1
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Pabba MK, Meyer J, Celikay K, Schermelleh L, Rohr K, Cardoso MC. DNA choreography: correlating mobility and organization of DNA across different resolutions from loops to chromosomes. Histochem Cell Biol 2024; 162:109-131. [PMID: 38758428 PMCID: PMC11227476 DOI: 10.1007/s00418-024-02285-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/27/2024] [Indexed: 05/18/2024]
Abstract
The dynamics of DNA in the cell nucleus plays a role in cellular processes and fates but the interplay of DNA mobility with the hierarchical levels of DNA organization is still underexplored. Here, we made use of DNA replication to directly label genomic DNA in an unbiased genome-wide manner. This was followed by live-cell time-lapse microscopy of the labeled DNA combining imaging at different resolutions levels simultaneously and allowing one to trace DNA motion across organization levels within the same cells. Quantification of the labeled DNA segments at different microscopic resolution levels revealed sizes comparable to the ones reported for DNA loops using 3D super-resolution microscopy, topologically associated domains (TAD) using 3D widefield microscopy, and also entire chromosomes. By employing advanced chromatin tracking and image registration, we discovered that DNA exhibited higher mobility at the individual loop level compared to the TAD level and even less at the chromosome level. Additionally, our findings indicate that chromatin movement, regardless of the resolution, slowed down during the S phase of the cell cycle compared to the G1/G2 phases. Furthermore, we found that a fraction of DNA loops and TADs exhibited directed movement with the majority depicting constrained movement. Our data also indicated spatial mobility differences with DNA loops and TADs at the nuclear periphery and the nuclear interior exhibiting lower velocity and radius of gyration than the intermediate locations. On the basis of these insights, we propose that there is a link between DNA mobility and its organizational structure including spatial distribution, which impacts cellular processes.
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Affiliation(s)
- Maruthi K Pabba
- Department of Biology, Technical University of Darmstadt, Darmstadt, Germany
| | - Janis Meyer
- Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg University, Heidelberg, Germany
| | - Kerem Celikay
- Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg University, Heidelberg, Germany
| | | | - Karl Rohr
- Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg University, Heidelberg, Germany.
| | - M Cristina Cardoso
- Department of Biology, Technical University of Darmstadt, Darmstadt, Germany.
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2
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Ritter C, Lee JY, Pham MT, Pabba MK, Cardoso MC, Bartenschlager R, Rohr K. Multi-detector fusion and Bayesian smoothing for tracking viral and chromatin structures. Med Image Anal 2024; 97:103227. [PMID: 38897031 DOI: 10.1016/j.media.2024.103227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/15/2023] [Accepted: 05/27/2024] [Indexed: 06/21/2024]
Abstract
Automatic tracking of viral and intracellular structures displayed as spots with varying sizes in fluorescence microscopy images is an important task to quantify cellular processes. We propose a novel probabilistic tracking approach for multiple particle tracking based on multi-detector and multi-scale data fusion as well as Bayesian smoothing. The approach integrates results from multiple detectors using a novel intensity-based covariance intersection method which takes into account information about the image intensities, positions, and uncertainties. The method ensures a consistent estimate of multiple fused particle detections and does not require an optimization step. Our probabilistic tracking approach performs data fusion of detections from classical and deep learning methods as well as exploits single-scale and multi-scale detections. In addition, we use Bayesian smoothing to fuse information of predictions from both past and future time points. We evaluated our approach using image data of the Particle Tracking Challenge and achieved state-of-the-art results or outperformed previous methods. Our method was also assessed on challenging live cell fluorescence microscopy image data of viral and cellular proteins expressed in hepatitis C virus-infected cells and chromatin structures in non-infected cells, acquired at different spatial-temporal resolutions. We found that the proposed approach outperforms existing methods.
