {Communications Biology}, volume = {4}, journal = {Communications Biology}, doi = {10.1038/s42003-021-01939-z}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-259830}, pages = {407}, year = {2021}, abstract = {Revealing the molecular organization of anatomically precisely defined brain regions is necessary for refined understanding of synaptic plasticity. Although three-dimensional (3D) single-molecule localization microscopy can provide the required resolution, 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 mu m, we developed a method for targeted 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 enabled 3D-dSTORM imaging of 25 mu 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. Pauli et al. develop targeted volumetric dSTORM in order to image large hippocampal mossy fiber boutons (MFBs) in brain slices. They can identify synaptic targets of individual MFBs and measured size and density of Bassoon clusters within individual untruncated MFBs at nanoscopic resolution.}, language = {en} } @article{BritzMarkertWitvlietetal.2021, author = {Britz, Sebastian and Markert, Sebastian Matthias and Witvliet, Daniel and Steyer, Anna Maria and Tr{\"o}ger, Sarah and Mulcahy, Ben and Kollmannsberger, Philip and Schwab, Yannick and Zhen, Mei and Stigloher, Christian}, title = {Structural Analysis of the Caenorhabditis elegans Dauer Larval Anterior Sensilla by Focused Ion Beam-Scanning Electron Microscopy}, series = {Frontiers in Neuroanatomy}, volume = {15}, journal = {Frontiers in Neuroanatomy}, issn = {1662-5129}, doi = {10.3389/fnana.2021.732520}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-249622}, year = {2021}, abstract = {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 the following: (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; and (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.}, language = {en} } @article{MarquardtSolimandoKerscheretal.2021, author = {Marquardt, Andr{\´e} and Solimando, Antonio Giovanni and Kerscher, Alexander and Bittrich, Max and Kalogirou, Charis and K{\"u}bler, Hubert and Rosenwald, Andreas and Bargou, Ralf and Kollmannsberger, Philip and Schilling, Bastian and Meierjohann, Svenja and Krebs, Markus}, title = {Subgroup-Independent Mapping of Renal Cell Carcinoma — Machine Learning Reveals Prognostic Mitochondrial Gene Signature Beyond Histopathologic Boundaries}, series = {Frontiers in Oncology}, volume = {11}, journal = {Frontiers in Oncology}, issn = {2234-943X}, doi = {10.3389/fonc.2021.621278}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-232107}, year = {2021}, abstract = {Background: Renal cell carcinoma (RCC) is divided into three major histopathologic groups—clear cell (ccRCC), papillary (pRCC) and chromophobe RCC (chRCC). We performed a comprehensive re-analysis of publicly available RCC datasets from the TCGA (The Cancer Genome Atlas) database, thereby combining samples from all three subgroups, for an exploratory transcriptome profiling of RCC subgroups. Materials and Methods: We used FPKM (fragments per kilobase per million) files derived from the ccRCC, pRCC and chRCC cohorts of the TCGA database, representing transcriptomic data of 891 patients. Using principal component analysis, we visualized datasets as t-SNE plot for cluster detection. Clusters were characterized by machine learning, resulting gene signatures were validated by correlation analyses in the TCGA dataset and three external datasets (ICGC RECA-EU, CPTAC-3-Kidney, and GSE157256). Results: Many RCC samples co-clustered according to histopathology. However, a substantial number of samples clustered independently from histopathologic origin (mixed subgroup)—demonstrating divergence between histopathology and transcriptomic data. Further analyses of mixed subgroup via machine learning revealed a predominant mitochondrial gene signature—a trait previously known for chRCC—across all histopathologic subgroups. Additionally, ccRCC samples from mixed subgroup presented an inverse correlation of mitochondrial and angiogenesis-related genes in the TCGA and in three external validation cohorts. Moreover, mixed subgroup affiliation was associated with a highly significant shorter overall survival for patients with ccRCC—and a highly significant longer overall survival for chRCC patients. Conclusions: Pan-RCC clustering according to RNA-sequencing data revealed a distinct histology-independent subgroup characterized by strengthened mitochondrial and weakened angiogenesis-related gene signatures. Moreover, affiliation to mixed subgroup went along with a significantly shorter overall survival for ccRCC and a longer overall survival for chRCC patients. Further research could offer a therapy stratification by specifically addressing the mitochondrial metabolism of such tumors and its microenvironment.}, language = {en} } @article{MarquardtLandwehrRonchietal.2021, author = {Marquardt, Andr{\´e} and Landwehr, Laura-Sophie and Ronchi, Cristina L. and di Dalmazi, Guido and Riester, Anna and Kollmannsberger, Philip and Altieri, Barbara and Fassnacht, Martin and Sbiera, Silviu}, title = {Identifying New Potential Biomarkers in Adrenocortical Tumors Based on mRNA Expression Data Using Machine Learning}, series = {Cancers}, volume = {13}, journal = {Cancers}, number = {18}, issn = {2072-6694}, doi = {10.3390/cancers13184671}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-246245}, year = {2021}, abstract = {Simple Summary Using a visual-based clustering method on the TCGA RNA sequencing data of a large adrenocortical carcinoma (ACC) cohort, we were able to classify these tumors in two distinct clusters largely overlapping with previously identified ones. As previously shown, the identified clusters also correlated with patient survival. Applying the visual clustering method to a second dataset also including benign adrenocortical samples additionally revealed that one of the ACC clusters is more closely located to the benign samples, providing a possible explanation for the better survival of this ACC cluster. Furthermore, the subsequent use of machine learning identified new possible biomarker genes with prognostic potential for this rare disease, that are significantly differentially expressed in the different survival clusters and should be further evaluated. Abstract Adrenocortical carcinoma (ACC) is a rare disease, associated with poor survival. Several "multiple-omics" studies characterizing ACC on a molecular level identified two different clusters correlating with patient survival (C1A and C1B). We here used the publicly available transcriptome data from the TCGA-ACC dataset (n = 79), applying machine