Eltzner, Benjamin

[["dc.bibliographiccitation.artnumber","013012"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","New Journal of Physics"],["dc.bibliographiccitation.volume","19"],["dc.contributor.author","Bernhardt, M."],["dc.contributor.author","Nicolas, J. D."],["dc.contributor.author","Eckermann, M."],["dc.contributor.author","Eltzner, B."],["dc.contributor.author","Rehfeldt, F."],["dc.contributor.author","Salditt, Tim"],["dc.date.accessioned","2019-07-09T11:43:26Z"],["dc.date.available","2019-07-09T11:43:26Z"],["dc.date.issued","2017"],["dc.description.abstract","X-ray diffraction from biomolecular assemblies is a powerful technique which can provide structural information about complex architectures such as the locomotor systems underlying muscle contraction. However, in its conventional form, macromolecular diffraction averages over large ensembles. Progress in x-ray optics has now enabled to probe structures on sub-cellular scales, with the beam confined to a distinct organelle. Here, we use scanning small angle x-ray scattering (scanning SAXS) to probe the diffraction from cytoskeleton networks in cardiac tissue cells. In particular, we focus on actin-myosin composites, which we identify as the dominating contribution to the anisotropic diffraction patterns, by correlation with optical fluorescence microscopy. To this end, we use a principal component analysis approach to quantify direction, degree of orientation, nematic order, and the second moment of the scattering distribution in each scan point.Wecompare the fiber orientation from micrographs of fluorescently labeled actin fibers to the structure orientation of the x-ray dataset and thus correlate signals of two different measurements: the native electron density distribution of the local probing area versus specifically labeled constituents of the sample. Further, we develop a robust and automated fitting approach based on a power law expansion, in order to describe the local structure factor in each scan point over a broad range of the momentum transfer qr. Finally, we demonstrate how the methodology shown for freeze dried cells in the first part of the paper can be translated to alive cell recordings."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2017"],["dc.identifier.doi","10.1088/1367-2630/19/1/013012"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14526"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/58886"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1367-2630"],["dc.relation.orgunit","Institut für Röntgenphysik"],["dc.relation.orgunit","Fakultät für Physik"],["dc.relation.workinggroup","RG Salditt (Structure of Biomolecular Assemblies and X-Ray Physics)"],["dc.rights","CC BY 3.0"],["dc.subject.ddc","530"],["dc.subject.gro","x-ray scattering"],["dc.title","Anisotropic x-ray scattering and orientation fields in cardiac tissue cells"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","submitted_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Applied Magnetic Resonance"],["dc.contributor.author","Hiller, M."],["dc.contributor.author","Tkach, I."],["dc.contributor.author","Wiechers, H."],["dc.contributor.author","Eltzner, Benjamin"],["dc.contributor.author","Huckemann, Stephan F."],["dc.contributor.author","Pokern, Y."],["dc.contributor.author","Bennati, Marina"],["dc.date.accessioned","2021-09-03T12:57:30Z"],["dc.date.available","2021-09-03T12:57:30Z"],["dc.date.issued","2021-08-26"],["dc.description.abstract","H ENDOR spectra of tyrosyl radicals (Y∙) have been the subject of numerous EPR spectroscopic studies due to their importance in biology. Nevertheless, assignment of all internal 1H hyperfine couplings has been challenging because of substantial spectral overlap. Recently, using 263 GHz ENDOR in conjunction with statistical analysis, we could identify the signature of the Hβ2 coupling in the essential Y122 radical of Escherichia coli ribonucleotide reductase, and modeled it with a distribution of radical conformations. Here, we demonstrate that this analysis can be extended to the full-width 1H ENDOR spectra that contain the larger Hβ1 coupling. The Hβ2 and Hβ1 couplings are related to each other through the ring dihedral and report on the amino acid conformation. The 263 GHz ENDOR data, acquired in batches instead of averaging, and data processing by a new “drift model” allow reconstructing the ENDOR spectra with statistically meaningful confidence intervals and separating them from baseline distortions. Spectral simulations using a distribution of ring dihedral angles confirm the presence of a conformational distribution, consistent with the previous analysis of the Hβ2 coupling. The analysis was corroborated by 94 GHz 2H ENDOR of deuterated Y∙122. These studies provide a starting point to investigate low populated states of tyrosyl radicals in greater detail."],["dc.identifier.doi","10.1007/s00723-021-01411-5"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89260"],["dc.language.iso","en"],["dc.relation","SFB 1456 | Cluster A | A01: Geometric and Bayesian statistics to reconstruct protein radical structures from ENDOR spectroscopy"],["dc.relation","SFB 1456 | Cluster A: Data with Geometric Nonlinearities"],["dc.relation","SFB 1456: Mathematik