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Affiliation(s)
- C Ritter
- Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg University, Im Neuenheimer Feld 267, Heidelberg, Germany.
| | - J-Y Lee
- Department of Infectious Diseases, Molecular Virology, Heidelberg University, Im Neuenheimer Feld 344, Heidelberg, Germany; German Center for Infection Research (DZIF), Heidelberg Partner Site, Germany
| | - M-T Pham
- Department of Infectious Diseases, Molecular Virology, Heidelberg University, Im Neuenheimer Feld 344, Heidelberg, Germany; German Center for Infection Research (DZIF), Heidelberg Partner Site, Germany
| | - M K Pabba
- Department of Biology, Cell Biology and Epigenetics, Technical University of Darmstadt, Schnittspahnstraße 10, Darmstadt, Germany
| | - M C Cardoso
- Department of Biology, Cell Biology and Epigenetics, Technical University of Darmstadt, Schnittspahnstraße 10, Darmstadt, Germany
| | - R Bartenschlager
- Department of Infectious Diseases, Molecular Virology, Heidelberg University, Im Neuenheimer Feld 344, Heidelberg, Germany; German Center for Infection Research (DZIF), Heidelberg Partner Site, Germany
| | - K Rohr
- Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg University, Im Neuenheimer Feld 267, Heidelberg, Germany.
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3
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Chen Y, Yan Z, Zhang X. Auxiliary two-filter particle smoothing for one generalized hidden Markov model. ISA TRANSACTIONS 2024; 149:266-280. [PMID: 38627161 DOI: 10.1016/j.isatra.2024.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 04/09/2024] [Accepted: 04/09/2024] [Indexed: 06/05/2024]
Abstract
This paper develops two-filter particle smoothing (TFPS) algorithms for the nonlinear fixed-interval smoothing problem of one generalized hidden Markov model (GHMM), where the current observation depends not only on the current state, but also on one-step previous state. Firstly, by Bayesian approach, the two-filter smoothing (TFS) formula for GHMM is established to calculate smoothing densities. In this TFS formula, the backward information prediction density is generally not a density of the state. This results in a difficulty that the normal sequential Monte Carlo (SMC) sampling technique cannot be directly applied to design corresponding TFPS algorithms based on the TFS formula. To solve this difficulty, a generalized TFS formula for GHMM is then proposed by introducing a sequence of artificial densities. By combining this generalized TFS formula, SMC, and the auxiliary variable sampling technique, a basic auxiliary TFPS (ATFPS) algorithm with quadratic computational complexity is proposed, and a simplified ATFPS algorithm with linear computational complexity is further developed. Finally, the effectiveness and superiority of the two proposed ATFPS algorithms for GHMM are verified via simulation examples and real experimental data.
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Affiliation(s)
- Yunqi Chen
- Shenzhen Key Laboratory of Control Theory and Intelligent Systems, Southern University of Science and Technology, Shenzhen 518055, China.
| | - Zhibin Yan
- School of Science, Harbin Institute of Technology-Shenzhen, Shenzhen 518055, China.
| | - Xing Zhang
- School of Mathematics and Information Science, Guangxi University, Nanning, 530004, China.
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4
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Martens KJA, Turkowyd B, Hohlbein J, Endesfelder U. Temporal analysis of relative distances (TARDIS) is a robust, parameter-free alternative to single-particle tracking. Nat Methods 2024; 21:1074-1081. [PMID: 38225387 DOI: 10.1038/s41592-023-02149-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 12/08/2023] [Indexed: 01/17/2024]
Abstract
In single-particle tracking, individual particles are localized and tracked over time to probe their diffusion and molecular interactions. Temporal crossing of trajectories, blinking particles, and false-positive localizations present computational challenges that have remained difficult to overcome. Here we introduce a robust, parameter-free alternative to single-particle tracking: temporal analysis of relative distances (TARDIS). In TARDIS, an all-to-all distance analysis between localizations is performed with increasing temporal shifts. These pairwise distances represent either intraparticle distances originating from the same particle, or interparticle distances originating from unrelated particles, and are fitted analytically to obtain quantitative measures on particle dynamics. We showcase that TARDIS outperforms tracking algorithms, benchmarked on simulated and experimental data of varying complexity. We further show that TARDIS performs accurately in complex conditions characterized by high particle density, strong emitter blinking or false-positive localizations, and is in fact limited by the capabilities of localization algorithms. TARDIS' robustness enables fivefold shorter measurements without loss of information.
