Detection and Sizing of Submicron Particles in Biologics With Interferometric Scattering Microscopy

Wong, Nathan; Uchida, Nina V.; Dissanayake, Thilini; Patel, Mehulkumar; Iqbal, Maira; Woehl, Taylor J.

doi:10.1016/j.xphs.2019.05.003

Cited by 5 publications

(4 citation statements)

References 40 publications

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“…For example, novel microscopy techniques such as interferometric scattering microscopy (iSCAT) have made it possible to quantify the optical scattering contrast of nanoparticles close to an interface. For particles larger than the wavelength of light, the angular distribution of the scattering intensity depends strongly on particle size, which has been exploited to characterize the size of particles and protein aggregates, , both using classical image analysis as well as deep-learning methods . However, quantifying size and refractive index of particles smaller than the wavelength of light from optical scattering patterns in microscopy images remains challenging: The dependence of the angular distribution of the scattering on particle size is very weak, and in the limit of Rayleigh scatterers ( r ≪ λ), the angular distribution is independent of size.…”

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confidence: 99%

Fast and Accurate Nanoparticle Characterization Using Deep-Learning-Enhanced Off-Axis Holography

et al. 2021

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Characterization of suspended nanoparticles in their native environment plays a central role in a wide range of fields, from medical diagnostics and nanoparticle-enhanced drug delivery to nanosafety and environmental nanopollution assessment. Standard optical approaches for nanoparticle sizing assess the size via the diffusion constant and, as a consequence, require long trajectories and that the medium has a known and uniform viscosity. However, in most biological applications, only short trajectories are available, while simultaneously, the medium viscosity is unknown and tends to display spatiotemporal variations. In this work, we demonstrate a label-free method to quantify not only size but also refractive index of individual subwavelength particles using 2 orders of magnitude shorter trajectories than required by standard methods and without prior knowledge about the physicochemical properties of the medium. We achieved this by developing a weighted average convolutional neural network to analyze holographic images of single particles, which was successfully applied to distinguish and quantify both size and refractive index of subwavelength silica and polystyrene particles without prior knowledge of solute viscosity or refractive index. We further demonstrate how these features make it possible to temporally resolve aggregation dynamics of 31 nm polystyrene nanoparticles, revealing previously unobserved time-resolved dynamics of the monomer number and fractal dimension of individual subwavelength aggregates.

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confidence: 99%

Fast and Accurate Nanoparticle Characterization Using Deep-Learning-Enhanced Off-Axis Holography

et al. 2021

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“…The protein was provided at a concentration of 100 mg/mL and was diluted to 1 mg/mL in histidine buffer. Protein aggregates were formed by magnetically stirring the solution at 1000 rpm for ~5 hours, which formed a broad distribution of protein aggregate sizes ranging from 100 -1000 nm as measured by light scattering and scanning electron microscopy (SEM) [7].…”

Section: Maryland United Statesmentioning

confidence: 99%

“…We have recently adopted a super-resolution optical imaging technique, interferometry scattering microscopy (IFS), to quantify the size distribution of sub-micron protein aggregates [7]. IFS utilizes a widefield optical microscope and a layered silicon sensor to enable visualizing and quantifying the size of low contrast nanoparticles down to sizes of ~100 nm [8].…”

Section: Maryland United Statesmentioning

confidence: 99%

Probing the Surface Structure of Monoclonal Antibody Aggregates with Multiscale Microscopy

2020

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“…For example, novel microscopy techniques such as interferometric scattering microscopy (iSCAT 13 ) have made it possible to quantify the optical scattering contrast of nanoparticles close to an interface. For particles larger than the wavelength of light, the angular distribution of the scattering intensity depends strongly on particle size, which has been exploited to characterise the size of particles in this regime 14,15 . However, quantifying size and refractive index of subwavelength particles from optical scattering patterns in microscopy images remains challenging: The dependence of the angular distribution of the scattering on particle size is very weak, and in the limit of Rayleigh scatterers (r λ) the angular distribution is independent of size.…”

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confidence: 99%

Holographic characterisation of subwavelength particles enhanced by deep learning

Midtvedt¹,

Olsén²,

Eklund³

et al. 2020

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The characterisation of the physical properties of nanoparticles in their native environment plays a central role in a wide range of fields, from nanoparticle-enhanced drug delivery to environmental nanopollution assessment. Standard optical approaches require long trajectories of nanoparticles dispersed in a medium with known viscosity to characterise their diffusion constant and, thus, their size. However, often only short trajectories are available, while the medium viscosity is unknown, e.g., in most biomedical applications. In this work, we demonstrate a label-free method to quantify size and refractive index of individual subwavelength particles using two orders of magnitude shorter trajectories than required by standard methods, and without assumptions about the physicochemical properties of the medium. We achieve this by developing a weighted average convolutional neural network to analyse the holographic images of the particles. As a proof of principle, we distinguish and quantify size and refractive index of silica and polystyrene particles without prior knowledge of solute viscosity or refractive index. As an example of an application beyond the state of the art, we demonstrate how this technique can monitor the aggregation of polystyrene nanoparticles, revealing the time-resolved dynamics of the monomer number and fractal dimension of individual subwavelength aggregates. This technique opens new possibilities for nanoparticle characterisation with a broad range of applications from biomedicine to environmental monitoring.Nanoparticles play a crucial role in many fields, including pharmaceutic sciences 1 , food production 2, 3 , and environmental monitoring 4 . As particle size and composition greatly influence particle function, fast and accurate characterisation tools are essential and, ideally, should work in the native environment of the particles. For example, in pharmaceutic applications, the interaction between nanoparticles (e.g., protein aggregates, extracellular vesicles and viruses) and biological cells depends crucially on particle size and composition 1, 5-8 , and studying this relation requires accurate characterisation of nanoparticles in the complex extra-and intracellular environments. In food production, nanoparticles are used to stabilise emulsions, improving food texture and shelf life 2 . In environmental monitoring, there is a need to identify and characterise nanoparticles that enter the air, water and soil as a byproduct of industrial processes and waste disposal 4 . In all these applications, it is often necessary to determine and monitor particle properties and activity in an environment whose physicochemical properties are unknown.Traditionally, individual submicron particles in dispersion have been indirectly sized by 1

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Detection and Sizing of Submicron Particles in Biologics With Interferometric Scattering Microscopy

Cited by 5 publications

References 40 publications

Fast and Accurate Nanoparticle Characterization Using Deep-Learning-Enhanced Off-Axis Holography

Fast and Accurate Nanoparticle Characterization Using Deep-Learning-Enhanced Off-Axis Holography

Probing the Surface Structure of Monoclonal Antibody Aggregates with Multiscale Microscopy

Holographic characterisation of subwavelength particles enhanced by deep learning

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