des Experiments: Die Herausforderung indirekter Messungen in den Naturwissenschaften"],["dc.relation.issn","0937-9347"],["dc.relation.issn","1613-7507"],["dc.relation.orgunit","Max-Planck-Institut für biophysikalische Chemie"],["dc.relation.orgunit","Fakultät für Chemie"],["dc.relation.orgunit","Institut für Mathematische Stochastik"],["dc.rights","CC BY 4.0"],["dc.title","Distribution of H$^\\beta$ Hyperfine Couplings in a Tyrosyl Radical Revealed by 263 GHz ENDOR Spectroscopy"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","25"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Image Analysis & Stereology"],["dc.bibliographiccitation.volume","36"],["dc.contributor.author","Benes, Viktor"],["dc.contributor.author","Vecera, Jakub"],["dc.contributor.author","Eltzner, Benjamin"],["dc.contributor.author","Wollnik, Carina"],["dc.contributor.author","Rehfeldt, Florian"],["dc.contributor.author","Kralova, Veronika"],["dc.contributor.author","Huckemann, Stephan"],["dc.date.accessioned","2017-09-07T11:50:29Z"],["dc.date.available","2017-09-07T11:50:29Z"],["dc.date.issued","2017"],["dc.identifier.doi","10.5566/ias.1627"],["dc.identifier.gro","3147619"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14778"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5090"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","chake"],["dc.publisher","Slovenian Society for Stereology and Quantitative Image Analysis"],["dc.relation.issn","1854-5165"],["dc.rights","CC BY-NC 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by-nc/4.0/"],["dc.title","ESTIMATION OF PARAMETERS IN A PLANAR SEGMENT PROCESS WITH A BIOLOGICAL APPLICATION"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","187"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","European Biophysics Journal"],["dc.bibliographiccitation.lastpage","209"],["dc.bibliographiccitation.volume","50"],["dc.contributor.author","Pein, Florian"],["dc.contributor.author","Eltzner, Benjamin"],["dc.contributor.author","Munk, Axel"],["dc.date.accessioned","2021-06-01T09:42:49Z"],["dc.date.available","2021-06-01T09:42:49Z"],["dc.date.issued","2021"],["dc.description.abstract","Abstract Analysis of patchclamp recordings is often a challenging issue. We give practical guidance how such recordings can be analyzed using the model-free multiscale idealization methodology JSMURF, JULES, and HILDE. We provide an operational manual how to use the accompanying software available as an R-package and as a graphical user interface. This includes selection of the right approach and tuning of parameters. We also discuss advantages and disadvantages of model-free approaches in comparison to hidden Markov model approaches and explain how they complement each other."],["dc.identifier.doi","10.1007/s00249-021-01506-8"],["dc.identifier.pmid","33837454"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/85363"],["dc.identifier.url","https://mbexc.uni-goettingen.de/literature/publications/272"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-425"],["dc.relation","EXC 2067: Multiscale Bioimaging"],["dc.relation.eissn","1432-1017"],["dc.relation.issn","0175-7571"],["dc.relation.workinggroup","RG Munk"],["dc.rights","CC BY 4.0"],["dc.title","Analysis of patchclamp recordings: model-free multiscale methods and software"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e0126346"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","PLOS ONE"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Eltzner, Benjamin"],["dc.contributor.author","Wollnik, Carina"],["dc.contributor.author","Gottschlich, Carsten"],["dc.contributor.author","Huckemann, Stephan"],["dc.contributor.author","Rehfeldt, Florian"],["dc.date.accessioned","2017-09-07T11:48:37Z"],["dc.date.available","2017-09-07T11:48:37Z"],["dc.date.issued","2015"],["dc.description.abstract","A reliable extraction of filament data from microscopic images is of high interest in the analysis of acto-myosin structures as early morphological markers in mechanically guided differentiation of human mesenchymal stem cells and the understanding of the underlying fiber arrangement processes. In this paper, we propose the filament sensor (FS), a fast and robust processing sequence which detects and records location, orientation, length, and width for each single filament of an image, and thus allows for the above described analysis. The extraction of these features has previously not been possible with existing methods. We evaluate the performance of the proposed FS in terms of accuracy and speed in comparison to three existing methods with respect to their limited output. Further, we provide a benchmark dataset of real cell images along with filaments manually marked by a human expert as well as simulated benchmark images. The FS clearly outperforms existing methods in terms of computational runtime and filament extraction accuracy. The implementation of the FS and the benchmark database are available as open source."