1
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Caughman N, Papanikolas M, Markovetz M, Freeman R, Hill DB, Forest MG, Lysy M. Statistical Methods for Microrheology of Airway Mucus with Extreme Heterogeneity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.20.567244. [PMID: 38045262 PMCID: PMC10690152 DOI: 10.1101/2023.11.20.567244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
The mucus lining of the human airway epithelium contains two gel-forming mucins, MUC5B and MUC5AC. During progression of cystic fibrosis (CF), mucus hyper-concentrates as its mucin ratio changes, coinciding with formation of insoluble, dense mucus flakes. We explore rheological heterogeneity of this pathology with reconstituted mucus matching three stages of CF progression and particle-tracking of 200 nm and 1 micron diameter beads. We introduce statistical data analysis methods specific to low signal-to-noise data within flakes. Each bead time series is decomposed into: (i) a fractional Brownian motion (fBm) classifier of the pure time-series signal; (ii) high-frequency static and dynamic noise; and (iii) low-frequency deterministic drift. Subsequent analysis focuses on the denoised fBm classifier ensemble from each mucus sample and bead diameter. Every ensemble fails a homogeneity test, compelling clustering methods to assess levels of heterogeneity. The first binary level detects beads within vs. outside flakes. A second binary level detects within-flake bead signals that can vs. cannot be disentangled from the experimental noise floor. We show all denoised ensembles, within- and outside-flakes, fail a homogeneity test, compelling additional clustering; next, all clusters with sufficient data fail a homogeneity test. These levels of heterogeneity are consistent with outcomes from a stochastic phase-separation process, and dictate applying the generalized Stokes-Einstein relation to each bead per cluster per sample, then frequency-domain averaging to assess rheological heterogeneity. Flakes exhibit a spectrum of gel-like and sol-like domains, outside-flake solutions a spectrum of sol-like domains, painting a rheological signature of the phase-separation process underlying flake-burdened mucus.
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Affiliation(s)
| | | | | | - Ronit Freeman
- Department of Applied Physical Sciences, UNC Chapel Hill
| | - David B. Hill
- Marsico Lung Institute, UNC Chapel Hill
- Department of Physics & Astronomy, UNC Chapel Hill
- Department of Biomedical Engineering, UNC Chapel Hill & NC State University
| | - M. Gregory Forest
- Department of Mathematics, UNC Chapel Hill
- Marsico Lung Institute, UNC Chapel Hill
- Department of Biomedical Engineering, UNC Chapel Hill & NC State University
| | - Martin Lysy
- Department of Statistics & Actuarial Science, University of Waterloo, CA
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2
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Waigh TA, Korabel N. Heterogeneous anomalous transport in cellular and molecular biology. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2023; 86:126601. [PMID: 37863075 DOI: 10.1088/1361-6633/ad058f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 10/20/2023] [Indexed: 10/22/2023]
Abstract
It is well established that a wide variety of phenomena in cellular and molecular biology involve anomalous transport e.g. the statistics for the motility of cells and molecules are fractional and do not conform to the archetypes of simple diffusion or ballistic transport. Recent research demonstrates that anomalous transport is in many cases heterogeneous in both time and space. Thus single anomalous exponents and single generalised diffusion coefficients are unable to satisfactorily describe many crucial phenomena in cellular and molecular biology. We consider advances in the field ofheterogeneous anomalous transport(HAT) highlighting: experimental techniques (single molecule methods, microscopy, image analysis, fluorescence correlation spectroscopy, inelastic neutron scattering, and nuclear magnetic resonance), theoretical tools for data analysis (robust statistical methods such as first passage probabilities, survival analysis, different varieties of mean square displacements, etc), analytic theory and generative theoretical models based on simulations. Special emphasis is made on high throughput analysis techniques based on machine learning and neural networks. Furthermore, we consider anomalous transport in the context of microrheology and the heterogeneous viscoelasticity of complex fluids. HAT in the wavefronts of reaction-diffusion systems is also considered since it plays an important role in morphogenesis and signalling. In addition, we present specific examples from cellular biology including embryonic cells, leucocytes, cancer cells, bacterial cells, bacterial biofilms, and eukaryotic microorganisms. Case studies from molecular biology include DNA, membranes, endosomal transport, endoplasmic reticula, mucins, globular proteins, and amyloids.
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Affiliation(s)
- Thomas Andrew Waigh
- Biological Physics, School of Physics and Astronomy, University of Manchester, Manchester M13 9PL, United Kingdom
| | - Nickolay Korabel
- Department of Mathematics, The University of Manchester, Manchester M13 9PL, United Kingdom
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3
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Verdier H, Laurent F, Cassé A, Vestergaard CL, Masson JB. Variational inference of fractional Brownian motion with linear computational complexity. Phys Rev E 2022; 106:055311. [PMID: 36559393 DOI: 10.1103/physreve.106.055311] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 10/11/2022] [Indexed: 06/17/2023]
Abstract
We introduce a simulation-based, amortized Bayesian inference scheme to infer the parameters of random walks. Our approach learns the posterior distribution of the walks' parameters with a likelihood-free method. In the first step a graph neural network is trained on simulated data to learn optimized low-dimensional summary statistics of the random walk. In the second step an invertible neural network generates the posterior distribution of the parameters from the learned summary statistics using variational inference. We apply our method to infer the parameters of the fractional Brownian motion model from single trajectories. The computational complexity of the amortized inference procedure scales linearly with trajectory length, and its precision scales similarly to the Cramér-Rao bound over a wide range of lengths. The approach is robust to positional noise, and generalizes to trajectories longer than those seen during training. Finally, we adapt this scheme to show that a finite decorrelation time in the environment can furthermore be inferred from individual trajectories.