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Affiliation(s)
- Koen J A Martens
- Institute for Microbiology and Biotechnology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.
- Department of Physics, Carnegie Mellon University, Pittsburgh, PA, USA.
- Laboratory of Biophysics, Wageningen University and Research, Wageningen, the Netherlands.
| | - Bartosz Turkowyd
- Institute for Microbiology and Biotechnology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
- Department of Physics, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Johannes Hohlbein
- Laboratory of Biophysics, Wageningen University and Research, Wageningen, the Netherlands
- Microspectroscopy Research Facility, Wageningen University and Research, Wageningen, the Netherlands
| | - Ulrike Endesfelder
- Institute for Microbiology and Biotechnology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
- Department of Physics, Carnegie Mellon University, Pittsburgh, PA, USA
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5
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Roudot P, Legant WR, Zou Q, Dean KM, Isogai T, Welf ES, David AF, Gerlich DW, Fiolka R, Betzig E, Danuser G. u-track3D: Measuring, navigating, and validating dense particle trajectories in three dimensions. CELL REPORTS METHODS 2023; 3:100655. [PMID: 38042149 PMCID: PMC10783629 DOI: 10.1016/j.crmeth.2023.100655] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 08/10/2023] [Accepted: 11/09/2023] [Indexed: 12/04/2023]
Abstract
We describe u-track3D, a software package that extends the versatile u-track framework established in 2D to address the specific challenges of 3D particle tracking. First, we present the performance of the new package in quantifying a variety of intracellular dynamics imaged by multiple 3D microcopy platforms and on the standard 3D test dataset of the particle tracking challenge. These analyses indicate that u-track3D presents a tracking solution that is competitive to both conventional and deep-learning-based approaches. We then present the concept of dynamic region of interest (dynROI), which allows an experimenter to interact with dynamic 3D processes in 2D views amenable to visual inspection. Third, we present an estimator of trackability that automatically defines a score for every trajectory, thereby overcoming the challenges of trajectory validation by visual inspection. With these combined strategies, u-track3D provides a complete framework for unbiased studies of molecular processes in complex volumetric sequences.
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Affiliation(s)
- Philippe Roudot
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA; Aix Marseille University, CNRS, Centrale Marseille, I2M, Turing Centre for Living Systems, Marseille, France.
| | - Wesley R Legant
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, North Carolina State University, Chapel Hill, NC, USA; Department of Pharmacology, University of North Carolina, Chapel Hill, NC, USA
| | - Qiongjing Zou
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA
| | - Kevin M Dean
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA
| | - Tadamoto Isogai
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA
| | - Erik S Welf
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA
| | - Ana F David
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna BioCenter, Vienna, Austria
| | - Daniel W Gerlich
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna BioCenter, Vienna, Austria
| | - Reto Fiolka
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA
| | - Eric Betzig
- Department of Molecular & Cell Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Gaudenz Danuser
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA.
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6
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Pabba MK, Ritter C, Chagin VO, Meyer J, Celikay K, Stear JH, Loerke D, Kolobynina K, Prorok P, Schmid AK, Leonhardt H, Rohr K, Cardoso MC. Replisome loading reduces chromatin motion independent of DNA synthesis. eLife 2023; 12:RP87572. [PMID: 37906089 PMCID: PMC10617993 DOI: 10.7554/elife.87572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2023] Open
Abstract
Chromatin has been shown to undergo diffusional motion, which is affected during gene transcription by RNA polymerase activity. However, the relationship between chromatin mobility and other genomic processes remains unclear. Hence, we set out to label the DNA directly in a sequence unbiased manner and followed labeled chromatin dynamics in interphase human cells expressing GFP-tagged proliferating cell nuclear antigen (PCNA), a cell cycle marker and core component of the DNA replication machinery. We detected decreased chromatin mobility during the S-phase compared to G1 and G2 phases in tumor as well as normal diploid cells using automated particle tracking. To gain insight into the dynamical organization of the genome during DNA replication, we determined labeled chromatin domain sizes and analyzed their motion in replicating cells. By correlating chromatin mobility proximal to the active sites of DNA synthesis, we showed that chromatin motion was locally constrained at the sites of DNA replication. Furthermore, inhibiting DNA synthesis led to increased loading of DNA polymerases. This was accompanied by accumulation of the single-stranded DNA binding protein on the chromatin and activation of DNA helicases further restricting local chromatin motion. We, therefore, propose that it is the loading of replisomes but not their catalytic activity that reduces the dynamics of replicating chromatin segments in the S-phase as well as their accessibility and probability of interactions with other genomic regions.