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2015"],["dc.identifier.doi","10.1371/journal.pone.0126346"],["dc.identifier.gro","3146928"],["dc.identifier.pmid","25996921"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/11812"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/4710"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","1932-6203"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","The Filament Sensor for Near Real-Time Detection of Cytoskeletal Fiber Structures"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","e2023615118"],["dc.bibliographiccitation.issue","27"],["dc.bibliographiccitation.journal","Proceedings of the National Academy of Sciences"],["dc.bibliographiccitation.volume","118"],["dc.contributor.author","Pokern, Yvo"],["dc.contributor.author","Eltzner, Benjamin"],["dc.contributor.author","Huckemann, Stephan F."],["dc.contributor.author","Beeken, Clemens"],["dc.contributor.author","Stubbe, JoAnne"],["dc.contributor.author","Tkach, Igor"],["dc.contributor.author","Bennati, Marina"],["dc.contributor.author","Hiller, Markus"],["dc.date.accessioned","2021-08-12T07:45:08Z"],["dc.date.available","2021-08-12T07:45:08Z"],["dc.date.issued","2021"],["dc.description.abstract","Electron–nuclear double resonance (ENDOR) measures the hyperfine interaction of magnetic nuclei with paramagnetic centers and is hence a powerful tool for spectroscopic investigations extending from biophysics to material science. Progress in microwave technology and the recent availability of commercial electron paramagnetic resonance (EPR) spectrometers up to an electron Larmor frequency of 263 GHz now open the opportunity for a more quantitative spectral analysis. Using representative spectra of a prototype amino acid radical in a biologically relevant enzyme, the Y 122 • in Escherichia coli ribonucleotide reductase, we developed a statistical model for ENDOR data and conducted statistical inference on the spectra including uncertainty estimation and hypothesis testing. Our approach in conjunction with 1 H/ 2 H isotopic labeling of Y 122 • in the protein unambiguously established new unexpected spectral contributions. Density functional theory (DFT) calculations and ENDOR spectral simulations indicated that these features result from the beta-methylene hyperfine coupling and are caused by a distribution of molecular conformations, likely important for the biological function of this essential radical. The results demonstrate that model-based statistical analysis in combination with state-of-the-art spectroscopy accesses information hitherto beyond standard approaches."],["dc.identifier.doi","10.1073/pnas.2023615118"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/88376"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-448"],["dc.relation","SFB 1456 | Cluster A | A01: Geometric and Bayesian statistics to reconstruct protein radical structures from ENDOR spectroscopy"],["dc.relation","SFB 1456 | Cluster A: Data with Geometric Nonlinearities"],["dc.relation","SFB 1456: Mathematik des Experiments: Die Herausforderung indirekter Messungen in den Naturwissenschaften"],["dc.relation.eissn","1091-6490"],["dc.relation.issn","0027-8424"],["dc.rights","CC BY 4.0"],["dc.title","Statistical analysis of ENDOR spectra"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","166"],["dc.bibliographiccitation.journal","Progress in biophysics and molecular biology"],["dc.bibliographiccitation.lastpage","176"],["dc.bibliographiccitation.volume","144"],["dc.contributor.author","Paknikar, Aishwarya K."],["dc.contributor.author","Eltzner, Benjamin"],["dc.contributor.author","Köster, Sarah"],["dc.date.accessioned","2019-07-22T12:56:44Z"],["dc.date.available","2019-07-22T12:56:44Z"],["dc.date.issued","2019"],["dc.description.abstract","Blood platelets are the key cellular players in blood clotting and thus of great biomedical importance. While spreading at the site of injury, they reorganize their cytoskeleton within minutes and assume a flat appearance. As platelets possess no nucleus, many standard methods for visualizing cytoskeletal components by means of fluorescence tags fail. Here we employ silicon-rhodamine actin and tubulin probes for imaging these important proteins in a time-resolved manner. We find two distinct timescales for platelet spread area development and for cytoskeletal reorganization, indicating that although cell spreading is most likely associated with actin polymerization at the cell edges, distinct, stress-fiber-like actin structures within the cell, which may be involved in the generation of contractile forces, form on their own timescale. Following microtubule dynamics allows us to distinguish the role of myosin, microtubules and actin during early spreading."],["dc.identifier.doi","10.1016/j.pbiomolbio.2018.05.001"],["dc.identifier.pmid","29843920"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16294"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/61800"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.relation.eissn","1873-1732"],["dc.relation.issn","0079-6107"],["dc.relation.orgunit","Institut für Röntgenphysik"],["dc.relation.orgunit","Fakultät für Physik"],["dc.relation.workinggroup","RG Köster (Cellular Biophysics)"],["dc.rights","CC BY-NC-ND 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by-nc-nd/4.0/"],["dc.subject.gro","cellular biophysics"],["dc.title","Direct characterization of cytoskeletal reorganization during blood platelet spreading"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]