1
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Nikitina N, Bursa N, Goelzer M, Goldfeldt M, Crandall C, Howard S, Rubin J, Zavala A, Satici A, Uzer G. Data-Driven and Cell-Specific Determination of Nuclei-Associated Actin Structure. SMALL STRUCTURES 2024; 5:2300204. [PMID: 39220563 PMCID: PMC11361466 DOI: 10.1002/sstr.202300204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
Quantitative and volumetric assessment of filamentous actin fibers (F-actin) remains challenging due to their interconnected nature, leading researchers to utilize threshold based or qualitative measurement methods with poor reproducibility. Here we introduce a novel machine learning based methodology for accurate quantification and reconstruction of nuclei-associated F-actin. Utilizing a Convolutional Neural Network (CNN), we segment actin filaments and nuclei from 3D confocal microscopy images and then reconstruct each fiber by connecting intersecting contours on cross-sectional slices. This allowed measurement of the total number of actin filaments and individual actin filament length and volume in a reproducible fashion. Focusing on the role of F-actin in supporting nucleocytoskeletal connectivity, we quantified apical F-actin, basal F-actin, and nuclear architecture in mesenchymal stem cells (MSCs) following the disruption of the Linker of Nucleoskeleton and Cytoskeleton (LINC) Complexes. Disabling LINC in mesenchymal stem cells (MSCs) generated F-actin disorganization at the nuclear envelope characterized by shorter length and volume of actin fibers contributing a less elongated nuclear shape. Our findings not only present a new tool for mechanobiology but introduce a novel pipeline for developing realistic computational models based on quantitative measures of F-actin.
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2
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Desroches S, Harris AR. Quantifying cytoskeletal organization from optical microscopy data. Front Cell Dev Biol 2024; 11:1327994. [PMID: 38234685 PMCID: PMC10792062 DOI: 10.3389/fcell.2023.1327994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 12/07/2023] [Indexed: 01/19/2024] Open
Abstract
The actin cytoskeleton plays a pivotal role in a broad range of physiological processes including directing cell shape and subcellular organization, determining cell mechanical properties, and sensing and transducing mechanical forces. The versatility of the actin cytoskeleton arises from the ability of actin filaments to assemble into higher order structures through their interaction with a vast set of regulatory proteins. Actin filaments assemble into bundles, meshes, and networks, where different combinations of these structures fulfill specific functional roles. Analyzing the organization and abundance of different actin structures from optical microscopy data provides a valuable metric for assessing cell physiological function and changes associated with disease. However, quantitative measurements of the size, abundance, orientation, and distribution of different types of actin structure remains challenging both from an experimental and image analysis perspective. In this review, we summarize image analysis methods for extracting quantitative values that can be used for characterizing the organization of actin structures and provide selected examples. We summarize the potential sample types and metric reported with different approaches as a guide for selecting an image analysis strategy.
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Affiliation(s)
- Sarah Desroches
- Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, ON, Canada
- Ottawa-Carleton Institute for Biomedical Engineering Graduate Program, Ottawa, ON, Canada
| | - Andrew R. Harris
- Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, ON, Canada
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3
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Sazzed S, Scheible P, He J, Wriggers W. Untangling Irregular Actin Cytoskeleton Architectures in Tomograms of the Cell with Struwwel Tracer. Int J Mol Sci 2023; 24:17183. [PMID: 38139012 PMCID: PMC10743648 DOI: 10.3390/ijms242417183] [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] [Received: 10/09/2023] [Revised: 11/09/2023] [Accepted: 11/15/2023] [Indexed: 12/24/2023] Open
Abstract
In this work, we established, validated, and optimized a novel computational framework for tracing arbitrarily oriented actin filaments in cryo-electron tomography maps. Our approach was designed for highly complex intracellular architectures in which a long-range cytoskeleton network extends throughout the cell bodies and protrusions. The irregular organization of the actin network, as well as cryo-electron-tomography-specific noise, missing wedge artifacts, and map dimensions call for a specialized implementation that is both robust and efficient. Our proposed solution, Struwwel Tracer, accumulates densities along paths of a specific length in various directions, starting from locally determined seed points. The highest-density paths originating from the seed points form short linear candidate filament segments, which are further scrutinized and classified by users via inspection of a novel pruning map, which visualizes the likelihood of being a part of longer filaments. The pruned linear candidate filament segments are then iteratively fused into continuous, longer, and curved filaments based on their relative orientations, gap spacings, and extendibility. When applied to the simulated phantom tomograms of a Dictyostelium discoideum filopodium under experimental conditions, Struwwel Tracer demonstrated high efficacy, with F1-scores ranging from 0.85 to 0.90, depending on the noise level. Furthermore, when applied to a previously untraced experimental tomogram of mouse fibroblast lamellipodia, the filaments predicted by Struwwel Tracer exhibited a good visual agreement with the experimental map. The Struwwel Tracer framework is highly time efficient and can complete the tracing process in just a few minutes. The source code is publicly available with version 3.2 of the free and open-source Situs software package.
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Affiliation(s)
- Salim Sazzed
- Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA; (S.S.)
| | - Peter Scheible
- Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA; (S.S.)
| | - Jing He
- Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA; (S.S.)
| | - Willy Wriggers
- Department of Mechanical and Aerospace Engineering, Old Dominion University, Norfolk, VA 23529, USA
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4
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Mougkogiannis P, Adamatzky A. Logical gates in ensembles of proteinoid microspheres. PLoS One 2023; 18:e0289433. [PMID: 37721941 PMCID: PMC10506713 DOI: 10.1371/journal.pone.0289433] [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: 07/16/2023] [Accepted: 08/31/2023] [Indexed: 09/20/2023] Open
Abstract
Proteinoids are thermal proteins which swell into microspheres in aqueous solution. Ensembles of proteinoids produce electrical spiking activity similar to that of neurons. We introduce a novel method for implementing logical gates in the ensembles of proteinoid microspheres using chronoamperometry. Chronoamperometry is a technique that involves applying a voltage pulse to proteinoid microspheres and measuring their current response. We have observed that proteinoids exhibit distinct current patterns that align with various logical outputs. We identify four types of logical gates: AND, OR, XOR, and NAND. These gates are determined by the current response of proteinoid microspheres. Additionally, we demonstrate that proteinoid microspheres have the ability to modify their current response over time, which is influenced by their previous exposure to voltage. This indicates that they possess a capacity for learning and are capable of adapting to their environment. Our research showcases the ability of proteinoid microspheres to perform logical operations and computations through their inherent electrical properties.