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Affiliation(s)
| | - Christian Ritter
- Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg UniversityHeidelbergGermany
| | - Vadim O Chagin
- Department of Biology, Technical University of DarmstadtDarmstadtGermany
- Institute of Cytology RASSt. PetersburgRussian Federation
| | - Janis Meyer
- Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg UniversityHeidelbergGermany
| | - Kerem Celikay
- Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg UniversityHeidelbergGermany
| | - Jeffrey H Stear
- EMBL Australia Node in Single Molecule Science, University of New South WalesSydneyAustralia
| | - Dinah Loerke
- Department of Physics & Astronomy, University of DenverDenverUnited States
| | - Ksenia Kolobynina
- Department of Biology, Technical University of DarmstadtDarmstadtGermany
| | - Paulina Prorok
- Department of Biology, Technical University of DarmstadtDarmstadtGermany
| | - Alice Kristin Schmid
- Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg UniversityHeidelbergGermany
| | | | - Karl Rohr
- Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg UniversityHeidelbergGermany
| | - M Cristina Cardoso
- Department of Biology, Technical University of DarmstadtDarmstadtGermany
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7
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Pham MT, Lee JY, Ritter C, Thielemann R, Meyer J, Haselmann U, Funaya C, Laketa V, Rohr K, Bartenschlager R. Endosomal egress and intercellular transmission of hepatic ApoE-containing lipoproteins and its exploitation by the hepatitis C virus. PLoS Pathog 2023; 19:e1011052. [PMID: 37506130 PMCID: PMC10411793 DOI: 10.1371/journal.ppat.1011052] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 08/09/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
Liver-generated plasma Apolipoprotein E (ApoE)-containing lipoproteins (LPs) (ApoE-LPs) play central roles in lipid transport and metabolism. Perturbations of ApoE can result in several metabolic disorders and ApoE genotypes have been associated with multiple diseases. ApoE is synthesized at the endoplasmic reticulum and transported to the Golgi apparatus for LP assembly; however, the ApoE-LPs transport pathway from there to the plasma membrane is largely unknown. Here, we established an integrative imaging approach based on a fully functional fluorescently tagged ApoE. We found that newly synthesized ApoE-LPs accumulate in CD63-positive endosomes of hepatocytes. In addition, we observed the co-egress of ApoE-LPs and CD63-positive intraluminal vesicles (ILVs), which are precursors of extracellular vesicles (EVs), along the late endosomal trafficking route in a microtubule-dependent manner. A fraction of ApoE-LPs associated with CD63-positive EVs appears to be co-transmitted from cell to cell. Given the important role of ApoE in viral infections, we employed as well-studied model the hepatitis C virus (HCV) and found that the viral replicase component nonstructural protein 5A (NS5A) is enriched in ApoE-containing ILVs. Interaction between NS5A and ApoE is required for the efficient release of ILVs containing HCV RNA. These vesicles are transported along the endosomal ApoE egress pathway. Taken together, our data argue for endosomal egress and transmission of hepatic ApoE-LPs, a pathway that is hijacked by HCV. Given the more general role of EV-mediated cell-to-cell communication, these insights provide new starting points for research into the pathophysiology of ApoE-related metabolic and infection-related disorders.