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Affiliation(s)
- Hippolyte Verdier
- Decision and Bayesian Computation, USR 3756 (C3BI/DBC) and Neuroscience Department CNRS UMR 3751, Institut Pasteur, Université de Paris, CNRS, 75015 Paris, France
- Histopathology and Bio-Imaging Group, Sanofi, R&D, 94400 Vitry-Sur-Seine, France
| | - François Laurent
- Decision and Bayesian Computation, USR 3756 (C3BI/DBC) and Neuroscience Department CNRS UMR 3751, Institut Pasteur, Université de Paris, CNRS, 75015 Paris, France
| | - Alhassan Cassé
- Histopathology and Bio-Imaging Group, Sanofi, R&D, 94400 Vitry-Sur-Seine, France
| | - Christian L Vestergaard
- Decision and Bayesian Computation, USR 3756 (C3BI/DBC) and Neuroscience Department CNRS UMR 3751, Institut Pasteur, Université de Paris, CNRS, 75015 Paris, France
| | - Jean-Baptiste Masson
- Decision and Bayesian Computation, USR 3756 (C3BI/DBC) and Neuroscience Department CNRS UMR 3751, Institut Pasteur, Université de Paris, CNRS, 75015 Paris, France
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4
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Hill DB, Button B, Rubinstein M, Boucher RC. Physiology and pathophysiology of human airway mucus. Physiol Rev 2022; 102:1757-1836. [PMID: 35001665 PMCID: PMC9665957 DOI: 10.1152/physrev.00004.2021] [Citation(s) in RCA: 87] [Impact Index Per Article: 43.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 12/13/2021] [Accepted: 12/19/2021] [Indexed: 01/27/2023] Open
Abstract
The mucus clearance system is the dominant mechanical host defense system of the human lung. Mucus is cleared from the lung by cilia and airflow, including both two-phase gas-liquid pumping and cough-dependent mechanisms, and mucus transport rates are heavily dependent on mucus concentration. Importantly, mucus transport rates are accurately predicted by the gel-on-brush model of the mucociliary apparatus from the relative osmotic moduli of the mucus and periciliary-glycocalyceal (PCL-G) layers. The fluid available to hydrate mucus is generated by transepithelial fluid transport. Feedback interactions between mucus concentrations and cilia beating, via purinergic signaling, coordinate Na+ absorptive vs Cl- secretory rates to maintain mucus hydration in health. In disease, mucus becomes hyperconcentrated (dehydrated). Multiple mechanisms derange the ion transport pathways that normally hydrate mucus in muco-obstructive lung diseases, e.g., cystic fibrosis (CF), chronic obstructive pulmonary disease (COPD), non-CF bronchiectasis (NCFB), and primary ciliary dyskinesia (PCD). A key step in muco-obstructive disease pathogenesis is the osmotic compression of the mucus layer onto the airway surface with the formation of adherent mucus plaques and plugs, particularly in distal airways. Mucus plaques create locally hypoxic conditions and produce airflow obstruction, inflammation, infection, and, ultimately, airway wall damage. Therapies to clear adherent mucus with hydrating and mucolytic agents are rational, and strategies to develop these agents are reviewed.
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Affiliation(s)
- David B Hill
- Marsico Lung Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Joint Department of Biomedical Engineering, The University of North Carolina and North Carolina State University, Chapel Hill, North Carolina
| | - Brian Button
- Marsico Lung Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Michael Rubinstein
- Marsico Lung Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Department of Mechanical Engineering and Materials Science, Biomedical Engineering, Physics, and Chemistry, Duke University, Durham, North Carolina
| | - Richard C Boucher
- Marsico Lung Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
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5
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Ling Y, Lysy M, Seim I, Newby J, Hill DB, Cribb J, Forest MG. Measurement error correction in particle tracking microrheology. Ann Appl Stat 2022. [DOI: 10.1214/21-aoas1565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Yun Ling
- Department of Statistics and Actuarial Science, University of Waterloo
| | - Martin Lysy
- Department of Statistics and Actuarial Science, University of Waterloo
| | - Ian Seim
- Department of Applied Physical Sciences, University of North Carolina at Chapel Hill
| | - Jay Newby
- Department of Mathematical and Statistical Sciences, University of Alberta
| | - David B. Hill
- Marsico Lung Institute, University of North Carolina at Chapel Hill
| | - Jeremy Cribb
- Department of Physics and Astronomy, University of North Carolina at Chapel Hill
| | - M. Gregory Forest
- Department of Biomedical Engineering, University of North Carolina at Chapel Hill
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6
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Vroylandt H, Goudenège L, Monmarché P, Pietrucci F, Rotenberg B. Likelihood-based non-Markovian models from molecular dynamics. Proc Natl Acad Sci U S A 2022; 119:e2117586119. [PMID: 35320038 PMCID: PMC9060509 DOI: 10.1073/pnas.2117586119] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 02/16/2022] [Indexed: 01/09/2023] Open
Abstract
SignificanceThe analysis of complex systems with many degrees of freedom generally involves the definition of low-dimensional collective variables more amenable to physical understanding. Their dynamics can be modeled by generalized Langevin equations, whose coefficients have to be estimated from simulations of the initial high-dimensional system. These equations feature a memory kernel describing the mutual influence of the low-dimensional variables and their environment. We introduce and implement an approach where the generalized Langevin equation is designed to maximize the statistical likelihood of the observed data. This provides an efficient way to generate reduced models to study dynamical properties of complex processes such as chemical reactions in solution, conformational changes in biomolecules, or phase transitions in condensed matter systems.