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Affiliation(s)
| | - Andrew Adamatzky
- Unconventional Computing Lab, University of the West of England, Bristol, United Kingdom
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5
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Nikitina N, Bursa N, Goelzer M, Goldfeldt M, Crandall C, Howard S, Rubin J, Satici A, Uzer G. Data driven and cell specific determination of nuclei-associated actin structure. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.06.535937. [PMID: 37066142 PMCID: PMC10104112 DOI: 10.1101/2023.04.06.535937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Quantitative and volumetric assessment of filamentous actin fibers (F-actin) remains challenging due to their interconnected nature, leading researchers to utilize threshold based or qualitative measurement methods with poor reproducibility. Here we introduce a novel machine learning based methodology for accurate quantification and reconstruction of nuclei-associated F-actin. Utilizing a Convolutional Neural Network (CNN), we segment actin filaments and nuclei from 3D confocal microscopy images and then reconstruct each fiber by connecting intersecting contours on cross-sectional slices. This allowed measurement of the total number of actin filaments and individual actin filament length and volume in a reproducible fashion. Focusing on the role of F-actin in supporting nucleocytoskeletal connectivity, we quantified apical F-actin, basal F-actin, and nuclear architecture in mesenchymal stem cells (MSCs) following the disruption of the Linker of Nucleoskeleton and Cytoskeleton (LINC) Complexes. Disabling LINC in mesenchymal stem cells (MSCs) generated F-actin disorganization at the nuclear envelope characterized by shorter length and volume of actin fibers contributing a less elongated nuclear shape. Our findings not only present a new tool for mechanobiology but introduce a novel pipeline for developing realistic computational models based on quantitative measures of F-actin.
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6
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Li P, Zhang Z, Tong Y, Foda BM, Day B. ILEE: Algorithms and toolbox for unguided and accurate quantitative analysis of cytoskeletal images. J Cell Biol 2023; 222:e202203024. [PMID: 36534166 PMCID: PMC9768434 DOI: 10.1083/jcb.202203024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 08/04/2022] [Accepted: 11/21/2022] [Indexed: 12/23/2022] Open
Abstract
The eukaryotic cytoskeleton plays essential roles in cell signaling and trafficking, broadly associated with immunity and diseases in humans and plants. To date, most studies describing cytoskeleton dynamics and function rely on qualitative/quantitative analyses of cytoskeletal images. While state-of-the-art, these approaches face general challenges: the diversity among filaments causes considerable inaccuracy, and the widely adopted image projection leads to bias and information loss. To solve these issues, we developed the Implicit Laplacian of Enhanced Edge (ILEE), an unguided, high-performance approach for 2D/3D-based quantification of cytoskeletal status and organization. Using ILEE, we constructed a Python library to enable automated cytoskeletal image analysis, providing biologically interpretable indices measuring the density, bundling, segmentation, branching, and directionality of the cytoskeleton. Our data demonstrated that ILEE resolves the defects of traditional approaches, enables the detection of novel cytoskeletal features, and yields data with superior accuracy, stability, and robustness. The ILEE toolbox is available for public use through PyPI and Google Colab.
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Affiliation(s)
- Pai Li
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI
- Department of Plant Biology, Michigan State University, East Lansing, MI
| | - Ze Zhang
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI
| | - Yiying Tong
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI
| | - Bardees M. Foda
- Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI
- Molecular Genetics and Enzymology Department, National Research Centre, Dokki, Egypt
| | - Brad Day
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI
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7
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Hauke L, Primeßnig A, Eltzner B, Radwitz J, Huckemann SF, Rehfeldt F. FilamentSensor 2.0: An open-source modular toolbox for 2D/3D cytoskeletal filament tracking. PLoS One 2023; 18:e0279336. [PMID: 36745610 PMCID: PMC9901806 DOI: 10.1371/journal.pone.0279336] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 12/05/2022] [Indexed: 02/07/2023] Open
Abstract
Cytoskeletal pattern formation and structural dynamics are key to a variety of biological functions and a detailed and quantitative analysis yields insight into finely tuned and well-balanced homeostasis and potential pathological alterations. High content life cell imaging of fluorescently labeled cytoskeletal elements under physiological conditions is nowadays state-of-the-art and can record time lapse data for detailed experimental studies. However, systematic quantification of structures and in particular the dynamics (i.e. frame-to-frame tracking) are essential. Here, an unbiased, quantitative, and robust analysis workflow that can be highly automatized is needed. For this purpose we upgraded and expanded our fiber detection algorithm FilamentSensor (FS) to the FilamentSensor 2.0 (FS2.0) toolbox, allowing for automatic detection and segmentation of fibrous structures and the extraction of relevant data (center of mass, length, width, orientation, curvature) in real-time as well as tracking of these objects over time and cell event monitoring.
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Affiliation(s)
- Lara Hauke
- Third Institute of Physics—Biophysics, Georg-August-University Göttingen, Göttingen, Germany
- Institute of Pharmacology and Toxicology, University Medical Center, Göttingen, Germany
- CIDAS (Campus Institute Data Science), University of Göttingen, Göttingen, Germany
- * E-mail: (LH); (FR)
| | - Andreas Primeßnig
- Third Institute of Physics—Biophysics, Georg-August-University Göttingen, Göttingen, Germany
- Institute of Pharmacology and Toxicology, University Medical Center, Göttingen, Germany
| | - Benjamin Eltzner
- Research Group Computational Biomolecular Dynamics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
- Felix-Bernstein-Institute for Mathematical Statistics in the Biosciences, Georg-August-University Göttingen, Göttingen, Germany
| | - Jennifer Radwitz
- Third Institute of Physics—Biophysics, Georg-August-University Göttingen, Göttingen, Germany
- Department of Molecular Neurogenetics, ZMNH, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan F. Huckemann
- Felix-Bernstein-Institute for Mathematical Statistics in the Biosciences, Georg-August-University Göttingen, Göttingen, Germany
| | - Florian Rehfeldt
- Third Institute of Physics—Biophysics, Georg-August-University Göttingen, Göttingen, Germany
- Experimental Physics I, University of Bayreuth, Bayreuth, Germany
- * E-mail: (LH); (FR)
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8
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Østerlund I, Persson S, Nikoloski Z. Tracing and tracking filamentous structures across scales: A systematic review. Comput Struct Biotechnol J 2022; 21:452-462. [PMID: 36618983 PMCID: PMC9804014 DOI: 10.1016/j.csbj.2022.12.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 12/14/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Filamentous structures are ubiquitous in nature, are studied in diverse scientific fields, and span vastly different spatial scales. Filamentous structures in biological systems fulfill different functions and often form dynamic networks that respond to perturbations. Therefore, characterizing the properties of filamentous structures and the networks they form is important to gain better understanding of systems level functions and dynamics. Filamentous structures are captured by various imaging technologies, and analysis of the resulting imaging data addresses two problems: (i) identification (tracing) of filamentous structures in a single snapshot and (ii) characterizing the dynamics (i.e., tracking) of filamentous structures over time. Therefore, considerable research efforts have been made in developing automated methods for tracing and tracking of filamentous structures. Here, we provide a systematic review in which we present, categorize, and discuss the state-of-the-art methods for tracing and tracking of filamentous structures in sparse and dense networks. We highlight the mathematical approaches, assumptions, and constraints particular for each method, allowing us to pinpoint outstanding challenges and offer perspectives for future research aimed at gaining better understanding of filamentous structures in biological systems.