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Affiliation(s)
- Minh-Tu Pham
- Department of Infectious Diseases, Molecular Virology, Center for Integrative Infectious Diseases Research, Heidelberg University, Heidelberg, Germany
- German Center for Infection Research (DZIF), Partner Site Heidelberg, Heidelberg, Germany
| | - Ji-Young Lee
- Department of Infectious Diseases, Molecular Virology, Center for Integrative Infectious Diseases Research, Heidelberg University, Heidelberg, Germany
- German Center for Infection Research (DZIF), Partner Site Heidelberg, Heidelberg, Germany
| | - Christian Ritter
- BioQuant Center, IPMB, Biomedical Computer Vision Group, Heidelberg University, Heidelberg, Germany
| | - Roman Thielemann
- BioQuant Center, IPMB, Biomedical Computer Vision Group, Heidelberg University, Heidelberg, Germany
| | - Janis Meyer
- BioQuant Center, IPMB, Biomedical Computer Vision Group, Heidelberg University, Heidelberg, Germany
| | - Uta Haselmann
- Department of Infectious Diseases, Molecular Virology, Center for Integrative Infectious Diseases Research, Heidelberg University, Heidelberg, Germany
| | - Charlotta Funaya
- Electron Microscopy Core Facility (EMCF), Heidelberg University, Heidelberg, Germany
| | - Vibor Laketa
- German Center for Infection Research (DZIF), Partner Site Heidelberg, Heidelberg, Germany
- Department of Infectious Diseases, Virology, Center for Integrative Infectious Diseases Research, Heidelberg University, Heidelberg, Germany
| | - Karl Rohr
- BioQuant Center, IPMB, Biomedical Computer Vision Group, Heidelberg University, Heidelberg, Germany
| | - Ralf Bartenschlager
- Department of Infectious Diseases, Molecular Virology, Center for Integrative Infectious Diseases Research, Heidelberg University, Heidelberg, Germany
- German Center for Infection Research (DZIF), Partner Site Heidelberg, Heidelberg, Germany
- Division Virus-Associated Carcinogenesis, German Cancer Research Center (DKFZ), Heidelberg, Germany
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8
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Linka K, Cavinato C, Humphrey JD, Cyron CJ. Predicting and understanding arterial elasticity from key microstructural features by bidirectional deep learning. Acta Biomater 2022; 147:63-72. [PMID: 35643194 DOI: 10.1016/j.actbio.2022.05.039] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 05/19/2022] [Accepted: 05/19/2022] [Indexed: 01/15/2023]
Abstract
Microstructural features and mechanical properties are closely related in all soft biological tissues. Both yet exhibit considerable inter-individual differences and are affected by factors such as aging and disease and its progression. Histological analysis, modern in situ imaging, and biomechanical testing have deepened our understanding of these complex interrelations, yet two key questions remain: (1) Given the specific microstructure, can one predict the macroscopic mechanical properties without mechanical testing? (2) Can one quantify individual contributions of the different microstructural features to the macroscopic mechanical properties in an automated, systematic and largely unbiased way? Here we propose a bidirectional deep learning architecture to address these two questions. Our architecture uses data from standard histological analyses, two-photon microscopy and biaxial biomechanical testing. Its capabilities are demonstrated by predicting with high accuracy (R2=0.92) the evolving mechanical properties of the murine aorta during maturation and aging. Moreover, our architecture reveals that the extracellular matrix composition and organization are the most prominent factors governing the macroscopic mechanical properties of the tissues studied herein. STATEMENT OF SIGNIFICANCE: .
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Affiliation(s)
- Kevin Linka
- Institute for Continuum and Material Mechanics, Hamburg University of Technology, Hamburg, Germany
| | - Cristina Cavinato
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Jay D Humphrey
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Vascular Biology and Therapeutics Program, Yale School of Medicine, New Haven, CT, USA
| | - Christian J Cyron
- Institute for Continuum and Material Mechanics, Hamburg University of Technology, Hamburg, Germany; Institute of Material Systems Modeling, Helmholtz-Zentrum Hereon, Geesthacht, Germany.
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