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Affiliation(s)
- Hadrien Vroylandt
- Institut des Sciences du Calcul et des Données, Sorbonne Université, F-75005 Paris, France
| | - Ludovic Goudenège
- CNRS, FR 3487, Fédération de Mathématiques de CentraleSupélec, CentraleSupélec, Université Paris-Saclay, 91190 Gif-sur-Yvette, France
| | - Pierre Monmarché
- Laboratoire Jacques-Louis Lions, Sorbonne Université, F-75005 Paris, France
- Laboratoire de Chimie Théorique, Sorbonne Université, F-75005 Paris, France
| | - Fabio Pietrucci
- Muséum National d’Histoire Naturelle, UMR CNRS 7590, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, Sorbonne Université, F-75005 Paris, France
| | - Benjamin Rotenberg
- Physicochimie des Électrolytes et Nanosystèmes Interfaciaux, Sorbonne Université, CNRS, F-75005 Paris, France
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7
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Gu M, Luo Y, He Y, Helgeson ME, Valentine MT. Uncertainty quantification and estimation in differential dynamic microscopy. Phys Rev E 2021; 104:034610. [PMID: 34654087 DOI: 10.1103/physreve.104.034610] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Accepted: 09/07/2021] [Indexed: 12/26/2022]
Abstract
Differential dynamic microscopy (DDM) is a form of video image analysis that combines the sensitivity of scattering and the direct visualization benefits of microscopy. DDM is broadly useful in determining dynamical properties including the intermediate scattering function for many spatiotemporally correlated systems. Despite its straightforward analysis, DDM has not been fully adopted as a routine characterization tool, largely due to computational cost and lack of algorithmic robustness. We present statistical analysis that quantifies the noise, reduces the computational order, and enhances the robustness of DDM analysis. We propagate the image noise through the Fourier analysis, which allows us to comprehensively study the bias in different estimators of model parameters, and we derive a different way to detect whether the bias is negligible. Furthermore, through use of Gaussian process regression (GPR), we find that predictive samples of the image structure function require only around 0.5%-5% of the Fourier transforms of the observed quantities. This vastly reduces computational cost, while preserving information of the quantities of interest, such as quantiles of the image scattering function, for subsequent analysis. The approach, which we call DDM with uncertainty quantification (DDM-UQ), is validated using both simulations and experiments with respect to accuracy and computational efficiency, as compared with conventional DDM and multiple particle tracking. Overall, we propose that DDM-UQ lays the foundation for important new applications of DDM, as well as to high-throughput characterization.
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Affiliation(s)
- Mengyang Gu
- Department of Statistics and Applied Probability, University of California, Santa Barbara, California 93106, USA
| | - Yimin Luo
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA.,Department of Mechanical Engineering, University of California, Santa Barbara, California 93106, USA
| | - Yue He
- Department of Statistics and Applied Probability, University of California, Santa Barbara, California 93106, USA
| | - Matthew E Helgeson
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA
| | - Megan T Valentine
- Department of Mechanical Engineering, University of California, Santa Barbara, California 93106, USA
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8
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Zepeda O J, Bishop LDC, Dutta C, Sarkar-Banerjee S, Leung WW, Landes CF. Untying the Gordian KNOT: Unbiased Single Particle Tracking Using Point Clouds and Adaptive Motion Analysis. J Phys Chem A 2021; 125:8723-8733. [PMID: 34559965 DOI: 10.1021/acs.jpca.1c06100] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Achieving mechanistic understanding of transport in complex environments such as inside cells or at polymer interfaces is challenging. We need better ways to image transport in 3-D and better single particle tracking algorithms to determine transport that are not systemically biased toward any classical motion model. Here we present an unbiased single particle tracking algorithm: Knowing Nothing Outside Tracking (KNOT). KNOT uses point clouds provided by iterative deconvolution to educate individual particle localizations and link particle positions between frames to achieve 2-D and 3-D tracking. Information from prior point clouds fuels an independent adaptive motion model for each particle to avoid global models that could introduce biases. KNOT competes with or surpasses other 2-D methods from the 2012 particle tracking challenge while accurately tracking adsorption dynamics of proteins on polymer surfaces and early endosome transport in live cells in 3-D. We apply KNOT to study 3-D endosome transport to reveal new physical insight into locally directed and diffusive transport in live cells. Our analysis demonstrates better accuracy in classifying local motion and its direction compared to previous methods, revealing intricate intracellular transport heterogeneities.