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Affiliation(s)
- Isabella Østerlund
- Department of Plant and Environmental Sciences, University of Copenhagen, 1871 Frederiksberg, Denmark
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany
| | - Staffan Persson
- Department of Plant and Environmental Sciences, University of Copenhagen, 1871 Frederiksberg, Denmark
| | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
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9
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Basu A, Paul MK, Weiss S. The actin cytoskeleton: Morphological changes in pre- and fully developed lung cancer. BIOPHYSICS REVIEWS 2022; 3:041304. [PMID: 38505516 PMCID: PMC10903407 DOI: 10.1063/5.0096188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 12/09/2022] [Indexed: 03/21/2024]
Abstract
Actin, a primary component of the cell cytoskeleton can have multiple isoforms, each of which can have specific properties uniquely suited for their purpose. These monomers are then bound together to form polymeric filaments utilizing adenosine triphosphate hydrolysis as a source of energy. Proteins, such as Arp2/3, VASP, formin, profilin, and cofilin, serve important roles in the polymerization process. These filaments can further be linked to form stress fibers by proteins called actin-binding proteins, such as α-actinin, myosin, fascin, filamin, zyxin, and epsin. These stress fibers are responsible for mechanotransduction, maintaining cell shape, cell motility, and intracellular cargo transport. Cancer metastasis, specifically epithelial mesenchymal transition (EMT), which is one of the key steps of the process, is accompanied by the formation of thick stress fibers through the Rho-associated protein kinase, MAPK/ERK, and Wnt pathways. Recently, with the advent of "field cancerization," pre-malignant cells have also been demonstrated to possess stress fibers and related cytoskeletal features. Analytical methods ranging from western blot and RNA-sequencing to cryo-EM and fluorescent imaging have been employed to understand the structure and dynamics of actin and related proteins including polymerization/depolymerization. More recent methods involve quantifying properties of the actin cytoskeleton from fluorescent images and utilizing them to study biological processes, such as EMT. These image analysis approaches exploit the fact that filaments have a unique structure (curvilinear) compared to the noise or other artifacts to separate them. Line segments are extracted from these filament images that have assigned lengths and orientations. Coupling such methods with statistical analysis has resulted in development of a new reporter for EMT in lung cancer cells as well as their drug responses.
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Affiliation(s)
| | | | - Shimon Weiss
- Author to whom correspondence should be addressed:
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10
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Conze C, Trushina NI, Holtmannspötter M, Rierola M, Attanasio S, Bakota L, Piehler J, Brandt R. Super-resolution imaging and quantitative analysis of microtubule arrays in model neurons show that epothilone D increases the density but decreases the length and straightness of microtubules in axon-like processes. Brain Res Bull 2022; 190:234-243. [PMID: 36244582 PMCID: PMC9634454 DOI: 10.1016/j.brainresbull.2022.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 10/03/2022] [Accepted: 10/06/2022] [Indexed: 11/29/2022]
Abstract
Microtubules are essential for the development of neurons and the regulation of their structural plasticity. Microtubules also provide the structural basis for the long-distance transport of cargo. Various factors influence the organization and dynamics of neuronal microtubules, and disturbance of microtubule regulation is thought to play a central role in neurodegenerative diseases. However, imaging and quantitative assessment of the microtubule organization in the densely packed neuronal processes is challenging. The development of super-resolution techniques combined with the use of nanobodies offers new possibilities to visualize microtubules in neurites in high resolution. In combination with recently developed computational analysis tools, this allows automated quantification of neuronal microtubule organization with high precision. Here we have implemented three-dimensional DNA-PAINT (Point Accumulation in Nanoscale Topography), a single-molecule localization microscopy (SMLM) technique, which allows us to acquire 3D arrays of the microtubule lattice in axons of model neurons (neuronally differentiated PC12 cells) and dendrites of primary neurons. For the quantitative analysis of the microtubule organization, we used the open-source software package SMLM image filament extractor (SIFNE). We found that treatment with nanomolar concentrations of the microtubule-targeting drug epothilone D (EpoD) increased microtubule density in axon-like processes of model neurons and shifted the microtubule length distribution to shorter ones, with a mean microtubule length of 2.39 µm (without EpoD) and 1.98 µm (with EpoD). We also observed a significant decrease in microtubule straightness after EpoD treatment. The changes in microtubule density were consistent with live-cell imaging measurements of ensemble microtubule dynamics using a previously established Fluorescence Decay After Photoactivation (FDAP) assay. For comparison, we determined the organization of the microtubule array in dendrites of primary hippocampal neurons. We observed that dendritic microtubules have a very similar length distribution and straightness compared to microtubules in axon-like processes of a neuronal cell line. Our data show that super-resolution imaging of microtubules followed by algorithm-based image analysis represents a powerful tool to quantitatively assess changes in microtubule organization in neuronal processes, useful to determine the effect of microtubule-modulating conditions. We also provide evidence that the approach is robust and can be applied to neuronal cell lines or primary neurons, both after incorporation of labeled tubulin and by anti-tubulin antibody staining.
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Affiliation(s)
- Christian Conze
- Department of Neurobiology, Osnabrück University, Osnabrück, Germany
| | | | | | - Marina Rierola
- Department of Neurobiology, Osnabrück University, Osnabrück, Germany
| | - Simone Attanasio
- Department of Neurobiology, Osnabrück University, Osnabrück, Germany
| | - Lidia Bakota
- Department of Neurobiology, Osnabrück University, Osnabrück, Germany
| | - Jacob Piehler
- Center for Cellular Nanoanalytics, Osnabrück University, Osnabrück, Germany; Division of Biophysics, Osnabrück University, Osnabrück, Germany
| | - Roland Brandt
- Department of Neurobiology, Osnabrück University, Osnabrück, Germany; Center for Cellular Nanoanalytics, Osnabrück University, Osnabrück, Germany; Institute of Cognitive Science, Osnabrück University, Osnabrück, Germany.
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11
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Sazzed S, Scheible P, He J, Wriggers W. Spaghetti Tracer: A Framework for Tracing Semiregular Filamentous Densities in 3D Tomograms. Biomolecules 2022; 12:1022. [PMID: 35892332 PMCID: PMC9394354 DOI: 10.3390/biom12081022] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/13/2022] [Accepted: 07/17/2022] [Indexed: 11/30/2022] Open
Abstract
Within cells, cytoskeletal filaments are often arranged into loosely aligned bundles. These fibrous bundles are dense enough to exhibit a certain regularity and mean direction, however, their packing is not sufficient to impose a symmetry between-or specific shape on-individual filaments. This intermediate regularity is computationally difficult to handle because individual filaments have a certain directional freedom, however, the filament densities are not well segmented from each other (especially in the presence of noise, such as in cryo-electron tomography). In this paper, we develop a dynamic programming-based framework, Spaghetti Tracer, to characterizing the structural arrangement of filaments in the challenging 3D maps of subcellular components. Assuming that the tomogram can be rotated such that the filaments are oriented in a mean direction, the proposed framework first identifies local seed points for candidate filament segments, which are then grown from the seeds using a dynamic programming algorithm. We validate various algorithmic variations of our framework on simulated tomograms that closely mimic the noise and appearance of experimental maps. As we know the ground truth in the simulated tomograms, the statistical analysis consisting of precision, recall, and F1 scores allows us to optimize the performance of this new approach. We find that a bipyramidal accumulation scheme for path density is superior to straight-line accumulation. In addition, the multiplication of forward and backward path densities provides for an efficient filter that lifts the filament density above the noise level. Resulting from our tests is a robust method that can be expected to perform well (F1 scores 0.86-0.95) under experimental noise conditions.
