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Lin C, Ashwin P, Steinberg G. Modelling the motion of organelles in an elongated cell via the coordination of heterogeneous drift-diffusion and long-range transport. THE EUROPEAN PHYSICAL JOURNAL. E, SOFT MATTER 2021; 44:10. [PMID: 33683507 DOI: 10.1140/epje/s10189-020-00007-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 12/21/2020] [Indexed: 06/12/2023]
Abstract
Cellular distribution of organelles in living cells is achieved via a variety of transport mechanisms, including directed motion, mediated by molecular motors along microtubules (MTs), and diffusion which is predominantly heterogeneous in space. In this paper, we introduce a model for particle transport in elongated cells that couples poleward drift, long-range bidirectional transport and diffusion with spatial heterogeneity in a three-dimensional space. Using stochastic simulations and analysis of a related population model, we find parameter regions where the three-dimensional model can be reduced to a coupled one-dimensional model or even a one-dimensional scalar model. We explore the efficiency with which individual model components can overcome drift towards one of the cell poles to reach an approximately even distribution. In particular, we find that if lateral movement is well mixed, then increasing the binding ability of particles to MTs is an efficient way to overcome a poleward drift, whereas if lateral motion is not well mixed, then increasing the axial diffusivity away from MTs becomes an efficient way to overcome the poleward drift. Our three-dimensional model provides a new tool that will help to understand the mechanisms by which eukaryotic cells organize their organelles in an elongated cell, and in particular when the one-dimensional models are applicable.
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Affiliation(s)
- Congping Lin
- School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan, China.
- Center for Mathematical Sciences, Huazhong University of Science and Technology, Wuhan, China.
- Hubei Key Lab of Engineering Modeling and Scientific Computing, Huazhong University of Science and Technology, Wuhan, China.
| | - Peter Ashwin
- Department of Mathematics, University of Exeter, Exeter, UK
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Liu M, Li Q, Liang L, Li J, Wang K, Li J, Lv M, Chen N, Song H, Lee J, Shi J, Wang L, Lal R, Fan C. Real-time visualization of clustering and intracellular transport of gold nanoparticles by correlative imaging. Nat Commun 2017; 8:15646. [PMID: 28561031 PMCID: PMC5460036 DOI: 10.1038/ncomms15646] [Citation(s) in RCA: 153] [Impact Index Per Article: 19.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2016] [Accepted: 04/18/2017] [Indexed: 12/19/2022] Open
Abstract
Mechanistic understanding of the endocytosis and intracellular trafficking of nanoparticles is essential for designing smart theranostic carriers. Physico-chemical properties, including size, clustering and surface chemistry of nanoparticles regulate their cellular uptake and transport. Significantly, even single nanoparticles could cluster intracellularly, yet their clustering state and subsequent trafficking are not well understood. Here, we used DNA-decorated gold (fPlas-gold) nanoparticles as a dually emissive fluorescent and plasmonic probe to examine their clustering states and intracellular transport. Evidence from correlative fluorescence and plasmonic imaging shows that endocytosis of fPlas-gold follows multiple pathways. In the early stages of endocytosis, fPlas-gold nanoparticles appear mostly as single particles and they cluster during the vesicular transport and maturation. The speed of encapsulated fPlas-gold transport was critically dependent on the size of clusters but not on the types of organelle such as endosomes and lysosomes. Our results provide key strategies for engineering theranostic nanocarriers for efficient health management.
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Affiliation(s)
- Mengmeng Liu
- Division of Physical Biology & Bioimaging Center, Shanghai Synchrotron Radiation Facility, CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
- The Institute for Translational Nanomedicine, Shanghai East Hospital, Shanghai 200120, China
| | - Qian Li
- Division of Physical Biology & Bioimaging Center, Shanghai Synchrotron Radiation Facility, CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
| | - Le Liang
- Division of Physical Biology & Bioimaging Center, Shanghai Synchrotron Radiation Facility, CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
| | - Jiang Li
- Division of Physical Biology & Bioimaging Center, Shanghai Synchrotron Radiation Facility, CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
| | - Kun Wang
- Division of Physical Biology & Bioimaging Center, Shanghai Synchrotron Radiation Facility, CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
| | - Jiajun Li
- Division of Physical Biology & Bioimaging Center, Shanghai Synchrotron Radiation Facility, CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
| | - Min Lv
- Division of Physical Biology & Bioimaging Center, Shanghai Synchrotron Radiation Facility, CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
| | - Nan Chen
- Division of Physical Biology & Bioimaging Center, Shanghai Synchrotron Radiation Facility, CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
| | - Haiyun Song
- Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Joon Lee
- Department of Bioengineering, Materials Science and Engineering and Department of Mechanical and Aerospace Engineering, Institute of Engineering in Medicine, University of California San Diego, La Jolla, California 92093, USA
| | - Jiye Shi
- Kellogg College, University of Oxford, Oxford, OX2 6PN, UK
- UCB Pharma, Slough, Berkshire SL1 3WE, UK
| | - Lihua Wang
- Division of Physical Biology & Bioimaging Center, Shanghai Synchrotron Radiation Facility, CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
| | - Ratnesh Lal
- Department of Bioengineering, Materials Science and Engineering and Department of Mechanical and Aerospace Engineering, Institute of Engineering in Medicine, University of California San Diego, La Jolla, California 92093, USA
| | - Chunhai Fan
- Division of Physical Biology & Bioimaging Center, Shanghai Synchrotron Radiation Facility, CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
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Spatial organization of organelles in fungi: Insights from mathematical modelling. Fungal Genet Biol 2017; 103:55-59. [PMID: 28351675 PMCID: PMC5476193 DOI: 10.1016/j.fgb.2017.03.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Revised: 03/15/2017] [Accepted: 03/22/2017] [Indexed: 01/03/2023]
Abstract
Modelling of dynein motility reveals a stochastic role in dynein comet formation. Modelling helps to elucidate mechanisms in spatial organization of early endosomes. A combination of diffusion and directed motion distributes ribosomes and peroxisomes.
Mathematical modelling in cellular systems aims to describe biological processes in a quantitative manner. Most accurate modelling is based on robust experimental data. Here we review recent progress in the theoretical description of motor behaviour, early endosome motility, ribosome distribution and peroxisome transport in the fungal model system Ustilago maydis and illustrate the power of modelling in our quest to understand molecular details and cellular roles of membrane trafficking in filamentous fungi.
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