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Affiliation(s)
- Jorge Zepeda O
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas 77005, United States
| | - Logan D C Bishop
- Department of Chemistry, Rice University, Houston, Texas 77005, United States
| | - Chayan Dutta
- Department of Chemistry, Rice University, Houston, Texas 77005, United States
| | | | - Wesley W Leung
- Applied Physics Graduate Program, Smalley-Curl Institute, Rice University, Houston, Texas 77005, United States
| | - Christy F Landes
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas 77005, United States.,Department of Chemistry, Rice University, Houston, Texas 77005, United States.,Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas 77005, United States
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9
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Briane V, Vimond M, Kervrann C. An overview of diffusion models for intracellular dynamics analysis. Brief Bioinform 2021; 21:1136-1150. [PMID: 31204428 DOI: 10.1093/bib/bbz052] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 03/15/2019] [Accepted: 04/09/2019] [Indexed: 11/13/2022] Open
Abstract
We present an overview of diffusion models commonly used for quantifying the dynamics of intracellular particles (e.g. biomolecules) inside eukaryotic living cells. It is established that inference on the modes of mobility of molecules is central in cell biology since it reflects interactions between structures and determines functions of biomolecules in the cell. In that context, Brownian motion is a key component in short distance transportation (e.g. connectivity for signal transduction). Another dynamical process that has been heavily studied in the past decade is the motor-mediated transport (e.g. dynein, kinesin and myosin) of molecules. Primarily supported by actin filament and microtubule network, it ensures spatial organization and temporal synchronization in the intracellular mechanisms and structures. Nevertheless, the complexity of internal structures and molecular processes in the living cell influence the molecular dynamics and prevent the systematic application of pure Brownian or directed motion modeling. On the one hand, cytoskeleton density will hinder the free displacement of the particle, a phenomenon called subdiffusion. On the other hand, the cytoskeleton elasticity combined with thermal bending can contribute a phenomenon called superdiffusion. This paper discusses the basics of diffusion modes observed in eukariotic cells, by introducing the essential properties of these processes. Applications of diffusion models include protein trafficking and transport and membrane diffusion.
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Affiliation(s)
- Vincent Briane
- Inria, Centre Rennes-Bretagne Atlantique, SERPICO Project Team, Rennes, France.,CREST (Ensai, Université Bretagne Loire), Bruz, France
| | - Myriam Vimond
- CREST (Ensai, Université Bretagne Loire), Bruz, France
| | - Charles Kervrann
- Inria, Centre Rennes-Bretagne Atlantique, SERPICO Project Team, Rennes, France
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10
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Bullerjahn JT, von Bülow S, Hummer G. Optimal estimates of self-diffusion coefficients from molecular dynamics simulations. J Chem Phys 2020; 153:024116. [PMID: 32668929 DOI: 10.1063/5.0008312] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Translational diffusion coefficients are routinely estimated from molecular dynamics simulations. Linear fits to mean squared displacement (MSD) curves have become the de facto standard, from simple liquids to complex biomacromolecules. Nonlinearities in MSD curves at short times are handled with a wide variety of ad hoc practices, such as partial and piece-wise fitting of the data. Here, we present a rigorous framework to obtain reliable estimates of the self-diffusion coefficient and its statistical uncertainty. We also assess in a quantitative manner if the observed dynamics is, indeed, diffusive. By accounting for correlations between MSD values at different times, we reduce the statistical uncertainty of the estimator and, thereby, increase its efficiency. With a Kolmogorov-Smirnov test, we check for possible anomalous diffusion. We provide an easy-to-use Python data analysis script for the estimation of self-diffusion coefficients. As an illustration, we apply the formalism to molecular dynamics simulation data of pure TIP4P-D water and a single ubiquitin protein. In another paper [S. von Bülow, J. T. Bullerjahn, and G. Hummer, J. Chem. Phys. 153, 021101 (2020)], we demonstrate its ability to recognize deviations from regular diffusion caused by systematic errors in a common trajectory "unwrapping" scheme that is implemented in popular simulation and visualization software.
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Affiliation(s)
- Jakob Tómas Bullerjahn
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438 Frankfurt am Main, Germany
| | - Sören von Bülow
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438 Frankfurt am Main, Germany
| | - Gerhard Hummer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438 Frankfurt am Main, Germany
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11
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Markovetz MR, Subramani DB, Kissner WJ, Morrison CB, Garbarine IC, Ghio A, Ramsey KA, Arora H, Kumar P, Nix DB, Kumagai T, Krunkosky TM, Krause DC, Radicioni G, Alexis NE, Kesimer M, Tiemeyer M, Boucher RC, Ehre C, Hill DB. Endotracheal tube mucus as a source of airway mucus for rheological study. Am J Physiol Lung Cell Mol Physiol 2019; 317:L498-L509. [PMID: 31389736 DOI: 10.1152/ajplung.00238] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2023] Open
Abstract
Muco-obstructive lung diseases (MOLDs), like cystic fibrosis and chronic obstructive pulmonary disease, affect a spectrum of subjects globally. In MOLDs, the airway mucus becomes hyperconcentrated, increasing osmotic and viscoelastic moduli and impairing mucus clearance. MOLD research requires relevant sources of healthy airway mucus for experimental manipulation and analysis. Mucus collected from endotracheal tubes (ETT) may represent such a source with benefits, e.g., in vivo production, over canonical sample types such as sputum or human bronchial epithelial (HBE) mucus. Ionic and biochemical compositions of ETT mucus from healthy human subjects were characterized and a stock of pooled ETT samples generated. Pooled ETT mucus exhibited concentration-dependent rheologic properties that agreed across spatial scales with reported individual ETT samples and HBE mucus. We suggest that the practical benefits compared with other sample types make ETT mucus potentially useful for MOLD research.