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Affiliation(s)
- Salim Sazzed
- Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA; (S.S.); (P.S.)
| | - Peter Scheible
- Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA; (S.S.); (P.S.)
| | - Jing He
- Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA; (S.S.); (P.S.)
| | - Willy Wriggers
- Department of Mechanical and Aerospace Engineering, Old Dominion University, Norfolk, VA 23529, USA
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12
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Basu A, Paul MK, Alioscha-Perez M, Grosberg A, Sahli H, Dubinett SM, Weiss S. Statistical parametrization of cell cytoskeleton reveals lung cancer cytoskeletal phenotype with partial EMT signature. Commun Biol 2022; 5:407. [PMID: 35501466 PMCID: PMC9061773 DOI: 10.1038/s42003-022-03358-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 04/12/2022] [Indexed: 12/14/2022] Open
Abstract
Epithelial–mesenchymal Transition (EMT) is a multi-step process that involves cytoskeletal rearrangement. Here, developing and using an image quantification tool, Statistical Parametrization of Cell Cytoskeleton (SPOCC), we have identified an intermediate EMT state with a specific cytoskeletal signature. We have been able to partition EMT into two steps: (1) initial formation of transverse arcs and dorsal stress fibers and (2) their subsequent conversion to ventral stress fibers with a concurrent alignment of fibers. Using the Orientational Order Parameter (OOP) as a figure of merit, we have been able to track EMT progression in live cells as well as characterize and quantify their cytoskeletal response to drugs. SPOCC has improved throughput and is non-destructive, making it a viable candidate for studying a broad range of biological processes. Further, owing to the increased stiffness (and by inference invasiveness) of the intermediate EMT phenotype compared to mesenchymal cells, our work can be instrumental in aiding the search for future treatment strategies that combat metastasis by specifically targeting the fiber alignment process. A computational method for automated quantification of actin stress fiber alignment in fluorescence images of cultured cells is presented, used to detect changes in stress fiber organization during EMT, with pathways regulating actin dynamics manipulated leading to the discovery of a cytoskeletal phenotype.
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Affiliation(s)
- Arkaprabha Basu
- Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, CA, USA
| | - Manash K Paul
- Department of Medicine, University of California Los Angeles, Los Angles, CA, USA.,Division of Pulmonary and Critical Care Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Mitchel Alioscha-Perez
- Electronics and Informatics Department, Vrije Universiteit Brussel, Brussels, Belgium.,Interuniversity Microelectronics Centre, Heverlee, Belgium
| | - Anna Grosberg
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA, USA.,The Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California Irvine, Irvine, CA, USA
| | - Hichem Sahli
- Electronics and Informatics Department, Vrije Universiteit Brussel, Brussels, Belgium.,Interuniversity Microelectronics Centre, Heverlee, Belgium
| | - Steven M Dubinett
- Department of Medicine, University of California Los Angeles, Los Angles, CA, USA.,Division of Pulmonary and Critical Care Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,California NanoSystems Institute, Los Angeles, CA, USA.,VA Greater Los Angeles Health Care System, Los Angeles, CA, USA
| | - Shimon Weiss
- Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, CA, USA. .,California NanoSystems Institute, Los Angeles, CA, USA. .,Department of Physiology, University of California Los Angeles, Los Angeles, CA, USA.
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13
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Jiang CF, Sun YM. Label-free monitoring of spatiotemporal changes in the stem cell cytoskeletons in time-lapse phase-contrast microscopy. BIOMEDICAL OPTICS EXPRESS 2022; 13:2323-2333. [PMID: 35519244 PMCID: PMC9045902 DOI: 10.1364/boe.452822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 03/12/2022] [Accepted: 03/15/2022] [Indexed: 06/14/2023]
Abstract
Investigation of the dynamic structural changes in the actin cytoskeleton during cell migration provides crucial information about the physiological conditions of a stem cell during in-vitro culture. Here we proposed a quantitative analytical model associated with texture extraction with cell tracking techniques for in situ monitoring of the cytoskeletal density change of stem cells in phase-contrast microscopy without fluorescence staining. The reliability of the model in quantifying the texture density with different orientation was first validated using a series of simulated textural images. The capability of the method to reflect the spatiotemporal regulation of the cytoskeletal structure of a living stem cell was further proved by applying it to a set of 72 h phase-contrast microscopic video of the growth dynamics of mesenchymal stem cells in vitro culture.
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Affiliation(s)
- Ching-Fen Jiang
- Graduate Degree Program of Smart Healthcare & Bioinformatics, I-Shou University, Kaohsiung, Taiwan
| | - Yu-Man Sun
- Department of Biomedical Engineering, I-Shou University, Kaohsiung, Taiwan
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14
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Yang S, Zhao C, Ren J, Zheng K, Shao Z, Ling S. Acquiring structural and mechanical information of a fibrous network through deep learning. NANOSCALE 2022; 14:5044-5053. [PMID: 35293414 DOI: 10.1039/d2nr00372d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Fibrous networks play an essential role in the structure and properties of a variety of biological and engineered materials, such as cytoskeletons, protein filament-based hydrogels, and entangled or crosslinked polymer chains. Therefore, insight into the structural features of these fibrous networks and their constituent filaments is critical for discovering the structure-property-function relationships of these material systems. In this paper, a fibrous network-deep learning system (FN-DLS) is established to extract fibrous network structure information from atomic force microscopy images. FN-DLS accurately assesses the structural and mechanical characteristics of fibrous networks, such as contour length, number of nodes, persistence length, mesh size and fractal dimension. As an open-source system, FN-DLS is expected to serve a vast community of scientists working on very diverse disciplines and pave the way for new approaches on the study of biological and synthetic polymer and filament networks found in current applied and fundamental sciences.
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Affiliation(s)
- Shuo Yang
- School of Physical Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China.
| | - Chenxi Zhao
- School of Physical Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China.
| | - Jing Ren
- School of Physical Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China.
| | - Ke Zheng
- Biomass Molecular Engineering Center and Department of Materials Science and Engineering, School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei, Anhui 230036, China
| | - Zhengzhong Shao
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Laboratory of Advanced Materials, Fudan University, Shanghai 200433, China
| | - Shengjie Ling
- School of Physical Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China.
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15
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Windoffer R, Schwarz N, Yoon S, Piskova T, Scholkemper M, Stegmaier J, Bönsch A, Di Russo J, Leube R. Quantitative mapping of keratin networks in 3D. eLife 2022; 11:75894. [PMID: 35179484 PMCID: PMC8979588 DOI: 10.7554/elife.75894] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 02/15/2022] [Indexed: 11/26/2022] Open
Abstract
Mechanobiology requires precise quantitative information on processes taking place in specific 3D microenvironments. Connecting the abundance of microscopical, molecular, biochemical, and cell mechanical data with defined topologies has turned out to be extremely difficult. Establishing such structural and functional 3D maps needed for biophysical modeling is a particular challenge for the cytoskeleton, which consists of long and interwoven filamentous polymers coordinating subcellular processes and interactions of cells with their environment. To date, useful tools are available for the segmentation and modeling of actin filaments and microtubules but comprehensive tools for the mapping of intermediate filament organization are still lacking. In this work, we describe a workflow to model and examine the complete 3D arrangement of the keratin intermediate filament cytoskeleton in canine, murine, and human epithelial cells both, in vitro and in vivo. Numerical models are derived from confocal airyscan high-resolution 3D imaging of fluorescence-tagged keratin filaments. They are interrogated and annotated at different length scales using different modes of visualization including immersive virtual reality. In this way, information is provided on network organization at the subcellular level including mesh arrangement, density and isotropic configuration as well as details on filament morphology such as bundling, curvature, and orientation. We show that the comparison of these parameters helps to identify, in quantitative terms, similarities and differences of keratin network organization in epithelial cell types defining subcellular domains, notably basal, apical, lateral, and perinuclear systems. The described approach and the presented data are pivotal for generating mechanobiological models that can be experimentally tested.
