Microrheology for biomaterial design

Joyner, Katherine; Yang, Sydney; Duncan, Gregg A.

doi:10.1063/5.0013707

Cited by 26 publications

(27 citation statements)

References 143 publications

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“…Acquired fluorescence microscopy videos were processed using a previously developed MATLAB (The MathWorks, Natick, MA) based analysis code to isolate and track imaged particles (Crocker and Grier, 1996; Schuster et al, 2015; Joyner et al, 2020). For each video, the mean squared displacement (MSD) was calculated as 〈MSD( τ )〉 = 〈( x 2 + y 2 )〉, for each particle.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning–Informed Predictions of Nanoparticle Mobility and Fate in the Mucus Barrier

Kaler

Joyner

Duncan

2022

Preprint

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Diffusion and transport of nanomaterials through mucus, is of critical importance to many basic and applied areas of research such as drug delivery and infectious disease. However, it is often challenging to interpret the dynamics of nanoparticles within the mucus gel due to its inherently heterogeneous microstructure and biochemistry. In this study, we measured the diffusion of densely PEGylated nanoparticles (NP) in human airway mucus ex vivo using multiple particle tracking and utilized machine learning to classify NP movement as either traditional Brownian motion (BM) or one of two different models of anomalous diffusion, fractional Brownian motion (FBM) and continuous time random walk (CTRW). Specifically, we employed a physics-based neural network model to predict the modes of diffusion experienced by individual NP in human airway mucus. We observed rapidly diffusing NP primarily exhibit BM whereas CTRW and FBM exhibited lower diffusion rates. Given the use of muco-inert nanoparticles, the observed transition from diffusive (BM) to sub-diffusive (CTRW/FBM) motion is likely a result of patient-to-patient variation in mucus network pore size. Using mathematic models that account for the mode of NP diffusion, we predicted the percentage of nanoparticles that would cross the mucus barrier over time in human airway mucus with varied total solids concentration. We also applied this approach to explore the transport modes and predicted fate of influenza A virus within human mucus. These results provide new tools to evaluate the extent of synthetic and viral nanoparticle penetration through mucus in the lung and other tissues.

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Section: Methodsmentioning

confidence: 99%

Machine Learning–Informed Predictions of Nanoparticle Mobility and Fate in the Mucus Barrier

Kaler

Joyner

Duncan

2022

Preprint

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“…For a gel network, sub-diffusive nanoparticle movement should be observed where 0 < a <1. 24 Using the generalized Stokes-Einstein relation, measured MSD values were used to compute viscoelastic properties. The Laplace transform of áMSD(t)ñ, áMSD(s)ñ, is related to viscoelastic spectrum G & (s) using the equation G & (s)=2k B T/[πasMSD(s)⟩], where s is the complex Laplace frequency.…”

Section: Transmission Electron Microscopy (Tem)mentioning

confidence: 99%

“…The complex modulus can be calculated as G*(ω)=G'(ω)+G"(iω), with iω being substituted for s, where i is a complex number and w is frequency. 24 Hydrogel network pore size, ξ, is estimated based on G' using the equation, ξ≈(kBT/G') 1/3 . 24…”

Section: Transmission Electron Microscopy (Tem)mentioning

confidence: 99%

“…However, we should note our measurements are limited to relatively low frequencies (w ≤ 1 Hz) using MPT microrheology. 24 We estimated network size (x) within MaxGel based on measured G' at w = 1 Hz for individual nanoparticles (Fig. 3D) which varied over a range between ~200-600 nm (xavg = 415 nm).…”

Section: A B Cmentioning

confidence: 99%

“…These results can be interpreted based on prior experimental and computational studies demonstrating constrained motion perpendicular to the long axis can lead to enhancements in translational diffusion through a polymer mesh network. 24,27,28 For ~300 nm NR, their motion is comparable to 100 nm NS given their overall dimensions are smaller than the average network spacing in MaxGel (d < L < xavg). For ~700 nm NR, although in the size range (d < xavg < L) where we would predict faster diffusion due to reduced frictional drag parallel to the long axis, we do not observe enhanced diffusion which may be due in part to the natural heterogeneity of the ECM network and wide range of pore size which may interfere with these effects.…”

Section: A B Cmentioning

confidence: 99%

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Shaping nanoparticle diffusion through biological barriers to drug delivery

Lee

Cheema

Bader

et al. 2021

Preprint

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Nanoparticle drug delivery systems encounter many biological barriers, such as the extracellular matrix and mucus gels, that they must bypass to gain access to target cells. A design parameter that has recently gained attention is nanoparticle shape, as it has been shown elongated rod shaped nanoparticles achieve higher diffusion rates through biological gels. However, the optimal dimensions of rod shaped nanoparticles to enhance this effect has yet to be established. To systematically approach this, rod-shaped nanoparticles were synthesized by mechanically stretching 100 nm, 200 nm, and 500 nm spherical nanoparticles. Transmission electron microscopy confirmed this procedure yields a significant fraction of elongated rods and remaining spheres could be removed by centrifugation. Fluorescent microscopy and multiple particle tracking analysis was then used to characterize rod-shaped and spherical nanoparticle diffusion in MaxGel, a model extracellular matrix hydrogel. When dispersed in MaxGel, we found rod-shaped nanoparticles exhibited the greatest enhancement in diffusion rate when their length far exceeds the average hydrogel network size. These results further establish the importance of shape as a design criterion to improve nanoparticle based drug delivery systems.

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Synthetic mucus biomaterials for antimicrobial peptide delivery

Yang

Duncan

2023

J Biomedical Materials Res

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Despite the promise of antimicrobial peptides (AMPs) as treatments for antibiotic‐resistant infections, their therapeutic efficacy is limited due to the rapid degradation and low bioavailability of AMPs. To address this, we have developed and characterized a synthetic mucus (SM) biomaterial capable of delivering LL37 AMPs and enhancing their therapeutic effect. LL37 is an AMP that exhibits a wide range of antimicrobial activity against bacteria, including Pseudomonas aeruginosa. LL37 loaded SM hydrogels demonstrated controlled release with 70%–95% of loaded LL37 over 8 h due to charge‐mediated interactions between mucins and LL37 AMPs. Compared to treatment with LL37 alone where antimicrobial activity was reduced after 3 h, LL37‐SM hydrogels inhibited P. aeruginosa (PAO1) growth over 12 h. LL37‐SM hydrogel treatment reduced PAO1 viability over 6 h whereas a rebound in bacterial growth was observed when treated with LL37 only. These data demonstrate LL37‐SM hydrogels enhance antimicrobial activity by preserving LL37 AMP activity and bioavailability. Overall, this work establishes SM biomaterials as a platform for enhanced AMP delivery for antimicrobial applications.

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Microrheology for biomaterial design

Cited by 26 publications

References 143 publications

Machine Learning–Informed Predictions of Nanoparticle Mobility and Fate in the Mucus Barrier

Machine Learning–Informed Predictions of Nanoparticle Mobility and Fate in the Mucus Barrier

Shaping nanoparticle diffusion through biological barriers to drug delivery

Synthetic mucus biomaterials for antimicrobial peptide delivery

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