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Affiliation(s)
- Matthew R Markovetz
- Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina
| | - Durai B Subramani
- Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina
| | - William J Kissner
- Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina
| | - Cameron B Morrison
- Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina
| | - Ian C Garbarine
- Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina
| | - Andrew Ghio
- National Health and Environmental Effects Research Laboratory, United States Environmental Protection Agency, Research Triangle Park, North Carolina
| | - Kathryn A Ramsey
- Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina
| | - Harendra Arora
- Department of Anesthesiology, University of North Carolina, Chapel Hill, North Carolina
- Outcomes Research Consortium, Cleveland, Ohio
| | - Priya Kumar
- Department of Anesthesiology, University of North Carolina, Chapel Hill, North Carolina
- Outcomes Research Consortium, Cleveland, Ohio
| | - David B Nix
- Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia
| | - Tadahiro Kumagai
- Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia
| | | | - Duncan C Krause
- Department of Microbiology, University of Georgia, Athens, Georgia
| | - Giorgia Radicioni
- Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina
| | - Neil E Alexis
- Center for Environmental Medicine, Asthma, and Lung Biology, University of North Carolina, Chapel Hill, North Carolina
| | - Mehmet Kesimer
- Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina
- Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, North Carolina
| | - Michael Tiemeyer
- Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia
| | - Richard C Boucher
- Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina
| | - Camille Ehre
- Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina
| | - David B Hill
- Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina
- Department of Physics and Astronomy, University of North Carolina, Chapel Hill, North Carolina
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12
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Markovetz MR, Subramani DB, Kissner WJ, Morrison CB, Garbarine IC, Ghio A, Ramsey KA, Arora H, Kumar P, Nix DB, Kumagai T, Krunkosky TM, Krause DC, Radicioni G, Alexis NE, Kesimer M, Tiemeyer M, Boucher RC, Ehre C, Hill DB. Endotracheal tube mucus as a source of airway mucus for rheological study. Am J Physiol Lung Cell Mol Physiol 2019; 317:L498-L509. [PMID: 31389736 DOI: 10.1152/ajplung.00238.2019] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Muco-obstructive lung diseases (MOLDs), like cystic fibrosis and chronic obstructive pulmonary disease, affect a spectrum of subjects globally. In MOLDs, the airway mucus becomes hyperconcentrated, increasing osmotic and viscoelastic moduli and impairing mucus clearance. MOLD research requires relevant sources of healthy airway mucus for experimental manipulation and analysis. Mucus collected from endotracheal tubes (ETT) may represent such a source with benefits, e.g., in vivo production, over canonical sample types such as sputum or human bronchial epithelial (HBE) mucus. Ionic and biochemical compositions of ETT mucus from healthy human subjects were characterized and a stock of pooled ETT samples generated. Pooled ETT mucus exhibited concentration-dependent rheologic properties that agreed across spatial scales with reported individual ETT samples and HBE mucus. We suggest that the practical benefits compared with other sample types make ETT mucus potentially useful for MOLD research.
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Affiliation(s)
- Matthew R Markovetz
- Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina
| | - Durai B Subramani
- Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina
| | - William J Kissner
- Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina
| | - Cameron B Morrison
- Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina
| | - Ian C Garbarine
- Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina
| | - Andrew Ghio
- National Health and Environmental Effects Research Laboratory, United States Environmental Protection Agency, Research Triangle Park, North Carolina
| | - Kathryn A Ramsey
- Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina
| | - Harendra Arora
- Department of Anesthesiology, University of North Carolina, Chapel Hill, North Carolina.,Outcomes Research Consortium, Cleveland, Ohio
| | - Priya Kumar
- Department of Anesthesiology, University of North Carolina, Chapel Hill, North Carolina.,Outcomes Research Consortium, Cleveland, Ohio
| | - David B Nix
- Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia
| | - Tadahiro Kumagai
- Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia
| | | | - Duncan C Krause
- Department of Microbiology, University of Georgia, Athens, Georgia
| | - Giorgia Radicioni
- Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina
| | - Neil E Alexis
- Center for Environmental Medicine, Asthma, and Lung Biology, University of North Carolina, Chapel Hill, North Carolina
| | - Mehmet Kesimer
- Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina.,Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, North Carolina
| | - Michael Tiemeyer
- Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia
| | - Richard C Boucher
- Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina
| | - Camille Ehre
- Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina
| | - David B Hill
- Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina.,Department of Physics and Astronomy, University of North Carolina, Chapel Hill, North Carolina
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13
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Cherstvy AG, Thapa S, Wagner CE, Metzler R. Non-Gaussian, non-ergodic, and non-Fickian diffusion of tracers in mucin hydrogels. SOFT MATTER 2019; 15:2526-2551. [PMID: 30734041 DOI: 10.1039/c8sm02096e] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Native mucus is polymer-based soft-matter material of paramount biological importance. How non-Gaussian and non-ergodic is the diffusive spreading of pathogens in mucus? We study the passive, thermally driven motion of micron-sized tracers in hydrogels of mucins, the main polymeric component of mucus. We report the results of the Bayesian analysis for ranking several diffusion models for a set of tracer trajectories [C. E. Wagner et al., Biomacromolecules, 2017, 18, 3654]. The models with "diffusing diffusivity", fractional and standard Brownian motion are used. The likelihood functions and evidences of each model are computed, ranking the significance of each model for individual traces. We find that viscoelastic anomalous diffusion is often most probable, followed by Brownian motion, while the model with a diffusing diffusion coefficient is only realised rarely. Our analysis also clarifies the distribution of time-averaged displacements, correlations of scaling exponents and diffusion coefficients, and the degree of non-Gaussianity of displacements at varying pH levels. Weak ergodicity breaking is also quantified. We conclude that-consistent with the original study-diffusion of tracers in the mucin gels is most non-Gaussian and non-ergodic at low pH that corresponds to the most heterogeneous networks. Using the Bayesian approach with the nested-sampling algorithm, together with the quantitative analysis of multiple statistical measures, we report new insights into possible physical mechanisms of diffusion in mucin gels.