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Affiliation(s)
- Reinhard Windoffer
- Institute of Molecular and Cellular Anatomy, RWTH Aachen University, Aachen, Germany
| | - Nicole Schwarz
- Institute of Molecular and Cellular Anatomy, RWTH Aachen University, Aachen, Germany
| | - Sungjun Yoon
- Institute of Molecular and Cellular Anatomy, RWTH Aachen University, Aachen, Germany
| | - Teodora Piskova
- Institute of Molecular and Cellular Anatomy, RWTH Aachen University, Aachen, Germany
| | | | - Johannes Stegmaier
- Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany
| | - Andrea Bönsch
- Visual Computing Institute, RWTH Aachen University, Aachen, Germany
| | - Jacopo Di Russo
- Interdisciplinary Centre for Clinical Research, RWTH Aachen University, Aachen, Germany
| | - Rudolf Leube
- Institute of Molecular and Cellular Anatomy, RWTH Aachen University, Aachen, Germany
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16
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Rossen NS, Kyrsting A, Giaccia AJ, Erler JT, Oddershede LB. Fiber finding algorithm using stepwise tracing to identify biopolymer fibers in noisy 3D images. Biophys J 2021; 120:3860-3868. [PMID: 34411578 DOI: 10.1016/j.bpj.2021.08.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 05/22/2021] [Accepted: 08/11/2021] [Indexed: 10/20/2022] Open
Abstract
We present a novel fiber finding algorithm (FFA) that will permit researchers to detect and return traces of individual biopolymers. Determining the biophysical properties and structural cues of biopolymers can permit researchers to assess the progression and severity of disease. Confocal microscopy images are a useful method for observing biopolymer structures in three dimensions, but their utility for identifying individual biopolymers is impaired by noise inherent in the acquisition process, including convolution from the point spread function (PSF). The new, iterative FFA we present here 1) measures a microscope's PSF and uses it as a metric for identifying fibers against the background; 2) traces each fiber within a cone angle; and 3) blots out the identified trace before identifying another fiber. Blotting out the identified traces in each iteration allows the FFA to detect and return traces of single fibers accurately and efficiently-even within fiber bundles. We used the FFA to trace unlabeled collagen type I fibers-a biopolymer used to mimic the extracellular matrix in in vitro cancer assays-imaged by confocal reflectance microscopy in three dimensions, enabling quantification of fiber contour length, persistence length, and three-dimensional (3D) mesh size. Based on 3D confocal reflectance microscopy images and the PSF, we traced and measured the fibers to confirm that colder gelation temperatures increased fiber contour length, persistence length, and 3D mesh size-thereby demonstrating the FFA's use in quantifying biopolymers' structural and physical cues from noisy microscope images.
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Affiliation(s)
- Ninna Struck Rossen
- Biotech Research & Innovation Center, University of Copenhagen (UCPH), Copenhagen, Denmark; Department of Radiation Oncology, Stanford University, Palo Alto, California; Niels Bohr Institute, University of Copenhagen (UCPH), Copenhagen, Denmark.
| | - Anders Kyrsting
- Niels Bohr Institute, University of Copenhagen (UCPH), Copenhagen, Denmark
| | - Amato J Giaccia
- Department of Radiation Oncology, Stanford University, Palo Alto, California
| | - Janine Terra Erler
- Biotech Research & Innovation Center, University of Copenhagen (UCPH), Copenhagen, Denmark
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17
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Haspinger DC, Klinge S, Holzapfel GA. Numerical analysis of the impact of cytoskeletal actin filament density alterations onto the diffusive vesicle-mediated cell transport. PLoS Comput Biol 2021; 17:e1008784. [PMID: 33939706 PMCID: PMC8130967 DOI: 10.1371/journal.pcbi.1008784] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 05/18/2021] [Accepted: 02/09/2021] [Indexed: 11/21/2022] Open
Abstract
The interior of a eukaryotic cell is a highly complex composite material which consists of water, structural scaffoldings, organelles, and various biomolecular solutes. All these components serve as obstacles that impede the motion of vesicles. Hence, it is hypothesized that any alteration of the cytoskeletal network may directly impact or even disrupt the vesicle transport. A disruption of the vesicle-mediated cell transport is thought to contribute to several severe diseases and disorders, such as diabetes, Parkinson’s and Alzheimer’s disease, emphasizing the clinical relevance. To address the outlined objective, a multiscale finite element model of the diffusive vesicle transport is proposed on the basis of the concept of homogenization, owed to the complexity of the cytoskeletal network. In order to study the microscopic effects of specific nanoscopic actin filament network alterations onto the vesicle transport, a parametrized three-dimensional geometrical model of the actin filament network was generated on the basis of experimentally observed filament densities and network geometries in an adenocarcinomic human alveolar basal epithelial cell. Numerical analyzes of the obtained effective diffusion properties within two-dimensional sampling domains of the whole cell model revealed that the computed homogenized diffusion coefficients can be predicted statistically accurate by a simple two-parameter power law as soon as the inaccessible area fraction, due to the obstacle geometries and the finite size of the vesicles, is known. This relationship, in turn, leads to a massive reduction in computation time and allows to study the impact of a variety of different cytoskeletal alterations onto the vesicle transport. Hence, the numerical simulations predicted a 35% increase in transport time due to a uniformly distributed four-fold increase of the total filament amount. On the other hand, a hypothetically reduced expression of filament cross-linking proteins led to sparser filament networks and, thus, a speed up of the vesicle transport. Many vital processes in our eukaryotic cells and organs require an astonishingly precise routing of intermediate products to various intra- and extracellular destinations using vesicles as transporters. This can be illustrated by numerous examples, such as the production and destruction of proteins, the export of neurotransmitters or insulin to the extracellular domain, etc. However, the inside of a cell is tightly packed with numerous structural scaffoldings (filaments), which serve as obstacles and impede the vesicle motion. It is thought that any disturbances of the vesicle-mediated cell transport contribute to numerous degenerative diseases and disorders, which highlights the clinical relevance for investigating this intracellular transport mechanism by developing computational models and performing experimental studies. In this study, we numerically quantified how different specific alterations of the filament density inside a human lung cell—due to changed mechanical loadings or genetic disorders of proteins being responsible for filament branching—affect the diffusion of vesicles inside the intracellular fluid. Therefore, based on the concept of homogenization, a computationally efficient numerical method was developed and utilized to simulate the diffusion of vesicles inside the whole cell, considering the detailed structural information of the filament network.