learning (ML) methods to classify the ACC based on expression pattern in an unbiased manner. UMAP (uniform manifold approximation and projection)-based clustering resulted in two distinct groups, ACC-UMAP1 and ACC-UMAP2, that largely overlap with clusters C1B and C1A, respectively. However, subsequent use of random-forest-based learning revealed a set of new possible marker genes showing significant differential expression in the described clusters (e.g., SOAT1, EIF2A1). For validation purposes, we used a secondary dataset based on a previous study from our group, consisting of 4 normal adrenal glands and 52 benign and 7 malignant tumor samples. The results largely confirmed those obtained for the TCGA-ACC cohort. In addition, the ENSAT dataset showed a correlation between benign adrenocortical tumors and the good prognosis ACC cluster ACC-UMAP1/C1B. In conclusion, the use of ML approaches re-identified and redefined known prognostic ACC subgroups. On the other hand, the subsequent use of random-forest-based learning identified new possible prognostic marker genes for ACC.}, language = {en} } @article{PaulKollmannsberger2020, author = {Paul, Torsten Johann and Kollmannsberger, Philip}, title = {Biological network growth in complex environments: A computational framework}, series = {PLoS Computational Biology}, volume = {16}, journal = {PLoS Computational Biology}, number = {11}, doi = {10.1371/journal.pcbi.1008003}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-231373}, year = {2020}, abstract = {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 summary We 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.}, language = {en} } @article{MostosiSchindelinKollmannsbergeretal.2020, author = {Mostosi, Philipp and Schindelin, Hermann and Kollmannsberger, Philip and Thorn, Andrea}, title = {Haruspex: A Neural Network for the Automatic Identification of Oligonucleotides and Protein Secondary Structure in Cryo-Electron Microscopy Maps}, series = {Angewandte Chemie International Edition}, volume = {59}, journal = {Angewandte Chemie International Edition}, number = {35}, doi = {10.1002/anie.202000421}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-214763}, pages = {14788 -- 14795}, year = {2020}, abstract = {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. Herein, 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 RNA/DNA as well as protein secondary structure elements. It can be straightforwardly applied to newly reconstructed maps in order to support domain placement or as 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.}, language = {en} } @article{SahlolKollmannsbergerEwees2020, author = {Sahlol, Ahmed T. and Kollmannsberger, Philip and Ewees, Ahmed A.}, title = {Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features}, series = {Scientific Reports}, volume = {10}, journal = {Scientific Reports}, number = {1}, doi = {10.1038/s41598-020-59215-9}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-229398}, year = {2020}, abstract = {White Blood Cell (WBC) Leukaemia is caused by excessive production of leukocytes in the bone marrow, and image-based detection of malignant WBCs is important for its detection. Convolutional Neural Networks (CNNs) present the current state-of-the-art for this type of image classification, but their computational cost for training and deployment can be high. We here present an improved hybrid approach for efficient classification of WBC Leukemia. We first extract features from WBC images using VGGNet, a powerful CNN architecture, pre-trained on ImageNet. The extracted features are then filtered using a statistically enhanced Salp Swarm Algorithm (SESSA). This bio-inspired optimization algorithm selects the most relevant features and removes highly correlated and noisy features. We applied the proposed approach to two public WBC Leukemia reference datasets and achieve both high accuracy and reduced computational complexity. The SESSA optimization selected only 1 K out of 25 K features extracted with VGGNet, while improving accuracy at the same time. The results are among the best achieved on these datasets and outperform several convolutional network models. We expect that the combination of CNN feature extraction and SESSA feature optimization could be useful for many other image classification tasks.}, language = {en} } @article{KollmannsbergerKerschnitzkiReppetal.2017, author = {Kollmannsberger, Philip and Kerschnitzki, Michael and Repp, Felix and Wagermaier, Wolfgang and Weinkamer, Richard and Fratzl, Peter}, title = {The small world of osteocytes: connectomics of the lacuno-canalicular network in bone}, series = {New Journal of Physics}, volume = {19}, journal = {New Journal of Physics}, number = {073019}, doi = {10.1088/1367-2630/aa764b}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-170662}, year = {2017}, abstract = {Osteocytes and their cell processes reside in a large, interconnected network of voids pervading the mineralized bone matrix of most vertebrates. This osteocyte lacuno-canalicular network (OLCN) is believed to play important roles in mechanosensing, mineral homeostasis, and for the mechanical properties of bone. While the extracellular matrix structure of bone is extensively studied on ultrastructural and macroscopic scales, there is a lack of quantitative knowledge on how the cellular network is organized. Using a recently introduced imaging and quantification approach, we analyze the OLCN in different bone types from mouse and sheep that exhibit different degrees of structural organization not only of the cell network but also of the fibrous matrix deposited by the cells. We define a number of robust, quantitative measures that are derived from the theory of complex networks. These measures enable us to gain insights into how efficient the network is organized with regard to intercellular transport and communication. Our analysis shows that the cell network in regularly organized, slow-growing bone tissue from sheep is less connected, but more efficiently organized compared to irregular and fast-growing bone tissue from mice. On the level of statistical topological properties (edges per node, edge length and degree distribution), both network types are indistinguishable, highlighting that despite pronounced differences at the tissue level, the topological architecture of the osteocyte canalicular network at the subcellular level may be independent of species and bone type. Our results suggest a universal mechanism underlying the self-organization of individual cells into a large, interconnected network during bone formation and mineralization.}, language = {en} }