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Affiliation(s)
- Andrey G Cherstvy
- Institute for Physics & Astronomy, University of Potsdam, 14476 Potsdam-Golm, Germany.
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14
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Osunbayo O, Miles CE, Doval F, Reddy BJN, Keener JP, Vershinin MD. Complex nearly immotile behaviour of enzymatically driven cargos. SOFT MATTER 2019; 15:1847-1852. [PMID: 30698601 DOI: 10.1039/c8sm01893f] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
We report a minimal microtubule-based motile system displaying signatures of unconventional diffusion. The system consists of a single model cargo driven by an ensemble of N340K NCD motors along a single microtubule. Despite the absence of cytosolic or cytoskeleton complexity, the system shows complex behavior, characterized by sub-diffusive motion for short time lag scales and linear mean squared displacement dependence for longer time lags. The latter is also shown to have non-Gaussian character and cannot be ascribed to a canonical diffusion process. We use single particle tracking and analysis at varying temperatures and motor concentrations to identify the origin of these behaviors as enzymatic activity of mutant NCD. Our results show that signatures of non-Gaussian diffusivities can arise as a result of an active process and suggest that some immotility of cargos observed in cells may reflect the ensemble workings of mechanochemical enzymes and need not always reflect the properties of the cytoskeletal network or the cytosol.
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Affiliation(s)
- O Osunbayo
- Department of Biology, University of Utah, Salt Lake City, UT 84112, USA
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15
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Thapa S, Lomholt MA, Krog J, Cherstvy AG, Metzler R. Bayesian analysis of single-particle tracking data using the nested-sampling algorithm: maximum-likelihood model selection applied to stochastic-diffusivity data. Phys Chem Chem Phys 2018; 20:29018-29037. [PMID: 30255886 DOI: 10.1039/c8cp04043e] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
We employ Bayesian statistics using the nested-sampling algorithm to compare and rank multiple models of ergodic diffusion (including anomalous diffusion) as well as to assess their optimal parameters for in silico-generated and real time-series. We focus on the recently-introduced model of Brownian motion with "diffusing diffusivity"-giving rise to widely-observed non-Gaussian displacement statistics-and its comparison to Brownian and fractional Brownian motion, also for the time-series with some measurement noise. We conduct this model-assessment analysis using Bayesian statistics and the nested-sampling algorithm on the level of individual particle trajectories. We evaluate relative model probabilities and compute best-parameter sets for each diffusion model, comparing the estimated parameters to the true ones. We test the performance of the nested-sampling algorithm and its predictive power both for computer-generated (idealised) trajectories as well as for real single-particle-tracking trajectories. Our approach delivers new important insight into the objective selection of the most suitable stochastic model for a given time-series. We also present first model-ranking results in application to experimental data of tracer diffusion in polymer-based hydrogels.
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Affiliation(s)
- Samudrajit Thapa
- Institute for Physics & Astronomy, University of Potsdam, 14476 Potsdam-Golm, Germany
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16
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Zhang K, Crizer KPR, Schoenfisch MH, Hill DB, Didier G. Fluid heterogeneity detection based on the asymptotic distribution of the time-averaged mean squared displacement in single particle tracking experiments. JOURNAL OF PHYSICS. A, MATHEMATICAL AND THEORETICAL 2018; 51:445601. [PMID: 31037119 PMCID: PMC6486181 DOI: 10.1088/1751-8121/aae0af] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
A tracer particle is called anomalously diffusive if its mean squared displacement grows approximately as σ 2 t α as a function of time t for some constant σ 2, where the diffusion exponent satisfies α ≠ 1. In this article, we use recent results on the asymptotic distribution of the time-averaged mean squared displacement [20] to construct statistical tests for detecting physical heterogeneity in viscoelastic fluid samples starting from one or multiple observed anomalously diffusive paths. The methods are asymptotically valid for the range 0 < α < 3/2 and involve a mathematical characterization of time-averaged mean squared displacement bias and the effect of correlated disturbance errors. The assumptions on particle motion cover a broad family of fractional Gaussian processes, including fractional Brownian motion and many fractional instances of the generalized Langevin equation framework. We apply the proposed methods in experimental data from treated P. aeruginosa biofilms generated by the collaboration of the Hill and Schoenfisch Labs at UNC-Chapel Hill.