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Affiliation(s)
| | - Sandra Klinge
- Chair of Structural Mechanics and Analysis, TU Berlin, Berlin, Germany
| | - Gerhard A. Holzapfel
- Institute of Biomechanics, Graz University of Technology, Graz, Austria
- Faculty of Engineering Science and Technology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- * E-mail:
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18
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Özdemir B, Reski R. Automated and semi-automated enhancement, segmentation and tracing of cytoskeletal networks in microscopic images: A review. Comput Struct Biotechnol J 2021; 19:2106-2120. [PMID: 33995906 PMCID: PMC8085673 DOI: 10.1016/j.csbj.2021.04.019] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 04/05/2021] [Accepted: 04/07/2021] [Indexed: 11/28/2022] Open
Abstract
Cytoskeletal filaments are structures of utmost importance to biological cells and organisms due to their versatility and the significant functions they perform. These biopolymers are most often organised into network-like scaffolds with a complex morphology. Understanding the geometrical and topological organisation of these networks provides key insights into their functional roles. However, this non-trivial task requires a combination of high-resolution microscopy and sophisticated image processing/analysis software. The correct analysis of the network structure and connectivity needs precise segmentation of microscopic images. While segmentation of filament-like objects is a well-studied concept in biomedical imaging, where tracing of neurons and blood vessels is routine, there are comparatively fewer studies focusing on the segmentation of cytoskeletal filaments and networks from microscopic images. The developments in the fields of microscopy, computer vision and deep learning, however, began to facilitate the task, as reflected by an increase in the recent literature on the topic. Here, we aim to provide a short summary of the research on the (semi-)automated enhancement, segmentation and tracing methods that are particularly designed and developed for microscopic images of cytoskeletal networks. In addition to providing an overview of the conventional methods, we cover the recently introduced, deep-learning-assisted methods alongside the advantages they offer over classical methods.
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Affiliation(s)
- Bugra Özdemir
- Plant Biotechnology, Faculty of Biology, University of Freiburg, Freiburg, Germany.,Signalling Research Centres BIOSS and CIBSS, Freiburg, Germany
| | - Ralf Reski
- Plant Biotechnology, Faculty of Biology, University of Freiburg, Freiburg, Germany.,Signalling Research Centres BIOSS and CIBSS, Freiburg, Germany.,Cluster of Excellence livMatS @ FIT - Freiburg Centre for Interactive Materials and Bioinspired Technologies, Freiburg, Germany
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19
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Liu Y, Nedo A, Seward K, Caplan J, Kambhamettu C. QUANTIFYING ACTIN FILAMENTS IN MICROSCOPIC IMAGES USING KEYPOINT DETECTION TECHNIQUES AND A FAST MARCHING ALGORITHM. PROCEEDINGS. INTERNATIONAL CONFERENCE ON IMAGE PROCESSING 2020; 2020:2506-2510. [PMID: 33758579 PMCID: PMC7983297 DOI: 10.1109/icip40778.2020.9191337] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The actin filament plays a fundamental role in numerous cellular processes such as cell growth, proliferation, migration, division, and locomotion. The actin cytoskeleton is highly dynamical and can polymerize and depolymerize in a very short time under different stimuli. To study the mechanics of actin filament, quantifying the length and number of actin filaments in each time frame of microscopic images is fundamental. In this paper, we adopt a Convolutional Neural Network (CNN) to segment actin filaments first, and then we utilize a modified Resnet to detect junctions and endpoints of filaments. With binary segmentation and detected keypoints, we apply a fast marching algorithm to obtain the number and length of each actin filament in microscopic images. We have also collected a dataset of 10 microscopic images of actin filaments to test our method. Our experiments show that our approach outperforms other existing approaches tackling this problem regarding both accuracy and inference time.
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Affiliation(s)
- Yi Liu
- University of Delaware, Newark, DE, USA
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20
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Xue R, Cartmell S. A simple in vitro biomimetic perfusion system for mechanotransduction study. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2020; 21:635-640. [PMID: 33061836 PMCID: PMC7534211 DOI: 10.1080/14686996.2020.1808432] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 08/03/2020] [Accepted: 08/07/2020] [Indexed: 06/11/2023]
Abstract
In mechanotransduction studies, flow-induced shear stress (FSS) is often applied to two-dimensional (2D) cultured cells with a parallel-plate flow chamber (PPFC) due to its simple FSS estimation. However, cells behave differently under FSS inside a 3D scaffold (e.g. 10 mPa FSS was shown to induce osteogenesis of human mesenchymal stem cells (hMSC) in 3D but over 900 mPa was needed for 2D culture). Here, a simple in vitro biomimetic perfusion system using borosilicate glass capillary tubes has been developed to study the cellular behaviour under low-level FSS that mimics 3D culture. It has been shown that, compared to cells in the PPFC, hMSC in the capillary tubes had upregulated Runx-2 expression and osteogenic cytoskeleton actin network under 10 mPa FSS for 24 h. Also, an image analysis method based on Haralick texture measurement has been used to identify osteogenic actin network. The biomimetic perfusion system can be a valuable tool to study mechanotransduction in 3D for more clinical relevant tissue-engineering applications.
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Affiliation(s)
- Ruikang Xue
- Department of Materials, School of Natural Sciences, Faculty of Science and Engineering, University of Manchester, Manchester, UK
| | - Sarah Cartmell
- Department of Materials, School of Natural Sciences, Faculty of Science and Engineering, University of Manchester, Manchester, UK
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21
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Nowak J, Gennermann K, Persson S, Nikoloski Z. CytoSeg 2.0: automated extraction of actin filaments. Bioinformatics 2020; 36:2950-2951. [PMID: 31971582 PMCID: PMC7203740 DOI: 10.1093/bioinformatics/btaa035] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Revised: 12/23/2019] [Accepted: 01/19/2020] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Actin filaments (AFs) are dynamic structures that substantially change their organization over time. The dynamic behavior and the relatively low signal-to-noise ratio during live-cell imaging have rendered the quantification of the actin organization a difficult task. RESULTS We developed an automated image-based framework that extracts AFs from fluorescence microscopy images and represents them as networks, which are automatically analyzed to identify and compare biologically relevant features. Although the source code is freely available, we have now implemented the framework into a graphical user interface that can be installed as a Fiji plugin, thus enabling easy access by the research community. AVAILABILITY AND IMPLEMENTATION CytoSeg 2.0 is open-source software under the GPL and is available on Github: https://github.com/jnowak90/CytoSeg2.0. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jacqueline Nowak
- School of Biosciences, University of Melbourne, Parkville, VIC 3010, Australia.,Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany.,Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
| | - Kristin Gennermann
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany
| | - Staffan Persson
- School of Biosciences, University of Melbourne, Parkville, VIC 3010, Australia
| | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany.,Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
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22
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Xu T, Langouras C, Koudehi MA, Vos BE, Wang N, Koenderink GH, Huang X, Vavylonis D. Automated Tracking of Biopolymer Growth and Network Deformation with TSOAX. Sci Rep 2019; 9:1717. [PMID: 30737416 PMCID: PMC6368602 DOI: 10.1038/s41598-018-37182-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Accepted: 12/03/2018] [Indexed: 01/03/2023] Open
Abstract
Studies of how individual semi-flexible biopolymers and their network assemblies change over time reveal dynamical and mechanical properties important to the understanding of their function in tissues and living cells. Automatic tracking of biopolymer networks from fluorescence microscopy time-lapse sequences facilitates such quantitative studies. We present an open source software tool that combines a global and local correspondence algorithm to track biopolymer networks in 2D and 3D, using stretching open active contours. We demonstrate its application in fully automated tracking of elongating and intersecting actin filaments, detection of loop formation and constriction of tilted contractile rings in live cells, and tracking of network deformation under shear deformation.
