TY - JOUR A1 - Berberich, Andreas A1 - Kurz, Andreas A1 - Reinhard, Sebastian A1 - Paul, Torsten Johann A1 - Burd, Paul Ray A1 - Sauer, Markus A1 - Kollmannsberger, Philip T1 - Fourier Ring Correlation and anisotropic kernel density estimation improve deep learning based SMLM reconstruction of microtubules JF - Frontiers in Bioinformatics N2 - 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. KW - dSTORM KW - deep learning–artificial neural network (DL-ANN) KW - single molecule localization microscopy KW - microtubule cytoskeleton KW - super-resolution Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-261686 VL - 1 ER - TY - JOUR A1 - Britz, Sebastian A1 - Markert, Sebastian Matthias A1 - Witvliet, Daniel A1 - Steyer, Anna Maria A1 - Tröger, Sarah A1 - Mulcahy, Ben A1 - Kollmannsberger, Philip A1 - Schwab, Yannick A1 - Zhen, Mei A1 - Stigloher, Christian T1 - Structural Analysis of the Caenorhabditis elegans Dauer Larval Anterior Sensilla by Focused Ion Beam-Scanning Electron Microscopy JF - Frontiers in Neuroanatomy N2 - 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. KW - FIB-SEM KW - 3D reconstruction KW - neuroanatomy KW - IL2 branching KW - amphids KW - Caenorhabditis elegans (C. elegans) KW - dauer Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-249622 SN - 1662-5129 VL - 15 ER - TY - JOUR A1 - Dannhäuser, Sven A1 - Mrestani, Achmed A1 - Gundelach, Florian A1 - Pauli, Martin A1 - Komma, Fabian A1 - Kollmannsberger, Philip A1 - Sauer, Markus A1 - Heckmann, Manfred A1 - Paul, Mila M. T1 - Endogenous tagging of Unc-13 reveals nanoscale reorganization at active zones during presynaptic homeostatic potentiation JF - Frontiers in Cellular Neuroscience N2 - 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. KW - active zone KW - Unc-13 KW - MiMIC KW - presynaptic homeostasis KW - nanoarchitecture KW - localization microscopy KW - STORM KW - HDBSCAN Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-299440 SN - 1662-5102 VL - 16 ER - TY - JOUR A1 - Kaltdorf, Kristin Verena A1 - Schulze, Katja A1 - Helmprobst, Frederik A1 - Kollmannsberger, Philip A1 - Dandekar, Thomas A1 - Stigloher, Christian T1 - Fiji macro 3D ART VeSElecT: 3D automated reconstruction tool for vesicle structures of electron tomograms JF - PLoS Computational Biology N2 - 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. KW - Biology KW - Vesicles KW - Caenorhabditis elegans KW - Zebrafish KW - Septins KW - Synaptic vesicles KW - Neuromuscular junctions KW - Computer software KW - Synapses Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-172112 VL - 13 IS - 1 ER - TY - JOUR A1 - Kollmannsberger, Philip A1 - Kerschnitzki, Michael A1 - Repp, Felix A1 - Wagermaier, Wolfgang A1 - Weinkamer, Richard A1 - Fratzl, Peter T1 - The small world of osteocytes: connectomics of the lacuno-canalicular network in bone JF - New Journal of Physics N2 - 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. KW - bone KW - osteocytes KW - networks KW - biomaterials KW - mechanobiology KW - image analysis Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-170662 VL - 19 IS - 073019 ER - TY - JOUR A1 - Lichter, Katharina A1 - Paul, Mila Marie A1 - Pauli, Martin A1 - Schoch, Susanne A1 - Kollmannsberger, Philip A1 - Stigloher, Christian A1 - Heckmann, Manfred A1 - Sirén, Anna-Leena T1 - Ultrastructural analysis of wild-type and RIM1α knockout active zones in a large cortical synapse JF - Cell Reports N2 - 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. KW - active zone KW - acute brain slices KW - CA3 KW - electron tomography KW - high-pressure freezing KW - hippocampal mossy fiber bouton KW - RIM1α KW - SV pool KW - synaptic ultrastructure KW - presynaptic Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-300913 VL - 40 IS - 12 ER - TY - JOUR A1 - Marquardt, André A1 - Hartrampf, Philipp A1 - Kollmannsberger, Philip A1 - Solimando, Antonio G. A1 - Meierjohann, Svenja A1 - Kübler, Hubert A1 - Bargou, Ralf A1 - Schilling, Bastian A1 - Serfling, Sebastian E. A1 - Buck, Andreas A1 - Werner, Rudolf A. A1 - Lapa, Constantin A1 - Krebs, Markus T1 - Predicting microenvironment in CXCR4- and FAP-positive solid tumors — a pan-cancer machine learning workflow for theranostic target structures JF - Cancers N2 - (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. KW - machine learning KW - tumor microenvironment KW - immune infiltration KW - angiogenesis KW - mRNA KW - miRNA KW - transcriptome Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-305036 SN - 2072-6694 VL - 15 IS - 2 ER - TY - JOUR A1 - Marquardt, André A1 - Kollmannsberger, Philip A1 - Krebs, Markus A1 - Argentiero, Antonella A1 - Knott, Markus A1 - Solimando, Antonio Giovanni A1 - Kerscher, Alexander Georg T1 - Visual clustering of transcriptomic data from primary and metastatic tumors — dependencies and novel pitfalls JF - Genes N2 - 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. KW - visual clustering KW - t-SNE KW - UMAP KW - transcriptomic analysis KW - cancer KW - metastasis Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-281872 SN - 2073-4425 VL - 13 IS - 8 ER - TY - JOUR A1 - Marquardt, André A1 - Landwehr, Laura-Sophie A1 - Ronchi, Cristina L. A1 - di Dalmazi, Guido A1 - Riester, Anna A1 - Kollmannsberger, Philip A1 - Altieri, Barbara A1 - Fassnacht, Martin A1 - Sbiera, Silviu T1 - Identifying New Potential Biomarkers in Adrenocortical Tumors Based on mRNA Expression Data Using Machine Learning JF - Cancers N2 - 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. KW - adrenocortical carcinoma KW - in silico analysis KW - machine learning KW - bioinformatic clustering KW - biomarker prediction Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-246245 SN - 2072-6694 VL - 13 IS - 18 ER - TY - JOUR A1 - Marquardt, André A1 - Solimando, Antonio Giovanni A1 - Kerscher, Alexander A1 - Bittrich, Max A1 - Kalogirou, Charis A1 - Kübler, Hubert A1 - Rosenwald, Andreas A1 - Bargou, Ralf A1 - Kollmannsberger, Philip A1 - Schilling, Bastian A1 - Meierjohann, Svenja A1 - Krebs, Markus T1 - Subgroup-Independent Mapping of Renal Cell Carcinoma — Machine Learning Reveals Prognostic Mitochondrial Gene Signature Beyond Histopathologic Boundaries JF - Frontiers in Oncology N2 - 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. KW - kidney cancer KW - pan-RCC KW - machine learning KW - mitochondrial DNA KW - mtDNA KW - mTOR Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-232107 SN - 2234-943X VL - 11 ER -