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Affiliation(s)
- Kui Zhang
- Department of Mathematics, Tulane University
| | | | | | - David B Hill
- The Marsico Lung Institute and Department of Physics and Astronomy, University of North Carolina at Chapel Hill
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17
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Briane V, Kervrann C, Vimond M. Statistical analysis of particle trajectories in living cells. Phys Rev E 2018; 97:062121. [PMID: 30011544 DOI: 10.1103/physreve.97.062121] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Indexed: 11/07/2022]
Abstract
Recent advances in molecular biology and fluorescence microscopy imaging have made possible the inference of the dynamics of molecules in living cells. Such inference allows us to understand and determine the organization and function of the cell. The trajectories of particles (e.g., biomolecules) in living cells, computed with the help of object tracking methods, can be modeled with diffusion processes. Three types of diffusion are considered: (i) free diffusion, (ii) subdiffusion, and (iii) superdiffusion. The mean-square displacement (MSD) is generally used to discriminate the three types of particle dynamics. We propose here a nonparametric three-decision test as an alternative to the MSD method. The rejection of the null hypothesis, i.e., free diffusion, is accompanied by claims of the direction of the alternative (subdiffusion or superdiffusion). We study the asymptotic behavior of the test statistic under the null hypothesis and under parametric alternatives which are currently considered in the biophysics literature. In addition, we adapt the multiple-testing procedure of Benjamini and Hochberg to fit with the three-decision-test setting, in order to apply the test procedure to a collection of independent trajectories. The performance of our procedure is much better than the MSD method as confirmed by Monte Carlo experiments. The method is demonstrated on real data sets corresponding to protein dynamics observed in fluorescence microscopy.
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Affiliation(s)
- Vincent Briane
- Inria Rennes, Serpico Project Team, Campus Universitaire de Beaulieu, 35042 Rennes Cedex, France.,CREST, Ensai, Université Bretagne Loire, Rue Blaise Pascal, 35172 Bruz, France
| | - Charles Kervrann
- Inria Rennes, Serpico Project Team, Campus Universitaire de Beaulieu, 35042 Rennes Cedex, France
| | - Myriam Vimond
- CREST, Ensai, Université Bretagne Loire, Rue Blaise Pascal, 35172 Bruz, France
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18
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Newby JM, Schaefer AM, Lee PT, Forest MG, Lai SK. Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D. Proc Natl Acad Sci U S A 2018; 115:9026-9031. [PMID: 30135100 PMCID: PMC6130393 DOI: 10.1073/pnas.1804420115] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Particle tracking is a powerful biophysical tool that requires conversion of large video files into position time series, i.e., traces of the species of interest for data analysis. Current tracking methods, based on a limited set of input parameters to identify bright objects, are ill-equipped to handle the spectrum of spatiotemporal heterogeneity and poor signal-to-noise ratios typically presented by submicron species in complex biological environments. Extensive user involvement is frequently necessary to optimize and execute tracking methods, which is not only inefficient but introduces user bias. To develop a fully automated tracking method, we developed a convolutional neural network for particle localization from image data, comprising over 6,000 parameters, and used machine learning techniques to train the network on a diverse portfolio of video conditions. The neural network tracker provides unprecedented automation and accuracy, with exceptionally low false positive and false negative rates on both 2D and 3D simulated videos and 2D experimental videos of difficult-to-track species.
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Affiliation(s)
- Jay M Newby
- Department of Mathematics, University of Alberta, Edmonton, AB, Canada T6G 2R3
- Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Alison M Schaefer
- Division of Pharmacoengineering and Molecular Pharmaceutics, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Phoebe T Lee
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - M Gregory Forest
- Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599;
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
- Department of Mathematics and Applied Physical Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Samuel K Lai
- Division of Pharmacoengineering and Molecular Pharmaceutics, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599;
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
- Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
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Two-step wavelet-based estimation for Gaussian mixed fractional processes. STATISTICAL INFERENCE FOR STOCHASTIC PROCESSES 2018. [DOI: 10.1007/s11203-018-9190-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Newby JM, Seim I, Lysy M, Ling Y, Huckaby J, Lai SK, Forest MG. Technological strategies to estimate and control diffusive passage times through the mucus barrier in mucosal drug delivery. Adv Drug Deliv Rev 2018; 124:64-81. [PMID: 29246855 PMCID: PMC5809312 DOI: 10.1016/j.addr.2017.12.002] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 12/05/2017] [Accepted: 12/06/2017] [Indexed: 01/05/2023]
Abstract
In mucosal drug delivery, two design goals are desirable: 1) insure drug passage through the mucosal barrier to the epithelium prior to drug removal from the respective organ via mucus clearance; and 2) design carrier particles to achieve a prescribed arrival time and drug uptake schedule at the epithelium. Both goals are achievable if one can control "one-sided" diffusive passage times of drug carrier particles: from deposition at the mucus interface, through the mucosal barrier, to the epithelium. The passage time distribution must be, with high confidence, shorter than the timescales of mucus clearance to maximize drug uptake. For 100nm and smaller drug-loaded nanoparticulates, as well as pure drug powders or drug solutions, diffusion is normal (i.e., Brownian) and rapid, easily passing through the mucosal barrier prior to clearance. Major challenges in quantitative control over mucosal drug delivery lie with larger drug-loaded nanoparticulates that are comparable to or larger than the pores within the mucus gel network, for which diffusion is not simple Brownian motion and typically much less rapid; in these scenarios, a timescale competition ensues between particle passage through the mucus barrier and mucus clearance from the organ. In the lung, as a primary example, coordinated cilia and air drag continuously transport mucus toward the trachea, where mucus and trapped cargo are swallowed into the digestive tract. Mucus clearance times in lung airways range from minutes to hours or significantly longer depending on deposition in the upper, middle, lower airways and on lung health, giving a wide time window for drug-loaded particle design to achieve controlled delivery to the epithelium. We review the physical and chemical factors (of both particles and mucus) that dictate particle diffusion in mucus, and the technological strategies (theoretical and experimental) required to achieve the design goals. First we describe an idealized scenario - a homogeneous viscous fluid of uniform depth with a particle undergoing passive normal diffusion - where the theory of Brownian motion affords the ability to rigorously specify particle size distributions to meet a prescribed, one-sided, diffusive passage time distribution. Furthermore, we describe how the theory of Brownian motion provides the scaling of one-sided diffusive passage times with respect to mucus viscosity and layer depth, and under reasonable caveats, one can also prescribe passage time scaling due to heterogeneity in viscosity and layer depth. Small-molecule drugs and muco-inert, drug-loaded carrier particles 100nm and smaller fall into this class of rigorously controllable passage times for drug delivery. Second we describe the prevalent scenarios in which drug-loaded carrier particles in mucus violate simple Brownian motion, instead exhibiting anomalous sub-diffusion, for which all theoretical control over diffusive passage times is lost, and experiments are prohibitive if not impossible to measure one-sided passage times. We then discuss strategies to overcome these roadblocks, requiring new particle-tracking experiments and emerging advances in theory and computation of anomalous, sub-diffusive processes that are necessary to predict and control one-sided particle passage times from deposition at the mucosal interface to epithelial uptake. We highlight progress to date, remaining hurdles, and prospects for achieving the two design goals for 200nm and larger, drug-loaded, non-dissolving, nanoparticulates.