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Affiliation(s)
- Ting Xu
- Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA, 18015, USA
| | | | | | - Bart E Vos
- AMOLF, Living Matter Department, 1098 XG, Amsterdam, The Netherlands
| | - Ning Wang
- Department of Molecular Genetics, The Ohio State University, Columbus, OH, 43210, USA
- Howard Hughes Medical Institute and Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | | | - Xiaolei Huang
- Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA, 18015, USA.
- College of Information Sciences and Technology, Penn State University, University Park, PA, 16802, USA.
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23
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Abstract
Quantification of the actin cytoskeleton is of prime importance to unveil the cellular force sensing and transduction mechanism. Although fluorescence imaging provides a convenient tool for observing the morphology of the actin cytoskeleton, due to the lack of approaches to accurate actin cytoskeleton quantification, the dynamics of mechanotransduction is still poorly understood. Currently, the existing image-based actin cytoskeleton analysis tools are either incapable of quantifying both the orientation and the quantity of the actin cytoskeleton simultaneously or the quantified results are subject to analysis artifacts. In this study, we propose an image recognition-based actin cytoskeleton quantification (IRAQ) approach, which quantifies both the actin cytoskeleton orientation and quantity by using edge, line, and brightness detection algorithms. The actin cytoskeleton is quantified through three parameters: the partial actin-cytoskeletal deviation (PAD), the total actin-cytoskeletal deviation (TAD), and the average actin-cytoskeletal intensity (AAI). First, Canny and Sobel edge detectors are applied to skeletonize the actin cytoskeleton images, then PAD and TAD are quantified using the line directions detected by Hough transform, and AAI is calculated through the summational brightness over the detected cell area. To verify the quantification accuracy, the proposed IRAQ was applied to six artificially-generated actin cytoskeleton mesh work models. The average error for both the quantified PAD and TAD was less than 1.22 ∘ . Then, IRAQ was implemented to quantify the actin cytoskeleton of NIH/3T3 cells treated with an F-actin inhibitor (latrunculin B). The quantification results suggest that the local and total actin-cytoskeletal organization became more disordered with the increase of latrunculin B dosage, and the quantity of the actin cytoskeleton showed a monotonically decreasing relation with latrunculin B dosage.
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24
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Janco M, Böcking T, He S, Coster ACF. Interactions of tropomyosin Tpm1.1 on a single actin filament: A method for extraction and processing of high resolution TIRF microscopy data. PLoS One 2018; 13:e0208586. [PMID: 30532204 PMCID: PMC6287813 DOI: 10.1371/journal.pone.0208586] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2018] [Accepted: 11/20/2018] [Indexed: 12/14/2022] Open
Abstract
Skeletal muscle tropomyosin (Tpm1.1) is an elongated, rod-shaped, alpha-helical coiled-coil protein that forms continuous head-to-tail polymers along both sides of the actin filament. In this study we use single molecule fluorescence TIRF microscopy combined with a microfluidic device and fluorescently labelled proteins to measure Tpm1.1 association to and dissociation from single actin filaments. Our experimental setup allows us to clearly resolve Tpm1.1 interactions on both sides of the filaments. Here we provide a semi-automated method for the extraction and quantification of kymograph data for individual actin filaments bound at different Tpm1.1 concentrations. We determine boundaries on the kymograph on each side of the actin filament, based on intensity thresholding, performing fine manual editing of the boundaries (if needed) and extracting user defined kinetic properties of the system. Using our analytical tools we can determine (i) nucleation point(s) and rates, (ii) elongation rates of Tpm1.1, (iii) identify meeting points after the saturation of filament, and when dissociation occurs, (iv) initiation point(s), (v) the final dissociation point(s), as well as (vi) dissociation rates. All of these measurements can be extracted from both sides of the filament, allowing for the determination of possible differences in behaviour on the two sides of the filament, and across concentrations. The robust and repeatable nature of the method allows quantitative, semi-automated analyses to be made of large studies of acto-tropomyosin interactions, as well as for other actin binding proteins or filamentous structures, opening the way for dissection of the dynamics underlying these interactions.
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Affiliation(s)
- Miro Janco
- Single Molecule Science and ARC Centre of Excellence in Advanced Molecular Imaging, University of New South Wales, Sydney, New South Wales, Australia
| | - Till Böcking
- Single Molecule Science and ARC Centre of Excellence in Advanced Molecular Imaging, University of New South Wales, Sydney, New South Wales, Australia
| | - Stanley He
- School of Mathematics & Statistics, University of New South Wales, Sydney, New South Wales, Australia
| | - Adelle C. F. Coster
- School of Mathematics & Statistics, University of New South Wales, Sydney, New South Wales, Australia
- * E-mail:
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25
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Prevedello L, Michielin F, Balcon M, Savio E, Pavan P, Elvassore N. A Novel Microfluidic Platform for Biomechano-Stimulations on a Chip. Ann Biomed Eng 2018; 47:231-242. [PMID: 30218223 DOI: 10.1007/s10439-018-02121-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Accepted: 08/20/2018] [Indexed: 11/27/2022]
Abstract
Mechanical stress has been proven to be an important factor interfering with many biological functions through mechano-sensitive elements within the cells. Despite the current interest in mechano-transduction, the development of suitable experimental tools is still characterized by the strife to design a compact device that allows high-magnification real-time imaging of the stretched cells, thus enabling to follow the dynamics of cellular response to mechanical stimulations. Here we present a microfluidic multi-layered chip that allows mechanical deformation of adherent cells maintaining a fixed focal plane, while allowing independent control of the soluble microenvironment. The device was optimized with the aid of FEM simulation and fully characterized in terms of mechanical deformation. Different cell lines were exposed to tunable mechanical strain, which results in continuous area deformation up to 20%. Thanks to the coupling of chemical glass etching, 2-dimensional deformation of a thin elastomeric membrane and microfluidic cell culture, the developed device allows a unique combination of cell mechanical stimulation, in line imaging and accurate control of cell culture microenvironment.
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Affiliation(s)
- Lia Prevedello
- Department of Industrial Engineering (DII), University of Padova, Padua, Italy
| | - Federica Michielin
- Department of Industrial Engineering (DII), University of Padova, Padua, Italy.,Venetian Institute of Molecular Medicine (VIMM), Padua, Italy
| | - Manuel Balcon
- Department of Industrial Engineering (DII), University of Padova, Padua, Italy
| | - Enrico Savio
- Department of Industrial Engineering (DII), University of Padova, Padua, Italy
| | - Piero Pavan
- Department of Industrial Engineering (DII), University of Padova, Padua, Italy
| | - Nicola Elvassore
- Department of Industrial Engineering (DII), University of Padova, Padua, Italy. .,Venetian Institute of Molecular Medicine (VIMM), Padua, Italy. .,Great Ormond Street Institute of Child Health, University College London, London, UK. .,Shanghai Institute for Advanced Immunochemical Studies (SIAIS), ShanghaiTech University, Shanghai, China.