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Affiliation(s)
- Jay M Newby
- Department of Mathematics and Applied Physical Sciences, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, United States
| | - Ian Seim
- Department of Mathematics and Applied Physical Sciences, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, United States
| | - Martin Lysy
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON N2L 3G1, United States
| | - Yun Ling
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON N2L 3G1, United States
| | - Justin Huckaby
- Division of Pharmacoengineering and Molecular Pharmaceutics, Eshelman School of Pharmacy, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, United States
| | - Samuel K Lai
- Division of Pharmacoengineering and Molecular Pharmaceutics, Eshelman School of Pharmacy, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, United States; UNC-NCSU Joint Department of Biomedical Engineering, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, United States; Department of Microbiology and Immunology, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, United States
| | - M Gregory Forest
- Department of Mathematics and Applied Physical Sciences, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, United States; UNC-NCSU Joint Department of Biomedical Engineering, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, United States.
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21
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Cherstvy AG, Nagel O, Beta C, Metzler R. Non-Gaussianity, population heterogeneity, and transient superdiffusion in the spreading dynamics of amoeboid cells. Phys Chem Chem Phys 2018; 20:23034-23054. [DOI: 10.1039/c8cp04254c] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
What is the underlying diffusion process governing the spreading dynamics and search strategies employed by amoeboid cells?
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Affiliation(s)
- Andrey G. Cherstvy
- Institute for Physics & Astronomy
- University of Potsdam
- 14476 Potsdam-Golm
- Germany
| | - Oliver Nagel
- Institute for Physics & Astronomy
- University of Potsdam
- 14476 Potsdam-Golm
- Germany
| | - Carsten Beta
- Institute for Physics & Astronomy
- University of Potsdam
- 14476 Potsdam-Golm
- Germany
| | - Ralf Metzler
- Institute for Physics & Astronomy
- University of Potsdam
- 14476 Potsdam-Golm
- Germany
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22
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Recent progress in translational cystic fibrosis research using precision medicine strategies. J Cyst Fibros 2017; 17:S52-S60. [PMID: 28986017 DOI: 10.1016/j.jcf.2017.09.005] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2017] [Revised: 09/21/2017] [Accepted: 09/21/2017] [Indexed: 01/17/2023]
Abstract
Significant progress has been achieved in developing precision therapies for cystic fibrosis; however, highly effective treatments that target the ion channel, CFTR, are not yet available for many patients. As numerous CFTR therapeutics are currently in the clinical pipeline, reliable screening tools capable of predicting drug efficacy to support individualized treatment plans and translational research are essential. The utilization of bronchial, nasal, and rectal tissues from individual cystic fibrosis patients for drug testing using in vitro assays such as electrophysiological measurements of CFTR activity and evaluation of fluid movement in spheroid cultures, has advanced the prediction of patient-specific responses. However, for precise prediction of drug effects, in vitro models of CFTR rescue should incorporate the inflamed cystic fibrosis airway environment and mimic the complex tissue structures of airway epithelia. Furthermore, novel assays that monitor other aspects of successful CFTR rescue such as restoration of mucus characteristics, which is important for predicting mucociliary clearance, will allow for better prognoses of successful therapies in vivo. Additional cystic fibrosis treatment strategies are being intensively explored, such as development of drugs that target other ion channels, and novel technologies including pluripotent stem cells, gene therapy, and gene editing. The multiple therapeutic approaches available to treat the basic defect in cystic fibrosis combined with relevant precision medicine models provide a framework for identifying optimal and sustained treatments that will benefit all cystic fibrosis patients.
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23
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Hall EJ, Katsoulakis MA, Rey-Bellet L. Uncertainty quantification for generalized Langevin dynamics. J Chem Phys 2016; 145:224108. [DOI: 10.1063/1.4971433] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Affiliation(s)
- Eric J. Hall
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, Massachusetts 01003, USA
| | - Markos A. Katsoulakis
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, Massachusetts 01003, USA
| | - Luc Rey-Bellet
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, Massachusetts 01003, USA
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