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26
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Tracing Actin Filament Bundles in Three-Dimensional Electron Tomography Density Maps of Hair Cell Stereocilia. Molecules 2018; 23:molecules23040882. [PMID: 29641472 PMCID: PMC6017643 DOI: 10.3390/molecules23040882] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2017] [Revised: 03/14/2018] [Accepted: 03/22/2018] [Indexed: 12/20/2022] Open
Abstract
Cryo-electron tomography (cryo-ET) is a powerful method of visualizing the three-dimensional organization of supramolecular complexes, such as the cytoskeleton, in their native cell and tissue contexts. Due to its minimal electron dose and reconstruction artifacts arising from the missing wedge during data collection, cryo-ET typically results in noisy density maps that display anisotropic XY versus Z resolution. Molecular crowding further exacerbates the challenge of automatically detecting supramolecular complexes, such as the actin bundle in hair cell stereocilia. Stereocilia are pivotal to the mechanoelectrical transduction process in inner ear sensory epithelial hair cells. Given the complexity and dense arrangement of actin bundles, traditional approaches to filament detection and tracing have failed in these cases. In this study, we introduce BundleTrac, an effective method to trace hundreds of filaments in a bundle. A comparison between BundleTrac and manually tracing the actin filaments in a stereocilium showed that BundleTrac accurately built 326 of 330 filaments (98.8%), with an overall cross-distance of 1.3 voxels for the 330 filaments. BundleTrac is an effective semi-automatic modeling approach in which a seed point is provided for each filament and the rest of the filament is computationally identified. We also demonstrate the potential of a denoising method that uses a polynomial regression to address the resolution and high-noise anisotropic environment of the density map.
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27
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Asgharzadeh P, Özdemir B, Reski R, Röhrle O, Birkhold AI. Computational 3D imaging to quantify structural components and assembly of protein networks. Acta Biomater 2018; 69:206-217. [PMID: 29378323 DOI: 10.1016/j.actbio.2018.01.020] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2017] [Revised: 12/21/2017] [Accepted: 01/16/2018] [Indexed: 12/11/2022]
Abstract
Traditionally, protein structures have been described by the secondary structure architecture and fold arrangement. However, the relatively novel method of 3D confocal microscopy of fluorescent-protein-tagged networks in living cells allows resolving the detailed spatial organization of these networks. This provides new possibilities to predict network functionality, as structure and function seem to be linked at various scales. Here, we propose a quantitative approach using 3D confocal microscopy image data to describe protein networks based on their nano-structural characteristics. This analysis is constructed in four steps: (i) Segmentation of the microscopic raw data into a volume model and extraction of a spatial graph representing the protein network. (ii) Quantifying protein network gross morphology using the volume model. (iii) Quantifying protein network components using the spatial graph. (iv) Linking these two scales to obtain insights into network assembly. Here, we quantitatively describe the filamentous temperature sensitive Z protein network of the moss Physcomitrella patens and elucidate relations between network size and assembly details. Future applications will link network structure and functionality by tracking dynamic structural changes over time and comparing different states or types of networks, possibly allowing more precise identification of (mal) functions or the design of protein-engineered biomaterials for applications in regenerative medicine. STATEMENT OF SIGNIFICANCE Protein networks are highly complex and dynamic structures that play various roles in biological environments. Analyzing the detailed spatial structure of these networks may lead to new insight into biological functions and malfunctions. Here, we propose a tool set that extracts structural information at two scales of the protein network and allows therefore to address questions such as "how is the network built?" or "how networks grow?".
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Uzelac I, Ji YC, Hornung D, Schröder-Scheteling J, Luther S, Gray RA, Cherry EM, Fenton FH. Simultaneous Quantification of Spatially Discordant Alternans in Voltage and Intracellular Calcium in Langendorff-Perfused Rabbit Hearts and Inconsistencies with Models of Cardiac Action Potentials and Ca Transients. Front Physiol 2017; 8:819. [PMID: 29104543 PMCID: PMC5655020 DOI: 10.3389/fphys.2017.00819] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Accepted: 10/05/2017] [Indexed: 02/03/2023] Open
Abstract
Rationale: Discordant alternans, a phenomenon in which the action potential duration (APDs) and/or intracellular calcium transient durations (CaDs) in different spatial regions of cardiac tissue are out of phase, present a dynamical instability for complex spatial dispersion that can be associated with long-QT syndrome (LQTS) and the initiation of reentrant arrhythmias. Because the use of numerical simulations to investigate arrhythmic effects, such as acquired LQTS by drugs is beginning to be studied by the FDA, it is crucial to validate mathematical models that may be used during this process. Objective: In this study, we characterized with high spatio-temporal resolution the development of discordant alternans patterns in transmembrane voltage (Vm) and intracellular calcium concentration ([Cai]+2) as a function of pacing period in rabbit hearts. Then we compared the dynamics to that of the latest state-of-the-art model for ventricular action potentials and calcium transients to better understand the underlying mechanisms of discordant alternans and compared the experimental data to the mathematical models representing Vm and [Cai]+2 dynamics. Methods and Results: We performed simultaneous dual optical mapping imaging of Vm and [Cai]+2 in Langendorff-perfused rabbit hearts with higher spatial resolutions compared with previous studies. The rabbit hearts developed discordant alternans through decreased pacing period protocols and we quantified the presence of multiple nodal points along the direction of wave propagation, both in APD and CaD, and compared these findings with results from theoretical models. In experiments, the nodal lines of CaD alternans have a steeper slope than those of APD alternans, but not as steep as predicted by numerical simulations in rabbit models. We further quantified several additional discrepancies between models and experiments. Conclusions: Alternans in CaD have nodal lines that are about an order of magnitude steeper compared to those of APD alternans. Current action potential models lack the necessary coupling between voltage and calcium compared to experiments and fail to reproduce some key dynamics such as, voltage amplitude alternans, smooth development of calcium alternans in time, conduction velocity and the steepness of the nodal lines of APD and CaD.
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Affiliation(s)
- Ilija Uzelac
- School of Physics, Georgia Institute of Technology, Atlanta, GA, United States
| | - Yanyan C. Ji
- School of Physics, Georgia Institute of Technology, Atlanta, GA, United States
| | - Daniel Hornung
- Max Planck Institute for Dynamics and Self-Organization, Gottingen, Germany
| | | | - Stefan Luther
- Max Planck Institute for Dynamics and Self-Organization, Gottingen, Germany
| | - Richard A. Gray
- Center for Device and Radiological Health, Food and Drug Administration, Silver Spring, MD, United States
| | - Elizabeth M. Cherry
- School of Mathematical Sciences, Rochester Institute of Technology, Rochester, NY, United States
| | - Flavio H. Fenton
- School of Physics, Georgia Institute of Technology, Atlanta, GA, United States
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Abstract
Molecular self-assembly is the dominant form of chemical reaction in living systems, yet efforts at systems biology modeling are only beginning to appreciate the need for and challenges to accurate quantitative modeling of self-assembly. Self-assembly reactions are essential to nearly every important process in cell and molecular biology and handling them is thus a necessary step in building comprehensive models of complex cellular systems. They present exceptional challenges, however, to standard methods for simulating complex systems. While the general systems biology world is just beginning to deal with these challenges, there is an extensive literature dealing with them for more specialized self-assembly modeling. This review will examine the challenges of self-assembly modeling, nascent efforts to deal with these challenges in the systems modeling community, and some of the solutions offered in prior work on self-assembly specifically. The review concludes with some consideration of the likely role of self-assembly in the future of complex biological system models more generally.
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Affiliation(s)
- Marcus Thomas
- Computational Biology Department, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, United States of America. Joint Carnegie Mellon University/University of Pittsburgh Ph.D. Program in Computational Biology, 4400 Fifth Avenue, Pittsburgh, PA 15213, United States of America
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